diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/paint_by_example.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/paint_by_example.md new file mode 100644 index 0000000000000000000000000000000000000000..02bf6db7265d5b815a9f10424b23586bd05187b1 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/paint_by_example.md @@ -0,0 +1,39 @@ + + +> [!WARNING] +> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model. + +# Paint by Example + +[Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://huggingface.co/papers/2211.13227) is by Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, Fang Wen. + +The abstract from the paper is: + +*Language-guided image editing has achieved great success recently. In this paper, for the first time, we investigate exemplar-guided image editing for more precise control. We achieve this goal by leveraging self-supervised training to disentangle and re-organize the source image and the exemplar. However, the naive approach will cause obvious fusing artifacts. We carefully analyze it and propose an information bottleneck and strong augmentations to avoid the trivial solution of directly copying and pasting the exemplar image. Meanwhile, to ensure the controllability of the editing process, we design an arbitrary shape mask for the exemplar image and leverage the classifier-free guidance to increase the similarity to the exemplar image. The whole framework involves a single forward of the diffusion model without any iterative optimization. We demonstrate that our method achieves an impressive performance and enables controllable editing on in-the-wild images with high fidelity.* + +The original codebase can be found at [Fantasy-Studio/Paint-by-Example](https://github.com/Fantasy-Studio/Paint-by-Example), and you can try it out in a [demo](https://huggingface.co/spaces/Fantasy-Studio/Paint-by-Example). + +## Tips + +Paint by Example is supported by the official [Fantasy-Studio/Paint-by-Example](https://huggingface.co/Fantasy-Studio/Paint-by-Example) checkpoint. The checkpoint is warm-started from [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) to inpaint partly masked images conditioned on example and reference images. + +> [!TIP] +> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. + +## PaintByExamplePipeline +[[autodoc]] PaintByExamplePipeline + - all + - __call__ + +## StableDiffusionPipelineOutput +[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/panorama.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/panorama.md new file mode 100644 index 0000000000000000000000000000000000000000..b65e05dd0b51aca1b833d92575a8c1627b395dc3 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/panorama.md @@ -0,0 +1,54 @@ + + +> [!WARNING] +> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model. + +# MultiDiffusion + +
+ LoRA +
+ +[MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://huggingface.co/papers/2302.08113) is by Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. + +The abstract from the paper is: + +*Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fast adaptation to new tasks still remains an open challenge, currently mostly addressed by costly and long re-training and fine-tuning or ad-hoc adaptations to specific image generation tasks. In this work, we present MultiDiffusion, a unified framework that enables versatile and controllable image generation, using a pre-trained text-to-image diffusion model, without any further training or finetuning. At the center of our approach is a new generation process, based on an optimization task that binds together multiple diffusion generation processes with a shared set of parameters or constraints. We show that MultiDiffusion can be readily applied to generate high quality and diverse images that adhere to user-provided controls, such as desired aspect ratio (e.g., panorama), and spatial guiding signals, ranging from tight segmentation masks to bounding boxes.* + +You can find additional information about MultiDiffusion on the [project page](https://multidiffusion.github.io/), [original codebase](https://github.com/omerbt/MultiDiffusion), and try it out in a [demo](https://huggingface.co/spaces/weizmannscience/MultiDiffusion). + +## Tips + +While calling [`StableDiffusionPanoramaPipeline`], it's possible to specify the `view_batch_size` parameter to be > 1. +For some GPUs with high performance, this can speedup the generation process and increase VRAM usage. + +To generate panorama-like images make sure you pass the width parameter accordingly. We recommend a width value of 2048 which is the default. + +Circular padding is applied to ensure there are no stitching artifacts when working with panoramas to ensure a seamless transition from the rightmost part to the leftmost part. By enabling circular padding (set `circular_padding=True`), the operation applies additional crops after the rightmost point of the image, allowing the model to "see” the transition from the rightmost part to the leftmost part. This helps maintain visual consistency in a 360-degree sense and creates a proper “panorama” that can be viewed using 360-degree panorama viewers. When decoding latents in Stable Diffusion, circular padding is applied to ensure that the decoded latents match in the RGB space. + +For example, without circular padding, there is a stitching artifact (default): +![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/indoor_%20no_circular_padding.png) + +But with circular padding, the right and the left parts are matching (`circular_padding=True`): +![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/indoor_%20circular_padding.png) + +> [!TIP] +> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. + +## StableDiffusionPanoramaPipeline +[[autodoc]] StableDiffusionPanoramaPipeline + - __call__ + - all + +## StableDiffusionPipelineOutput +[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/pia.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/pia.md new file mode 100644 index 0000000000000000000000000000000000000000..eebfa4d4f8a6fa5a7168937af309ed40c2843113 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/pia.md @@ -0,0 +1,168 @@ + + +> [!WARNING] +> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model. + +# Image-to-Video Generation with PIA (Personalized Image Animator) + +
+ LoRA +
+ +## Overview + +[PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models](https://huggingface.co/papers/2312.13964) by Yiming Zhang, Zhening Xing, Yanhong Zeng, Youqing Fang, Kai Chen + +Recent advancements in personalized text-to-image (T2I) models have revolutionized content creation, empowering non-experts to generate stunning images with unique styles. While promising, adding realistic motions into these personalized images by text poses significant challenges in preserving distinct styles, high-fidelity details, and achieving motion controllability by text. In this paper, we present PIA, a Personalized Image Animator that excels in aligning with condition images, achieving motion controllability by text, and the compatibility with various personalized T2I models without specific tuning. To achieve these goals, PIA builds upon a base T2I model with well-trained temporal alignment layers, allowing for the seamless transformation of any personalized T2I model into an image animation model. A key component of PIA is the introduction of the condition module, which utilizes the condition frame and inter-frame affinity as input to transfer appearance information guided by the affinity hint for individual frame synthesis in the latent space. This design mitigates the challenges of appearance-related image alignment within and allows for a stronger focus on aligning with motion-related guidance. + +[Project page](https://pi-animator.github.io/) + +## Available Pipelines + +| Pipeline | Tasks | Demo +|---|---|:---:| +| [PIAPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pia/pipeline_pia.py) | *Image-to-Video Generation with PIA* | + +## Available checkpoints + +Motion Adapter checkpoints for PIA can be found under the [OpenMMLab org](https://huggingface.co/openmmlab/PIA-condition-adapter). These checkpoints are meant to work with any model based on Stable Diffusion 1.5 + +## Usage example + +PIA works with a MotionAdapter checkpoint and a Stable Diffusion 1.5 model checkpoint. The MotionAdapter is a collection of Motion Modules that are responsible for adding coherent motion across image frames. These modules are applied after the Resnet and Attention blocks in the Stable Diffusion UNet. In addition to the motion modules, PIA also replaces the input convolution layer of the SD 1.5 UNet model with a 9 channel input convolution layer. + +The following example demonstrates how to use PIA to generate a video from a single image. + +```python +import torch +from diffusers import ( + EulerDiscreteScheduler, + MotionAdapter, + PIAPipeline, +) +from diffusers.utils import export_to_gif, load_image + +adapter = MotionAdapter.from_pretrained("openmmlab/PIA-condition-adapter") +pipe = PIAPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", motion_adapter=adapter, torch_dtype=torch.float16) + +pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config) +pipe.enable_model_cpu_offload() +pipe.enable_vae_slicing() + +image = load_image( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true" +) +image = image.resize((512, 512)) +prompt = "cat in a field" +negative_prompt = "wrong white balance, dark, sketches,worst quality,low quality" + +generator = torch.Generator("cpu").manual_seed(0) +output = pipe(image=image, prompt=prompt, generator=generator) +frames = output.frames[0] +export_to_gif(frames, "pia-animation.gif") +``` + +Here are some sample outputs: + + + + + +
+ cat in a field. +
+ cat in a field +
+ + +> [!TIP] +> If you plan on using a scheduler that can clip samples, make sure to disable it by setting `clip_sample=False` in the scheduler as this can also have an adverse effect on generated samples. Additionally, the PIA checkpoints can be sensitive to the beta schedule of the scheduler. We recommend setting this to `linear`. + +## Using FreeInit + +[FreeInit: Bridging Initialization Gap in Video Diffusion Models](https://huggingface.co/papers/2312.07537) by Tianxing Wu, Chenyang Si, Yuming Jiang, Ziqi Huang, Ziwei Liu. + +FreeInit is an effective method that improves temporal consistency and overall quality of videos generated using video-diffusion-models without any addition training. It can be applied to PIA, AnimateDiff, ModelScope, VideoCrafter and various other video generation models seamlessly at inference time, and works by iteratively refining the latent-initialization noise. More details can be found it the paper. + +The following example demonstrates the usage of FreeInit. + +```python +import torch +from diffusers import ( + DDIMScheduler, + MotionAdapter, + PIAPipeline, +) +from diffusers.utils import export_to_gif, load_image + +adapter = MotionAdapter.from_pretrained("openmmlab/PIA-condition-adapter") +pipe = PIAPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", motion_adapter=adapter) + +# enable FreeInit +# Refer to the enable_free_init documentation for a full list of configurable parameters +pipe.enable_free_init(method="butterworth", use_fast_sampling=True) + +# Memory saving options +pipe.enable_model_cpu_offload() +pipe.enable_vae_slicing() + +pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) +image = load_image( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true" +) +image = image.resize((512, 512)) +prompt = "cat in a field" +negative_prompt = "wrong white balance, dark, sketches,worst quality,low quality" + +generator = torch.Generator("cpu").manual_seed(0) + +output = pipe(image=image, prompt=prompt, generator=generator) +frames = output.frames[0] +export_to_gif(frames, "pia-freeinit-animation.gif") +``` + + + + + +
+ cat in a field. +
+ cat in a field +
+ + +> [!WARNING] +> FreeInit is not really free - the improved quality comes at the cost of extra computation. It requires sampling a few extra times depending on the `num_iters` parameter that is set when enabling it. Setting the `use_fast_sampling` parameter to `True` can improve the overall performance (at the cost of lower quality compared to when `use_fast_sampling=False` but still better results than vanilla video generation models). + +## PIAPipeline + +[[autodoc]] PIAPipeline + - all + - __call__ + - enable_freeu + - disable_freeu + - enable_free_init + - disable_free_init + - enable_vae_slicing + - disable_vae_slicing + - enable_vae_tiling + - disable_vae_tiling + +## PIAPipelineOutput + +[[autodoc]] pipelines.pia.PIAPipelineOutput \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/pix2pix.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/pix2pix.md new file mode 100644 index 0000000000000000000000000000000000000000..84eb0cb5e5d39727cfb41b8460cb82f8c80dc792 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/pix2pix.md @@ -0,0 +1,41 @@ + + +# InstructPix2Pix + +
+ LoRA +
+ +[InstructPix2Pix: Learning to Follow Image Editing Instructions](https://huggingface.co/papers/2211.09800) is by Tim Brooks, Aleksander Holynski and Alexei A. Efros. + +The abstract from the paper is: + +*We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image. To obtain training data for this problem, we combine the knowledge of two large pretrained models -- a language model (GPT-3) and a text-to-image model (Stable Diffusion) -- to generate a large dataset of image editing examples. Our conditional diffusion model, InstructPix2Pix, is trained on our generated data, and generalizes to real images and user-written instructions at inference time. Since it performs edits in the forward pass and does not require per example fine-tuning or inversion, our model edits images quickly, in a matter of seconds. We show compelling editing results for a diverse collection of input images and written instructions.* + +You can find additional information about InstructPix2Pix on the [project page](https://www.timothybrooks.com/instruct-pix2pix), [original codebase](https://github.com/timothybrooks/instruct-pix2pix), and try it out in a [demo](https://huggingface.co/spaces/timbrooks/instruct-pix2pix). + +> [!TIP] +> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. + +## StableDiffusionInstructPix2PixPipeline +[[autodoc]] StableDiffusionInstructPix2PixPipeline + - __call__ + - all + - load_textual_inversion + - load_lora_weights + - save_lora_weights + +## StableDiffusionXLInstructPix2PixPipeline +[[autodoc]] StableDiffusionXLInstructPix2PixPipeline + - __call__ + - all diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/pixart.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/pixart.md new file mode 100644 index 0000000000000000000000000000000000000000..dbdc89857e5e7834de12569155efd5c65671aef1 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/pixart.md @@ -0,0 +1,139 @@ + + +# PixArt-α + +![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/pixart/header_collage.png) + +[PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis](https://huggingface.co/papers/2310.00426) is Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li. + +The abstract from the paper is: + +*The most advanced text-to-image (T2I) models require significant training costs (e.g., millions of GPU hours), seriously hindering the fundamental innovation for the AIGC community while increasing CO2 emissions. This paper introduces PIXART-α, a Transformer-based T2I diffusion model whose image generation quality is competitive with state-of-the-art image generators (e.g., Imagen, SDXL, and even Midjourney), reaching near-commercial application standards. Additionally, it supports high-resolution image synthesis up to 1024px resolution with low training cost, as shown in Figure 1 and 2. To achieve this goal, three core designs are proposed: (1) Training strategy decomposition: We devise three distinct training steps that separately optimize pixel dependency, text-image alignment, and image aesthetic quality; (2) Efficient T2I Transformer: We incorporate cross-attention modules into Diffusion Transformer (DiT) to inject text conditions and streamline the computation-intensive class-condition branch; (3) High-informative data: We emphasize the significance of concept density in text-image pairs and leverage a large Vision-Language model to auto-label dense pseudo-captions to assist text-image alignment learning. As a result, PIXART-α's training speed markedly surpasses existing large-scale T2I models, e.g., PIXART-α only takes 10.8% of Stable Diffusion v1.5's training time (675 vs. 6,250 A100 GPU days), saving nearly $300,000 ($26,000 vs. $320,000) and reducing 90% CO2 emissions. Moreover, compared with a larger SOTA model, RAPHAEL, our training cost is merely 1%. Extensive experiments demonstrate that PIXART-α excels in image quality, artistry, and semantic control. We hope PIXART-α will provide new insights to the AIGC community and startups to accelerate building their own high-quality yet low-cost generative models from scratch.* + +You can find the original codebase at [PixArt-alpha/PixArt-alpha](https://github.com/PixArt-alpha/PixArt-alpha) and all the available checkpoints at [PixArt-alpha](https://huggingface.co/PixArt-alpha). + +Some notes about this pipeline: + +* It uses a Transformer backbone (instead of a UNet) for denoising. As such it has a similar architecture as [DiT](./dit). +* It was trained using text conditions computed from T5. This aspect makes the pipeline better at following complex text prompts with intricate details. +* It is good at producing high-resolution images at different aspect ratios. To get the best results, the authors recommend some size brackets which can be found [here](https://github.com/PixArt-alpha/PixArt-alpha/blob/08fbbd281ec96866109bdd2cdb75f2f58fb17610/diffusion/data/datasets/utils.py). +* It rivals the quality of state-of-the-art text-to-image generation systems (as of this writing) such as Stable Diffusion XL, Imagen, and DALL-E 2, while being more efficient than them. + +> [!TIP] +> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. + +## Inference with under 8GB GPU VRAM + +Run the [`PixArtAlphaPipeline`] with under 8GB GPU VRAM by loading the text encoder in 8-bit precision. Let's walk through a full-fledged example. + +First, install the [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) library: + +```bash +pip install -U bitsandbytes +``` + +Then load the text encoder in 8-bit: + +```python +from transformers import T5EncoderModel +from diffusers import PixArtAlphaPipeline +import torch + +text_encoder = T5EncoderModel.from_pretrained( + "PixArt-alpha/PixArt-XL-2-1024-MS", + subfolder="text_encoder", + load_in_8bit=True, + device_map="auto", + +) +pipe = PixArtAlphaPipeline.from_pretrained( + "PixArt-alpha/PixArt-XL-2-1024-MS", + text_encoder=text_encoder, + transformer=None, + device_map="auto" +) +``` + +Now, use the `pipe` to encode a prompt: + +```python +with torch.no_grad(): + prompt = "cute cat" + prompt_embeds, prompt_attention_mask, negative_embeds, negative_prompt_attention_mask = pipe.encode_prompt(prompt) +``` + +Since text embeddings have been computed, remove the `text_encoder` and `pipe` from the memory, and free up some GPU VRAM: + +```python +import gc + +def flush(): + gc.collect() + torch.cuda.empty_cache() + +del text_encoder +del pipe +flush() +``` + +Then compute the latents with the prompt embeddings as inputs: + +```python +pipe = PixArtAlphaPipeline.from_pretrained( + "PixArt-alpha/PixArt-XL-2-1024-MS", + text_encoder=None, + torch_dtype=torch.float16, +).to("cuda") + +latents = pipe( + negative_prompt=None, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_embeds, + prompt_attention_mask=prompt_attention_mask, + negative_prompt_attention_mask=negative_prompt_attention_mask, + num_images_per_prompt=1, + output_type="latent", +).images + +del pipe.transformer +flush() +``` + +> [!TIP] +> Notice that while initializing `pipe`, you're setting `text_encoder` to `None` so that it's not loaded. + +Once the latents are computed, pass it off to the VAE to decode into a real image: + +```python +with torch.no_grad(): + image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0] +image = pipe.image_processor.postprocess(image, output_type="pil")[0] +image.save("cat.png") +``` + +By deleting components you aren't using and flushing the GPU VRAM, you should be able to run [`PixArtAlphaPipeline`] with under 8GB GPU VRAM. + +![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/pixart/8bits_cat.png) + +If you want a report of your memory-usage, run this [script](https://gist.github.com/sayakpaul/3ae0f847001d342af27018a96f467e4e). + +> [!WARNING] +> Text embeddings computed in 8-bit can impact the quality of the generated images because of the information loss in the representation space caused by the reduced precision. It's recommended to compare the outputs with and without 8-bit. + +While loading the `text_encoder`, you set `load_in_8bit` to `True`. You could also specify `load_in_4bit` to bring your memory requirements down even further to under 7GB. + +## PixArtAlphaPipeline + +[[autodoc]] PixArtAlphaPipeline + - all + - __call__ diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/pixart_sigma.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/pixart_sigma.md new file mode 100644 index 0000000000000000000000000000000000000000..06b54de43bbc9ea170da2a6131a5dd552fa9276d --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/pixart_sigma.md @@ -0,0 +1,143 @@ + + +# PixArt-Σ + +![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/pixart/header_collage_sigma.jpg) + +[PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation](https://huggingface.co/papers/2403.04692) is Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li. + +The abstract from the paper is: + +*In this paper, we introduce PixArt-Σ, a Diffusion Transformer model (DiT) capable of directly generating images at 4K resolution. PixArt-Σ represents a significant advancement over its predecessor, PixArt-α, offering images of markedly higher fidelity and improved alignment with text prompts. A key feature of PixArt-Σ is its training efficiency. Leveraging the foundational pre-training of PixArt-α, it evolves from the ‘weaker’ baseline to a ‘stronger’ model via incorporating higher quality data, a process we term “weak-to-strong training”. The advancements in PixArt-Σ are twofold: (1) High-Quality Training Data: PixArt-Σ incorporates superior-quality image data, paired with more precise and detailed image captions. (2) Efficient Token Compression: we propose a novel attention module within the DiT framework that compresses both keys and values, significantly improving efficiency and facilitating ultra-high-resolution image generation. Thanks to these improvements, PixArt-Σ achieves superior image quality and user prompt adherence capabilities with significantly smaller model size (0.6B parameters) than existing text-to-image diffusion models, such as SDXL (2.6B parameters) and SD Cascade (5.1B parameters). Moreover, PixArt-Σ’s capability to generate 4K images supports the creation of high-resolution posters and wallpapers, efficiently bolstering the production of highquality visual content in industries such as film and gaming.* + +You can find the original codebase at [PixArt-alpha/PixArt-sigma](https://github.com/PixArt-alpha/PixArt-sigma) and all the available checkpoints at [PixArt-alpha](https://huggingface.co/PixArt-alpha). + +Some notes about this pipeline: + +* It uses a Transformer backbone (instead of a UNet) for denoising. As such it has a similar architecture as [DiT](https://hf.co/docs/transformers/model_doc/dit). +* It was trained using text conditions computed from T5. This aspect makes the pipeline better at following complex text prompts with intricate details. +* It is good at producing high-resolution images at different aspect ratios. To get the best results, the authors recommend some size brackets which can be found [here](https://github.com/PixArt-alpha/PixArt-sigma/blob/master/diffusion/data/datasets/utils.py). +* It rivals the quality of state-of-the-art text-to-image generation systems (as of this writing) such as PixArt-α, Stable Diffusion XL, Playground V2.0 and DALL-E 3, while being more efficient than them. +* It shows the ability of generating super high resolution images, such as 2048px or even 4K. +* It shows that text-to-image models can grow from a weak model to a stronger one through several improvements (VAEs, datasets, and so on.) + +> [!TIP] +> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. + +> [!TIP] +> You can further improve generation quality by passing the generated image from [`PixArtSigmaPipeline`] to the [SDXL refiner](../../using-diffusers/sdxl#base-to-refiner-model) model. + +## Inference with under 8GB GPU VRAM + +Run the [`PixArtSigmaPipeline`] with under 8GB GPU VRAM by loading the text encoder in 8-bit precision. Let's walk through a full-fledged example. + +First, install the [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) library: + +```bash +pip install -U bitsandbytes +``` + +Then load the text encoder in 8-bit: + +```python +from transformers import T5EncoderModel +from diffusers import PixArtSigmaPipeline +import torch + +text_encoder = T5EncoderModel.from_pretrained( + "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS", + subfolder="text_encoder", + load_in_8bit=True, + device_map="auto", +) +pipe = PixArtSigmaPipeline.from_pretrained( + "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS", + text_encoder=text_encoder, + transformer=None, + device_map="balanced" +) +``` + +Now, use the `pipe` to encode a prompt: + +```python +with torch.no_grad(): + prompt = "cute cat" + prompt_embeds, prompt_attention_mask, negative_embeds, negative_prompt_attention_mask = pipe.encode_prompt(prompt) +``` + +Since text embeddings have been computed, remove the `text_encoder` and `pipe` from the memory, and free up some GPU VRAM: + +```python +import gc + +def flush(): + gc.collect() + torch.cuda.empty_cache() + +del text_encoder +del pipe +flush() +``` + +Then compute the latents with the prompt embeddings as inputs: + +```python +pipe = PixArtSigmaPipeline.from_pretrained( + "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS", + text_encoder=None, + torch_dtype=torch.float16, +).to("cuda") + +latents = pipe( + negative_prompt=None, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_embeds, + prompt_attention_mask=prompt_attention_mask, + negative_prompt_attention_mask=negative_prompt_attention_mask, + num_images_per_prompt=1, + output_type="latent", +).images + +del pipe.transformer +flush() +``` + +> [!TIP] +> Notice that while initializing `pipe`, you're setting `text_encoder` to `None` so that it's not loaded. + +Once the latents are computed, pass it off to the VAE to decode into a real image: + +```python +with torch.no_grad(): + image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0] +image = pipe.image_processor.postprocess(image, output_type="pil")[0] +image.save("cat.png") +``` + +By deleting components you aren't using and flushing the GPU VRAM, you should be able to run [`PixArtSigmaPipeline`] with under 8GB GPU VRAM. + +![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/pixart/8bits_cat.png) + +If you want a report of your memory-usage, run this [script](https://gist.github.com/sayakpaul/3ae0f847001d342af27018a96f467e4e). + +> [!WARNING] +> Text embeddings computed in 8-bit can impact the quality of the generated images because of the information loss in the representation space caused by the reduced precision. It's recommended to compare the outputs with and without 8-bit. + +While loading the `text_encoder`, you set `load_in_8bit` to `True`. You could also specify `load_in_4bit` to bring your memory requirements down even further to under 7GB. + +## PixArtSigmaPipeline + +[[autodoc]] PixArtSigmaPipeline + - all + - __call__ diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/prx.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/prx.md new file mode 100644 index 0000000000000000000000000000000000000000..16670f4bfc86eb5ec887ed943ca1ea7d1a245fbd --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/prx.md @@ -0,0 +1,131 @@ + + +# PRX + + +PRX generates high-quality images from text using a simplified MMDIT architecture where text tokens don't update through transformer blocks. It employs flow matching with discrete scheduling for efficient sampling and uses Google's T5Gemma-2B-2B-UL2 model for multi-language text encoding. The ~1.3B parameter transformer delivers fast inference without sacrificing quality. You can choose between Flux VAE (8x compression, 16 latent channels) for balanced quality and speed or DC-AE (32x compression, 32 latent channels) for latent compression and faster processing. + +## Available models + +PRX offers multiple variants with different VAE configurations, each optimized for specific resolutions. Base models excel with detailed prompts, capturing complex compositions and subtle details. Fine-tuned models trained on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) improve aesthetic quality, especially with simpler prompts. + + +| Model | Resolution | Fine-tuned | Distilled | Description | Suggested prompts | Suggested parameters | Recommended dtype | +|:-----:|:-----------------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:| +| [`Photoroom/prx-256-t2i`](https://huggingface.co/Photoroom/prx-256-t2i)| 256 | No | No | Base model pre-trained at 256 with Flux VAE|Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` | +| [`Photoroom/prx-256-t2i-sft`](https://huggingface.co/Photoroom/prx-256-t2i-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with Flux VAE | Can handle less detailed prompts|28 steps, cfg=5.0| `torch.bfloat16` | +| [`Photoroom/prx-512-t2i`](https://huggingface.co/Photoroom/prx-512-t2i)| 512 | No | No | Base model pre-trained at 512 with Flux VAE |Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` | +| [`Photoroom/prx-512-t2i-sft`](https://huggingface.co/Photoroom/prx-512-t2i-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with Flux VAE | Can handle less detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` | +| [`Photoroom/prx-512-t2i-sft-distilled`](https://huggingface.co/Photoroom/prx-512-t2i-sft-distilled)| 512 | Yes | Yes | 8-step distilled model from [`Photoroom/prx-512-t2i-sft`](https://huggingface.co/Photoroom/prx-512-t2i-sft) | Can handle less detailed prompts in natural language|8 steps, cfg=1.0| `torch.bfloat16` | +| [`Photoroom/prx-512-t2i-dc-ae`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae)| 512 | No | No | Base model pre-trained at 512 with [Deep Compression Autoencoder (DC-AE)](https://hanlab.mit.edu/projects/dc-ae)|Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` | +| [`Photoroom/prx-512-t2i-dc-ae-sft`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with [Deep Compression Autoencoder (DC-AE)](https://hanlab.mit.edu/projects/dc-ae) | Can handle less detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` | +| [`Photoroom/prx-512-t2i-dc-ae-sft-distilled`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae-sft-distilled)| 512 | Yes | Yes | 8-step distilled model from [`Photoroom/prx-512-t2i-dc-ae-sft-distilled`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae-sft-distilled) | Can handle less detailed prompts in natural language|8 steps, cfg=1.0| `torch.bfloat16` |s + +Refer to [this](https://huggingface.co/collections/Photoroom/prx-models-68e66254c202ebfab99ad38e) collection for more information. + +## Loading the pipeline + +Load the pipeline with [`~DiffusionPipeline.from_pretrained`]. + +```py +from diffusers.pipelines.prx import PRXPipeline + +# Load pipeline - VAE and text encoder will be loaded from HuggingFace +pipe = PRXPipeline.from_pretrained("Photoroom/prx-512-t2i-sft", torch_dtype=torch.bfloat16) +pipe.to("cuda") + +prompt = "A front-facing portrait of a lion the golden savanna at sunset." +image = pipe(prompt, num_inference_steps=28, guidance_scale=5.0).images[0] +image.save("prx_output.png") +``` + +### Manual Component Loading + +Load components individually to customize the pipeline for instance to use quantized models. + +```py +import torch +from diffusers.pipelines.prx import PRXPipeline +from diffusers.models import AutoencoderKL, AutoencoderDC +from diffusers.models.transformers.transformer_prx import PRXTransformer2DModel +from diffusers.schedulers import FlowMatchEulerDiscreteScheduler +from transformers import T5GemmaModel, GemmaTokenizerFast +from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig +from transformers import BitsAndBytesConfig as BitsAndBytesConfig + +quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True) +# Load transformer +transformer = PRXTransformer2DModel.from_pretrained( + "checkpoints/prx-512-t2i-sft", + subfolder="transformer", + quantization_config=quant_config, + torch_dtype=torch.bfloat16, +) + +# Load scheduler +scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained( + "checkpoints/prx-512-t2i-sft", subfolder="scheduler" +) + +# Load T5Gemma text encoder +t5gemma_model = T5GemmaModel.from_pretrained("google/t5gemma-2b-2b-ul2", + quantization_config=quant_config, + torch_dtype=torch.bfloat16) +text_encoder = t5gemma_model.encoder.to(dtype=torch.bfloat16) +tokenizer = GemmaTokenizerFast.from_pretrained("google/t5gemma-2b-2b-ul2") +tokenizer.model_max_length = 256 + +# Load VAE - choose either Flux VAE or DC-AE +# Flux VAE +vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", + subfolder="vae", + quantization_config=quant_config, + torch_dtype=torch.bfloat16) + +pipe = PRXPipeline( + transformer=transformer, + scheduler=scheduler, + text_encoder=text_encoder, + tokenizer=tokenizer, + vae=vae +) +pipe.to("cuda") +``` + + +## Memory Optimization + +For memory-constrained environments: + +```py +import torch +from diffusers.pipelines.prx import PRXPipeline + +pipe = PRXPipeline.from_pretrained("Photoroom/prx-512-t2i-sft", torch_dtype=torch.bfloat16) +pipe.enable_model_cpu_offload() # Offload components to CPU when not in use + +# Or use sequential CPU offload for even lower memory +pipe.enable_sequential_cpu_offload() +``` + +## PRXPipeline + +[[autodoc]] PRXPipeline + - all + - __call__ + +## PRXPipelineOutput + +[[autodoc]] pipelines.prx.pipeline_output.PRXPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/qwenimage.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/qwenimage.md new file mode 100644 index 0000000000000000000000000000000000000000..b3dd3dd936186604fcca1ad41131d3cf50ea14df --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/qwenimage.md @@ -0,0 +1,161 @@ + + +# QwenImage + +
+ LoRA +
+ +Qwen-Image from the Qwen team is an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. Experiments show strong general capabilities in both image generation and editing, with exceptional performance in text rendering, especially for Chinese. + +Qwen-Image comes in the following variants: + +| model type | model id | +|:----------:|:--------:| +| Qwen-Image | [`Qwen/Qwen-Image`](https://huggingface.co/Qwen/Qwen-Image) | +| Qwen-Image-Edit | [`Qwen/Qwen-Image-Edit`](https://huggingface.co/Qwen/Qwen-Image-Edit) | +| Qwen-Image-Edit Plus | [Qwen/Qwen-Image-Edit-2509](https://huggingface.co/Qwen/Qwen-Image-Edit-2509) | + +> [!TIP] +> [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs. + +## LoRA for faster inference + +Use a LoRA from `lightx2v/Qwen-Image-Lightning` to speed up inference by reducing the +number of steps. Refer to the code snippet below: + +
+Code + +```py +from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler +import torch +import math + +ckpt_id = "Qwen/Qwen-Image" + +# From +# https://github.com/ModelTC/Qwen-Image-Lightning/blob/342260e8f5468d2f24d084ce04f55e101007118b/generate_with_diffusers.py#L82C9-L97C10 +scheduler_config = { + "base_image_seq_len": 256, + "base_shift": math.log(3), # We use shift=3 in distillation + "invert_sigmas": False, + "max_image_seq_len": 8192, + "max_shift": math.log(3), # We use shift=3 in distillation + "num_train_timesteps": 1000, + "shift": 1.0, + "shift_terminal": None, # set shift_terminal to None + "stochastic_sampling": False, + "time_shift_type": "exponential", + "use_beta_sigmas": False, + "use_dynamic_shifting": True, + "use_exponential_sigmas": False, + "use_karras_sigmas": False, +} +scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config) +pipe = DiffusionPipeline.from_pretrained( + ckpt_id, scheduler=scheduler, torch_dtype=torch.bfloat16 +).to("cuda") +pipe.load_lora_weights( + "lightx2v/Qwen-Image-Lightning", weight_name="Qwen-Image-Lightning-8steps-V1.0.safetensors" +) + +prompt = "a tiny astronaut hatching from an egg on the moon, Ultra HD, 4K, cinematic composition." +negative_prompt = " " +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + width=1024, + height=1024, + num_inference_steps=8, + true_cfg_scale=1.0, + generator=torch.manual_seed(0), +).images[0] +image.save("qwen_fewsteps.png") +``` + +
+ +> [!TIP] +> The `guidance_scale` parameter in the pipeline is there to support future guidance-distilled models when they come up. Note that passing `guidance_scale` to the pipeline is ineffective. To enable classifier-free guidance, please pass `true_cfg_scale` and `negative_prompt` (even an empty negative prompt like " ") should enable classifier-free guidance computations. + +## Multi-image reference with QwenImageEditPlusPipeline + +With [`QwenImageEditPlusPipeline`], one can provide multiple images as input reference. + +``` +import torch +from PIL import Image +from diffusers import QwenImageEditPlusPipeline +from diffusers.utils import load_image + +pipe = QwenImageEditPlusPipeline.from_pretrained( + "Qwen/Qwen-Image-Edit-2509", torch_dtype=torch.bfloat16 +).to("cuda") + +image_1 = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/grumpy.jpg") +image_2 = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peng.png") +image = pipe( + image=[image_1, image_2], + prompt='''put the penguin and the cat at a game show called "Qwen Edit Plus Games"''', + num_inference_steps=50 +).images[0] +``` + +## QwenImagePipeline + +[[autodoc]] QwenImagePipeline + - all + - __call__ + +## QwenImageImg2ImgPipeline + +[[autodoc]] QwenImageImg2ImgPipeline + - all + - __call__ + +## QwenImageInpaintPipeline + +[[autodoc]] QwenImageInpaintPipeline + - all + - __call__ + +## QwenImageEditPipeline + +[[autodoc]] QwenImageEditPipeline + - all + - __call__ + +## QwenImageEditInpaintPipeline + +[[autodoc]] QwenImageEditInpaintPipeline + - all + - __call__ + +## QwenImageControlNetPipeline + +[[autodoc]] QwenImageControlNetPipeline + - all + - __call__ + +## QwenImageEditPlusPipeline + +[[autodoc]] QwenImageEditPlusPipeline + - all + - __call__ + +## QwenImagePipelineOutput + +[[autodoc]] pipelines.qwenimage.pipeline_output.QwenImagePipelineOutput \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/sana.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/sana.md new file mode 100644 index 0000000000000000000000000000000000000000..a948620f96cb9245438a2cbcd69f03f570ce5ac0 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/sana.md @@ -0,0 +1,106 @@ + + +# SanaPipeline + +
+ LoRA + MPS +
+ +[SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers](https://huggingface.co/papers/2410.10629) from NVIDIA and MIT HAN Lab, by Enze Xie, Junsong Chen, Junyu Chen, Han Cai, Haotian Tang, Yujun Lin, Zhekai Zhang, Muyang Li, Ligeng Zhu, Yao Lu, Song Han. + +The abstract from the paper is: + +*We introduce Sana, a text-to-image framework that can efficiently generate images up to 4096×4096 resolution. Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU. Core designs include: (1) Deep compression autoencoder: unlike traditional AEs, which compress images only 8×, we trained an AE that can compress images 32×, effectively reducing the number of latent tokens. (2) Linear DiT: we replace all vanilla attention in DiT with linear attention, which is more efficient at high resolutions without sacrificing quality. (3) Decoder-only text encoder: we replaced T5 with modern decoder-only small LLM as the text encoder and designed complex human instruction with in-context learning to enhance the image-text alignment. (4) Efficient training and sampling: we propose Flow-DPM-Solver to reduce sampling steps, with efficient caption labeling and selection to accelerate convergence. As a result, Sana-0.6B is very competitive with modern giant diffusion model (e.g. Flux-12B), being 20 times smaller and 100+ times faster in measured throughput. Moreover, Sana-0.6B can be deployed on a 16GB laptop GPU, taking less than 1 second to generate a 1024×1024 resolution image. Sana enables content creation at low cost. Code and model will be publicly released.* + +> [!TIP] +> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. + +This pipeline was contributed by [lawrence-cj](https://github.com/lawrence-cj) and [chenjy2003](https://github.com/chenjy2003). The original codebase can be found [here](https://github.com/NVlabs/Sana). The original weights can be found under [hf.co/Efficient-Large-Model](https://huggingface.co/Efficient-Large-Model). + +Available models: + +| Model | Recommended dtype | +|:-----:|:-----------------:| +| [`Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers) | `torch.bfloat16` | +| [`Efficient-Large-Model/Sana_1600M_1024px_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_diffusers) | `torch.float16` | +| [`Efficient-Large-Model/Sana_1600M_1024px_MultiLing_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_MultiLing_diffusers) | `torch.float16` | +| [`Efficient-Large-Model/Sana_1600M_512px_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_1600M_512px_diffusers) | `torch.float16` | +| [`Efficient-Large-Model/Sana_1600M_512px_MultiLing_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_1600M_512px_MultiLing_diffusers) | `torch.float16` | +| [`Efficient-Large-Model/Sana_600M_1024px_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_600M_1024px_diffusers) | `torch.float16` | +| [`Efficient-Large-Model/Sana_600M_512px_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_600M_512px_diffusers) | `torch.float16` | + +Refer to [this](https://huggingface.co/collections/Efficient-Large-Model/sana-673efba2a57ed99843f11f9e) collection for more information. + +Note: The recommended dtype mentioned is for the transformer weights. The text encoder and VAE weights must stay in `torch.bfloat16` or `torch.float32` for the model to work correctly. Please refer to the inference example below to see how to load the model with the recommended dtype. + +> [!TIP] +> Make sure to pass the `variant` argument for downloaded checkpoints to use lower disk space. Set it to `"fp16"` for models with recommended dtype as `torch.float16`, and `"bf16"` for models with recommended dtype as `torch.bfloat16`. By default, `torch.float32` weights are downloaded, which use twice the amount of disk storage. Additionally, `torch.float32` weights can be downcasted on-the-fly by specifying the `torch_dtype` argument. Read about it in the [docs](https://huggingface.co/docs/diffusers/v0.31.0/en/api/pipelines/overview#diffusers.DiffusionPipeline.from_pretrained). + +## Quantization + +Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model. + +Refer to the [Quantization](../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`SanaPipeline`] for inference with bitsandbytes. + +```py +import torch +from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, SanaTransformer2DModel, SanaPipeline +from transformers import BitsAndBytesConfig as BitsAndBytesConfig, AutoModel + +quant_config = BitsAndBytesConfig(load_in_8bit=True) +text_encoder_8bit = AutoModel.from_pretrained( + "Efficient-Large-Model/Sana_1600M_1024px_diffusers", + subfolder="text_encoder", + quantization_config=quant_config, + torch_dtype=torch.float16, +) + +quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True) +transformer_8bit = SanaTransformer2DModel.from_pretrained( + "Efficient-Large-Model/Sana_1600M_1024px_diffusers", + subfolder="transformer", + quantization_config=quant_config, + torch_dtype=torch.float16, +) + +pipeline = SanaPipeline.from_pretrained( + "Efficient-Large-Model/Sana_1600M_1024px_diffusers", + text_encoder=text_encoder_8bit, + transformer=transformer_8bit, + torch_dtype=torch.float16, + device_map="balanced", +) + +prompt = "a tiny astronaut hatching from an egg on the moon" +image = pipeline(prompt).images[0] +image.save("sana.png") +``` + +## SanaPipeline + +[[autodoc]] SanaPipeline + - all + - __call__ + +## SanaPAGPipeline + +[[autodoc]] SanaPAGPipeline + - all + - __call__ + +## SanaPipelineOutput + +[[autodoc]] pipelines.sana.pipeline_output.SanaPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/sana_sprint.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/sana_sprint.md new file mode 100644 index 0000000000000000000000000000000000000000..46cdc13302ecea53ea29f386093f250ac8635e13 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/sana_sprint.md @@ -0,0 +1,128 @@ + + +# SANA-Sprint + +
+ LoRA +
+ +[SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillation](https://huggingface.co/papers/2503.09641) from NVIDIA, MIT HAN Lab, and Hugging Face by Junsong Chen, Shuchen Xue, Yuyang Zhao, Jincheng Yu, Sayak Paul, Junyu Chen, Han Cai, Enze Xie, Song Han + +The abstract from the paper is: + +*This paper presents SANA-Sprint, an efficient diffusion model for ultra-fast text-to-image (T2I) generation. SANA-Sprint is built on a pre-trained foundation model and augmented with hybrid distillation, dramatically reducing inference steps from 20 to 1-4. We introduce three key innovations: (1) We propose a training-free approach that transforms a pre-trained flow-matching model for continuous-time consistency distillation (sCM), eliminating costly training from scratch and achieving high training efficiency. Our hybrid distillation strategy combines sCM with latent adversarial distillation (LADD): sCM ensures alignment with the teacher model, while LADD enhances single-step generation fidelity. (2) SANA-Sprint is a unified step-adaptive model that achieves high-quality generation in 1-4 steps, eliminating step-specific training and improving efficiency. (3) We integrate ControlNet with SANA-Sprint for real-time interactive image generation, enabling instant visual feedback for user interaction. SANA-Sprint establishes a new Pareto frontier in speed-quality tradeoffs, achieving state-of-the-art performance with 7.59 FID and 0.74 GenEval in only 1 step — outperforming FLUX-schnell (7.94 FID / 0.71 GenEval) while being 10× faster (0.1s vs 1.1s on H100). It also achieves 0.1s (T2I) and 0.25s (ControlNet) latency for 1024×1024 images on H100, and 0.31s (T2I) on an RTX 4090, showcasing its exceptional efficiency and potential for AI-powered consumer applications (AIPC). Code and pre-trained models will be open-sourced.* + +This pipeline was contributed by [lawrence-cj](https://github.com/lawrence-cj), [shuchen Xue](https://github.com/scxue) and [Enze Xie](https://github.com/xieenze). The original codebase can be found [here](https://github.com/NVlabs/Sana). The original weights can be found under [hf.co/Efficient-Large-Model](https://huggingface.co/Efficient-Large-Model/). + +Available models: + +| Model | Recommended dtype | +|:-------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------:| +| [`Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers) | `torch.bfloat16` | +| [`Efficient-Large-Model/Sana_Sprint_0.6B_1024px_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_Sprint_0.6B_1024px_diffusers) | `torch.bfloat16` | + +Refer to [this](https://huggingface.co/collections/Efficient-Large-Model/sana-sprint-67d6810d65235085b3b17c76) collection for more information. + +Note: The recommended dtype mentioned is for the transformer weights. The text encoder must stay in `torch.bfloat16` and VAE weights must stay in `torch.bfloat16` or `torch.float32` for the model to work correctly. Please refer to the inference example below to see how to load the model with the recommended dtype. + + +## Quantization + +Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model. + +Refer to the [Quantization](../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`SanaSprintPipeline`] for inference with bitsandbytes. + +```py +import torch +from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, SanaTransformer2DModel, SanaSprintPipeline +from transformers import BitsAndBytesConfig as BitsAndBytesConfig, AutoModel + +quant_config = BitsAndBytesConfig(load_in_8bit=True) +text_encoder_8bit = AutoModel.from_pretrained( + "Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers", + subfolder="text_encoder", + quantization_config=quant_config, + torch_dtype=torch.bfloat16, +) + +quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True) +transformer_8bit = SanaTransformer2DModel.from_pretrained( + "Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers", + subfolder="transformer", + quantization_config=quant_config, + torch_dtype=torch.bfloat16, +) + +pipeline = SanaSprintPipeline.from_pretrained( + "Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers", + text_encoder=text_encoder_8bit, + transformer=transformer_8bit, + torch_dtype=torch.bfloat16, + device_map="balanced", +) + +prompt = "a tiny astronaut hatching from an egg on the moon" +image = pipeline(prompt).images[0] +image.save("sana.png") +``` + +## Setting `max_timesteps` + +Users can tweak the `max_timesteps` value for experimenting with the visual quality of the generated outputs. The default `max_timesteps` value was obtained with an inference-time search process. For more details about it, check out the paper. + +## Image to Image + +The [`SanaSprintImg2ImgPipeline`] is a pipeline for image-to-image generation. It takes an input image and a prompt, and generates a new image based on the input image and the prompt. + +```py +import torch +from diffusers import SanaSprintImg2ImgPipeline +from diffusers.utils.loading_utils import load_image + +image = load_image( + "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/penguin.png" +) + +pipe = SanaSprintImg2ImgPipeline.from_pretrained( + "Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers", + torch_dtype=torch.bfloat16) +pipe.to("cuda") + +image = pipe( + prompt="a cute pink bear", + image=image, + strength=0.5, + height=832, + width=480 +).images[0] +image.save("output.png") +``` + +## SanaSprintPipeline + +[[autodoc]] SanaSprintPipeline + - all + - __call__ + +## SanaSprintImg2ImgPipeline + +[[autodoc]] SanaSprintImg2ImgPipeline + - all + - __call__ + + +## SanaPipelineOutput + +[[autodoc]] pipelines.sana.pipeline_output.SanaPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/sana_video.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/sana_video.md new file mode 100644 index 0000000000000000000000000000000000000000..9e330c758318e55275cd2d18fb75a6ac64663d97 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/sana_video.md @@ -0,0 +1,189 @@ + + +# Sana-Video + +
+ LoRA + MPS +
+ +[SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer](https://huggingface.co/papers/2509.24695) from NVIDIA and MIT HAN Lab, by Junsong Chen, Yuyang Zhao, Jincheng Yu, Ruihang Chu, Junyu Chen, Shuai Yang, Xianbang Wang, Yicheng Pan, Daquan Zhou, Huan Ling, Haozhe Liu, Hongwei Yi, Hao Zhang, Muyang Li, Yukang Chen, Han Cai, Sanja Fidler, Ping Luo, Song Han, Enze Xie. + +The abstract from the paper is: + +*We introduce SANA-Video, a small diffusion model that can efficiently generate videos up to 720x1280 resolution and minute-length duration. SANA-Video synthesizes high-resolution, high-quality and long videos with strong text-video alignment at a remarkably fast speed, deployable on RTX 5090 GPU. Two core designs ensure our efficient, effective and long video generation: (1) Linear DiT: We leverage linear attention as the core operation, which is more efficient than vanilla attention given the large number of tokens processed in video generation. (2) Constant-Memory KV cache for Block Linear Attention: we design block-wise autoregressive approach for long video generation by employing a constant-memory state, derived from the cumulative properties of linear attention. This KV cache provides the Linear DiT with global context at a fixed memory cost, eliminating the need for a traditional KV cache and enabling efficient, minute-long video generation. In addition, we explore effective data filters and model training strategies, narrowing the training cost to 12 days on 64 H100 GPUs, which is only 1% of the cost of MovieGen. Given its low cost, SANA-Video achieves competitive performance compared to modern state-of-the-art small diffusion models (e.g., Wan 2.1-1.3B and SkyReel-V2-1.3B) while being 16x faster in measured latency. Moreover, SANA-Video can be deployed on RTX 5090 GPUs with NVFP4 precision, accelerating the inference speed of generating a 5-second 720p video from 71s to 29s (2.4x speedup). In summary, SANA-Video enables low-cost, high-quality video generation. [this https URL](https://github.com/NVlabs/SANA).* + +This pipeline was contributed by SANA Team. The original codebase can be found [here](https://github.com/NVlabs/Sana). The original weights can be found under [hf.co/Efficient-Large-Model](https://hf.co/collections/Efficient-Large-Model/sana-video). + +Available models: + +| Model | Recommended dtype | +|:-----:|:-----------------:| +| [`Efficient-Large-Model/SANA-Video_2B_480p_diffusers`](https://huggingface.co/Efficient-Large-Model/ANA-Video_2B_480p_diffusers) | `torch.bfloat16` | + +Refer to [this](https://huggingface.co/collections/Efficient-Large-Model/sana-video) collection for more information. + +Note: The recommended dtype mentioned is for the transformer weights. The text encoder and VAE weights must stay in `torch.bfloat16` or `torch.float32` for the model to work correctly. Please refer to the inference example below to see how to load the model with the recommended dtype. + + +## Generation Pipelines + +` + + +The example below demonstrates how to use the text-to-video pipeline to generate a video using a text description. + +```python +pipe = SanaVideoPipeline.from_pretrained( + "Efficient-Large-Model/SANA-Video_2B_480p_diffusers", + torch_dtype=torch.bfloat16, +) +pipe.text_encoder.to(torch.bfloat16) +pipe.vae.to(torch.float32) +pipe.to("cuda") + +prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window." +negative_prompt = "A chaotic sequence with misshapen, deformed limbs in heavy motion blur, sudden disappearance, jump cuts, jerky movements, rapid shot changes, frames out of sync, inconsistent character shapes, temporal artifacts, jitter, and ghosting effects, creating a disorienting visual experience." +motion_scale = 30 +motion_prompt = f" motion score: {motion_scale}." +prompt = prompt + motion_prompt + +video = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + height=480, + width=832, + frames=81, + guidance_scale=6, + num_inference_steps=50, + generator=torch.Generator(device="cuda").manual_seed(0), +).frames[0] + +export_to_video(video, "sana_video.mp4", fps=16) +``` + + + + +The example below demonstrates how to use the image-to-video pipeline to generate a video using a text description and a starting frame. + +```python +pipe = SanaImageToVideoPipeline.from_pretrained( + "Efficient-Large-Model/SANA-Video_2B_480p_diffusers", + torch_dtype=torch.bfloat16, +) +pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(pipe.scheduler.config, flow_shift=8.0) +pipe.vae.to(torch.float32) +pipe.text_encoder.to(torch.bfloat16) +pipe.to("cuda") + +image = load_image("https://raw.githubusercontent.com/NVlabs/Sana/refs/heads/main/asset/samples/i2v-1.png") +prompt = "A woman stands against a stunning sunset backdrop, her long, wavy brown hair gently blowing in the breeze. She wears a sleeveless, light-colored blouse with a deep V-neckline, which accentuates her graceful posture. The warm hues of the setting sun cast a golden glow across her face and hair, creating a serene and ethereal atmosphere. The background features a blurred landscape with soft, rolling hills and scattered clouds, adding depth to the scene. The camera remains steady, capturing the tranquil moment from a medium close-up angle." +negative_prompt = "A chaotic sequence with misshapen, deformed limbs in heavy motion blur, sudden disappearance, jump cuts, jerky movements, rapid shot changes, frames out of sync, inconsistent character shapes, temporal artifacts, jitter, and ghosting effects, creating a disorienting visual experience." +motion_scale = 30 +motion_prompt = f" motion score: {motion_scale}." +prompt = prompt + motion_prompt + +motion_scale = 30.0 + +video = pipe( + image=image, + prompt=prompt, + negative_prompt=negative_prompt, + height=480, + width=832, + frames=81, + guidance_scale=6, + num_inference_steps=50, + generator=torch.Generator(device="cuda").manual_seed(0), +).frames[0] + +export_to_video(video, "sana-i2v.mp4", fps=16) +``` + + + + + +## Quantization + +Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model. + +Refer to the [Quantization](../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`SanaVideoPipeline`] for inference with bitsandbytes. + +```py +import torch +from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, SanaVideoTransformer3DModel, SanaVideoPipeline +from transformers import BitsAndBytesConfig as BitsAndBytesConfig, AutoModel + +quant_config = BitsAndBytesConfig(load_in_8bit=True) +text_encoder_8bit = AutoModel.from_pretrained( + "Efficient-Large-Model/SANA-Video_2B_480p_diffusers", + subfolder="text_encoder", + quantization_config=quant_config, + torch_dtype=torch.float16, +) + +quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True) +transformer_8bit = SanaVideoTransformer3DModel.from_pretrained( + "Efficient-Large-Model/SANA-Video_2B_480p_diffusers", + subfolder="transformer", + quantization_config=quant_config, + torch_dtype=torch.float16, +) + +pipeline = SanaVideoPipeline.from_pretrained( + "Efficient-Large-Model/SANA-Video_2B_480p_diffusers", + text_encoder=text_encoder_8bit, + transformer=transformer_8bit, + torch_dtype=torch.float16, + device_map="balanced", +) + +model_score = 30 +prompt = "Evening, backlight, side lighting, soft light, high contrast, mid-shot, centered composition, clean solo shot, warm color. A young Caucasian man stands in a forest, golden light glimmers on his hair as sunlight filters through the leaves. He wears a light shirt, wind gently blowing his hair and collar, light dances across his face with his movements. The background is blurred, with dappled light and soft tree shadows in the distance. The camera focuses on his lifted gaze, clear and emotional." +negative_prompt = "A chaotic sequence with misshapen, deformed limbs in heavy motion blur, sudden disappearance, jump cuts, jerky movements, rapid shot changes, frames out of sync, inconsistent character shapes, temporal artifacts, jitter, and ghosting effects, creating a disorienting visual experience." +motion_prompt = f" motion score: {model_score}." +prompt = prompt + motion_prompt + +output = pipeline( + prompt=prompt, + negative_prompt=negative_prompt, + height=480, + width=832, + num_frames=81, + guidance_scale=6.0, + num_inference_steps=50 +).frames[0] +export_to_video(output, "sana-video-output.mp4", fps=16) +``` + +## SanaVideoPipeline + +[[autodoc]] SanaVideoPipeline + - all + - __call__ + + +## SanaImageToVideoPipeline + +[[autodoc]] SanaImageToVideoPipeline + - all + - __call__ + + +## SanaVideoPipelineOutput + +[[autodoc]] pipelines.sana_video.pipeline_sana_video.SanaVideoPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/self_attention_guidance.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/self_attention_guidance.md new file mode 100644 index 0000000000000000000000000000000000000000..8d411598ae6dd7c2cda15357df325d582359a78c --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/self_attention_guidance.md @@ -0,0 +1,35 @@ + + +> [!WARNING] +> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model. + +# Self-Attention Guidance + +[Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://huggingface.co/papers/2210.00939) is by Susung Hong et al. + +The abstract from the paper is: + +*Denoising diffusion models (DDMs) have attracted attention for their exceptional generation quality and diversity. This success is largely attributed to the use of class- or text-conditional diffusion guidance methods, such as classifier and classifier-free guidance. In this paper, we present a more comprehensive perspective that goes beyond the traditional guidance methods. From this generalized perspective, we introduce novel condition- and training-free strategies to enhance the quality of generated images. As a simple solution, blur guidance improves the suitability of intermediate samples for their fine-scale information and structures, enabling diffusion models to generate higher quality samples with a moderate guidance scale. Improving upon this, Self-Attention Guidance (SAG) uses the intermediate self-attention maps of diffusion models to enhance their stability and efficacy. Specifically, SAG adversarially blurs only the regions that diffusion models attend to at each iteration and guides them accordingly. Our experimental results show that our SAG improves the performance of various diffusion models, including ADM, IDDPM, Stable Diffusion, and DiT. Moreover, combining SAG with conventional guidance methods leads to further improvement.* + +You can find additional information about Self-Attention Guidance on the [project page](https://ku-cvlab.github.io/Self-Attention-Guidance), [original codebase](https://github.com/KU-CVLAB/Self-Attention-Guidance), and try it out in a [demo](https://huggingface.co/spaces/susunghong/Self-Attention-Guidance) or [notebook](https://colab.research.google.com/github/SusungHong/Self-Attention-Guidance/blob/main/SAG_Stable.ipynb). + +> [!TIP] +> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. + +## StableDiffusionSAGPipeline +[[autodoc]] StableDiffusionSAGPipeline + - __call__ + - all + +## StableDiffusionOutput +[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/semantic_stable_diffusion.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/semantic_stable_diffusion.md new file mode 100644 index 0000000000000000000000000000000000000000..dda428e80f8f1834af718783d4cc85a372cbb0fa --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/semantic_stable_diffusion.md @@ -0,0 +1,35 @@ + + +> [!WARNING] +> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model. + +# Semantic Guidance + +Semantic Guidance for Diffusion Models was proposed in [SEGA: Instructing Text-to-Image Models using Semantic Guidance](https://huggingface.co/papers/2301.12247) and provides strong semantic control over image generation. +Small changes to the text prompt usually result in entirely different output images. However, with SEGA a variety of changes to the image are enabled that can be controlled easily and intuitively, while staying true to the original image composition. + +The abstract from the paper is: + +*Text-to-image diffusion models have recently received a lot of interest for their astonishing ability to produce high-fidelity images from text only. However, achieving one-shot generation that aligns with the user's intent is nearly impossible, yet small changes to the input prompt often result in very different images. This leaves the user with little semantic control. To put the user in control, we show how to interact with the diffusion process to flexibly steer it along semantic directions. This semantic guidance (SEGA) generalizes to any generative architecture using classifier-free guidance. More importantly, it allows for subtle and extensive edits, changes in composition and style, as well as optimizing the overall artistic conception. We demonstrate SEGA's effectiveness on both latent and pixel-based diffusion models such as Stable Diffusion, Paella, and DeepFloyd-IF using a variety of tasks, thus providing strong evidence for its versatility, flexibility, and improvements over existing methods.* + +> [!TIP] +> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. + +## SemanticStableDiffusionPipeline +[[autodoc]] SemanticStableDiffusionPipeline + - all + - __call__ + +## SemanticStableDiffusionPipelineOutput +[[autodoc]] pipelines.semantic_stable_diffusion.pipeline_output.SemanticStableDiffusionPipelineOutput + - all diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/shap_e.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/shap_e.md new file mode 100644 index 0000000000000000000000000000000000000000..3e505894ca80ca07738903aecdea7fe18c80bc80 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/shap_e.md @@ -0,0 +1,34 @@ + + +# Shap-E + +The Shap-E model was proposed in [Shap-E: Generating Conditional 3D Implicit Functions](https://huggingface.co/papers/2305.02463) by Alex Nichol and Heewoo Jun from [OpenAI](https://github.com/openai). + +The abstract from the paper is: + +*We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds. When compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space.* + +The original codebase can be found at [openai/shap-e](https://github.com/openai/shap-e). + +> [!TIP] +> See the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. + +## ShapEPipeline +[[autodoc]] ShapEPipeline + - all + - __call__ + +## ShapEImg2ImgPipeline +[[autodoc]] ShapEImg2ImgPipeline + - all + - __call__ + +## ShapEPipelineOutput +[[autodoc]] pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/skyreels_v2.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/skyreels_v2.md new file mode 100644 index 0000000000000000000000000000000000000000..6730f1551607d8019dedf728f66907e7f83978d0 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/skyreels_v2.md @@ -0,0 +1,346 @@ + + +
+
+ + LoRA + +
+
+ +# SkyReels-V2: Infinite-length Film Generative model + +[SkyReels-V2](https://huggingface.co/papers/2504.13074) by the SkyReels Team from Skywork AI. + +*Recent advances in video generation have been driven by diffusion models and autoregressive frameworks, yet critical challenges persist in harmonizing prompt adherence, visual quality, motion dynamics, and duration: compromises in motion dynamics to enhance temporal visual quality, constrained video duration (5-10 seconds) to prioritize resolution, and inadequate shot-aware generation stemming from general-purpose MLLMs' inability to interpret cinematic grammar, such as shot composition, actor expressions, and camera motions. These intertwined limitations hinder realistic long-form synthesis and professional film-style generation. To address these limitations, we propose SkyReels-V2, an Infinite-length Film Generative Model, that synergizes Multi-modal Large Language Model (MLLM), Multi-stage Pretraining, Reinforcement Learning, and Diffusion Forcing Framework. Firstly, we design a comprehensive structural representation of video that combines the general descriptions by the Multi-modal LLM and the detailed shot language by sub-expert models. Aided with human annotation, we then train a unified Video Captioner, named SkyCaptioner-V1, to efficiently label the video data. Secondly, we establish progressive-resolution pretraining for the fundamental video generation, followed by a four-stage post-training enhancement: Initial concept-balanced Supervised Fine-Tuning (SFT) improves baseline quality; Motion-specific Reinforcement Learning (RL) training with human-annotated and synthetic distortion data addresses dynamic artifacts; Our diffusion forcing framework with non-decreasing noise schedules enables long-video synthesis in an efficient search space; Final high-quality SFT refines visual fidelity. All the code and models are available at [this https URL](https://github.com/SkyworkAI/SkyReels-V2).* + +You can find all the original SkyReels-V2 checkpoints under the [Skywork](https://huggingface.co/collections/Skywork/skyreels-v2-6801b1b93df627d441d0d0d9) organization. + +The following SkyReels-V2 models are supported in Diffusers: +- [SkyReels-V2 DF 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers) +- [SkyReels-V2 DF 14B - 540P](https://huggingface.co/Skywork/SkyReels-V2-DF-14B-540P-Diffusers) +- [SkyReels-V2 DF 14B - 720P](https://huggingface.co/Skywork/SkyReels-V2-DF-14B-720P-Diffusers) +- [SkyReels-V2 T2V 14B - 540P](https://huggingface.co/Skywork/SkyReels-V2-T2V-14B-540P-Diffusers) +- [SkyReels-V2 T2V 14B - 720P](https://huggingface.co/Skywork/SkyReels-V2-T2V-14B-720P-Diffusers) +- [SkyReels-V2 I2V 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-I2V-1.3B-540P-Diffusers) +- [SkyReels-V2 I2V 14B - 540P](https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-540P-Diffusers) +- [SkyReels-V2 I2V 14B - 720P](https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-720P-Diffusers) +- [SkyReels-V2 FLF2V 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-FLF2V-1.3B-540P-Diffusers) + +> [!TIP] +> Click on the SkyReels-V2 models in the right sidebar for more examples of video generation. + +### A _Visual_ Demonstration + +The example below has the following parameters: + +- `base_num_frames=97` +- `num_frames=97` +- `num_inference_steps=30` +- `ar_step=5` +- `causal_block_size=5` + +With `vae_scale_factor_temporal=4`, expect `5` blocks of `5` frames each as calculated by: + +`num_latent_frames: (97-1)//vae_scale_factor_temporal+1 = 25 frames -> 5 blocks of 5 frames each` + +And the maximum context length in the latent space is calculated with `base_num_latent_frames`: + +`base_num_latent_frames = (97-1)//vae_scale_factor_temporal+1 = 25 -> 25//5 = 5 blocks` + +Asynchronous Processing Timeline: +```text +┌─────────────────────────────────────────────────────────────────┐ +│ Steps: 1 6 11 16 21 26 31 36 41 46 50 │ +│ Block 1: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] │ +│ Block 2: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] │ +│ Block 3: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] │ +│ Block 4: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] │ +│ Block 5: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] │ +└─────────────────────────────────────────────────────────────────┘ +``` + +For Long Videos (`num_frames` > `base_num_frames`): +`base_num_frames` acts as the "sliding window size" for processing long videos. + +Example: `257`-frame video with `base_num_frames=97`, `overlap_history=17` +```text +┌──── Iteration 1 (frames 1-97) ────┐ +│ Processing window: 97 frames │ → 5 blocks, +│ Generates: frames 1-97 │ async processing +└───────────────────────────────────┘ + ┌────── Iteration 2 (frames 81-177) ──────┐ + │ Processing window: 97 frames │ + │ Overlap: 17 frames (81-97) from prev │ → 5 blocks, + │ Generates: frames 98-177 │ async processing + └─────────────────────────────────────────┘ + ┌────── Iteration 3 (frames 161-257) ──────┐ + │ Processing window: 97 frames │ + │ Overlap: 17 frames (161-177) from prev │ → 5 blocks, + │ Generates: frames 178-257 │ async processing + └──────────────────────────────────────────┘ +``` + +Each iteration independently runs the asynchronous processing with its own `5` blocks. +`base_num_frames` controls: +1. Memory usage (larger window = more VRAM) +2. Model context length (must match training constraints) +3. Number of blocks per iteration (`base_num_latent_frames // causal_block_size`) + +Each block takes `30` steps to complete denoising. +Block N starts at step: `1 + (N-1) x ar_step` +Total steps: `30 + (5-1) x 5 = 50` steps + + +Synchronous mode (`ar_step=0`) would process all blocks/frames simultaneously: +```text +┌──────────────────────────────────────────────┐ +│ Steps: 1 ... 30 │ +│ All blocks: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] │ +└──────────────────────────────────────────────┘ +``` +Total steps: `30` steps + + +An example on how the step matrix is constructed for asynchronous processing: +Given the parameters: (`num_inference_steps=30, flow_shift=8, num_frames=97, ar_step=5, causal_block_size=5`) +``` +- num_latent_frames = (97 frames - 1) // (4 temporal downsampling) + 1 = 25 +- step_template = [999, 995, 991, 986, 980, 975, 969, 963, 956, 948, + 941, 932, 922, 912, 901, 888, 874, 859, 841, 822, + 799, 773, 743, 708, 666, 615, 551, 470, 363, 216] +``` + +The algorithm creates a `50x25` `step_matrix` where: +``` +- Row 1: [999×5, 999×5, 999×5, 999×5, 999×5] +- Row 2: [995×5, 999×5, 999×5, 999×5, 999×5] +- Row 3: [991×5, 999×5, 999×5, 999×5, 999×5] +- ... +- Row 7: [969×5, 995×5, 999×5, 999×5, 999×5] +- ... +- Row 21: [799×5, 888×5, 941×5, 975×5, 999×5] +- ... +- Row 35: [ 0×5, 216×5, 666×5, 822×5, 901×5] +- ... +- Row 42: [ 0×5, 0×5, 0×5, 551×5, 773×5] +- ... +- Row 50: [ 0×5, 0×5, 0×5, 0×5, 216×5] +``` + +Detailed Row `6` Analysis: +``` +- step_matrix[5]: [ 975×5, 999×5, 999×5, 999×5, 999×5] +- step_index[5]: [ 6×5, 1×5, 0×5, 0×5, 0×5] +- step_update_mask[5]: [True×5, True×5, False×5, False×5, False×5] +- valid_interval[5]: (0, 25) +``` + +Key Pattern: Block `i` lags behind Block `i-1` by exactly `ar_step=5` timesteps, creating the +staggered "diffusion forcing" effect where later blocks condition on cleaner earlier blocks. + + +### Text-to-Video Generation + +The example below demonstrates how to generate a video from text. + + + + +Refer to the [Reduce memory usage](../../optimization/memory) guide for more details about the various memory saving techniques. + +From the original repo: +>You can use --ar_step 5 to enable asynchronous inference. When asynchronous inference, --causal_block_size 5 is recommended while it is not supposed to be set for synchronous generation... Asynchronous inference will take more steps to diffuse the whole sequence which means it will be SLOWER than synchronous mode. In our experiments, asynchronous inference may improve the instruction following and visual consistent performance. + +```py +import torch +from diffusers import AutoModel, SkyReelsV2DiffusionForcingPipeline, UniPCMultistepScheduler +from diffusers.utils import export_to_video + + +model_id = "Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers" +vae = AutoModel.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32) + +pipeline = SkyReelsV2DiffusionForcingPipeline.from_pretrained( + model_id, + vae=vae, + torch_dtype=torch.bfloat16, +) +pipeline.to("cuda") +flow_shift = 8.0 # 8.0 for T2V, 5.0 for I2V +pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config, flow_shift=flow_shift) + +prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window." + +output = pipeline( + prompt=prompt, + num_inference_steps=30, + height=544, # 720 for 720P + width=960, # 1280 for 720P + num_frames=97, + base_num_frames=97, # 121 for 720P + ar_step=5, # Controls asynchronous inference (0 for synchronous mode) + causal_block_size=5, # Number of frames in each block for asynchronous processing + overlap_history=None, # Number of frames to overlap for smooth transitions in long videos; 17 for long video generations + addnoise_condition=20, # Improves consistency in long video generation +).frames[0] +export_to_video(output, "video.mp4", fps=24, quality=8) +``` + + + + +### First-Last-Frame-to-Video Generation + +The example below demonstrates how to use the image-to-video pipeline to generate a video using a text description, a starting frame, and an ending frame. + + + + +```python +import numpy as np +import torch +import torchvision.transforms.functional as TF +from diffusers import AutoencoderKLWan, SkyReelsV2DiffusionForcingImageToVideoPipeline, UniPCMultistepScheduler +from diffusers.utils import export_to_video, load_image + + +model_id = "Skywork/SkyReels-V2-DF-1.3B-720P-Diffusers" +vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32) +pipeline = SkyReelsV2DiffusionForcingImageToVideoPipeline.from_pretrained( + model_id, vae=vae, torch_dtype=torch.bfloat16 +) +pipeline.to("cuda") +flow_shift = 5.0 # 8.0 for T2V, 5.0 for I2V +pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config, flow_shift=flow_shift) + +first_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_first_frame.png") +last_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_last_frame.png") + +def aspect_ratio_resize(image, pipeline, max_area=720 * 1280): + aspect_ratio = image.height / image.width + mod_value = pipeline.vae_scale_factor_spatial * pipeline.transformer.config.patch_size[1] + height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value + width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value + image = image.resize((width, height)) + return image, height, width + +def center_crop_resize(image, height, width): + # Calculate resize ratio to match first frame dimensions + resize_ratio = max(width / image.width, height / image.height) + + # Resize the image + width = round(image.width * resize_ratio) + height = round(image.height * resize_ratio) + size = [width, height] + image = TF.center_crop(image, size) + + return image, height, width + +first_frame, height, width = aspect_ratio_resize(first_frame, pipeline) +if last_frame.size != first_frame.size: + last_frame, _, _ = center_crop_resize(last_frame, height, width) + +prompt = "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird's feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective." + +output = pipeline( + image=first_frame, last_image=last_frame, prompt=prompt, height=height, width=width, guidance_scale=5.0 +).frames[0] +export_to_video(output, "video.mp4", fps=24, quality=8) +``` + + + + + +### Video-to-Video Generation + + + + +`SkyReelsV2DiffusionForcingVideoToVideoPipeline` extends a given video. + +```python +import numpy as np +import torch +import torchvision.transforms.functional as TF +from diffusers import AutoencoderKLWan, SkyReelsV2DiffusionForcingVideoToVideoPipeline, UniPCMultistepScheduler +from diffusers.utils import export_to_video, load_video + + +model_id = "Skywork/SkyReels-V2-DF-1.3B-720P-Diffusers" +vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32) +pipeline = SkyReelsV2DiffusionForcingVideoToVideoPipeline.from_pretrained( + model_id, vae=vae, torch_dtype=torch.bfloat16 +) +pipeline.to("cuda") +flow_shift = 5.0 # 8.0 for T2V, 5.0 for I2V +pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config, flow_shift=flow_shift) + +video = load_video("input_video.mp4") + +prompt = "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird's feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective." + +output = pipeline( + video=video, prompt=prompt, height=720, width=1280, guidance_scale=5.0, overlap_history=17, + num_inference_steps=30, num_frames=257, base_num_frames=121#, ar_step=5, causal_block_size=5, +).frames[0] +export_to_video(output, "video.mp4", fps=24, quality=8) +# Total frames will be the number of frames of the given video + 257 +``` + + + + +## Notes + +- SkyReels-V2 supports LoRAs with [`~loaders.SkyReelsV2LoraLoaderMixin.load_lora_weights`]. + +`SkyReelsV2Pipeline` and `SkyReelsV2ImageToVideoPipeline` are also available without Diffusion Forcing framework applied. + + +## SkyReelsV2DiffusionForcingPipeline + +[[autodoc]] SkyReelsV2DiffusionForcingPipeline + - all + - __call__ + +## SkyReelsV2DiffusionForcingImageToVideoPipeline + +[[autodoc]] SkyReelsV2DiffusionForcingImageToVideoPipeline + - all + - __call__ + +## SkyReelsV2DiffusionForcingVideoToVideoPipeline + +[[autodoc]] SkyReelsV2DiffusionForcingVideoToVideoPipeline + - all + - __call__ + +## SkyReelsV2Pipeline + +[[autodoc]] SkyReelsV2Pipeline + - all + - __call__ + +## SkyReelsV2ImageToVideoPipeline + +[[autodoc]] SkyReelsV2ImageToVideoPipeline + - all + - __call__ + +## SkyReelsV2PipelineOutput + +[[autodoc]] pipelines.skyreels_v2.pipeline_output.SkyReelsV2PipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_audio.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_audio.md new file mode 100644 index 0000000000000000000000000000000000000000..82763a52a94213f76fb7e650b14988ae7a5ffa4c --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_audio.md @@ -0,0 +1,93 @@ + + +# Stable Audio + +Stable Audio was proposed in [Stable Audio Open](https://huggingface.co/papers/2407.14358) by Zach Evans et al. . it takes a text prompt as input and predicts the corresponding sound or music sample. + +Stable Audio Open generates variable-length (up to 47s) stereo audio at 44.1kHz from text prompts. It comprises three components: an autoencoder that compresses waveforms into a manageable sequence length, a T5-based text embedding for text conditioning, and a transformer-based diffusion (DiT) model that operates in the latent space of the autoencoder. + +Stable Audio is trained on a corpus of around 48k audio recordings, where around 47k are from Freesound and the rest are from the Free Music Archive (FMA). All audio files are licensed under CC0, CC BY, or CC Sampling+. This data is used to train the autoencoder and the DiT. + +The abstract of the paper is the following: +*Open generative models are vitally important for the community, allowing for fine-tunes and serving as baselines when presenting new models. However, most current text-to-audio models are private and not accessible for artists and researchers to build upon. Here we describe the architecture and training process of a new open-weights text-to-audio model trained with Creative Commons data. Our evaluation shows that the model's performance is competitive with the state-of-the-art across various metrics. Notably, the reported FDopenl3 results (measuring the realism of the generations) showcase its potential for high-quality stereo sound synthesis at 44.1kHz.* + +This pipeline was contributed by [Yoach Lacombe](https://huggingface.co/ylacombe). The original codebase can be found at [Stability-AI/stable-audio-tools](https://github.com/Stability-AI/stable-audio-tools). + +## Tips + +When constructing a prompt, keep in mind: + +* Descriptive prompt inputs work best; use adjectives to describe the sound (for example, "high quality" or "clear") and make the prompt context specific where possible (e.g. "melodic techno with a fast beat and synths" works better than "techno"). +* Using a *negative prompt* can significantly improve the quality of the generated audio. Try using a negative prompt of "low quality, average quality". + +During inference: + +* The _quality_ of the generated audio sample can be controlled by the `num_inference_steps` argument; higher steps give higher quality audio at the expense of slower inference. +* Multiple waveforms can be generated in one go: set `num_waveforms_per_prompt` to a value greater than 1 to enable. Automatic scoring will be performed between the generated waveforms and prompt text, and the audios ranked from best to worst accordingly. + +## Quantization + +Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model. + +Refer to the [Quantization](../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`StableAudioPipeline`] for inference with bitsandbytes. + +```py +import torch +from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, StableAudioDiTModel, StableAudioPipeline +from diffusers.utils import export_to_video +from transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel + +quant_config = BitsAndBytesConfig(load_in_8bit=True) +text_encoder_8bit = T5EncoderModel.from_pretrained( + "stabilityai/stable-audio-open-1.0", + subfolder="text_encoder", + quantization_config=quant_config, + torch_dtype=torch.float16, +) + +quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True) +transformer_8bit = StableAudioDiTModel.from_pretrained( + "stabilityai/stable-audio-open-1.0", + subfolder="transformer", + quantization_config=quant_config, + torch_dtype=torch.float16, +) + +pipeline = StableAudioPipeline.from_pretrained( + "stabilityai/stable-audio-open-1.0", + text_encoder=text_encoder_8bit, + transformer=transformer_8bit, + torch_dtype=torch.float16, + device_map="balanced", +) + +prompt = "The sound of a hammer hitting a wooden surface." +negative_prompt = "Low quality." +audio = pipeline( + prompt, + negative_prompt=negative_prompt, + num_inference_steps=200, + audio_end_in_s=10.0, + num_waveforms_per_prompt=3, + generator=generator, +).audios + +output = audio[0].T.float().cpu().numpy() +sf.write("hammer.wav", output, pipeline.vae.sampling_rate) +``` + + +## StableAudioPipeline +[[autodoc]] StableAudioPipeline + - all + - __call__ diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_cascade.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_cascade.md new file mode 100644 index 0000000000000000000000000000000000000000..70de6776e98f6dd986e94356a7bf5cbbfc8ae223 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_cascade.md @@ -0,0 +1,226 @@ + + +# Stable Cascade + +This model is built upon the [Würstchen](https://openreview.net/forum?id=gU58d5QeGv) architecture and its main +difference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this +important? The smaller the latent space, the **faster** you can run inference and the **cheaper** the training becomes. +How small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being +encoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a +1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the +highly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable +Diffusion 1.5. + +Therefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions +like finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well. + +The original codebase can be found at [Stability-AI/StableCascade](https://github.com/Stability-AI/StableCascade). + +## Model Overview +Stable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images, +hence the name "Stable Cascade". + +Stage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion. +However, with this setup, a much higher compression of images can be achieved. While the Stable Diffusion models use a +spatial compression factor of 8, encoding an image with resolution of 1024 x 1024 to 128 x 128, Stable Cascade achieves +a compression factor of 42. This encodes a 1024 x 1024 image to 24 x 24, while being able to accurately decode the +image. This comes with the great benefit of cheaper training and inference. Furthermore, Stage C is responsible +for generating the small 24 x 24 latents given a text prompt. + +The Stage C model operates on the small 24 x 24 latents and denoises the latents conditioned on text prompts. The model is also the largest component in the Cascade pipeline and is meant to be used with the `StableCascadePriorPipeline` + +The Stage B and Stage A models are used with the `StableCascadeDecoderPipeline` and are responsible for generating the final image given the small 24 x 24 latents. + +> [!WARNING] +> There are some restrictions on data types that can be used with the Stable Cascade models. The official checkpoints for the `StableCascadePriorPipeline` do not support the `torch.float16` data type. Please use `torch.bfloat16` instead. +> +> In order to use the `torch.bfloat16` data type with the `StableCascadeDecoderPipeline` you need to have PyTorch 2.2.0 or higher installed. This also means that using the `StableCascadeCombinedPipeline` with `torch.bfloat16` requires PyTorch 2.2.0 or higher, since it calls the `StableCascadeDecoderPipeline` internally. +> +> If it is not possible to install PyTorch 2.2.0 or higher in your environment, the `StableCascadeDecoderPipeline` can be used on its own with the `torch.float16` data type. You can download the full precision or `bf16` variant weights for the pipeline and cast the weights to `torch.float16`. + +## Usage example + +```python +import torch +from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline + +prompt = "an image of a shiba inu, donning a spacesuit and helmet" +negative_prompt = "" + +prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", variant="bf16", torch_dtype=torch.bfloat16) +decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", variant="bf16", torch_dtype=torch.float16) + +prior.enable_model_cpu_offload() +prior_output = prior( + prompt=prompt, + height=1024, + width=1024, + negative_prompt=negative_prompt, + guidance_scale=4.0, + num_images_per_prompt=1, + num_inference_steps=20 +) + +decoder.enable_model_cpu_offload() +decoder_output = decoder( + image_embeddings=prior_output.image_embeddings.to(torch.float16), + prompt=prompt, + negative_prompt=negative_prompt, + guidance_scale=0.0, + output_type="pil", + num_inference_steps=10 +).images[0] +decoder_output.save("cascade.png") +``` + +## Using the Lite Versions of the Stage B and Stage C models + +```python +import torch +from diffusers import ( + StableCascadeDecoderPipeline, + StableCascadePriorPipeline, + StableCascadeUNet, +) + +prompt = "an image of a shiba inu, donning a spacesuit and helmet" +negative_prompt = "" + +prior_unet = StableCascadeUNet.from_pretrained("stabilityai/stable-cascade-prior", subfolder="prior_lite") +decoder_unet = StableCascadeUNet.from_pretrained("stabilityai/stable-cascade", subfolder="decoder_lite") + +prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", prior=prior_unet) +decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", decoder=decoder_unet) + +prior.enable_model_cpu_offload() +prior_output = prior( + prompt=prompt, + height=1024, + width=1024, + negative_prompt=negative_prompt, + guidance_scale=4.0, + num_images_per_prompt=1, + num_inference_steps=20 +) + +decoder.enable_model_cpu_offload() +decoder_output = decoder( + image_embeddings=prior_output.image_embeddings, + prompt=prompt, + negative_prompt=negative_prompt, + guidance_scale=0.0, + output_type="pil", + num_inference_steps=10 +).images[0] +decoder_output.save("cascade.png") +``` + +## Loading original checkpoints with `from_single_file` + +Loading the original format checkpoints is supported via `from_single_file` method in the StableCascadeUNet. + +```python +import torch +from diffusers import ( + StableCascadeDecoderPipeline, + StableCascadePriorPipeline, + StableCascadeUNet, +) + +prompt = "an image of a shiba inu, donning a spacesuit and helmet" +negative_prompt = "" + +prior_unet = StableCascadeUNet.from_single_file( + "https://huggingface.co/stabilityai/stable-cascade/resolve/main/stage_c_bf16.safetensors", + torch_dtype=torch.bfloat16 +) +decoder_unet = StableCascadeUNet.from_single_file( + "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_bf16.safetensors", + torch_dtype=torch.bfloat16 +) + +prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", prior=prior_unet, torch_dtype=torch.bfloat16) +decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", decoder=decoder_unet, torch_dtype=torch.bfloat16) + +prior.enable_model_cpu_offload() +prior_output = prior( + prompt=prompt, + height=1024, + width=1024, + negative_prompt=negative_prompt, + guidance_scale=4.0, + num_images_per_prompt=1, + num_inference_steps=20 +) + +decoder.enable_model_cpu_offload() +decoder_output = decoder( + image_embeddings=prior_output.image_embeddings, + prompt=prompt, + negative_prompt=negative_prompt, + guidance_scale=0.0, + output_type="pil", + num_inference_steps=10 +).images[0] +decoder_output.save("cascade-single-file.png") +``` + +## Uses + +### Direct Use + +The model is intended for research purposes for now. Possible research areas and tasks include + +- Research on generative models. +- Safe deployment of models which have the potential to generate harmful content. +- Probing and understanding the limitations and biases of generative models. +- Generation of artworks and use in design and other artistic processes. +- Applications in educational or creative tools. + +Excluded uses are described below. + +### Out-of-Scope Use + +The model was not trained to be factual or true representations of people or events, +and therefore using the model to generate such content is out-of-scope for the abilities of this model. +The model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy). + +## Limitations and Bias + +### Limitations +- Faces and people in general may not be generated properly. +- The autoencoding part of the model is lossy. + + +## StableCascadeCombinedPipeline + +[[autodoc]] StableCascadeCombinedPipeline + - all + - __call__ + +## StableCascadePriorPipeline + +[[autodoc]] StableCascadePriorPipeline + - all + - __call__ + +## StableCascadePriorPipelineOutput + +[[autodoc]] pipelines.stable_cascade.pipeline_stable_cascade_prior.StableCascadePriorPipelineOutput + +## StableCascadeDecoderPipeline + +[[autodoc]] StableCascadeDecoderPipeline + - all + - __call__ + diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/adapter.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/adapter.md new file mode 100644 index 0000000000000000000000000000000000000000..af1e9afe97df97827bf5f445fe532f063d56a8b6 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/adapter.md @@ -0,0 +1,47 @@ + + +# T2I-Adapter + +[T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.08453) by Chong Mou, Xintao Wang, Liangbin Xie, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie. + +Using the pretrained models we can provide control images (for example, a depth map) to control Stable Diffusion text-to-image generation so that it follows the structure of the depth image and fills in the details. + +The abstract of the paper is the following: + +*The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated strong power of learning complex structures and meaningful semantics. However, relying solely on text prompts cannot fully take advantage of the knowledge learned by the model, especially when flexible and accurate controlling (e.g., color and structure) is needed. In this paper, we aim to ``dig out" the capabilities that T2I models have implicitly learned, and then explicitly use them to control the generation more granularly. Specifically, we propose to learn simple and lightweight T2I-Adapters to align internal knowledge in T2I models with external control signals, while freezing the original large T2I models. In this way, we can train various adapters according to different conditions, achieving rich control and editing effects in the color and structure of the generation results. Further, the proposed T2I-Adapters have attractive properties of practical value, such as composability and generalization ability. Extensive experiments demonstrate that our T2I-Adapter has promising generation quality and a wide range of applications.* + +This model was contributed by the community contributor [HimariO](https://github.com/HimariO) ❤️ . + +## StableDiffusionAdapterPipeline + +[[autodoc]] StableDiffusionAdapterPipeline + - all + - __call__ + - enable_attention_slicing + - disable_attention_slicing + - enable_vae_slicing + - disable_vae_slicing + - enable_xformers_memory_efficient_attention + - disable_xformers_memory_efficient_attention + +## StableDiffusionXLAdapterPipeline + +[[autodoc]] StableDiffusionXLAdapterPipeline + - all + - __call__ + - enable_attention_slicing + - disable_attention_slicing + - enable_vae_slicing + - disable_vae_slicing + - enable_xformers_memory_efficient_attention + - disable_xformers_memory_efficient_attention diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/depth2img.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/depth2img.md new file mode 100644 index 0000000000000000000000000000000000000000..9ac9b7f90619018b62b6d08bda86ca2047c23135 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/depth2img.md @@ -0,0 +1,41 @@ + + +# Depth-to-image + +
+ LoRA +
+ +The Stable Diffusion model can also infer depth based on an image using [MiDaS](https://github.com/isl-org/MiDaS). This allows you to pass a text prompt and an initial image to condition the generation of new images as well as a `depth_map` to preserve the image structure. + +> [!TIP] +> Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! +> +> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! + +## StableDiffusionDepth2ImgPipeline + +[[autodoc]] StableDiffusionDepth2ImgPipeline + - all + - __call__ + - enable_attention_slicing + - disable_attention_slicing + - enable_xformers_memory_efficient_attention + - disable_xformers_memory_efficient_attention + - load_textual_inversion + - load_lora_weights + - save_lora_weights + +## StableDiffusionPipelineOutput + +[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/gligen.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/gligen.md new file mode 100644 index 0000000000000000000000000000000000000000..c8297fb7b3de8c23271d8bac5738e745b1b2ded5 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/gligen.md @@ -0,0 +1,59 @@ + + +> [!WARNING] +> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model. + +# GLIGEN (Grounded Language-to-Image Generation) + +The GLIGEN model was created by researchers and engineers from [University of Wisconsin-Madison, Columbia University, and Microsoft](https://github.com/gligen/GLIGEN). The [`StableDiffusionGLIGENPipeline`] and [`StableDiffusionGLIGENTextImagePipeline`] can generate photorealistic images conditioned on grounding inputs. Along with text and bounding boxes with [`StableDiffusionGLIGENPipeline`], if input images are given, [`StableDiffusionGLIGENTextImagePipeline`] can insert objects described by text at the region defined by bounding boxes. Otherwise, it'll generate an image described by the caption/prompt and insert objects described by text at the region defined by bounding boxes. It's trained on COCO2014D and COCO2014CD datasets, and the model uses a frozen CLIP ViT-L/14 text encoder to condition itself on grounding inputs. + +The abstract from the [paper](https://huggingface.co/papers/2301.07093) is: + +*Large-scale text-to-image diffusion models have made amazing advances. However, the status quo is to use text input alone, which can impede controllability. In this work, we propose GLIGEN, Grounded-Language-to-Image Generation, a novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them to also be conditioned on grounding inputs. To preserve the vast concept knowledge of the pre-trained model, we freeze all of its weights and inject the grounding information into new trainable layers via a gated mechanism. Our model achieves open-world grounded text2img generation with caption and bounding box condition inputs, and the grounding ability generalizes well to novel spatial configurations and concepts. GLIGEN’s zeroshot performance on COCO and LVIS outperforms existing supervised layout-to-image baselines by a large margin.* + +> [!TIP] +> Make sure to check out the Stable Diffusion [Tips](https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality and how to reuse pipeline components efficiently! +> +> If you want to use one of the official checkpoints for a task, explore the [gligen](https://huggingface.co/gligen) Hub organizations! + +[`StableDiffusionGLIGENPipeline`] was contributed by [Nikhil Gajendrakumar](https://github.com/nikhil-masterful) and [`StableDiffusionGLIGENTextImagePipeline`] was contributed by [Nguyễn Công Tú Anh](https://github.com/tuanh123789). + +## StableDiffusionGLIGENPipeline + +[[autodoc]] StableDiffusionGLIGENPipeline + - all + - __call__ + - enable_vae_slicing + - disable_vae_slicing + - enable_vae_tiling + - disable_vae_tiling + - enable_model_cpu_offload + - prepare_latents + - enable_fuser + +## StableDiffusionGLIGENTextImagePipeline + +[[autodoc]] StableDiffusionGLIGENTextImagePipeline + - all + - __call__ + - enable_vae_slicing + - disable_vae_slicing + - enable_vae_tiling + - disable_vae_tiling + - enable_model_cpu_offload + - prepare_latents + - enable_fuser + +## StableDiffusionPipelineOutput + +[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/image_variation.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/image_variation.md new file mode 100644 index 0000000000000000000000000000000000000000..b1b7146b336ff7f55f800fadb3591f6cbc6b36c8 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/image_variation.md @@ -0,0 +1,34 @@ + + +# Image variation + +The Stable Diffusion model can also generate variations from an input image. It uses a fine-tuned version of a Stable Diffusion model by [Justin Pinkney](https://www.justinpinkney.com/) from [Lambda](https://lambdalabs.com/). + +The original codebase can be found at [LambdaLabsML/lambda-diffusers](https://github.com/LambdaLabsML/lambda-diffusers#stable-diffusion-image-variations) and additional official checkpoints for image variation can be found at [lambdalabs/sd-image-variations-diffusers](https://huggingface.co/lambdalabs/sd-image-variations-diffusers). + +> [!TIP] +> Make sure to check out the Stable Diffusion [Tips](./overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! + +## StableDiffusionImageVariationPipeline + +[[autodoc]] StableDiffusionImageVariationPipeline + - all + - __call__ + - enable_attention_slicing + - disable_attention_slicing + - enable_xformers_memory_efficient_attention + - disable_xformers_memory_efficient_attention + +## StableDiffusionPipelineOutput + +[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/img2img.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/img2img.md new file mode 100644 index 0000000000000000000000000000000000000000..f9e4476427de94eb1ca3ad7a9632da342ccfc243 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/img2img.md @@ -0,0 +1,46 @@ + + +# Image-to-image + +
+ LoRA +
+ +The Stable Diffusion model can also be applied to image-to-image generation by passing a text prompt and an initial image to condition the generation of new images. + +The [`StableDiffusionImg2ImgPipeline`] uses the diffusion-denoising mechanism proposed in [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://huggingface.co/papers/2108.01073) by Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon. + +The abstract from the paper is: + +*Guided image synthesis enables everyday users to create and edit photo-realistic images with minimum effort. The key challenge is balancing faithfulness to the user input (e.g., hand-drawn colored strokes) and realism of the synthesized image. Existing GAN-based methods attempt to achieve such balance using either conditional GANs or GAN inversions, which are challenging and often require additional training data or loss functions for individual applications. To address these issues, we introduce a new image synthesis and editing method, Stochastic Differential Editing (SDEdit), based on a diffusion model generative prior, which synthesizes realistic images by iteratively denoising through a stochastic differential equation (SDE). Given an input image with user guide of any type, SDEdit first adds noise to the input, then subsequently denoises the resulting image through the SDE prior to increase its realism. SDEdit does not require task-specific training or inversions and can naturally achieve the balance between realism and faithfulness. SDEdit significantly outperforms state-of-the-art GAN-based methods by up to 98.09% on realism and 91.72% on overall satisfaction scores, according to a human perception study, on multiple tasks, including stroke-based image synthesis and editing as well as image compositing.* + +> [!TIP] +> Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! + +## StableDiffusionImg2ImgPipeline + +[[autodoc]] StableDiffusionImg2ImgPipeline + - all + - __call__ + - enable_attention_slicing + - disable_attention_slicing + - enable_xformers_memory_efficient_attention + - disable_xformers_memory_efficient_attention + - load_textual_inversion + - from_single_file + - load_lora_weights + - save_lora_weights + +## StableDiffusionPipelineOutput + +[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/inpaint.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/inpaint.md new file mode 100644 index 0000000000000000000000000000000000000000..d60f472d0a7fcbc67f0359e3b05a7400c85a608a --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/inpaint.md @@ -0,0 +1,48 @@ + + +# Inpainting + +
+ LoRA +
+ +The Stable Diffusion model can also be applied to inpainting which lets you edit specific parts of an image by providing a mask and a text prompt using Stable Diffusion. + +## Tips + +It is recommended to use this pipeline with checkpoints that have been specifically fine-tuned for inpainting, such +as [stable-diffusion-v1-5/stable-diffusion-inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting). Default +text-to-image Stable Diffusion checkpoints, such as +[stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) are also compatible but they might be less performant. + +> [!TIP] +> Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! +> +> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! + +## StableDiffusionInpaintPipeline + +[[autodoc]] StableDiffusionInpaintPipeline + - all + - __call__ + - enable_attention_slicing + - disable_attention_slicing + - enable_xformers_memory_efficient_attention + - disable_xformers_memory_efficient_attention + - load_textual_inversion + - load_lora_weights + - save_lora_weights + +## StableDiffusionPipelineOutput + +[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/k_diffusion.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/k_diffusion.md new file mode 100644 index 0000000000000000000000000000000000000000..75f052b08f135ba31cb2510d86396eb5d72adf2b --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/k_diffusion.md @@ -0,0 +1,30 @@ + + +> [!WARNING] +> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model. + +# K-Diffusion + +[k-diffusion](https://github.com/crowsonkb/k-diffusion) is a popular library created by [Katherine Crowson](https://github.com/crowsonkb/). We provide `StableDiffusionKDiffusionPipeline` and `StableDiffusionXLKDiffusionPipeline` that allow you to run Stable DIffusion with samplers from k-diffusion. + +Note that most the samplers from k-diffusion are implemented in Diffusers and we recommend using existing schedulers. You can find a mapping between k-diffusion samplers and schedulers in Diffusers [here](https://huggingface.co/docs/diffusers/api/schedulers/overview) + + +## StableDiffusionKDiffusionPipeline + +[[autodoc]] StableDiffusionKDiffusionPipeline + + +## StableDiffusionXLKDiffusionPipeline + +[[autodoc]] StableDiffusionXLKDiffusionPipeline \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md new file mode 100644 index 0000000000000000000000000000000000000000..19eae9a9ce444a8afa1ebca290acd86c5b89ce69 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md @@ -0,0 +1,35 @@ + + +# Latent upscaler + +The Stable Diffusion latent upscaler model was created by [Katherine Crowson](https://github.com/crowsonkb/k-diffusion) in collaboration with [Stability AI](https://stability.ai/). It is used to enhance the output image resolution by a factor of 2 (see this demo [notebook](https://colab.research.google.com/drive/1o1qYJcFeywzCIdkfKJy7cTpgZTCM2EI4) for a demonstration of the original implementation). + +> [!TIP] +> Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! +> +> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! + +## StableDiffusionLatentUpscalePipeline + +[[autodoc]] StableDiffusionLatentUpscalePipeline + - all + - __call__ + - enable_sequential_cpu_offload + - enable_attention_slicing + - disable_attention_slicing + - enable_xformers_memory_efficient_attention + - disable_xformers_memory_efficient_attention + +## StableDiffusionPipelineOutput + +[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/ldm3d_diffusion.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/ldm3d_diffusion.md new file mode 100644 index 0000000000000000000000000000000000000000..15f9f1db851f521a38f6fd38811252528d94254a --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/ldm3d_diffusion.md @@ -0,0 +1,59 @@ + + +> [!WARNING] +> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model. + +# Text-to-(RGB, depth) + +
+ LoRA +
+ +LDM3D was proposed in [LDM3D: Latent Diffusion Model for 3D](https://huggingface.co/papers/2305.10853) by Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden, Will Saxton, Jean Yu, Estelle Aflalo, Shao-Yen Tseng, Fabio Nonato, Matthias Muller, and Vasudev Lal. LDM3D generates an image and a depth map from a given text prompt unlike the existing text-to-image diffusion models such as [Stable Diffusion](./overview) which only generates an image. With almost the same number of parameters, LDM3D achieves to create a latent space that can compress both the RGB images and the depth maps. + +Two checkpoints are available for use: +- [ldm3d-original](https://huggingface.co/Intel/ldm3d). The original checkpoint used in the [paper](https://huggingface.co/papers/2305.10853) +- [ldm3d-4c](https://huggingface.co/Intel/ldm3d-4c). The new version of LDM3D using 4 channels inputs instead of 6-channels inputs and finetuned on higher resolution images. + + +The abstract from the paper is: + +*This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. The LDM3D model is fine-tuned on a dataset of tuples containing an RGB image, depth map and caption, and validated through extensive experiments. We also develop an application called DepthFusion, which uses the generated RGB images and depth maps to create immersive and interactive 360-degree-view experiences using TouchDesigner. This technology has the potential to transform a wide range of industries, from entertainment and gaming to architecture and design. Overall, this paper presents a significant contribution to the field of generative AI and computer vision, and showcases the potential of LDM3D and DepthFusion to revolutionize content creation and digital experiences. A short video summarizing the approach can be found at [this url](https://t.ly/tdi2).* + +> [!TIP] +> Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! + +## StableDiffusionLDM3DPipeline + +[[autodoc]] pipelines.stable_diffusion_ldm3d.pipeline_stable_diffusion_ldm3d.StableDiffusionLDM3DPipeline + - all + - __call__ + + +## LDM3DPipelineOutput + +[[autodoc]] pipelines.stable_diffusion_ldm3d.pipeline_stable_diffusion_ldm3d.LDM3DPipelineOutput + - all + - __call__ + +# Upscaler + +[LDM3D-VR](https://huggingface.co/papers/2311.03226) is an extended version of LDM3D. + +The abstract from the paper is: +*Latent diffusion models have proven to be state-of-the-art in the creation and manipulation of visual outputs. However, as far as we know, the generation of depth maps jointly with RGB is still limited. We introduce LDM3D-VR, a suite of diffusion models targeting virtual reality development that includes LDM3D-pano and LDM3D-SR. These models enable the generation of panoramic RGBD based on textual prompts and the upscaling of low-resolution inputs to high-resolution RGBD, respectively. Our models are fine-tuned from existing pretrained models on datasets containing panoramic/high-resolution RGB images, depth maps and captions. Both models are evaluated in comparison to existing related methods* + +Two checkpoints are available for use: +- [ldm3d-pano](https://huggingface.co/Intel/ldm3d-pano). This checkpoint enables the generation of panoramic images and requires the StableDiffusionLDM3DPipeline pipeline to be used. +- [ldm3d-sr](https://huggingface.co/Intel/ldm3d-sr). This checkpoint enables the upscaling of RGB and depth images. Can be used in cascade after the original LDM3D pipeline using the StableDiffusionUpscaleLDM3DPipeline from communauty pipeline. + diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/overview.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/overview.md new file mode 100644 index 0000000000000000000000000000000000000000..2d2de39c91a8bd17abe7724e1109e70f277d36bb --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/overview.md @@ -0,0 +1,216 @@ + + +# Stable Diffusion pipelines + +
+ LoRA +
+ +Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/). Latent diffusion applies the diffusion process over a lower dimensional latent space to reduce memory and compute complexity. This specific type of diffusion model was proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2112.10752) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer. + +Stable Diffusion is trained on 512x512 images from a subset of the LAION-5B dataset. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and can run on consumer GPUs. + +For more details about how Stable Diffusion works and how it differs from the base latent diffusion model, take a look at the Stability AI [announcement](https://stability.ai/blog/stable-diffusion-announcement) and our own [blog post](https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work) for more technical details. + +You can find the original codebase for Stable Diffusion v1.0 at [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) and Stable Diffusion v2.0 at [Stability-AI/stablediffusion](https://github.com/Stability-AI/stablediffusion) as well as their original scripts for various tasks. Additional official checkpoints for the different Stable Diffusion versions and tasks can be found on the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations. Explore these organizations to find the best checkpoint for your use-case! + +The table below summarizes the available Stable Diffusion pipelines, their supported tasks, and an interactive demo: + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Pipeline + + Supported tasks + + 🤗 Space +
+ StableDiffusion + text-to-image +
+ StableDiffusionImg2Img + image-to-image +
+ StableDiffusionInpaint + inpainting +
+ StableDiffusionDepth2Img + depth-to-image +
+ StableDiffusionImageVariation + image variation +
+ StableDiffusionPipelineSafe + filtered text-to-image +
+ StableDiffusion2 + text-to-image, inpainting, depth-to-image, super-resolution +
+ StableDiffusionXL + text-to-image, image-to-image +
+ StableDiffusionLatentUpscale + super-resolution +
+ StableDiffusionUpscale + super-resolution
+ StableDiffusionLDM3D + text-to-rgb, text-to-depth, text-to-pano +
+ StableDiffusionUpscaleLDM3D + ldm3d super-resolution
+
+
+ +## Tips + +To help you get the most out of the Stable Diffusion pipelines, here are a few tips for improving performance and usability. These tips are applicable to all Stable Diffusion pipelines. + +### Explore tradeoff between speed and quality + +[`StableDiffusionPipeline`] uses the [`PNDMScheduler`] by default, but 🤗 Diffusers provides many other schedulers (some of which are faster or output better quality) that are compatible. For example, if you want to use the [`EulerDiscreteScheduler`] instead of the default: + +```py +from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler + +pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4") +pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config) + +# or +euler_scheduler = EulerDiscreteScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler") +pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=euler_scheduler) +``` + +### Reuse pipeline components to save memory + +To save memory and use the same components across multiple pipelines, use the `.components` method to avoid loading weights into RAM more than once. + +```py +from diffusers import ( + StableDiffusionPipeline, + StableDiffusionImg2ImgPipeline, + StableDiffusionInpaintPipeline, +) + +text2img = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4") +img2img = StableDiffusionImg2ImgPipeline(**text2img.components) +inpaint = StableDiffusionInpaintPipeline(**text2img.components) + +# now you can use text2img(...), img2img(...), inpaint(...) just like the call methods of each respective pipeline +``` + +### Create web demos using `gradio` + +The Stable Diffusion pipelines are automatically supported in [Gradio](https://github.com/gradio-app/gradio/), a library that makes creating beautiful and user-friendly machine learning apps on the web a breeze. First, make sure you have Gradio installed: + +```sh +pip install -U gradio +``` + +Then, create a web demo around any Stable Diffusion-based pipeline. For example, you can create an image generation pipeline in a single line of code with Gradio's [`Interface.from_pipeline`](https://www.gradio.app/docs/interface#interface-from-pipeline) function: + +```py +from diffusers import StableDiffusionPipeline +import gradio as gr + +pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4") + +gr.Interface.from_pipeline(pipe).launch() +``` + +which opens an intuitive drag-and-drop interface in your browser: + +![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/gradio-panda.png) + +Similarly, you could create a demo for an image-to-image pipeline with: + +```py +from diffusers import StableDiffusionImg2ImgPipeline +import gradio as gr + + +pipe = StableDiffusionImg2ImgPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5") + +gr.Interface.from_pipeline(pipe).launch() +``` + +By default, the web demo runs on a local server. If you'd like to share it with others, you can generate a temporary public +link by setting `share=True` in `launch()`. Or, you can host your demo on [Hugging Face Spaces](https://huggingface.co/spaces)https://huggingface.co/spaces for a permanent link. \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/sdxl_turbo.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/sdxl_turbo.md new file mode 100644 index 0000000000000000000000000000000000000000..7964db4c9d7ec089078dd991867868fc4174709c --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/sdxl_turbo.md @@ -0,0 +1,32 @@ + + +# SDXL Turbo + +Stable Diffusion XL (SDXL) Turbo was proposed in [Adversarial Diffusion Distillation](https://stability.ai/research/adversarial-diffusion-distillation) by Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach. + +The abstract from the paper is: + +*We introduce Adversarial Diffusion Distillation (ADD), a novel training approach that efficiently samples large-scale foundational image diffusion models in just 1–4 steps while maintaining high image quality. We use score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal in combination with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps. Our analyses show that our model clearly outperforms existing few-step methods (GANs,Latent Consistency Models) in a single step and reaches the performance of state-of-the-art diffusion models (SDXL) in only four steps. ADD is the first method to unlock single-step, real-time image synthesis with foundation models.* + +## Tips + +- SDXL Turbo uses the exact same architecture as [SDXL](./stable_diffusion_xl), which means it also has the same API. Please refer to the [SDXL](./stable_diffusion_xl) API reference for more details. +- SDXL Turbo should disable guidance scale by setting `guidance_scale=0.0`. +- SDXL Turbo should use `timestep_spacing='trailing'` for the scheduler and use between 1 and 4 steps. +- SDXL Turbo has been trained to generate images of size 512x512. +- SDXL Turbo is open-access, but not open-source meaning that one might have to buy a model license in order to use it for commercial applications. Make sure to read the [official model card](https://huggingface.co/stabilityai/sdxl-turbo) to learn more. + +> [!TIP] +> To learn how to use SDXL Turbo for various tasks, how to optimize performance, and other usage examples, take a look at the [SDXL Turbo](../../../using-diffusers/sdxl_turbo) guide. +> +> Check out the [Stability AI](https://huggingface.co/stabilityai) Hub organization for the official base and refiner model checkpoints! diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md new file mode 100644 index 0000000000000000000000000000000000000000..31164fb9ae8ffbb21383350856698fc45b0c3a91 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md @@ -0,0 +1,122 @@ + + +# Stable Diffusion 2 + +Stable Diffusion 2 is a text-to-image _latent diffusion_ model built upon the work of the original [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release), and it was led by Robin Rombach and Katherine Crowson from [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/). + +*The Stable Diffusion 2.0 release includes robust text-to-image models trained using a brand new text encoder (OpenCLIP), developed by LAION with support from Stability AI, which greatly improves the quality of the generated images compared to earlier V1 releases. The text-to-image models in this release can generate images with default resolutions of both 512x512 pixels and 768x768 pixels. +These models are trained on an aesthetic subset of the [LAION-5B dataset](https://laion.ai/blog/laion-5b/) created by the DeepFloyd team at Stability AI, which is then further filtered to remove adult content using [LAION’s NSFW filter](https://openreview.net/forum?id=M3Y74vmsMcY).* + +For more details about how Stable Diffusion 2 works and how it differs from the original Stable Diffusion, please refer to the official [announcement post](https://stability.ai/blog/stable-diffusion-v2-release). + +The architecture of Stable Diffusion 2 is more or less identical to the original [Stable Diffusion model](./text2img) so check out it's API documentation for how to use Stable Diffusion 2. We recommend using the [`DPMSolverMultistepScheduler`] as it gives a reasonable speed/quality trade-off and can be run with as little as 20 steps. + +Stable Diffusion 2 is available for tasks like text-to-image, inpainting, super-resolution, and depth-to-image: + +| Task | Repository | +|-------------------------|---------------------------------------------------------------------------------------------------------------| +| text-to-image (512x512) | [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) | +| text-to-image (768x768) | [stabilityai/stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) | +| inpainting | [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) | +| super-resolution | [stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler) | +| depth-to-image | [stabilityai/stable-diffusion-2-depth](https://huggingface.co/stabilityai/stable-diffusion-2-depth) | + +Here are some examples for how to use Stable Diffusion 2 for each task: + +> [!TIP] +> Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! +> +> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! + +## Text-to-image + +```py +from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler +import torch + +repo_id = "stabilityai/stable-diffusion-2-base" +pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, variant="fp16") + +pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) +pipe = pipe.to("cuda") + +prompt = "High quality photo of an astronaut riding a horse in space" +image = pipe(prompt, num_inference_steps=25).images[0] +image +``` + +## Inpainting + +```py +import torch +from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler +from diffusers.utils import load_image, make_image_grid + +img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" +mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" + +init_image = load_image(img_url).resize((512, 512)) +mask_image = load_image(mask_url).resize((512, 512)) + +repo_id = "stabilityai/stable-diffusion-2-inpainting" +pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, variant="fp16") + +pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) +pipe = pipe.to("cuda") + +prompt = "Face of a yellow cat, high resolution, sitting on a park bench" +image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=25).images[0] +make_image_grid([init_image, mask_image, image], rows=1, cols=3) +``` + +## Super-resolution + +```py +from diffusers import StableDiffusionUpscalePipeline +from diffusers.utils import load_image, make_image_grid +import torch + +# load model and scheduler +model_id = "stabilityai/stable-diffusion-x4-upscaler" +pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16) +pipeline = pipeline.to("cuda") + +# let's download an image +url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png" +low_res_img = load_image(url) +low_res_img = low_res_img.resize((128, 128)) +prompt = "a white cat" +upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0] +make_image_grid([low_res_img.resize((512, 512)), upscaled_image.resize((512, 512))], rows=1, cols=2) +``` + +## Depth-to-image + +```py +import torch +from diffusers import StableDiffusionDepth2ImgPipeline +from diffusers.utils import load_image, make_image_grid + +pipe = StableDiffusionDepth2ImgPipeline.from_pretrained( + "stabilityai/stable-diffusion-2-depth", + torch_dtype=torch.float16, +).to("cuda") + + +url = "http://images.cocodataset.org/val2017/000000039769.jpg" +init_image = load_image(url) +prompt = "two tigers" +negative_prompt = "bad, deformed, ugly, bad anotomy" +image = pipe(prompt=prompt, image=init_image, negative_prompt=negative_prompt, strength=0.7).images[0] +make_image_grid([init_image, image], rows=1, cols=2) +``` diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md new file mode 100644 index 0000000000000000000000000000000000000000..4c7e5b1073162cf7747b12c6c2cb0cf61c7904d7 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md @@ -0,0 +1,443 @@ + + +# Stable Diffusion 3 + +
+ LoRA + MPS +
+ +Stable Diffusion 3 (SD3) was proposed in [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://huggingface.co/papers/2403.03206) by Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Muller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach. + +The abstract from the paper is: + +*Diffusion models create data from noise by inverting the forward paths of data towards noise and have emerged as a powerful generative modeling technique for high-dimensional, perceptual data such as images and videos. Rectified flow is a recent generative model formulation that connects data and noise in a straight line. Despite its better theoretical properties and conceptual simplicity, it is not yet decisively established as standard practice. In this work, we improve existing noise sampling techniques for training rectified flow models by biasing them towards perceptually relevant scales. Through a large-scale study, we demonstrate the superior performance of this approach compared to established diffusion formulations for high-resolution text-to-image synthesis. Additionally, we present a novel transformer-based architecture for text-to-image generation that uses separate weights for the two modalities and enables a bidirectional flow of information between image and text tokens, improving text comprehension typography, and human preference ratings. We demonstrate that this architecture follows predictable scaling trends and correlates lower validation loss to improved text-to-image synthesis as measured by various metrics and human evaluations.* + + +## Usage Example + +_As the model is gated, before using it with diffusers you first need to go to the [Stable Diffusion 3 Medium Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers), fill in the form and accept the gate. Once you are in, you need to login so that your system knows you’ve accepted the gate._ + +Use the command below to log in: + +```bash +hf auth login +``` + +> [!TIP] +> The SD3 pipeline uses three text encoders to generate an image. Model offloading is necessary in order for it to run on most commodity hardware. Please use the `torch.float16` data type for additional memory savings. + +```python +import torch +from diffusers import StableDiffusion3Pipeline + +pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16) +pipe.to("cuda") + +image = pipe( + prompt="a photo of a cat holding a sign that says hello world", + negative_prompt="", + num_inference_steps=28, + height=1024, + width=1024, + guidance_scale=7.0, +).images[0] + +image.save("sd3_hello_world.png") +``` + +**Note:** Stable Diffusion 3.5 can also be run using the SD3 pipeline, and all mentioned optimizations and techniques apply to it as well. In total there are three official models in the SD3 family: +- [`stabilityai/stable-diffusion-3-medium-diffusers`](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers) +- [`stabilityai/stable-diffusion-3.5-large`](https://huggingface.co/stabilityai/stable-diffusion-3-5-large) +- [`stabilityai/stable-diffusion-3.5-large-turbo`](https://huggingface.co/stabilityai/stable-diffusion-3-5-large-turbo) + +## Image Prompting with IP-Adapters + +An IP-Adapter lets you prompt SD3 with images, in addition to the text prompt. This is especially useful when describing complex concepts that are difficult to articulate through text alone and you have reference images. To load and use an IP-Adapter, you need: + +- `image_encoder`: Pre-trained vision model used to obtain image features, usually a CLIP image encoder. +- `feature_extractor`: Image processor that prepares the input image for the chosen `image_encoder`. +- `ip_adapter_id`: Checkpoint containing parameters of image cross attention layers and image projection. + +IP-Adapters are trained for a specific model architecture, so they also work in finetuned variations of the base model. You can use the [`~SD3IPAdapterMixin.set_ip_adapter_scale`] function to adjust how strongly the output aligns with the image prompt. The higher the value, the more closely the model follows the image prompt. A default value of 0.5 is typically a good balance, ensuring the model considers both the text and image prompts equally. + +```python +import torch +from PIL import Image + +from diffusers import StableDiffusion3Pipeline +from transformers import SiglipVisionModel, SiglipImageProcessor + +image_encoder_id = "google/siglip-so400m-patch14-384" +ip_adapter_id = "InstantX/SD3.5-Large-IP-Adapter" + +feature_extractor = SiglipImageProcessor.from_pretrained( + image_encoder_id, + torch_dtype=torch.float16 +) +image_encoder = SiglipVisionModel.from_pretrained( + image_encoder_id, + torch_dtype=torch.float16 +).to( "cuda") + +pipe = StableDiffusion3Pipeline.from_pretrained( + "stabilityai/stable-diffusion-3.5-large", + torch_dtype=torch.float16, + feature_extractor=feature_extractor, + image_encoder=image_encoder, +).to("cuda") + +pipe.load_ip_adapter(ip_adapter_id) +pipe.set_ip_adapter_scale(0.6) + +ref_img = Image.open("image.jpg").convert('RGB') + +image = pipe( + width=1024, + height=1024, + prompt="a cat", + negative_prompt="lowres, low quality, worst quality", + num_inference_steps=24, + guidance_scale=5.0, + ip_adapter_image=ref_img +).images[0] + +image.save("result.jpg") +``` + +
+ +
IP-Adapter examples with prompt "a cat"
+
+ + +> [!TIP] +> Check out [IP-Adapter](../../../using-diffusers/ip_adapter) to learn more about how IP-Adapters work. + + +## Memory Optimisations for SD3 + +SD3 uses three text encoders, one of which is the very large T5-XXL model. This makes it challenging to run the model on GPUs with less than 24GB of VRAM, even when using `fp16` precision. The following section outlines a few memory optimizations in Diffusers that make it easier to run SD3 on low resource hardware. + +### Running Inference with Model Offloading + +The most basic memory optimization available in Diffusers allows you to offload the components of the model to CPU during inference in order to save memory, while seeing a slight increase in inference latency. Model offloading will only move a model component onto the GPU when it needs to be executed, while keeping the remaining components on the CPU. + +```python +import torch +from diffusers import StableDiffusion3Pipeline + +pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16) +pipe.enable_model_cpu_offload() + +image = pipe( + prompt="a photo of a cat holding a sign that says hello world", + negative_prompt="", + num_inference_steps=28, + height=1024, + width=1024, + guidance_scale=7.0, +).images[0] + +image.save("sd3_hello_world.png") +``` + +### Dropping the T5 Text Encoder during Inference + +Removing the memory-intensive 4.7B parameter T5-XXL text encoder during inference can significantly decrease the memory requirements for SD3 with only a slight loss in performance. + +```python +import torch +from diffusers import StableDiffusion3Pipeline + +pipe = StableDiffusion3Pipeline.from_pretrained( + "stabilityai/stable-diffusion-3-medium-diffusers", + text_encoder_3=None, + tokenizer_3=None, + torch_dtype=torch.float16 +) +pipe.to("cuda") + +image = pipe( + prompt="a photo of a cat holding a sign that says hello world", + negative_prompt="", + num_inference_steps=28, + height=1024, + width=1024, + guidance_scale=7.0, +).images[0] + +image.save("sd3_hello_world-no-T5.png") +``` + +### Using a Quantized Version of the T5 Text Encoder + +We can leverage the `bitsandbytes` library to load and quantize the T5-XXL text encoder to 8-bit precision. This allows you to keep using all three text encoders while only slightly impacting performance. + +First install the `bitsandbytes` library. + +```shell +pip install bitsandbytes +``` + +Then load the T5-XXL model using the `BitsAndBytesConfig`. + +```python +import torch +from diffusers import StableDiffusion3Pipeline +from transformers import T5EncoderModel, BitsAndBytesConfig + +quantization_config = BitsAndBytesConfig(load_in_8bit=True) + +model_id = "stabilityai/stable-diffusion-3-medium-diffusers" +text_encoder = T5EncoderModel.from_pretrained( + model_id, + subfolder="text_encoder_3", + quantization_config=quantization_config, +) +pipe = StableDiffusion3Pipeline.from_pretrained( + model_id, + text_encoder_3=text_encoder, + device_map="balanced", + torch_dtype=torch.float16 +) + +image = pipe( + prompt="a photo of a cat holding a sign that says hello world", + negative_prompt="", + num_inference_steps=28, + height=1024, + width=1024, + guidance_scale=7.0, +).images[0] + +image.save("sd3_hello_world-8bit-T5.png") +``` + +You can find the end-to-end script [here](https://gist.github.com/sayakpaul/82acb5976509851f2db1a83456e504f1). + +## Performance Optimizations for SD3 + +### Using Torch Compile to Speed Up Inference + +Using compiled components in the SD3 pipeline can speed up inference by as much as 4X. The following code snippet demonstrates how to compile the Transformer and VAE components of the SD3 pipeline. + +```python +import torch +from diffusers import StableDiffusion3Pipeline + +torch.set_float32_matmul_precision("high") + +torch._inductor.config.conv_1x1_as_mm = True +torch._inductor.config.coordinate_descent_tuning = True +torch._inductor.config.epilogue_fusion = False +torch._inductor.config.coordinate_descent_check_all_directions = True + +pipe = StableDiffusion3Pipeline.from_pretrained( + "stabilityai/stable-diffusion-3-medium-diffusers", + torch_dtype=torch.float16 +).to("cuda") +pipe.set_progress_bar_config(disable=True) + +pipe.transformer.to(memory_format=torch.channels_last) +pipe.vae.to(memory_format=torch.channels_last) + +pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True) +pipe.vae.decode = torch.compile(pipe.vae.decode, mode="max-autotune", fullgraph=True) + +# Warm Up +prompt = "a photo of a cat holding a sign that says hello world" +for _ in range(3): + _ = pipe(prompt=prompt, generator=torch.manual_seed(1)) + +# Run Inference +image = pipe(prompt=prompt, generator=torch.manual_seed(1)).images[0] +image.save("sd3_hello_world.png") +``` + +Check out the full script [here](https://gist.github.com/sayakpaul/508d89d7aad4f454900813da5d42ca97). + +## Quantization + +Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model. + +Refer to the [Quantization](../../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`StableDiffusion3Pipeline`] for inference with bitsandbytes. + +```py +import torch +from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, SD3Transformer2DModel, StableDiffusion3Pipeline +from transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel + +quant_config = BitsAndBytesConfig(load_in_8bit=True) +text_encoder_8bit = T5EncoderModel.from_pretrained( + "stabilityai/stable-diffusion-3.5-large", + subfolder="text_encoder_3", + quantization_config=quant_config, + torch_dtype=torch.float16, +) + +quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True) +transformer_8bit = SD3Transformer2DModel.from_pretrained( + "stabilityai/stable-diffusion-3.5-large", + subfolder="transformer", + quantization_config=quant_config, + torch_dtype=torch.float16, +) + +pipeline = StableDiffusion3Pipeline.from_pretrained( + "stabilityai/stable-diffusion-3.5-large", + text_encoder=text_encoder_8bit, + transformer=transformer_8bit, + torch_dtype=torch.float16, + device_map="balanced", +) + +prompt = "a tiny astronaut hatching from an egg on the moon" +image = pipeline(prompt, num_inference_steps=28, guidance_scale=7.0).images[0] +image.save("sd3.png") +``` + +## Using Long Prompts with the T5 Text Encoder + +By default, the T5 Text Encoder prompt uses a maximum sequence length of `256`. This can be adjusted by setting the `max_sequence_length` to accept fewer or more tokens. Keep in mind that longer sequences require additional resources and result in longer generation times, such as during batch inference. + +```python +prompt = "A whimsical and creative image depicting a hybrid creature that is a mix of a waffle and a hippopotamus, basking in a river of melted butter amidst a breakfast-themed landscape. It features the distinctive, bulky body shape of a hippo. However, instead of the usual grey skin, the creature’s body resembles a golden-brown, crispy waffle fresh off the griddle. The skin is textured with the familiar grid pattern of a waffle, each square filled with a glistening sheen of syrup. The environment combines the natural habitat of a hippo with elements of a breakfast table setting, a river of warm, melted butter, with oversized utensils or plates peeking out from the lush, pancake-like foliage in the background, a towering pepper mill standing in for a tree. As the sun rises in this fantastical world, it casts a warm, buttery glow over the scene. The creature, content in its butter river, lets out a yawn. Nearby, a flock of birds take flight" + +image = pipe( + prompt=prompt, + negative_prompt="", + num_inference_steps=28, + guidance_scale=4.5, + max_sequence_length=512, +).images[0] +``` + +### Sending a different prompt to the T5 Text Encoder + +You can send a different prompt to the CLIP Text Encoders and the T5 Text Encoder to prevent the prompt from being truncated by the CLIP Text Encoders and to improve generation. + +> [!TIP] +> The prompt with the CLIP Text Encoders is still truncated to the 77 token limit. + +```python +prompt = "A whimsical and creative image depicting a hybrid creature that is a mix of a waffle and a hippopotamus, basking in a river of melted butter amidst a breakfast-themed landscape. A river of warm, melted butter, pancake-like foliage in the background, a towering pepper mill standing in for a tree." + +prompt_3 = "A whimsical and creative image depicting a hybrid creature that is a mix of a waffle and a hippopotamus, basking in a river of melted butter amidst a breakfast-themed landscape. It features the distinctive, bulky body shape of a hippo. However, instead of the usual grey skin, the creature’s body resembles a golden-brown, crispy waffle fresh off the griddle. The skin is textured with the familiar grid pattern of a waffle, each square filled with a glistening sheen of syrup. The environment combines the natural habitat of a hippo with elements of a breakfast table setting, a river of warm, melted butter, with oversized utensils or plates peeking out from the lush, pancake-like foliage in the background, a towering pepper mill standing in for a tree. As the sun rises in this fantastical world, it casts a warm, buttery glow over the scene. The creature, content in its butter river, lets out a yawn. Nearby, a flock of birds take flight" + +image = pipe( + prompt=prompt, + prompt_3=prompt_3, + negative_prompt="", + num_inference_steps=28, + guidance_scale=4.5, + max_sequence_length=512, +).images[0] +``` + +## Tiny AutoEncoder for Stable Diffusion 3 + +Tiny AutoEncoder for Stable Diffusion (TAESD3) is a tiny distilled version of Stable Diffusion 3's VAE by [Ollin Boer Bohan](https://github.com/madebyollin/taesd) that can decode [`StableDiffusion3Pipeline`] latents almost instantly. + +To use with Stable Diffusion 3: + +```python +import torch +from diffusers import StableDiffusion3Pipeline, AutoencoderTiny + +pipe = StableDiffusion3Pipeline.from_pretrained( + "stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16 +) +pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesd3", torch_dtype=torch.float16) +pipe = pipe.to("cuda") + +prompt = "slice of delicious New York-style berry cheesecake" +image = pipe(prompt, num_inference_steps=25).images[0] +image.save("cheesecake.png") +``` + +## Loading the original checkpoints via `from_single_file` + +The `SD3Transformer2DModel` and `StableDiffusion3Pipeline` classes support loading the original checkpoints via the `from_single_file` method. This method allows you to load the original checkpoint files that were used to train the models. + +## Loading the original checkpoints for the `SD3Transformer2DModel` + +```python +from diffusers import SD3Transformer2DModel + +model = SD3Transformer2DModel.from_single_file("https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/sd3_medium.safetensors") +``` + +## Loading the single checkpoint for the `StableDiffusion3Pipeline` + +### Loading the single file checkpoint without T5 + +```python +import torch +from diffusers import StableDiffusion3Pipeline + +pipe = StableDiffusion3Pipeline.from_single_file( + "https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/sd3_medium_incl_clips.safetensors", + torch_dtype=torch.float16, + text_encoder_3=None +) +pipe.enable_model_cpu_offload() + +image = pipe("a picture of a cat holding a sign that says hello world").images[0] +image.save('sd3-single-file.png') +``` + +### Loading the single file checkpoint with T5 + +> [!TIP] +> The following example loads a checkpoint stored in a 8-bit floating point format which requires PyTorch 2.3 or later. + +```python +import torch +from diffusers import StableDiffusion3Pipeline + +pipe = StableDiffusion3Pipeline.from_single_file( + "https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/sd3_medium_incl_clips_t5xxlfp8.safetensors", + torch_dtype=torch.float16, +) +pipe.enable_model_cpu_offload() + +image = pipe("a picture of a cat holding a sign that says hello world").images[0] +image.save('sd3-single-file-t5-fp8.png') +``` + +### Loading the single file checkpoint for the Stable Diffusion 3.5 Transformer Model + +```python +import torch +from diffusers import SD3Transformer2DModel, StableDiffusion3Pipeline + +transformer = SD3Transformer2DModel.from_single_file( + "https://huggingface.co/stabilityai/stable-diffusion-3.5-large-turbo/blob/main/sd3.5_large.safetensors", + torch_dtype=torch.bfloat16, +) +pipe = StableDiffusion3Pipeline.from_pretrained( + "stabilityai/stable-diffusion-3.5-large", + transformer=transformer, + torch_dtype=torch.bfloat16, +) +pipe.enable_model_cpu_offload() +image = pipe("a cat holding a sign that says hello world").images[0] +image.save("sd35.png") +``` + +## StableDiffusion3Pipeline + +[[autodoc]] StableDiffusion3Pipeline + - all + - __call__ diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_safe.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_safe.md new file mode 100644 index 0000000000000000000000000000000000000000..151b0b8a6507a27b76862f1e193771e7d82bd816 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_safe.md @@ -0,0 +1,61 @@ + + +> [!WARNING] +> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model. + +# Safe Stable Diffusion + +Safe Stable Diffusion was proposed in [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://huggingface.co/papers/2211.05105) and mitigates inappropriate degeneration from Stable Diffusion models because they're trained on unfiltered web-crawled datasets. For instance Stable Diffusion may unexpectedly generate nudity, violence, images depicting self-harm, and otherwise offensive content. Safe Stable Diffusion is an extension of Stable Diffusion that drastically reduces this type of content. + +The abstract from the paper is: + +*Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the internet, they also suffer, as we demonstrate, from degenerated and biased human behavior. In turn, they may even reinforce such biases. To help combat these undesired side effects, we present safe latent diffusion (SLD). Specifically, to measure the inappropriate degeneration due to unfiltered and imbalanced training sets, we establish a novel image generation test bed-inappropriate image prompts (I2P)-containing dedicated, real-world image-to-text prompts covering concepts such as nudity and violence. As our exhaustive empirical evaluation demonstrates, the introduced SLD removes and suppresses inappropriate image parts during the diffusion process, with no additional training required and no adverse effect on overall image quality or text alignment.* + +## Tips + +Use the `safety_concept` property of [`StableDiffusionPipelineSafe`] to check and edit the current safety concept: + +```python +>>> from diffusers import StableDiffusionPipelineSafe + +>>> pipeline = StableDiffusionPipelineSafe.from_pretrained("AIML-TUDA/stable-diffusion-safe") +>>> pipeline.safety_concept +'an image showing hate, harassment, violence, suffering, humiliation, harm, suicide, sexual, nudity, bodily fluids, blood, obscene gestures, illegal activity, drug use, theft, vandalism, weapons, child abuse, brutality, cruelty' +``` +For each image generation the active concept is also contained in [`StableDiffusionSafePipelineOutput`]. + +There are 4 configurations (`SafetyConfig.WEAK`, `SafetyConfig.MEDIUM`, `SafetyConfig.STRONG`, and `SafetyConfig.MAX`) that can be applied: + +```python +>>> from diffusers import StableDiffusionPipelineSafe +>>> from diffusers.pipelines.stable_diffusion_safe import SafetyConfig + +>>> pipeline = StableDiffusionPipelineSafe.from_pretrained("AIML-TUDA/stable-diffusion-safe") +>>> prompt = "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c. leyendecker" +>>> out = pipeline(prompt=prompt, **SafetyConfig.MAX) +``` + +> [!TIP] +> Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! + +## StableDiffusionPipelineSafe + +[[autodoc]] StableDiffusionPipelineSafe + - all + - __call__ + +## StableDiffusionSafePipelineOutput + +[[autodoc]] pipelines.stable_diffusion_safe.StableDiffusionSafePipelineOutput + - all + - __call__ diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md new file mode 100644 index 0000000000000000000000000000000000000000..6863d408b5fdea20ace19c1946d6ec934a7cf403 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md @@ -0,0 +1,57 @@ + + +# Stable Diffusion XL + +
+ LoRA + MPS +
+ +Stable Diffusion XL (SDXL) was proposed in [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://huggingface.co/papers/2307.01952) by Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. + +The abstract from the paper is: + +*We present SDXL, a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. We design multiple novel conditioning schemes and train SDXL on multiple aspect ratios. We also introduce a refinement model which is used to improve the visual fidelity of samples generated by SDXL using a post-hoc image-to-image technique. We demonstrate that SDXL shows drastically improved performance compared the previous versions of Stable Diffusion and achieves results competitive with those of black-box state-of-the-art image generators.* + +## Tips + +- Using SDXL with a DPM++ scheduler for less than 50 steps is known to produce [visual artifacts](https://github.com/huggingface/diffusers/issues/5433) because the solver becomes numerically unstable. To fix this issue, take a look at this [PR](https://github.com/huggingface/diffusers/pull/5541) which recommends for ODE/SDE solvers: + - set `use_karras_sigmas=True` or `lu_lambdas=True` to improve image quality + - set `euler_at_final=True` if you're using a solver with uniform step sizes (DPM++2M or DPM++2M SDE) +- Most SDXL checkpoints work best with an image size of 1024x1024. Image sizes of 768x768 and 512x512 are also supported, but the results aren't as good. Anything below 512x512 is not recommended and likely won't be for default checkpoints like [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0). +- SDXL can pass a different prompt for each of the text encoders it was trained on. We can even pass different parts of the same prompt to the text encoders. +- SDXL output images can be improved by making use of a refiner model in an image-to-image setting. +- SDXL offers `negative_original_size`, `negative_crops_coords_top_left`, and `negative_target_size` to negatively condition the model on image resolution and cropping parameters. + +> [!TIP] +> To learn how to use SDXL for various tasks, how to optimize performance, and other usage examples, take a look at the [Stable Diffusion XL](../../../using-diffusers/sdxl) guide. +> +> Check out the [Stability AI](https://huggingface.co/stabilityai) Hub organization for the official base and refiner model checkpoints! + +## StableDiffusionXLPipeline + +[[autodoc]] StableDiffusionXLPipeline + - all + - __call__ + +## StableDiffusionXLImg2ImgPipeline + +[[autodoc]] StableDiffusionXLImg2ImgPipeline + - all + - __call__ + +## StableDiffusionXLInpaintPipeline + +[[autodoc]] StableDiffusionXLInpaintPipeline + - all + - __call__ diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/svd.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/svd.md new file mode 100644 index 0000000000000000000000000000000000000000..a00dd3ef6d85c5c101a5fc550eaa9a1500721f95 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/svd.md @@ -0,0 +1,40 @@ + + +# Stable Video Diffusion + +Stable Video Diffusion was proposed in [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://hf.co/papers/2311.15127) by Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, Varun Jampani, Robin Rombach. + +The abstract from the paper is: + +*We present Stable Video Diffusion - a latent video diffusion model for high-resolution, state-of-the-art text-to-video and image-to-video generation. Recently, latent diffusion models trained for 2D image synthesis have been turned into generative video models by inserting temporal layers and finetuning them on small, high-quality video datasets. However, training methods in the literature vary widely, and the field has yet to agree on a unified strategy for curating video data. In this paper, we identify and evaluate three different stages for successful training of video LDMs: text-to-image pretraining, video pretraining, and high-quality video finetuning. Furthermore, we demonstrate the necessity of a well-curated pretraining dataset for generating high-quality videos and present a systematic curation process to train a strong base model, including captioning and filtering strategies. We then explore the impact of finetuning our base model on high-quality data and train a text-to-video model that is competitive with closed-source video generation. We also show that our base model provides a powerful motion representation for downstream tasks such as image-to-video generation and adaptability to camera motion-specific LoRA modules. Finally, we demonstrate that our model provides a strong multi-view 3D-prior and can serve as a base to finetune a multi-view diffusion model that jointly generates multiple views of objects in a feedforward fashion, outperforming image-based methods at a fraction of their compute budget. We release code and model weights at this https URL.* + +> [!TIP] +> To learn how to use Stable Video Diffusion, take a look at the [Stable Video Diffusion](../../../using-diffusers/svd) guide. +> +>
+> +> Check out the [Stability AI](https://huggingface.co/stabilityai) Hub organization for the [base](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid) and [extended frame](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt) checkpoints! + +## Tips + +Video generation is memory-intensive and one way to reduce your memory usage is to set `enable_forward_chunking` on the pipeline's UNet so you don't run the entire feedforward layer at once. Breaking it up into chunks in a loop is more efficient. + +Check out the [Text or image-to-video](../../../using-diffusers/text-img2vid) guide for more details about how certain parameters can affect video generation and how to optimize inference by reducing memory usage. + +## StableVideoDiffusionPipeline + +[[autodoc]] StableVideoDiffusionPipeline + +## StableVideoDiffusionPipelineOutput + +[[autodoc]] pipelines.stable_video_diffusion.StableVideoDiffusionPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/text2img.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/text2img.md new file mode 100644 index 0000000000000000000000000000000000000000..2033638a1a36bf901570d3407cdd80f1b8e3c961 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/text2img.md @@ -0,0 +1,50 @@ + + +# Text-to-image + +
+ LoRA +
+ +The Stable Diffusion model was created by researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), [Runway](https://github.com/runwayml), and [LAION](https://laion.ai/). The [`StableDiffusionPipeline`] is capable of generating photorealistic images given any text input. It's trained on 512x512 images from a subset of the LAION-5B dataset. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and can run on consumer GPUs. Latent diffusion is the research on top of which Stable Diffusion was built. It was proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2112.10752) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer. + +The abstract from the paper is: + +*By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs. Code is available at https://github.com/CompVis/latent-diffusion.* + +> [!TIP] +> Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! +> +> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! + +## StableDiffusionPipeline + +[[autodoc]] StableDiffusionPipeline + - all + - __call__ + - enable_attention_slicing + - disable_attention_slicing + - enable_vae_slicing + - disable_vae_slicing + - enable_xformers_memory_efficient_attention + - disable_xformers_memory_efficient_attention + - enable_vae_tiling + - disable_vae_tiling + - load_textual_inversion + - from_single_file + - load_lora_weights + - save_lora_weights + +## StableDiffusionPipelineOutput + +[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/upscale.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/upscale.md new file mode 100644 index 0000000000000000000000000000000000000000..e7d571e73cdf7ecd7d80489c7d5bdd41aaa63575 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_diffusion/upscale.md @@ -0,0 +1,38 @@ + + +# Super-resolution + +
+ LoRA +
+ +The Stable Diffusion upscaler diffusion model was created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), and [LAION](https://laion.ai/). It is used to enhance the resolution of input images by a factor of 4. + +> [!TIP] +> Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! +> +> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! + +## StableDiffusionUpscalePipeline + +[[autodoc]] StableDiffusionUpscalePipeline + - all + - __call__ + - enable_attention_slicing + - disable_attention_slicing + - enable_xformers_memory_efficient_attention + - disable_xformers_memory_efficient_attention + +## StableDiffusionPipelineOutput + +[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_unclip.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_unclip.md new file mode 100644 index 0000000000000000000000000000000000000000..09100201bb1e5db9660f30e110424115ed7a99ba --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/stable_unclip.md @@ -0,0 +1,127 @@ + + +# Stable unCLIP + +
+ LoRA +
+ +Stable unCLIP checkpoints are finetuned from [Stable Diffusion 2.1](./stable_diffusion/stable_diffusion_2) checkpoints to condition on CLIP image embeddings. +Stable unCLIP still conditions on text embeddings. Given the two separate conditionings, stable unCLIP can be used +for text guided image variation. When combined with an unCLIP prior, it can also be used for full text to image generation. + +The abstract from the paper is: + +*Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. Moreover, the joint embedding space of CLIP enables language-guided image manipulations in a zero-shot fashion. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples.* + +## Tips + +Stable unCLIP takes `noise_level` as input during inference which determines how much noise is added to the image embeddings. A higher `noise_level` increases variation in the final un-noised images. By default, we do not add any additional noise to the image embeddings (`noise_level = 0`). + +### Text-to-Image Generation +Stable unCLIP can be leveraged for text-to-image generation by pipelining it with the prior model of KakaoBrain's open source DALL-E 2 replication [Karlo](https://huggingface.co/kakaobrain/karlo-v1-alpha): + +```python +import torch +from diffusers import UnCLIPScheduler, DDPMScheduler, StableUnCLIPPipeline +from diffusers.models import PriorTransformer +from transformers import CLIPTokenizer, CLIPTextModelWithProjection + +prior_model_id = "kakaobrain/karlo-v1-alpha" +data_type = torch.float16 +prior = PriorTransformer.from_pretrained(prior_model_id, subfolder="prior", torch_dtype=data_type) + +prior_text_model_id = "openai/clip-vit-large-patch14" +prior_tokenizer = CLIPTokenizer.from_pretrained(prior_text_model_id) +prior_text_model = CLIPTextModelWithProjection.from_pretrained(prior_text_model_id, torch_dtype=data_type) +prior_scheduler = UnCLIPScheduler.from_pretrained(prior_model_id, subfolder="prior_scheduler") +prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config) + +stable_unclip_model_id = "stabilityai/stable-diffusion-2-1-unclip-small" + +pipe = StableUnCLIPPipeline.from_pretrained( + stable_unclip_model_id, + torch_dtype=data_type, + variant="fp16", + prior_tokenizer=prior_tokenizer, + prior_text_encoder=prior_text_model, + prior=prior, + prior_scheduler=prior_scheduler, +) + +pipe = pipe.to("cuda") +wave_prompt = "dramatic wave, the Oceans roar, Strong wave spiral across the oceans as the waves unfurl into roaring crests; perfect wave form; perfect wave shape; dramatic wave shape; wave shape unbelievable; wave; wave shape spectacular" + +image = pipe(prompt=wave_prompt).images[0] +image +``` +> [!WARNING] +> For text-to-image we use `stabilityai/stable-diffusion-2-1-unclip-small` as it was trained on CLIP ViT-L/14 embedding, the same as the Karlo model prior. [stabilityai/stable-diffusion-2-1-unclip](https://hf.co/stabilityai/stable-diffusion-2-1-unclip) was trained on OpenCLIP ViT-H, so we don't recommend its use. + +### Text guided Image-to-Image Variation + +```python +from diffusers import StableUnCLIPImg2ImgPipeline +from diffusers.utils import load_image +import torch + +pipe = StableUnCLIPImg2ImgPipeline.from_pretrained( + "stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16" +) +pipe = pipe.to("cuda") + +url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png" +init_image = load_image(url) + +images = pipe(init_image).images +images[0].save("variation_image.png") +``` + +Optionally, you can also pass a prompt to `pipe` such as: + +```python +prompt = "A fantasy landscape, trending on artstation" + +image = pipe(init_image, prompt=prompt).images[0] +image +``` + +> [!TIP] +> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. + +## StableUnCLIPPipeline + +[[autodoc]] StableUnCLIPPipeline + - all + - __call__ + - enable_attention_slicing + - disable_attention_slicing + - enable_vae_slicing + - disable_vae_slicing + - enable_xformers_memory_efficient_attention + - disable_xformers_memory_efficient_attention + +## StableUnCLIPImg2ImgPipeline + +[[autodoc]] StableUnCLIPImg2ImgPipeline + - all + - __call__ + - enable_attention_slicing + - disable_attention_slicing + - enable_vae_slicing + - disable_vae_slicing + - enable_xformers_memory_efficient_attention + - disable_xformers_memory_efficient_attention + +## ImagePipelineOutput +[[autodoc]] pipelines.ImagePipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/text_to_video.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/text_to_video.md new file mode 100644 index 0000000000000000000000000000000000000000..d9f6d8e722ac6c20a8dfd9b136f8a5252ba832cb --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/text_to_video.md @@ -0,0 +1,191 @@ + + +> [!WARNING] +> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model. + +# Text-to-video + +
+ LoRA +
+ +[ModelScope Text-to-Video Technical Report](https://huggingface.co/papers/2308.06571) is by Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, Shiwei Zhang. + +The abstract from the paper is: + +*This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a text-to-image synthesis model (i.e., Stable Diffusion). ModelScopeT2V incorporates spatio-temporal blocks to ensure consistent frame generation and smooth movement transitions. The model could adapt to varying frame numbers during training and inference, rendering it suitable for both image-text and video-text datasets. ModelScopeT2V brings together three components (i.e., VQGAN, a text encoder, and a denoising UNet), totally comprising 1.7 billion parameters, in which 0.5 billion parameters are dedicated to temporal capabilities. The model demonstrates superior performance over state-of-the-art methods across three evaluation metrics. The code and an online demo are available at https://modelscope.cn/models/damo/text-to-video-synthesis/summary.* + +You can find additional information about Text-to-Video on the [project page](https://modelscope.cn/models/damo/text-to-video-synthesis/summary), [original codebase](https://github.com/modelscope/modelscope/), and try it out in a [demo](https://huggingface.co/spaces/damo-vilab/modelscope-text-to-video-synthesis). Official checkpoints can be found at [damo-vilab](https://huggingface.co/damo-vilab) and [cerspense](https://huggingface.co/cerspense). + +## Usage example + +### `text-to-video-ms-1.7b` + +Let's start by generating a short video with the default length of 16 frames (2s at 8 fps): + +```python +import torch +from diffusers import DiffusionPipeline +from diffusers.utils import export_to_video + +pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16") +pipe = pipe.to("cuda") + +prompt = "Spiderman is surfing" +video_frames = pipe(prompt).frames[0] +video_path = export_to_video(video_frames) +video_path +``` + +Diffusers supports different optimization techniques to improve the latency +and memory footprint of a pipeline. Since videos are often more memory-heavy than images, +we can enable CPU offloading and VAE slicing to keep the memory footprint at bay. + +Let's generate a video of 8 seconds (64 frames) on the same GPU using CPU offloading and VAE slicing: + +```python +import torch +from diffusers import DiffusionPipeline +from diffusers.utils import export_to_video + +pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16") +pipe.enable_model_cpu_offload() + +# memory optimization +pipe.enable_vae_slicing() + +prompt = "Darth Vader surfing a wave" +video_frames = pipe(prompt, num_frames=64).frames[0] +video_path = export_to_video(video_frames) +video_path +``` + +It just takes **7 GBs of GPU memory** to generate the 64 video frames using PyTorch 2.0, "fp16" precision and the techniques mentioned above. + +We can also use a different scheduler easily, using the same method we'd use for Stable Diffusion: + +```python +import torch +from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler +from diffusers.utils import export_to_video + +pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16") +pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) +pipe.enable_model_cpu_offload() + +prompt = "Spiderman is surfing" +video_frames = pipe(prompt, num_inference_steps=25).frames[0] +video_path = export_to_video(video_frames) +video_path +``` + +Here are some sample outputs: + + + + + + +
+ An astronaut riding a horse. +
+ An astronaut riding a horse. +
+ Darth vader surfing in waves. +
+ Darth vader surfing in waves. +
+ +### `cerspense/zeroscope_v2_576w` & `cerspense/zeroscope_v2_XL` + +Zeroscope are watermark-free model and have been trained on specific sizes such as `576x320` and `1024x576`. +One should first generate a video using the lower resolution checkpoint [`cerspense/zeroscope_v2_576w`](https://huggingface.co/cerspense/zeroscope_v2_576w) with [`TextToVideoSDPipeline`], +which can then be upscaled using [`VideoToVideoSDPipeline`] and [`cerspense/zeroscope_v2_XL`](https://huggingface.co/cerspense/zeroscope_v2_XL). + + +```py +import torch +from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler +from diffusers.utils import export_to_video +from PIL import Image + +pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16) +pipe.enable_model_cpu_offload() + +# memory optimization +pipe.unet.enable_forward_chunking(chunk_size=1, dim=1) +pipe.enable_vae_slicing() + +prompt = "Darth Vader surfing a wave" +video_frames = pipe(prompt, num_frames=24).frames[0] +video_path = export_to_video(video_frames) +video_path +``` + +Now the video can be upscaled: + +```py +pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_XL", torch_dtype=torch.float16) +pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) +pipe.enable_model_cpu_offload() + +# memory optimization +pipe.unet.enable_forward_chunking(chunk_size=1, dim=1) +pipe.enable_vae_slicing() + +video = [Image.fromarray(frame).resize((1024, 576)) for frame in video_frames] + +video_frames = pipe(prompt, video=video, strength=0.6).frames[0] +video_path = export_to_video(video_frames) +video_path +``` + +Here are some sample outputs: + + + + + +
+ Darth vader surfing in waves. +
+ Darth vader surfing in waves. +
+ +## Tips + +Video generation is memory-intensive and one way to reduce your memory usage is to set `enable_forward_chunking` on the pipeline's UNet so you don't run the entire feedforward layer at once. Breaking it up into chunks in a loop is more efficient. + +Check out the [Text or image-to-video](../../using-diffusers/text-img2vid) guide for more details about how certain parameters can affect video generation and how to optimize inference by reducing memory usage. + +> [!TIP] +> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. + +## TextToVideoSDPipeline +[[autodoc]] TextToVideoSDPipeline + - all + - __call__ + +## VideoToVideoSDPipeline +[[autodoc]] VideoToVideoSDPipeline + - all + - __call__ + +## TextToVideoSDPipelineOutput +[[autodoc]] pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/text_to_video_zero.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/text_to_video_zero.md new file mode 100644 index 0000000000000000000000000000000000000000..50e7620760f30d25fc1100c0f7a69777e82b1c62 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/text_to_video_zero.md @@ -0,0 +1,306 @@ + + +> [!WARNING] +> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model. + +# Text2Video-Zero + +
+ LoRA +
+ +[Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://huggingface.co/papers/2303.13439) is by Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan, Roberto Henschel, [Zhangyang Wang](https://www.ece.utexas.edu/people/faculty/atlas-wang), Shant Navasardyan, [Humphrey Shi](https://www.humphreyshi.com). + +Text2Video-Zero enables zero-shot video generation using either: +1. A textual prompt +2. A prompt combined with guidance from poses or edges +3. Video Instruct-Pix2Pix (instruction-guided video editing) + +Results are temporally consistent and closely follow the guidance and textual prompts. + +![teaser-img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/t2v_zero_teaser.png) + +The abstract from the paper is: + +*Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. In this paper, we introduce a new task of zero-shot text-to-video generation and propose a low-cost approach (without any training or optimization) by leveraging the power of existing text-to-image synthesis methods (e.g., Stable Diffusion), making them suitable for the video domain. +Our key modifications include (i) enriching the latent codes of the generated frames with motion dynamics to keep the global scene and the background time consistent; and (ii) reprogramming frame-level self-attention using a new cross-frame attention of each frame on the first frame, to preserve the context, appearance, and identity of the foreground object. +Experiments show that this leads to low overhead, yet high-quality and remarkably consistent video generation. Moreover, our approach is not limited to text-to-video synthesis but is also applicable to other tasks such as conditional and content-specialized video generation, and Video Instruct-Pix2Pix, i.e., instruction-guided video editing. +As experiments show, our method performs comparably or sometimes better than recent approaches, despite not being trained on additional video data.* + +You can find additional information about Text2Video-Zero on the [project page](https://text2video-zero.github.io/), [paper](https://huggingface.co/papers/2303.13439), and [original codebase](https://github.com/Picsart-AI-Research/Text2Video-Zero). + +## Usage example + +### Text-To-Video + +To generate a video from prompt, run the following Python code: +```python +import torch +from diffusers import TextToVideoZeroPipeline +import imageio + +model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5" +pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") + +prompt = "A panda is playing guitar on times square" +result = pipe(prompt=prompt).images +result = [(r * 255).astype("uint8") for r in result] +imageio.mimsave("video.mp4", result, fps=4) +``` +You can change these parameters in the pipeline call: +* Motion field strength (see the [paper](https://huggingface.co/papers/2303.13439), Sect. 3.3.1): + * `motion_field_strength_x` and `motion_field_strength_y`. Default: `motion_field_strength_x=12`, `motion_field_strength_y=12` +* `T` and `T'` (see the [paper](https://huggingface.co/papers/2303.13439), Sect. 3.3.1) + * `t0` and `t1` in the range `{0, ..., num_inference_steps}`. Default: `t0=45`, `t1=48` +* Video length: + * `video_length`, the number of frames video_length to be generated. Default: `video_length=8` + +We can also generate longer videos by doing the processing in a chunk-by-chunk manner: +```python +import torch +from diffusers import TextToVideoZeroPipeline +import numpy as np + +model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5" +pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") +seed = 0 +video_length = 24 #24 ÷ 4fps = 6 seconds +chunk_size = 8 +prompt = "A panda is playing guitar on times square" + +# Generate the video chunk-by-chunk +result = [] +chunk_ids = np.arange(0, video_length, chunk_size - 1) +generator = torch.Generator(device="cuda") +for i in range(len(chunk_ids)): + print(f"Processing chunk {i + 1} / {len(chunk_ids)}") + ch_start = chunk_ids[i] + ch_end = video_length if i == len(chunk_ids) - 1 else chunk_ids[i + 1] + # Attach the first frame for Cross Frame Attention + frame_ids = [0] + list(range(ch_start, ch_end)) + # Fix the seed for the temporal consistency + generator.manual_seed(seed) + output = pipe(prompt=prompt, video_length=len(frame_ids), generator=generator, frame_ids=frame_ids) + result.append(output.images[1:]) + +# Concatenate chunks and save +result = np.concatenate(result) +result = [(r * 255).astype("uint8") for r in result] +imageio.mimsave("video.mp4", result, fps=4) +``` + + +- #### SDXL Support +In order to use the SDXL model when generating a video from prompt, use the `TextToVideoZeroSDXLPipeline` pipeline: + +```python +import torch +from diffusers import TextToVideoZeroSDXLPipeline + +model_id = "stabilityai/stable-diffusion-xl-base-1.0" +pipe = TextToVideoZeroSDXLPipeline.from_pretrained( + model_id, torch_dtype=torch.float16, variant="fp16", use_safetensors=True +).to("cuda") +``` + +### Text-To-Video with Pose Control +To generate a video from prompt with additional pose control + +1. Download a demo video + + ```python + from huggingface_hub import hf_hub_download + + filename = "__assets__/poses_skeleton_gifs/dance1_corr.mp4" + repo_id = "PAIR/Text2Video-Zero" + video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename) + ``` + + +2. Read video containing extracted pose images + ```python + from PIL import Image + import imageio + + reader = imageio.get_reader(video_path, "ffmpeg") + frame_count = 8 + pose_images = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)] + ``` + To extract pose from actual video, read [ControlNet documentation](controlnet). + +3. Run `StableDiffusionControlNetPipeline` with our custom attention processor + + ```python + import torch + from diffusers import StableDiffusionControlNetPipeline, ControlNetModel + from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor + + model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5" + controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16) + pipe = StableDiffusionControlNetPipeline.from_pretrained( + model_id, controlnet=controlnet, torch_dtype=torch.float16 + ).to("cuda") + + # Set the attention processor + pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2)) + pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2)) + + # fix latents for all frames + latents = torch.randn((1, 4, 64, 64), device="cuda", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1) + + prompt = "Darth Vader dancing in a desert" + result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images + imageio.mimsave("video.mp4", result, fps=4) + ``` +- #### SDXL Support + + Since our attention processor also works with SDXL, it can be utilized to generate a video from prompt using ControlNet models powered by SDXL: + ```python + import torch + from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel + from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor + + controlnet_model_id = 'thibaud/controlnet-openpose-sdxl-1.0' + model_id = 'stabilityai/stable-diffusion-xl-base-1.0' + + controlnet = ControlNetModel.from_pretrained(controlnet_model_id, torch_dtype=torch.float16) + pipe = StableDiffusionControlNetPipeline.from_pretrained( + model_id, controlnet=controlnet, torch_dtype=torch.float16 + ).to('cuda') + + # Set the attention processor + pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2)) + pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2)) + + # fix latents for all frames + latents = torch.randn((1, 4, 128, 128), device="cuda", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1) + + prompt = "Darth Vader dancing in a desert" + result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images + imageio.mimsave("video.mp4", result, fps=4) + ``` + +### Text-To-Video with Edge Control + +To generate a video from prompt with additional Canny edge control, follow the same steps described above for pose-guided generation using [Canny edge ControlNet model](https://huggingface.co/lllyasviel/sd-controlnet-canny). + + +### Video Instruct-Pix2Pix + +To perform text-guided video editing (with [InstructPix2Pix](pix2pix)): + +1. Download a demo video + + ```python + from huggingface_hub import hf_hub_download + + filename = "__assets__/pix2pix video/camel.mp4" + repo_id = "PAIR/Text2Video-Zero" + video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename) + ``` + +2. Read video from path + ```python + from PIL import Image + import imageio + + reader = imageio.get_reader(video_path, "ffmpeg") + frame_count = 8 + video = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)] + ``` + +3. Run `StableDiffusionInstructPix2PixPipeline` with our custom attention processor + ```python + import torch + from diffusers import StableDiffusionInstructPix2PixPipeline + from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor + + model_id = "timbrooks/instruct-pix2pix" + pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") + pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=3)) + + prompt = "make it Van Gogh Starry Night style" + result = pipe(prompt=[prompt] * len(video), image=video).images + imageio.mimsave("edited_video.mp4", result, fps=4) + ``` + + +### DreamBooth specialization + +Methods **Text-To-Video**, **Text-To-Video with Pose Control** and **Text-To-Video with Edge Control** +can run with custom [DreamBooth](../../training/dreambooth) models, as shown below for +[Canny edge ControlNet model](https://huggingface.co/lllyasviel/sd-controlnet-canny) and +[Avatar style DreamBooth](https://huggingface.co/PAIR/text2video-zero-controlnet-canny-avatar) model: + +1. Download a demo video + + ```python + from huggingface_hub import hf_hub_download + + filename = "__assets__/canny_videos_mp4/girl_turning.mp4" + repo_id = "PAIR/Text2Video-Zero" + video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename) + ``` + +2. Read video from path + ```python + from PIL import Image + import imageio + + reader = imageio.get_reader(video_path, "ffmpeg") + frame_count = 8 + canny_edges = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)] + ``` + +3. Run `StableDiffusionControlNetPipeline` with custom trained DreamBooth model + ```python + import torch + from diffusers import StableDiffusionControlNetPipeline, ControlNetModel + from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor + + # set model id to custom model + model_id = "PAIR/text2video-zero-controlnet-canny-avatar" + controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) + pipe = StableDiffusionControlNetPipeline.from_pretrained( + model_id, controlnet=controlnet, torch_dtype=torch.float16 + ).to("cuda") + + # Set the attention processor + pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2)) + pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2)) + + # fix latents for all frames + latents = torch.randn((1, 4, 64, 64), device="cuda", dtype=torch.float16).repeat(len(canny_edges), 1, 1, 1) + + prompt = "oil painting of a beautiful girl avatar style" + result = pipe(prompt=[prompt] * len(canny_edges), image=canny_edges, latents=latents).images + imageio.mimsave("video.mp4", result, fps=4) + ``` + +You can filter out some available DreamBooth-trained models with [this link](https://huggingface.co/models?search=dreambooth). + +> [!TIP] +> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. + +## TextToVideoZeroPipeline +[[autodoc]] TextToVideoZeroPipeline + - all + - __call__ + +## TextToVideoZeroSDXLPipeline +[[autodoc]] TextToVideoZeroSDXLPipeline + - all + - __call__ + +## TextToVideoPipelineOutput +[[autodoc]] pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/unclip.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/unclip.md new file mode 100644 index 0000000000000000000000000000000000000000..7c5c2b0d9ab95f3f2939db78666c3d4c706fc10c --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/unclip.md @@ -0,0 +1,37 @@ + + +> [!WARNING] +> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model. + +# unCLIP + +[Hierarchical Text-Conditional Image Generation with CLIP Latents](https://huggingface.co/papers/2204.06125) is by Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen. The unCLIP model in 🤗 Diffusers comes from kakaobrain's [karlo](https://github.com/kakaobrain/karlo). + +The abstract from the paper is following: + +*Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. Moreover, the joint embedding space of CLIP enables language-guided image manipulations in a zero-shot fashion. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples.* + +You can find lucidrains' DALL-E 2 recreation at [lucidrains/DALLE2-pytorch](https://github.com/lucidrains/DALLE2-pytorch). + +> [!TIP] +> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. + +## UnCLIPPipeline +[[autodoc]] UnCLIPPipeline + - all + - __call__ + +## UnCLIPImageVariationPipeline +[[autodoc]] UnCLIPImageVariationPipeline + - all + - __call__ + +## ImagePipelineOutput +[[autodoc]] pipelines.ImagePipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/unidiffuser.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/unidiffuser.md new file mode 100644 index 0000000000000000000000000000000000000000..2ff700e4b8be2a3b0aa779c7dd553ab7f7d4e9d4 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/unidiffuser.md @@ -0,0 +1,206 @@ + + +> [!WARNING] +> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model. + +# UniDiffuser + +
+ LoRA +
+ +The UniDiffuser model was proposed in [One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale](https://huggingface.co/papers/2303.06555) by Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu. + +The abstract from the paper is: + +*This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fit all distributions relevant to a set of multi-modal data in one model. Our key insight is -- learning diffusion models for marginal, conditional, and joint distributions can be unified as predicting the noise in the perturbed data, where the perturbation levels (i.e. timesteps) can be different for different modalities. Inspired by the unified view, UniDiffuser learns all distributions simultaneously with a minimal modification to the original diffusion model -- perturbs data in all modalities instead of a single modality, inputs individual timesteps in different modalities, and predicts the noise of all modalities instead of a single modality. UniDiffuser is parameterized by a transformer for diffusion models to handle input types of different modalities. Implemented on large-scale paired image-text data, UniDiffuser is able to perform image, text, text-to-image, image-to-text, and image-text pair generation by setting proper timesteps without additional overhead. In particular, UniDiffuser is able to produce perceptually realistic samples in all tasks and its quantitative results (e.g., the FID and CLIP score) are not only superior to existing general-purpose models but also comparable to the bespoken models (e.g., Stable Diffusion and DALL-E 2) in representative tasks (e.g., text-to-image generation).* + +You can find the original codebase at [thu-ml/unidiffuser](https://github.com/thu-ml/unidiffuser) and additional checkpoints at [thu-ml](https://huggingface.co/thu-ml). + +> [!WARNING] +> There is currently an issue on PyTorch 1.X where the output images are all black or the pixel values become `NaNs`. This issue can be mitigated by switching to PyTorch 2.X. + +This pipeline was contributed by [dg845](https://github.com/dg845). ❤️ + +## Usage Examples + +Because the UniDiffuser model is trained to model the joint distribution of (image, text) pairs, it is capable of performing a diverse range of generation tasks: + +### Unconditional Image and Text Generation + +Unconditional generation (where we start from only latents sampled from a standard Gaussian prior) from a [`UniDiffuserPipeline`] will produce a (image, text) pair: + +```python +import torch + +from diffusers import UniDiffuserPipeline + +device = "cuda" +model_id_or_path = "thu-ml/unidiffuser-v1" +pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16) +pipe.to(device) + +# Unconditional image and text generation. The generation task is automatically inferred. +sample = pipe(num_inference_steps=20, guidance_scale=8.0) +image = sample.images[0] +text = sample.text[0] +image.save("unidiffuser_joint_sample_image.png") +print(text) +``` + +This is also called "joint" generation in the UniDiffuser paper, since we are sampling from the joint image-text distribution. + +Note that the generation task is inferred from the inputs used when calling the pipeline. +It is also possible to manually specify the unconditional generation task ("mode") manually with [`UniDiffuserPipeline.set_joint_mode`]: + +```python +# Equivalent to the above. +pipe.set_joint_mode() +sample = pipe(num_inference_steps=20, guidance_scale=8.0) +``` + +When the mode is set manually, subsequent calls to the pipeline will use the set mode without attempting to infer the mode. +You can reset the mode with [`UniDiffuserPipeline.reset_mode`], after which the pipeline will once again infer the mode. + +You can also generate only an image or only text (which the UniDiffuser paper calls "marginal" generation since we sample from the marginal distribution of images and text, respectively): + +```python +# Unlike other generation tasks, image-only and text-only generation don't use classifier-free guidance +# Image-only generation +pipe.set_image_mode() +sample_image = pipe(num_inference_steps=20).images[0] +# Text-only generation +pipe.set_text_mode() +sample_text = pipe(num_inference_steps=20).text[0] +``` + +### Text-to-Image Generation + +UniDiffuser is also capable of sampling from conditional distributions; that is, the distribution of images conditioned on a text prompt or the distribution of texts conditioned on an image. +Here is an example of sampling from the conditional image distribution (text-to-image generation or text-conditioned image generation): + +```python +import torch + +from diffusers import UniDiffuserPipeline + +device = "cuda" +model_id_or_path = "thu-ml/unidiffuser-v1" +pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16) +pipe.to(device) + +# Text-to-image generation +prompt = "an elephant under the sea" + +sample = pipe(prompt=prompt, num_inference_steps=20, guidance_scale=8.0) +t2i_image = sample.images[0] +t2i_image +``` + +The `text2img` mode requires that either an input `prompt` or `prompt_embeds` be supplied. You can set the `text2img` mode manually with [`UniDiffuserPipeline.set_text_to_image_mode`]. + +### Image-to-Text Generation + +Similarly, UniDiffuser can also produce text samples given an image (image-to-text or image-conditioned text generation): + +```python +import torch + +from diffusers import UniDiffuserPipeline +from diffusers.utils import load_image + +device = "cuda" +model_id_or_path = "thu-ml/unidiffuser-v1" +pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16) +pipe.to(device) + +# Image-to-text generation +image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg" +init_image = load_image(image_url).resize((512, 512)) + +sample = pipe(image=init_image, num_inference_steps=20, guidance_scale=8.0) +i2t_text = sample.text[0] +print(i2t_text) +``` + +The `img2text` mode requires that an input `image` be supplied. You can set the `img2text` mode manually with [`UniDiffuserPipeline.set_image_to_text_mode`]. + +### Image Variation + +The UniDiffuser authors suggest performing image variation through a "round-trip" generation method, where given an input image, we first perform an image-to-text generation, and then perform a text-to-image generation on the outputs of the first generation. +This produces a new image which is semantically similar to the input image: + +```python +import torch + +from diffusers import UniDiffuserPipeline +from diffusers.utils import load_image + +device = "cuda" +model_id_or_path = "thu-ml/unidiffuser-v1" +pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16) +pipe.to(device) + +# Image variation can be performed with an image-to-text generation followed by a text-to-image generation: +# 1. Image-to-text generation +image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg" +init_image = load_image(image_url).resize((512, 512)) + +sample = pipe(image=init_image, num_inference_steps=20, guidance_scale=8.0) +i2t_text = sample.text[0] +print(i2t_text) + +# 2. Text-to-image generation +sample = pipe(prompt=i2t_text, num_inference_steps=20, guidance_scale=8.0) +final_image = sample.images[0] +final_image.save("unidiffuser_image_variation_sample.png") +``` + +### Text Variation + +Similarly, text variation can be performed on an input prompt with a text-to-image generation followed by a image-to-text generation: + +```python +import torch + +from diffusers import UniDiffuserPipeline + +device = "cuda" +model_id_or_path = "thu-ml/unidiffuser-v1" +pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16) +pipe.to(device) + +# Text variation can be performed with a text-to-image generation followed by a image-to-text generation: +# 1. Text-to-image generation +prompt = "an elephant under the sea" + +sample = pipe(prompt=prompt, num_inference_steps=20, guidance_scale=8.0) +t2i_image = sample.images[0] +t2i_image.save("unidiffuser_text2img_sample_image.png") + +# 2. Image-to-text generation +sample = pipe(image=t2i_image, num_inference_steps=20, guidance_scale=8.0) +final_prompt = sample.text[0] +print(final_prompt) +``` + +> [!TIP] +> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. + +## UniDiffuserPipeline +[[autodoc]] UniDiffuserPipeline + - all + - __call__ + +## ImageTextPipelineOutput +[[autodoc]] pipelines.ImageTextPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/value_guided_sampling.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/value_guided_sampling.md new file mode 100644 index 0000000000000000000000000000000000000000..d050ea309ca5215702a055d3d7f669bddbf8c01a --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/value_guided_sampling.md @@ -0,0 +1,32 @@ + + +# Value-guided planning + +> [!WARNING] +> 🧪 This is an experimental pipeline for reinforcement learning! + +This pipeline is based on the [Planning with Diffusion for Flexible Behavior Synthesis](https://huggingface.co/papers/2205.09991) paper by Michael Janner, Yilun Du, Joshua B. Tenenbaum, Sergey Levine. + +The abstract from the paper is: + +*Model-based reinforcement learning methods often use learning only for the purpose of estimating an approximate dynamics model, offloading the rest of the decision-making work to classical trajectory optimizers. While conceptually simple, this combination has a number of empirical shortcomings, suggesting that learned models may not be well-suited to standard trajectory optimization. In this paper, we consider what it would look like to fold as much of the trajectory optimization pipeline as possible into the modeling problem, such that sampling from the model and planning with it become nearly identical. The core of our technical approach lies in a diffusion probabilistic model that plans by iteratively denoising trajectories. We show how classifier-guided sampling and image inpainting can be reinterpreted as coherent planning strategies, explore the unusual and useful properties of diffusion-based planning methods, and demonstrate the effectiveness of our framework in control settings that emphasize long-horizon decision-making and test-time flexibility.* + +You can find additional information about the model on the [project page](https://diffusion-planning.github.io/), the [original codebase](https://github.com/jannerm/diffuser), or try it out in a demo [notebook](https://colab.research.google.com/drive/1rXm8CX4ZdN5qivjJ2lhwhkOmt_m0CvU0#scrollTo=6HXJvhyqcITc&uniqifier=1). + +The script to run the model is available [here](https://github.com/huggingface/diffusers/tree/main/examples/reinforcement_learning). + +> [!TIP] +> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. + +## ValueGuidedRLPipeline +[[autodoc]] diffusers.experimental.ValueGuidedRLPipeline diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/visualcloze.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/visualcloze.md new file mode 100644 index 0000000000000000000000000000000000000000..1a4f96a50d638099adacef370a0c0467f15e7074 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/visualcloze.md @@ -0,0 +1,300 @@ + + +# VisualCloze + +[VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning](https://huggingface.co/papers/2504.07960) is an innovative in-context learning based universal image generation framework that offers key capabilities: +1. Support for various in-domain tasks +2. Generalization to unseen tasks through in-context learning +3. Unify multiple tasks into one step and generate both target image and intermediate results +4. Support reverse-engineering conditions from target images + +## Overview + +The abstract from the paper is: + +*Recent progress in diffusion models significantly advances various image generation tasks. However, the current mainstream approach remains focused on building task-specific models, which have limited efficiency when supporting a wide range of different needs. While universal models attempt to address this limitation, they face critical challenges, including generalizable task instruction, appropriate task distributions, and unified architectural design. To tackle these challenges, we propose VisualCloze, a universal image generation framework, which supports a wide range of in-domain tasks, generalization to unseen ones, unseen unification of multiple tasks, and reverse generation. Unlike existing methods that rely on language-based task instruction, leading to task ambiguity and weak generalization, we integrate visual in-context learning, allowing models to identify tasks from visual demonstrations. Meanwhile, the inherent sparsity of visual task distributions hampers the learning of transferable knowledge across tasks. To this end, we introduce Graph200K, a graph-structured dataset that establishes various interrelated tasks, enhancing task density and transferable knowledge. Furthermore, we uncover that our unified image generation formulation shared a consistent objective with image infilling, enabling us to leverage the strong generative priors of pre-trained infilling models without modifying the architectures. The codes, dataset, and models are available at https://visualcloze.github.io.* + +## Inference + +### Model loading + +VisualCloze is a two-stage cascade pipeline, containing `VisualClozeGenerationPipeline` and `VisualClozeUpsamplingPipeline`. +- In `VisualClozeGenerationPipeline`, each image is downsampled before concatenating images into a grid layout, avoiding excessively high resolutions. VisualCloze releases two models suitable for diffusers, i.e., [VisualClozePipeline-384](https://huggingface.co/VisualCloze/VisualClozePipeline-384) and [VisualClozePipeline-512](https://huggingface.co/VisualCloze/VisualClozePipeline-384), which downsample images to resolutions of 384 and 512, respectively. +- `VisualClozeUpsamplingPipeline` uses [SDEdit](https://huggingface.co/papers/2108.01073) to enable high-resolution image synthesis. + +The `VisualClozePipeline` integrates both stages to support convenient end-to-end sampling, while also allowing users to utilize each pipeline independently as needed. + +### Input Specifications + +#### Task and Content Prompts +- Task prompt: Required to describe the generation task intention +- Content prompt: Optional description or caption of the target image +- When content prompt is not needed, pass `None` +- For batch inference, pass `List[str|None]` + +#### Image Input Format +- Format: `List[List[Image|None]]` +- Structure: + - All rows except the last represent in-context examples + - Last row represents the current query (target image set to `None`) +- For batch inference, pass `List[List[List[Image|None]]]` + +#### Resolution Control +- Default behavior: + - Initial generation in the first stage: area of ${pipe.resolution}^2$ + - Upsampling in the second stage: 3x factor +- Custom resolution: Adjust using `upsampling_height` and `upsampling_width` parameters + +### Examples + +For comprehensive examples covering a wide range of tasks, please refer to the [Online Demo](https://huggingface.co/spaces/VisualCloze/VisualCloze) and [GitHub Repository](https://github.com/lzyhha/VisualCloze). Below are simple examples for three cases: mask-to-image conversion, edge detection, and subject-driven generation. + +#### Example for mask2image + +```python +import torch +from diffusers import VisualClozePipeline +from diffusers.utils import load_image + +pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16) +pipe.to("cuda") + +# Load in-context images (make sure the paths are correct and accessible) +image_paths = [ + # in-context examples + [ + load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_mask.jpg'), + load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_image.jpg'), + ], + # query with the target image + [ + load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_query_mask.jpg'), + None, # No image needed for the target image + ], +] + +# Task and content prompt +task_prompt = "In each row, a logical task is demonstrated to achieve [IMAGE2] an aesthetically pleasing photograph based on [IMAGE1] sam 2-generated masks with rich color coding." +content_prompt = """Majestic photo of a golden eagle perched on a rocky outcrop in a mountainous landscape. +The eagle is positioned in the right foreground, facing left, with its sharp beak and keen eyes prominently visible. +Its plumage is a mix of dark brown and golden hues, with intricate feather details. +The background features a soft-focus view of snow-capped mountains under a cloudy sky, creating a serene and grandiose atmosphere. +The foreground includes rugged rocks and patches of green moss. Photorealistic, medium depth of field, +soft natural lighting, cool color palette, high contrast, sharp focus on the eagle, blurred background, +tranquil, majestic, wildlife photography.""" + +# Run the pipeline +image_result = pipe( + task_prompt=task_prompt, + content_prompt=content_prompt, + image=image_paths, + upsampling_width=1344, + upsampling_height=768, + upsampling_strength=0.4, + guidance_scale=30, + num_inference_steps=30, + max_sequence_length=512, + generator=torch.Generator("cpu").manual_seed(0) +).images[0][0] + +# Save the resulting image +image_result.save("visualcloze.png") +``` + +#### Example for edge-detection + +```python +import torch +from diffusers import VisualClozePipeline +from diffusers.utils import load_image + +pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16) +pipe.to("cuda") + +# Load in-context images (make sure the paths are correct and accessible) +image_paths = [ + # in-context examples + [ + load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_incontext-example-1_image.jpg'), + load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_incontext-example-1_edge.jpg'), + ], + [ + load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_incontext-example-2_image.jpg'), + load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_incontext-example-2_edge.jpg'), + ], + # query with the target image + [ + load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_query_image.jpg'), + None, # No image needed for the target image + ], +] + +# Task and content prompt +task_prompt = "Each row illustrates a pathway from [IMAGE1] a sharp and beautifully composed photograph to [IMAGE2] edge map with natural well-connected outlines using a clear logical task." +content_prompt = "" + +# Run the pipeline +image_result = pipe( + task_prompt=task_prompt, + content_prompt=content_prompt, + image=image_paths, + upsampling_width=864, + upsampling_height=1152, + upsampling_strength=0.4, + guidance_scale=30, + num_inference_steps=30, + max_sequence_length=512, + generator=torch.Generator("cpu").manual_seed(0) +).images[0][0] + +# Save the resulting image +image_result.save("visualcloze.png") +``` + +#### Example for subject-driven generation + +```python +import torch +from diffusers import VisualClozePipeline +from diffusers.utils import load_image + +pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16) +pipe.to("cuda") + +# Load in-context images (make sure the paths are correct and accessible) +image_paths = [ + # in-context examples + [ + load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-1_reference.jpg'), + load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-1_depth.jpg'), + load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-1_image.jpg'), + ], + [ + load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-2_reference.jpg'), + load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-2_depth.jpg'), + load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-2_image.jpg'), + ], + # query with the target image + [ + load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_query_reference.jpg'), + load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_query_depth.jpg'), + None, # No image needed for the target image + ], +] + +# Task and content prompt +task_prompt = """Each row describes a process that begins with [IMAGE1] an image containing the key object, +[IMAGE2] depth map revealing gray-toned spatial layers and results in +[IMAGE3] an image with artistic qualitya high-quality image with exceptional detail.""" +content_prompt = """A vintage porcelain collector's item. Beneath a blossoming cherry tree in early spring, +this treasure is photographed up close, with soft pink petals drifting through the air and vibrant blossoms framing the scene.""" + +# Run the pipeline +image_result = pipe( + task_prompt=task_prompt, + content_prompt=content_prompt, + image=image_paths, + upsampling_width=1024, + upsampling_height=1024, + upsampling_strength=0.2, + guidance_scale=30, + num_inference_steps=30, + max_sequence_length=512, + generator=torch.Generator("cpu").manual_seed(0) +).images[0][0] + +# Save the resulting image +image_result.save("visualcloze.png") +``` + +#### Utilize each pipeline independently + +```python +import torch +from diffusers import VisualClozeGenerationPipeline, FluxFillPipeline as VisualClozeUpsamplingPipeline +from diffusers.utils import load_image +from PIL import Image + +pipe = VisualClozeGenerationPipeline.from_pretrained( + "VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16 +) +pipe.to("cuda") + +image_paths = [ + # in-context examples + [ + load_image( + "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_mask.jpg" + ), + load_image( + "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_image.jpg" + ), + ], + # query with the target image + [ + load_image( + "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_query_mask.jpg" + ), + None, # No image needed for the target image + ], +] +task_prompt = "In each row, a logical task is demonstrated to achieve [IMAGE2] an aesthetically pleasing photograph based on [IMAGE1] sam 2-generated masks with rich color coding." +content_prompt = "Majestic photo of a golden eagle perched on a rocky outcrop in a mountainous landscape. The eagle is positioned in the right foreground, facing left, with its sharp beak and keen eyes prominently visible. Its plumage is a mix of dark brown and golden hues, with intricate feather details. The background features a soft-focus view of snow-capped mountains under a cloudy sky, creating a serene and grandiose atmosphere. The foreground includes rugged rocks and patches of green moss. Photorealistic, medium depth of field, soft natural lighting, cool color palette, high contrast, sharp focus on the eagle, blurred background, tranquil, majestic, wildlife photography." + +# Stage 1: Generate initial image +image = pipe( + task_prompt=task_prompt, + content_prompt=content_prompt, + image=image_paths, + guidance_scale=30, + num_inference_steps=30, + max_sequence_length=512, + generator=torch.Generator("cpu").manual_seed(0), +).images[0][0] + +# Stage 2 (optional): Upsample the generated image +pipe_upsample = VisualClozeUpsamplingPipeline.from_pipe(pipe) +pipe_upsample.to("cuda") + +mask_image = Image.new("RGB", image.size, (255, 255, 255)) + +image = pipe_upsample( + image=image, + mask_image=mask_image, + prompt=content_prompt, + width=1344, + height=768, + strength=0.4, + guidance_scale=30, + num_inference_steps=30, + max_sequence_length=512, + generator=torch.Generator("cpu").manual_seed(0), +).images[0] + +image.save("visualcloze.png") +``` + +## VisualClozePipeline + +[[autodoc]] VisualClozePipeline + - all + - __call__ + +## VisualClozeGenerationPipeline + +[[autodoc]] VisualClozeGenerationPipeline + - all + - __call__ diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/wan.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/wan.md new file mode 100644 index 0000000000000000000000000000000000000000..6aab6c5b33b9ccd0d33d6f79412de43783fb6a2c --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/wan.md @@ -0,0 +1,573 @@ + + +
+
+ + LoRA + +
+
+ +# Wan + +[Wan-2.1](https://huggingface.co/papers/2503.20314) by the Wan Team. + +*This report presents Wan, a comprehensive and open suite of video foundation models designed to push the boundaries of video generation. Built upon the mainstream diffusion transformer paradigm, Wan achieves significant advancements in generative capabilities through a series of innovations, including our novel VAE, scalable pre-training strategies, large-scale data curation, and automated evaluation metrics. These contributions collectively enhance the model's performance and versatility. Specifically, Wan is characterized by four key features: Leading Performance: The 14B model of Wan, trained on a vast dataset comprising billions of images and videos, demonstrates the scaling laws of video generation with respect to both data and model size. It consistently outperforms the existing open-source models as well as state-of-the-art commercial solutions across multiple internal and external benchmarks, demonstrating a clear and significant performance superiority. Comprehensiveness: Wan offers two capable models, i.e., 1.3B and 14B parameters, for efficiency and effectiveness respectively. It also covers multiple downstream applications, including image-to-video, instruction-guided video editing, and personal video generation, encompassing up to eight tasks. Consumer-Grade Efficiency: The 1.3B model demonstrates exceptional resource efficiency, requiring only 8.19 GB VRAM, making it compatible with a wide range of consumer-grade GPUs. Openness: We open-source the entire series of Wan, including source code and all models, with the goal of fostering the growth of the video generation community. This openness seeks to significantly expand the creative possibilities of video production in the industry and provide academia with high-quality video foundation models. All the code and models are available at [this https URL](https://github.com/Wan-Video/Wan2.1).* + +You can find all the original Wan2.1 checkpoints under the [Wan-AI](https://huggingface.co/Wan-AI) organization. + +The following Wan models are supported in Diffusers: + +- [Wan 2.1 T2V 1.3B](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers) +- [Wan 2.1 T2V 14B](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B-Diffusers) +- [Wan 2.1 I2V 14B - 480P](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P-Diffusers) +- [Wan 2.1 I2V 14B - 720P](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P-Diffusers) +- [Wan 2.1 FLF2V 14B - 720P](https://huggingface.co/Wan-AI/Wan2.1-FLF2V-14B-720P-diffusers) +- [Wan 2.1 VACE 1.3B](https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B-diffusers) +- [Wan 2.1 VACE 14B](https://huggingface.co/Wan-AI/Wan2.1-VACE-14B-diffusers) +- [Wan 2.2 T2V 14B](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers) +- [Wan 2.2 I2V 14B](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers) +- [Wan 2.2 TI2V 5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers) +- [Wan 2.2 Animate 14B](https://huggingface.co/Wan-AI/Wan2.2-Animate-14B-Diffusers) + +> [!TIP] +> Click on the Wan models in the right sidebar for more examples of video generation. + +### Text-to-Video Generation + +The example below demonstrates how to generate a video from text optimized for memory or inference speed. + + + + +Refer to the [Reduce memory usage](../../optimization/memory) guide for more details about the various memory saving techniques. + +The Wan2.1 text-to-video model below requires ~13GB of VRAM. + +```py +# pip install ftfy +import torch +import numpy as np +from diffusers import AutoModel, WanPipeline +from diffusers.quantizers import PipelineQuantizationConfig +from diffusers.hooks.group_offloading import apply_group_offloading +from diffusers.utils import export_to_video, load_image +from transformers import UMT5EncoderModel + +text_encoder = UMT5EncoderModel.from_pretrained("Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="text_encoder", torch_dtype=torch.bfloat16) +vae = AutoModel.from_pretrained("Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="vae", torch_dtype=torch.float32) +transformer = AutoModel.from_pretrained("Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16) + +# group-offloading +onload_device = torch.device("cuda") +offload_device = torch.device("cpu") +apply_group_offloading(text_encoder, + onload_device=onload_device, + offload_device=offload_device, + offload_type="block_level", + num_blocks_per_group=4 +) +transformer.enable_group_offload( + onload_device=onload_device, + offload_device=offload_device, + offload_type="leaf_level", + use_stream=True +) + +pipeline = WanPipeline.from_pretrained( + "Wan-AI/Wan2.1-T2V-14B-Diffusers", + vae=vae, + transformer=transformer, + text_encoder=text_encoder, + torch_dtype=torch.bfloat16 +) +pipeline.to("cuda") + +prompt = """ +The camera rushes from far to near in a low-angle shot, +revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in +for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground. +Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic +shadows and warm highlights. Medium composition, front view, low angle, with depth of field. +""" +negative_prompt = """ +Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, +low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, +misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards +""" + +output = pipeline( + prompt=prompt, + negative_prompt=negative_prompt, + num_frames=81, + guidance_scale=5.0, +).frames[0] +export_to_video(output, "output.mp4", fps=16) +``` + + + + +[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster. [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs. + +```py +# pip install ftfy +import torch +import numpy as np +from diffusers import AutoModel, WanPipeline +from diffusers.hooks.group_offloading import apply_group_offloading +from diffusers.utils import export_to_video, load_image +from transformers import UMT5EncoderModel + +text_encoder = UMT5EncoderModel.from_pretrained("Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="text_encoder", torch_dtype=torch.bfloat16) +vae = AutoModel.from_pretrained("Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="vae", torch_dtype=torch.float32) +transformer = AutoModel.from_pretrained("Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16) + +pipeline = WanPipeline.from_pretrained( + "Wan-AI/Wan2.1-T2V-14B-Diffusers", + vae=vae, + transformer=transformer, + text_encoder=text_encoder, + torch_dtype=torch.bfloat16 +) +pipeline.to("cuda") + +# torch.compile +pipeline.transformer.to(memory_format=torch.channels_last) +pipeline.transformer = torch.compile( + pipeline.transformer, mode="max-autotune", fullgraph=True +) + +prompt = """ +The camera rushes from far to near in a low-angle shot, +revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in +for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground. +Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic +shadows and warm highlights. Medium composition, front view, low angle, with depth of field. +""" +negative_prompt = """ +Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, +low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, +misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards +""" + +output = pipeline( + prompt=prompt, + negative_prompt=negative_prompt, + num_frames=81, + guidance_scale=5.0, +).frames[0] +export_to_video(output, "output.mp4", fps=16) +``` + + + + +### First-Last-Frame-to-Video Generation + +The example below demonstrates how to use the image-to-video pipeline to generate a video using a text description, a starting frame, and an ending frame. + + + + +```python +import numpy as np +import torch +import torchvision.transforms.functional as TF +from diffusers import AutoencoderKLWan, WanImageToVideoPipeline +from diffusers.utils import export_to_video, load_image +from transformers import CLIPVisionModel + + +model_id = "Wan-AI/Wan2.1-FLF2V-14B-720P-diffusers" +image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32) +vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32) +pipe = WanImageToVideoPipeline.from_pretrained( + model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16 +) +pipe.to("cuda") + +first_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_first_frame.png") +last_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_last_frame.png") + +def aspect_ratio_resize(image, pipe, max_area=720 * 1280): + aspect_ratio = image.height / image.width + mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1] + height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value + width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value + image = image.resize((width, height)) + return image, height, width + +def center_crop_resize(image, height, width): + # Calculate resize ratio to match first frame dimensions + resize_ratio = max(width / image.width, height / image.height) + + # Resize the image + width = round(image.width * resize_ratio) + height = round(image.height * resize_ratio) + size = [width, height] + image = TF.center_crop(image, size) + + return image, height, width + +first_frame, height, width = aspect_ratio_resize(first_frame, pipe) +if last_frame.size != first_frame.size: + last_frame, _, _ = center_crop_resize(last_frame, height, width) + +prompt = "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird's feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective." + +output = pipe( + image=first_frame, last_image=last_frame, prompt=prompt, height=height, width=width, guidance_scale=5.5 +).frames[0] +export_to_video(output, "output.mp4", fps=16) +``` + + + + +### Any-to-Video Controllable Generation + +Wan VACE supports various generation techniques which achieve controllable video generation. Some of the capabilities include: +- Control to Video (Depth, Pose, Sketch, Flow, Grayscale, Scribble, Layout, Boundary Box, etc.). Recommended library for preprocessing videos to obtain control videos: [huggingface/controlnet_aux]() +- Image/Video to Video (first frame, last frame, starting clip, ending clip, random clips) +- Inpainting and Outpainting +- Subject to Video (faces, object, characters, etc.) +- Composition to Video (reference anything, animate anything, swap anything, expand anything, move anything, etc.) + +The code snippets available in [this](https://github.com/huggingface/diffusers/pull/11582) pull request demonstrate some examples of how videos can be generated with controllability signals. + +The general rule of thumb to keep in mind when preparing inputs for the VACE pipeline is that the input images, or frames of a video that you want to use for conditioning, should have a corresponding mask that is black in color. The black mask signifies that the model will not generate new content for that area, and only use those parts for conditioning the generation process. For parts/frames that should be generated by the model, the mask should be white in color. + + + + +### Wan-Animate: Unified Character Animation and Replacement with Holistic Replication + +[Wan-Animate](https://huggingface.co/papers/2509.14055) by the Wan Team. + +*We introduce Wan-Animate, a unified framework for character animation and replacement. Given a character image and a reference video, Wan-Animate can animate the character by precisely replicating the expressions and movements of the character in the video to generate high-fidelity character videos. Alternatively, it can integrate the animated character into the reference video to replace the original character, replicating the scene's lighting and color tone to achieve seamless environmental integration. Wan-Animate is built upon the Wan model. To adapt it for character animation tasks, we employ a modified input paradigm to differentiate between reference conditions and regions for generation. This design unifies multiple tasks into a common symbolic representation. We use spatially-aligned skeleton signals to replicate body motion and implicit facial features extracted from source images to reenact expressions, enabling the generation of character videos with high controllability and expressiveness. Furthermore, to enhance environmental integration during character replacement, we develop an auxiliary Relighting LoRA. This module preserves the character's appearance consistency while applying the appropriate environmental lighting and color tone. Experimental results demonstrate that Wan-Animate achieves state-of-the-art performance. We are committed to open-sourcing the model weights and its source code.* + +The project page: https://humanaigc.github.io/wan-animate + +This model was mostly contributed by [M. Tolga Cangöz](https://github.com/tolgacangoz). + +#### Usage + +The Wan-Animate pipeline supports two modes of operation: + +1. **Animation Mode** (default): Animates a character image based on motion and expression from reference videos +2. **Replacement Mode**: Replaces a character in a background video with a new character while preserving the scene + +##### Prerequisites + +Before using the pipeline, you need to preprocess your reference video to extract: +- **Pose video**: Contains skeletal keypoints representing body motion +- **Face video**: Contains facial feature representations for expression control + +For replacement mode, you additionally need: +- **Background video**: The original video containing the scene +- **Mask video**: A mask indicating where to generate content (white) vs. preserve original (black) + +> [!NOTE] +> Raw videos should not be used for inputs such as `pose_video`, which the pipeline expects to be preprocessed to extract the proper information. Preprocessing scripts to prepare these inputs are available in the [original Wan-Animate repository](https://github.com/Wan-Video/Wan2.2?tab=readme-ov-file#1-preprocessing). Integration of these preprocessing steps into Diffusers is planned for a future release. + +The example below demonstrates how to use the Wan-Animate pipeline: + + + + +```python +import numpy as np +import torch +from diffusers import AutoencoderKLWan, WanAnimatePipeline +from diffusers.utils import export_to_video, load_image, load_video + +model_id = "Wan-AI/Wan2.2-Animate-14B-Diffusers" +vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32) +pipe = WanAnimatePipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16) +pipe.to("cuda") + +# Load character image and preprocessed videos +image = load_image("path/to/character.jpg") +pose_video = load_video("path/to/pose_video.mp4") # Preprocessed skeletal keypoints +face_video = load_video("path/to/face_video.mp4") # Preprocessed facial features + +# Resize image to match VAE constraints +def aspect_ratio_resize(image, pipe, max_area=720 * 1280): + aspect_ratio = image.height / image.width + mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1] + height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value + width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value + image = image.resize((width, height)) + return image, height, width + +image, height, width = aspect_ratio_resize(image, pipe) + +prompt = "A person dancing energetically in a studio with dynamic lighting and professional camera work" +negative_prompt = "blurry, low quality, distorted, deformed, static, poorly drawn" + +# Generate animated video +output = pipe( + image=image, + pose_video=pose_video, + face_video=face_video, + prompt=prompt, + negative_prompt=negative_prompt, + height=height, + width=width, + segment_frame_length=77, + guidance_scale=1.0, + mode="animate", # Animation mode (default) +).frames[0] +export_to_video(output, "animated_character.mp4", fps=30) +``` + + + + +```python +import numpy as np +import torch +from diffusers import AutoencoderKLWan, WanAnimatePipeline +from diffusers.utils import export_to_video, load_image, load_video + +model_id = "Wan-AI/Wan2.2-Animate-14B-Diffusers" +vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32) +pipe = WanAnimatePipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16) +pipe.to("cuda") + +# Load all required inputs for replacement mode +image = load_image("path/to/new_character.jpg") +pose_video = load_video("path/to/pose_video.mp4") # Preprocessed skeletal keypoints +face_video = load_video("path/to/face_video.mp4") # Preprocessed facial features +background_video = load_video("path/to/background_video.mp4") # Original scene +mask_video = load_video("path/to/mask_video.mp4") # Black: preserve, White: generate + +# Resize image to match video dimensions +def aspect_ratio_resize(image, pipe, max_area=720 * 1280): + aspect_ratio = image.height / image.width + mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1] + height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value + width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value + image = image.resize((width, height)) + return image, height, width + +image, height, width = aspect_ratio_resize(image, pipe) + +prompt = "A person seamlessly integrated into the scene with consistent lighting and environment" +negative_prompt = "blurry, low quality, inconsistent lighting, floating, disconnected from scene" + +# Replace character in background video +output = pipe( + image=image, + pose_video=pose_video, + face_video=face_video, + background_video=background_video, + mask_video=mask_video, + prompt=prompt, + negative_prompt=negative_prompt, + height=height, + width=width, + segment_frame_lengths=77, + guidance_scale=1.0, + mode="replace", # Replacement mode +).frames[0] +export_to_video(output, "character_replaced.mp4", fps=30) +``` + + + + +```python +import numpy as np +import torch +from diffusers import AutoencoderKLWan, WanAnimatePipeline +from diffusers.utils import export_to_video, load_image, load_video + +model_id = "Wan-AI/Wan2.2-Animate-14B-Diffusers" +vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32) +pipe = WanAnimatePipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16) +pipe.to("cuda") + +image = load_image("path/to/character.jpg") +pose_video = load_video("path/to/pose_video.mp4") +face_video = load_video("path/to/face_video.mp4") + +def aspect_ratio_resize(image, pipe, max_area=720 * 1280): + aspect_ratio = image.height / image.width + mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1] + height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value + width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value + image = image.resize((width, height)) + return image, height, width + +image, height, width = aspect_ratio_resize(image, pipe) + +prompt = "A person dancing energetically in a studio" +negative_prompt = "blurry, low quality" + +# Advanced: Use temporal guidance and custom callback +def callback_fn(pipe, step_index, timestep, callback_kwargs): + # You can modify latents or other tensors here + print(f"Step {step_index}, Timestep {timestep}") + return callback_kwargs + +output = pipe( + image=image, + pose_video=pose_video, + face_video=face_video, + prompt=prompt, + negative_prompt=negative_prompt, + height=height, + width=width, + segment_frame_length=77, + num_inference_steps=50, + guidance_scale=5.0, + prev_segment_conditioning_frames=5, # Use 5 frames for temporal guidance (1 or 5 recommended) + callback_on_step_end=callback_fn, + callback_on_step_end_tensor_inputs=["latents"], +).frames[0] +export_to_video(output, "animated_advanced.mp4", fps=30) +``` + + + + +#### Key Parameters + +- **mode**: Choose between `"animate"` (default) or `"replace"` +- **prev_segment_conditioning_frames**: Number of frames for temporal guidance (1 or 5 recommended). Using 5 provides better temporal consistency but requires more memory +- **guidance_scale**: Controls how closely the output follows the text prompt. Higher values (5-7) produce results more aligned with the prompt. For Wan-Animate, CFG is disabled by default (`guidance_scale=1.0`) but can be enabled to support negative prompts and finer control over facial expressions. (Note that CFG will only target the text prompt and face conditioning.) + + +## Notes + +- Wan2.1 supports LoRAs with [`~loaders.WanLoraLoaderMixin.load_lora_weights`]. + +
+ Show example code + + ```py + # pip install ftfy + import torch + from diffusers import AutoModel, WanPipeline + from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler + from diffusers.utils import export_to_video + + vae = AutoModel.from_pretrained( + "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="vae", torch_dtype=torch.float32 + ) + pipeline = WanPipeline.from_pretrained( + "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", vae=vae, torch_dtype=torch.bfloat16 + ) + pipeline.scheduler = UniPCMultistepScheduler.from_config( + pipeline.scheduler.config, flow_shift=5.0 + ) + pipeline.to("cuda") + + pipeline.load_lora_weights("benjamin-paine/steamboat-willie-1.3b", adapter_name="steamboat-willie") + pipeline.set_adapters("steamboat-willie") + + pipeline.enable_model_cpu_offload() + + # use "steamboat willie style" to trigger the LoRA + prompt = """ + steamboat willie style, golden era animation, The camera rushes from far to near in a low-angle shot, + revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in + for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground. + Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic + shadows and warm highlights. Medium composition, front view, low angle, with depth of field. + """ + + output = pipeline( + prompt=prompt, + num_frames=81, + guidance_scale=5.0, + ).frames[0] + export_to_video(output, "output.mp4", fps=16) + ``` + +
+ +- [`WanTransformer3DModel`] and [`AutoencoderKLWan`] supports loading from single files with [`~loaders.FromSingleFileMixin.from_single_file`]. + +
+ Show example code + + ```py + # pip install ftfy + import torch + from diffusers import WanPipeline, WanTransformer3DModel, AutoencoderKLWan + + vae = AutoencoderKLWan.from_single_file( + "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/vae/wan_2.1_vae.safetensors" + ) + transformer = WanTransformer3DModel.from_single_file( + "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/diffusion_models/wan2.1_t2v_1.3B_bf16.safetensors", + torch_dtype=torch.bfloat16 + ) + pipeline = WanPipeline.from_pretrained( + "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", + vae=vae, + transformer=transformer, + torch_dtype=torch.bfloat16 + ) + ``` + +
+ +- Set the [`AutoencoderKLWan`] dtype to `torch.float32` for better decoding quality. + +- The number of frames per second (fps) or `k` should be calculated by `4 * k + 1`. + +- Try lower `shift` values (`2.0` to `5.0`) for lower resolution videos and higher `shift` values (`7.0` to `12.0`) for higher resolution images. + +- Wan 2.1 and 2.2 support using [LightX2V LoRAs](https://huggingface.co/Kijai/WanVideo_comfy/tree/main/Lightx2v) to speed up inference. Using them on Wan 2.2 is slightly more involed. Refer to [this code snippet](https://github.com/huggingface/diffusers/pull/12040#issuecomment-3144185272) to learn more. + +- Wan 2.2 has two denoisers. By default, LoRAs are only loaded into the first denoiser. One can set `load_into_transformer_2=True` to load LoRAs into the second denoiser. Refer to [this](https://github.com/huggingface/diffusers/pull/12074#issue-3292620048) and [this](https://github.com/huggingface/diffusers/pull/12074#issuecomment-3155896144) examples to learn more. + +## WanPipeline + +[[autodoc]] WanPipeline + - all + - __call__ + +## WanImageToVideoPipeline + +[[autodoc]] WanImageToVideoPipeline + - all + - __call__ + +## WanVACEPipeline + +[[autodoc]] WanVACEPipeline + - all + - __call__ + +## WanVideoToVideoPipeline + +[[autodoc]] WanVideoToVideoPipeline + - all + - __call__ + +## WanAnimatePipeline + +[[autodoc]] WanAnimatePipeline + - all + - __call__ + +## WanPipelineOutput + +[[autodoc]] pipelines.wan.pipeline_output.WanPipelineOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/wuerstchen.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/wuerstchen.md new file mode 100644 index 0000000000000000000000000000000000000000..2be3631d845675bb767c2f2fc83d272660e8a423 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/pipelines/wuerstchen.md @@ -0,0 +1,170 @@ + + +# Würstchen + +> [!WARNING] +> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model. + +
+ LoRA +
+ + + +[Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models](https://huggingface.co/papers/2306.00637) is by Pablo Pernias, Dominic Rampas, Mats L. Richter and Christopher Pal and Marc Aubreville. + +The abstract from the paper is: + +*We introduce Würstchen, a novel architecture for text-to-image synthesis that combines competitive performance with unprecedented cost-effectiveness for large-scale text-to-image diffusion models. A key contribution of our work is to develop a latent diffusion technique in which we learn a detailed but extremely compact semantic image representation used to guide the diffusion process. This highly compressed representation of an image provides much more detailed guidance compared to latent representations of language and this significantly reduces the computational requirements to achieve state-of-the-art results. Our approach also improves the quality of text-conditioned image generation based on our user preference study. The training requirements of our approach consists of 24,602 A100-GPU hours - compared to Stable Diffusion 2.1's 200,000 GPU hours. Our approach also requires less training data to achieve these results. Furthermore, our compact latent representations allows us to perform inference over twice as fast, slashing the usual costs and carbon footprint of a state-of-the-art (SOTA) diffusion model significantly, without compromising the end performance. In a broader comparison against SOTA models our approach is substantially more efficient and compares favorably in terms of image quality. We believe that this work motivates more emphasis on the prioritization of both performance and computational accessibility.* + +## Würstchen Overview +Würstchen is a diffusion model, whose text-conditional model works in a highly compressed latent space of images. Why is this important? Compressing data can reduce computational costs for both training and inference by magnitudes. Training on 1024x1024 images is way more expensive than training on 32x32. Usually, other works make use of a relatively small compression, in the range of 4x - 8x spatial compression. Würstchen takes this to an extreme. Through its novel design, we achieve a 42x spatial compression. This was unseen before because common methods fail to faithfully reconstruct detailed images after 16x spatial compression. Würstchen employs a two-stage compression, what we call Stage A and Stage B. Stage A is a VQGAN, and Stage B is a Diffusion Autoencoder (more details can be found in the [paper](https://huggingface.co/papers/2306.00637)). A third model, Stage C, is learned in that highly compressed latent space. This training requires fractions of the compute used for current top-performing models, while also allowing cheaper and faster inference. + +## Würstchen v2 comes to Diffusers + +After the initial paper release, we have improved numerous things in the architecture, training and sampling, making Würstchen competitive to current state-of-the-art models in many ways. We are excited to release this new version together with Diffusers. Here is a list of the improvements. + +- Higher resolution (1024x1024 up to 2048x2048) +- Faster inference +- Multi Aspect Resolution Sampling +- Better quality + + +We are releasing 3 checkpoints for the text-conditional image generation model (Stage C). Those are: + +- v2-base +- v2-aesthetic +- **(default)** v2-interpolated (50% interpolation between v2-base and v2-aesthetic) + +We recommend using v2-interpolated, as it has a nice touch of both photorealism and aesthetics. Use v2-base for finetunings as it does not have a style bias and use v2-aesthetic for very artistic generations. +A comparison can be seen here: + + + +## Text-to-Image Generation + +For the sake of usability, Würstchen can be used with a single pipeline. This pipeline can be used as follows: + +```python +import torch +from diffusers import AutoPipelineForText2Image +from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS + +pipe = AutoPipelineForText2Image.from_pretrained("warp-ai/wuerstchen", torch_dtype=torch.float16).to("cuda") + +caption = "Anthropomorphic cat dressed as a fire fighter" +images = pipe( + caption, + width=1024, + height=1536, + prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS, + prior_guidance_scale=4.0, + num_images_per_prompt=2, +).images +``` + +For explanation purposes, we can also initialize the two main pipelines of Würstchen individually. Würstchen consists of 3 stages: Stage C, Stage B, Stage A. They all have different jobs and work only together. When generating text-conditional images, Stage C will first generate the latents in a very compressed latent space. This is what happens in the `prior_pipeline`. Afterwards, the generated latents will be passed to Stage B, which decompresses the latents into a bigger latent space of a VQGAN. These latents can then be decoded by Stage A, which is a VQGAN, into the pixel-space. Stage B & Stage A are both encapsulated in the `decoder_pipeline`. For more details, take a look at the [paper](https://huggingface.co/papers/2306.00637). + +```python +import torch +from diffusers import WuerstchenDecoderPipeline, WuerstchenPriorPipeline +from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS + +device = "cuda" +dtype = torch.float16 +num_images_per_prompt = 2 + +prior_pipeline = WuerstchenPriorPipeline.from_pretrained( + "warp-ai/wuerstchen-prior", torch_dtype=dtype +).to(device) +decoder_pipeline = WuerstchenDecoderPipeline.from_pretrained( + "warp-ai/wuerstchen", torch_dtype=dtype +).to(device) + +caption = "Anthropomorphic cat dressed as a fire fighter" +negative_prompt = "" + +prior_output = prior_pipeline( + prompt=caption, + height=1024, + width=1536, + timesteps=DEFAULT_STAGE_C_TIMESTEPS, + negative_prompt=negative_prompt, + guidance_scale=4.0, + num_images_per_prompt=num_images_per_prompt, +) +decoder_output = decoder_pipeline( + image_embeddings=prior_output.image_embeddings, + prompt=caption, + negative_prompt=negative_prompt, + guidance_scale=0.0, + output_type="pil", +).images[0] +decoder_output +``` + +## Speed-Up Inference +You can make use of `torch.compile` function and gain a speed-up of about 2-3x: + +```python +prior_pipeline.prior = torch.compile(prior_pipeline.prior, mode="reduce-overhead", fullgraph=True) +decoder_pipeline.decoder = torch.compile(decoder_pipeline.decoder, mode="reduce-overhead", fullgraph=True) +``` + +## Limitations + +- Due to the high compression employed by Würstchen, generations can lack a good amount +of detail. To our human eye, this is especially noticeable in faces, hands etc. +- **Images can only be generated in 128-pixel steps**, e.g. the next higher resolution +after 1024x1024 is 1152x1152 +- The model lacks the ability to render correct text in images +- The model often does not achieve photorealism +- Difficult compositional prompts are hard for the model + +The original codebase, as well as experimental ideas, can be found at [dome272/Wuerstchen](https://github.com/dome272/Wuerstchen). + + +## WuerstchenCombinedPipeline + +[[autodoc]] WuerstchenCombinedPipeline + - all + - __call__ + +## WuerstchenPriorPipeline + +[[autodoc]] WuerstchenPriorPipeline + - all + - __call__ + +## WuerstchenPriorPipelineOutput + +[[autodoc]] pipelines.wuerstchen.pipeline_wuerstchen_prior.WuerstchenPriorPipelineOutput + +## WuerstchenDecoderPipeline + +[[autodoc]] WuerstchenDecoderPipeline + - all + - __call__ + +## Citation + +```bibtex + @misc{pernias2023wuerstchen, + title={Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models}, + author={Pablo Pernias and Dominic Rampas and Mats L. Richter and Christopher J. Pal and Marc Aubreville}, + year={2023}, + eprint={2306.00637}, + archivePrefix={arXiv}, + primaryClass={cs.CV} + } +``` diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/quantization.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/quantization.md new file mode 100644 index 0000000000000000000000000000000000000000..7fa7c7c9d016836caf730ae9e089d089850424e7 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/quantization.md @@ -0,0 +1,43 @@ + + +# Quantization + +Quantization techniques reduce memory and computational costs by representing weights and activations with lower-precision data types like 8-bit integers (int8). This enables loading larger models you normally wouldn't be able to fit into memory, and speeding up inference. + +> [!TIP] +> Learn how to quantize models in the [Quantization](../quantization/overview) guide. + +## PipelineQuantizationConfig + +[[autodoc]] quantizers.PipelineQuantizationConfig + +## BitsAndBytesConfig + +[[autodoc]] quantizers.quantization_config.BitsAndBytesConfig + +## GGUFQuantizationConfig + +[[autodoc]] quantizers.quantization_config.GGUFQuantizationConfig + +## QuantoConfig + +[[autodoc]] quantizers.quantization_config.QuantoConfig + +## TorchAoConfig + +[[autodoc]] quantizers.quantization_config.TorchAoConfig + +## DiffusersQuantizer + +[[autodoc]] quantizers.base.DiffusersQuantizer diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/cm_stochastic_iterative.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/cm_stochastic_iterative.md new file mode 100644 index 0000000000000000000000000000000000000000..edc10529db2c75c517453564a57ebddb4c77a9a8 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/cm_stochastic_iterative.md @@ -0,0 +1,27 @@ + + +# CMStochasticIterativeScheduler + +[Consistency Models](https://huggingface.co/papers/2303.01469) by Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever introduced a multistep and onestep scheduler (Algorithm 1) that is capable of generating good samples in one or a small number of steps. + +The abstract from the paper is: + +*Diffusion models have significantly advanced the fields of image, audio, and video generation, but they depend on an iterative sampling process that causes slow generation. To overcome this limitation, we propose consistency models, a new family of models that generate high quality samples by directly mapping noise to data. They support fast one-step generation by design, while still allowing multistep sampling to trade compute for sample quality. They also support zero-shot data editing, such as image inpainting, colorization, and super-resolution, without requiring explicit training on these tasks. Consistency models can be trained either by distilling pre-trained diffusion models, or as standalone generative models altogether. Through extensive experiments, we demonstrate that they outperform existing distillation techniques for diffusion models in one- and few-step sampling, achieving the new state-of-the-art FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64x64 for one-step generation. When trained in isolation, consistency models become a new family of generative models that can outperform existing one-step, non-adversarial generative models on standard benchmarks such as CIFAR-10, ImageNet 64x64 and LSUN 256x256.* + +The original codebase can be found at [openai/consistency_models](https://github.com/openai/consistency_models). + +## CMStochasticIterativeScheduler +[[autodoc]] CMStochasticIterativeScheduler + +## CMStochasticIterativeSchedulerOutput +[[autodoc]] schedulers.scheduling_consistency_models.CMStochasticIterativeSchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/consistency_decoder.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/consistency_decoder.md new file mode 100644 index 0000000000000000000000000000000000000000..7fe2d0a2216fb1b4619f4ffcc78a80cf7df27cd9 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/consistency_decoder.md @@ -0,0 +1,21 @@ + + +# ConsistencyDecoderScheduler + +This scheduler is a part of the [`ConsistencyDecoderPipeline`] and was introduced in [DALL-E 3](https://openai.com/dall-e-3). + +The original codebase can be found at [openai/consistency_models](https://github.com/openai/consistency_models). + + +## ConsistencyDecoderScheduler +[[autodoc]] schedulers.scheduling_consistency_decoder.ConsistencyDecoderScheduler diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/cosine_dpm.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/cosine_dpm.md new file mode 100644 index 0000000000000000000000000000000000000000..bf6c47fb31f8cf0f688a56adc5bb172306ebcb16 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/cosine_dpm.md @@ -0,0 +1,24 @@ + + +# CosineDPMSolverMultistepScheduler + +The [`CosineDPMSolverMultistepScheduler`] is a variant of [`DPMSolverMultistepScheduler`] with cosine schedule, proposed by Nichol and Dhariwal (2021). +It is being used in the [Stable Audio Open](https://huggingface.co/papers/2407.14358) paper and the [Stability-AI/stable-audio-tool](https://github.com/Stability-AI/stable-audio-tools) codebase. + +This scheduler was contributed by [Yoach Lacombe](https://huggingface.co/ylacombe). + +## CosineDPMSolverMultistepScheduler +[[autodoc]] CosineDPMSolverMultistepScheduler + +## SchedulerOutput +[[autodoc]] schedulers.scheduling_utils.SchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/ddim.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/ddim.md new file mode 100644 index 0000000000000000000000000000000000000000..61ef30c786f946b67d40cd8252a45ed8e3d7d691 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/ddim.md @@ -0,0 +1,79 @@ + + +# DDIMScheduler + +[Denoising Diffusion Implicit Models](https://huggingface.co/papers/2010.02502) (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon. + +The abstract from the paper is: + +*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. +To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models +with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. +We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from. +We empirically demonstrate that DDIMs can produce high quality samples 10× to 50× faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space.* + +The original codebase of this paper can be found at [ermongroup/ddim](https://github.com/ermongroup/ddim), and you can contact the author on [tsong.me](https://tsong.me/). + +## Tips + +The paper [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) claims that a mismatch between the training and inference settings leads to suboptimal inference generation results for Stable Diffusion. To fix this, the authors propose: + +> [!WARNING] +> 🧪 This is an experimental feature! + +1. rescale the noise schedule to enforce zero terminal signal-to-noise ratio (SNR) + +```py +pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, rescale_betas_zero_snr=True) +``` + +2. train a model with `v_prediction` (add the following argument to the [train_text_to_image.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) or [train_text_to_image_lora.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py) scripts) + +```bash +--prediction_type="v_prediction" +``` + +3. change the sampler to always start from the last timestep + +```py +pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") +``` + +4. rescale classifier-free guidance to prevent over-exposure + +```py +image = pipe(prompt, guidance_rescale=0.7).images[0] +``` + +For example: + +```py +from diffusers import DiffusionPipeline, DDIMScheduler +import torch + +pipe = DiffusionPipeline.from_pretrained("ptx0/pseudo-journey-v2", torch_dtype=torch.float16) +pipe.scheduler = DDIMScheduler.from_config( + pipe.scheduler.config, rescale_betas_zero_snr=True, timestep_spacing="trailing" +) +pipe.to("cuda") + +prompt = "A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k" +image = pipe(prompt, guidance_rescale=0.7).images[0] +image +``` + +## DDIMScheduler +[[autodoc]] DDIMScheduler + +## DDIMSchedulerOutput +[[autodoc]] schedulers.scheduling_ddim.DDIMSchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/ddim_cogvideox.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/ddim_cogvideox.md new file mode 100644 index 0000000000000000000000000000000000000000..967d890ecb910c56b03c37bc18aacd55ff350eda --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/ddim_cogvideox.md @@ -0,0 +1,19 @@ + + +# CogVideoXDDIMScheduler + +`CogVideoXDDIMScheduler` is based on [Denoising Diffusion Implicit Models](https://huggingface.co/papers/2010.02502), specifically for CogVideoX models. + +## CogVideoXDDIMScheduler + +[[autodoc]] CogVideoXDDIMScheduler diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/ddim_inverse.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/ddim_inverse.md new file mode 100644 index 0000000000000000000000000000000000000000..fdae50c2ab27ad5254f9466113a5a5befda218d5 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/ddim_inverse.md @@ -0,0 +1,19 @@ + + +# DDIMInverseScheduler + +`DDIMInverseScheduler` is the inverted scheduler from [Denoising Diffusion Implicit Models](https://huggingface.co/papers/2010.02502) (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon. +The implementation is mostly based on the DDIM inversion definition from [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://huggingface.co/papers/2211.09794). + +## DDIMInverseScheduler +[[autodoc]] DDIMInverseScheduler diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/ddpm.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/ddpm.md new file mode 100644 index 0000000000000000000000000000000000000000..ef584fcfc275f277c4d7bd729001b24385f6f192 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/ddpm.md @@ -0,0 +1,25 @@ + + +# DDPMScheduler + +[Denoising Diffusion Probabilistic Models](https://huggingface.co/papers/2006.11239) (DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes a diffusion based model of the same name. In the context of the 🤗 Diffusers library, DDPM refers to the discrete denoising scheduler from the paper as well as the pipeline. + +The abstract from the paper is: + +*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at [this https URL](https://github.com/hojonathanho/diffusion).* + +## DDPMScheduler +[[autodoc]] DDPMScheduler + +## DDPMSchedulerOutput +[[autodoc]] schedulers.scheduling_ddpm.DDPMSchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/deis.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/deis.md new file mode 100644 index 0000000000000000000000000000000000000000..23a8499f5365f5862d55f0a19e9b3a7a37159fe2 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/deis.md @@ -0,0 +1,34 @@ + + +# DEISMultistepScheduler + +Diffusion Exponential Integrator Sampler (DEIS) is proposed in [Fast Sampling of Diffusion Models with Exponential Integrator](https://huggingface.co/papers/2204.13902) by Qinsheng Zhang and Yongxin Chen. `DEISMultistepScheduler` is a fast high order solver for diffusion ordinary differential equations (ODEs). + +This implementation modifies the polynomial fitting formula in log-rho space instead of the original linear `t` space in the DEIS paper. The modification enjoys closed-form coefficients for exponential multistep update instead of replying on the numerical solver. + +The abstract from the paper is: + +*The past few years have witnessed the great success of Diffusion models~(DMs) in generating high-fidelity samples in generative modeling tasks. A major limitation of the DM is its notoriously slow sampling procedure which normally requires hundreds to thousands of time discretization steps of the learned diffusion process to reach the desired accuracy. Our goal is to develop a fast sampling method for DMs with a much less number of steps while retaining high sample quality. To this end, we systematically analyze the sampling procedure in DMs and identify key factors that affect the sample quality, among which the method of discretization is most crucial. By carefully examining the learned diffusion process, we propose Diffusion Exponential Integrator Sampler~(DEIS). It is based on the Exponential Integrator designed for discretizing ordinary differential equations (ODEs) and leverages a semilinear structure of the learned diffusion process to reduce the discretization error. The proposed method can be applied to any DMs and can generate high-fidelity samples in as few as 10 steps. In our experiments, it takes about 3 minutes on one A6000 GPU to generate 50k images from CIFAR10. Moreover, by directly using pre-trained DMs, we achieve the state-of-art sampling performance when the number of score function evaluation~(NFE) is limited, e.g., 4.17 FID with 10 NFEs, 3.37 FID, and 9.74 IS with only 15 NFEs on CIFAR10. Code is available at [this https URL](https://github.com/qsh-zh/deis).* + +## Tips + +It is recommended to set `solver_order` to 2 or 3, while `solver_order=1` is equivalent to [`DDIMScheduler`]. + +Dynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space +diffusion models, you can set `thresholding=True` to use the dynamic thresholding. + +## DEISMultistepScheduler +[[autodoc]] DEISMultistepScheduler + +## SchedulerOutput +[[autodoc]] schedulers.scheduling_utils.SchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/dpm_discrete.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/dpm_discrete.md new file mode 100644 index 0000000000000000000000000000000000000000..5ef96fd0fa9c79df3821c7f65c13c3f87f8d636a --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/dpm_discrete.md @@ -0,0 +1,23 @@ + + +# KDPM2DiscreteScheduler + +The `KDPM2DiscreteScheduler` is inspired by the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper, and the scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/). + +The original codebase can be found at [crowsonkb/k-diffusion](https://github.com/crowsonkb/k-diffusion). + +## KDPM2DiscreteScheduler +[[autodoc]] KDPM2DiscreteScheduler + +## SchedulerOutput +[[autodoc]] schedulers.scheduling_utils.SchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/dpm_discrete_ancestral.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/dpm_discrete_ancestral.md new file mode 100644 index 0000000000000000000000000000000000000000..1973155fb1e07591ec053a1164129ad12aa6e995 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/dpm_discrete_ancestral.md @@ -0,0 +1,23 @@ + + +# KDPM2AncestralDiscreteScheduler + +The `KDPM2DiscreteScheduler` with ancestral sampling is inspired by the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper, and the scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/). + +The original codebase can be found at [crowsonkb/k-diffusion](https://github.com/crowsonkb/k-diffusion). + +## KDPM2AncestralDiscreteScheduler +[[autodoc]] KDPM2AncestralDiscreteScheduler + +## SchedulerOutput +[[autodoc]] schedulers.scheduling_utils.SchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/dpm_sde.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/dpm_sde.md new file mode 100644 index 0000000000000000000000000000000000000000..1c1c26907ec6852657bb4ec641604b92e67a0447 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/dpm_sde.md @@ -0,0 +1,21 @@ + + +# DPMSolverSDEScheduler + +The `DPMSolverSDEScheduler` is inspired by the stochastic sampler from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper, and the scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/). + +## DPMSolverSDEScheduler +[[autodoc]] DPMSolverSDEScheduler + +## SchedulerOutput +[[autodoc]] schedulers.scheduling_utils.SchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/edm_euler.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/edm_euler.md new file mode 100644 index 0000000000000000000000000000000000000000..350b36fa95c1038a7917b82b674498954e038c3f --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/edm_euler.md @@ -0,0 +1,22 @@ + + +# EDMEulerScheduler + +The Karras formulation of the Euler scheduler (Algorithm 2) from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper by Karras et al. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original [k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51) implementation by [Katherine Crowson](https://github.com/crowsonkb/). + + +## EDMEulerScheduler +[[autodoc]] EDMEulerScheduler + +## EDMEulerSchedulerOutput +[[autodoc]] schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md new file mode 100644 index 0000000000000000000000000000000000000000..bac8e08c64da0e328f60e3b4b0b39448426f2cfc --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md @@ -0,0 +1,24 @@ + + +# EDMDPMSolverMultistepScheduler + +`EDMDPMSolverMultistepScheduler` is a [Karras formulation](https://huggingface.co/papers/2206.00364) of `DPMSolverMultistepScheduler`, a multistep scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. + +DPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality +samples, and it can generate quite good samples even in 10 steps. + +## EDMDPMSolverMultistepScheduler +[[autodoc]] EDMDPMSolverMultistepScheduler + +## SchedulerOutput +[[autodoc]] schedulers.scheduling_utils.SchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/euler.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/euler.md new file mode 100644 index 0000000000000000000000000000000000000000..1755f365b3ea65fae190a98d7bf48cf6b2ee2edb --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/euler.md @@ -0,0 +1,22 @@ + + +# EulerDiscreteScheduler + +The Euler scheduler (Algorithm 2) is from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper by Karras et al. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original [k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51) implementation by [Katherine Crowson](https://github.com/crowsonkb/). + + +## EulerDiscreteScheduler +[[autodoc]] EulerDiscreteScheduler + +## EulerDiscreteSchedulerOutput +[[autodoc]] schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/euler_ancestral.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/euler_ancestral.md new file mode 100644 index 0000000000000000000000000000000000000000..a3f67c94c882adfad31b265025c416466ba5bf49 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/euler_ancestral.md @@ -0,0 +1,21 @@ + + +# EulerAncestralDiscreteScheduler + +A scheduler that uses ancestral sampling with Euler method steps. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original [k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72) implementation by [Katherine Crowson](https://github.com/crowsonkb/). + +## EulerAncestralDiscreteScheduler +[[autodoc]] EulerAncestralDiscreteScheduler + +## EulerAncestralDiscreteSchedulerOutput +[[autodoc]] schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteSchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/flow_match_euler_discrete.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/flow_match_euler_discrete.md new file mode 100644 index 0000000000000000000000000000000000000000..612755adc7252ddb2396ea441c96f61f7eee0b80 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/flow_match_euler_discrete.md @@ -0,0 +1,18 @@ + + +# FlowMatchEulerDiscreteScheduler + +`FlowMatchEulerDiscreteScheduler` is based on the flow-matching sampling introduced in [Stable Diffusion 3](https://huggingface.co/papers/2403.03206). + +## FlowMatchEulerDiscreteScheduler +[[autodoc]] FlowMatchEulerDiscreteScheduler diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/flow_match_heun_discrete.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/flow_match_heun_discrete.md new file mode 100644 index 0000000000000000000000000000000000000000..c6359ed843d0e3b59ade070a5edee3c64df7bb29 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/flow_match_heun_discrete.md @@ -0,0 +1,18 @@ + + +# FlowMatchHeunDiscreteScheduler + +`FlowMatchHeunDiscreteScheduler` is based on the flow-matching sampling introduced in [EDM](https://huggingface.co/papers/2403.03206). + +## FlowMatchHeunDiscreteScheduler +[[autodoc]] FlowMatchHeunDiscreteScheduler diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/heun.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/heun.md new file mode 100644 index 0000000000000000000000000000000000000000..efe6ad23a37378521ebf483ce550c7e9a7a07092 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/heun.md @@ -0,0 +1,21 @@ + + +# HeunDiscreteScheduler + +The Heun scheduler (Algorithm 1) is from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper by Karras et al. The scheduler is ported from the [k-diffusion](https://github.com/crowsonkb/k-diffusion) library and created by [Katherine Crowson](https://github.com/crowsonkb/). + +## HeunDiscreteScheduler +[[autodoc]] HeunDiscreteScheduler + +## SchedulerOutput +[[autodoc]] schedulers.scheduling_utils.SchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/ipndm.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/ipndm.md new file mode 100644 index 0000000000000000000000000000000000000000..085316d6d4a16f4b9bc8a36c2300fae1934b3725 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/ipndm.md @@ -0,0 +1,21 @@ + + +# IPNDMScheduler + +`IPNDMScheduler` is a fourth-order Improved Pseudo Linear Multistep scheduler. The original implementation can be found at [crowsonkb/v-diffusion-pytorch](https://github.com/crowsonkb/v-diffusion-pytorch/blob/987f8985e38208345c1959b0ea767a625831cc9b/diffusion/sampling.py#L296). + +## IPNDMScheduler +[[autodoc]] IPNDMScheduler + +## SchedulerOutput +[[autodoc]] schedulers.scheduling_utils.SchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/lcm.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/lcm.md new file mode 100644 index 0000000000000000000000000000000000000000..e237e53faed11efc553ff769793e5c80616ea949 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/lcm.md @@ -0,0 +1,21 @@ + + +# Latent Consistency Model Multistep Scheduler + +## Overview + +Multistep and onestep scheduler (Algorithm 3) introduced alongside latent consistency models in the paper [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://huggingface.co/papers/2310.04378) by Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. +This scheduler should be able to generate good samples from [`LatentConsistencyModelPipeline`] in 1-8 steps. + +## LCMScheduler +[[autodoc]] LCMScheduler diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/lms_discrete.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/lms_discrete.md new file mode 100644 index 0000000000000000000000000000000000000000..394c350f31ae96c89fcd9b93c4f62ab8f2f19788 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/lms_discrete.md @@ -0,0 +1,21 @@ + + +# LMSDiscreteScheduler + +`LMSDiscreteScheduler` is a linear multistep scheduler for discrete beta schedules. The scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/), and the original implementation can be found at [crowsonkb/k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181). + +## LMSDiscreteScheduler +[[autodoc]] LMSDiscreteScheduler + +## LMSDiscreteSchedulerOutput +[[autodoc]] schedulers.scheduling_lms_discrete.LMSDiscreteSchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/multistep_dpm_solver.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/multistep_dpm_solver.md new file mode 100644 index 0000000000000000000000000000000000000000..faf50e7ce9c4fd127cdb25ffa08f5f7870756b3a --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/multistep_dpm_solver.md @@ -0,0 +1,35 @@ + + +# DPMSolverMultistepScheduler + +`DPMSolverMultistepScheduler` is a multistep scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. + +DPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality +samples, and it can generate quite good samples even in 10 steps. + +## Tips + +It is recommended to set `solver_order` to 2 for guide sampling, and `solver_order=3` for unconditional sampling. + +Dynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space +diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use the dynamic +thresholding. This thresholding method is unsuitable for latent-space diffusion models such as +Stable Diffusion. + +The SDE variant of DPMSolver and DPM-Solver++ is also supported, but only for the first and second-order solvers. This is a fast SDE solver for the reverse diffusion SDE. It is recommended to use the second-order `sde-dpmsolver++`. + +## DPMSolverMultistepScheduler +[[autodoc]] DPMSolverMultistepScheduler + +## SchedulerOutput +[[autodoc]] schedulers.scheduling_utils.SchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/multistep_dpm_solver_cogvideox.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/multistep_dpm_solver_cogvideox.md new file mode 100644 index 0000000000000000000000000000000000000000..c59831525d2d4a366f5628f2c6f3f96d5d4454e4 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/multistep_dpm_solver_cogvideox.md @@ -0,0 +1,19 @@ + + +# CogVideoXDPMScheduler + +`CogVideoXDPMScheduler` is based on [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095), specifically for CogVideoX models. + +## CogVideoXDPMScheduler + +[[autodoc]] CogVideoXDPMScheduler diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/multistep_dpm_solver_inverse.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/multistep_dpm_solver_inverse.md new file mode 100644 index 0000000000000000000000000000000000000000..80b8cb182395e5fe27beef1236a4c77b3d3e6641 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/multistep_dpm_solver_inverse.md @@ -0,0 +1,30 @@ + + +# DPMSolverMultistepInverse + +`DPMSolverMultistepInverse` is the inverted scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. + +The implementation is mostly based on the DDIM inversion definition of [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://huggingface.co/papers/2211.09794) and notebook implementation of the [`DiffEdit`] latent inversion from [Xiang-cd/DiffEdit-stable-diffusion](https://github.com/Xiang-cd/DiffEdit-stable-diffusion/blob/main/diffedit.ipynb). + +## Tips + +Dynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space +diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use the dynamic +thresholding. This thresholding method is unsuitable for latent-space diffusion models such as +Stable Diffusion. + +## DPMSolverMultistepInverseScheduler +[[autodoc]] DPMSolverMultistepInverseScheduler + +## SchedulerOutput +[[autodoc]] schedulers.scheduling_utils.SchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/overview.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/overview.md new file mode 100644 index 0000000000000000000000000000000000000000..a57e99a3e46e1a88d325eb5ca8574fb59d5142ad --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/overview.md @@ -0,0 +1,73 @@ + + +# Schedulers + +🤗 Diffusers provides many scheduler functions for the diffusion process. A scheduler takes a model's output (the sample which the diffusion process is iterating on) and a timestep to return a denoised sample. The timestep is important because it dictates where in the diffusion process the step is; data is generated by iterating forward *n* timesteps and inference occurs by propagating backward through the timesteps. Based on the timestep, a scheduler may be *discrete* in which case the timestep is an `int` or *continuous* in which case the timestep is a `float`. + +Depending on the context, a scheduler defines how to iteratively add noise to an image or how to update a sample based on a model's output: + +- during *training*, a scheduler adds noise (there are different algorithms for how to add noise) to a sample to train a diffusion model +- during *inference*, a scheduler defines how to update a sample based on a pretrained model's output + +Many schedulers are implemented from the [k-diffusion](https://github.com/crowsonkb/k-diffusion) library by [Katherine Crowson](https://github.com/crowsonkb/), and they're also widely used in A1111. To help you map the schedulers from k-diffusion and A1111 to the schedulers in 🤗 Diffusers, take a look at the table below: + +| A1111/k-diffusion | 🤗 Diffusers | Usage | +|---------------------|-------------------------------------|---------------------------------------------------------------------------------------------------------------| +| DPM++ 2M | [`DPMSolverMultistepScheduler`] | | +| DPM++ 2M Karras | [`DPMSolverMultistepScheduler`] | init with `use_karras_sigmas=True` | +| DPM++ 2M SDE | [`DPMSolverMultistepScheduler`] | init with `algorithm_type="sde-dpmsolver++"` | +| DPM++ 2M SDE Karras | [`DPMSolverMultistepScheduler`] | init with `use_karras_sigmas=True` and `algorithm_type="sde-dpmsolver++"` | +| DPM++ 2S a | N/A | very similar to `DPMSolverSinglestepScheduler` | +| DPM++ 2S a Karras | N/A | very similar to `DPMSolverSinglestepScheduler(use_karras_sigmas=True, ...)` | +| DPM++ SDE | [`DPMSolverSinglestepScheduler`] | | +| DPM++ SDE Karras | [`DPMSolverSinglestepScheduler`] | init with `use_karras_sigmas=True` | +| DPM2 | [`KDPM2DiscreteScheduler`] | | +| DPM2 Karras | [`KDPM2DiscreteScheduler`] | init with `use_karras_sigmas=True` | +| DPM2 a | [`KDPM2AncestralDiscreteScheduler`] | | +| DPM2 a Karras | [`KDPM2AncestralDiscreteScheduler`] | init with `use_karras_sigmas=True` | +| DPM adaptive | N/A | | +| DPM fast | N/A | | +| Euler | [`EulerDiscreteScheduler`] | | +| Euler a | [`EulerAncestralDiscreteScheduler`] | | +| Heun | [`HeunDiscreteScheduler`] | | +| LMS | [`LMSDiscreteScheduler`] | | +| LMS Karras | [`LMSDiscreteScheduler`] | init with `use_karras_sigmas=True` | +| N/A | [`DEISMultistepScheduler`] | | +| N/A | [`UniPCMultistepScheduler`] | | + +## Noise schedules and schedule types +| A1111/k-diffusion | 🤗 Diffusers | +|--------------------------|----------------------------------------------------------------------------| +| Karras | init with `use_karras_sigmas=True` | +| sgm_uniform | init with `timestep_spacing="trailing"` | +| simple | init with `timestep_spacing="trailing"` | +| exponential | init with `timestep_spacing="linspace"`, `use_exponential_sigmas=True` | +| beta | init with `timestep_spacing="linspace"`, `use_beta_sigmas=True` | + +All schedulers are built from the base [`SchedulerMixin`] class which implements low level utilities shared by all schedulers. + +## SchedulerMixin +[[autodoc]] SchedulerMixin + +## SchedulerOutput +[[autodoc]] schedulers.scheduling_utils.SchedulerOutput + +## KarrasDiffusionSchedulers + +[`KarrasDiffusionSchedulers`] are a broad generalization of schedulers in 🤗 Diffusers. The schedulers in this class are distinguished at a high level by their noise sampling strategy, the type of network and scaling, the training strategy, and how the loss is weighed. + +The different schedulers in this class, depending on the ordinary differential equations (ODE) solver type, fall into the above taxonomy and provide a good abstraction for the design of the main schedulers implemented in 🤗 Diffusers. The schedulers in this class are given [here](https://github.com/huggingface/diffusers/blob/a69754bb879ed55b9b6dc9dd0b3cf4fa4124c765/src/diffusers/schedulers/scheduling_utils.py#L32). + +## PushToHubMixin + +[[autodoc]] utils.PushToHubMixin diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/pndm.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/pndm.md new file mode 100644 index 0000000000000000000000000000000000000000..407fc49c65d61955497981b1ef401be28e2b8d80 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/pndm.md @@ -0,0 +1,21 @@ + + +# PNDMScheduler + +`PNDMScheduler`, or pseudo numerical methods for diffusion models, uses more advanced ODE integration techniques like the Runge-Kutta and linear multi-step method. The original implementation can be found at [crowsonkb/k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181). + +## PNDMScheduler +[[autodoc]] PNDMScheduler + +## SchedulerOutput +[[autodoc]] schedulers.scheduling_utils.SchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/repaint.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/repaint.md new file mode 100644 index 0000000000000000000000000000000000000000..afb983b8b8349fb4059dfbf595350e25085c30dc --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/repaint.md @@ -0,0 +1,27 @@ + + +# RePaintScheduler + +`RePaintScheduler` is a DDPM-based inpainting scheduler for unsupervised inpainting with extreme masks. It is designed to be used with the [`RePaintPipeline`], and it is based on the paper [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://huggingface.co/papers/2201.09865) by Andreas Lugmayr et al. + +The abstract from the paper is: + +*Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pretrained unconditional DDPM as the generative prior. To condition the generation process, we only alter the reverse diffusion iterations by sampling the unmasked regions using the given image information. Since this technique does not modify or condition the original DDPM network itself, the model produces high-quality and diverse output images for any inpainting form. We validate our method for both faces and general-purpose image inpainting using standard and extreme masks. RePaint outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions. GitHub Repository: [this http URL](http://git.io/RePaint).* + +The original implementation can be found at [andreas128/RePaint](https://github.com/andreas128/). + +## RePaintScheduler +[[autodoc]] RePaintScheduler + +## RePaintSchedulerOutput +[[autodoc]] schedulers.scheduling_repaint.RePaintSchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/score_sde_ve.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/score_sde_ve.md new file mode 100644 index 0000000000000000000000000000000000000000..b9b8891eff9c6c59d807f995e0aec62b3a768022 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/score_sde_ve.md @@ -0,0 +1,25 @@ + + +# ScoreSdeVeScheduler + +`ScoreSdeVeScheduler` is a variance exploding stochastic differential equation (SDE) scheduler. It was introduced in the [Score-Based Generative Modeling through Stochastic Differential Equations](https://huggingface.co/papers/2011.13456) paper by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole. + +The abstract from the paper is: + +*Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\aka, score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.* + +## ScoreSdeVeScheduler +[[autodoc]] ScoreSdeVeScheduler + +## SdeVeOutput +[[autodoc]] schedulers.scheduling_sde_ve.SdeVeOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/score_sde_vp.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/score_sde_vp.md new file mode 100644 index 0000000000000000000000000000000000000000..8ce9800ee3e17fcd6cc4a126646a2fa312efd6fd --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/score_sde_vp.md @@ -0,0 +1,25 @@ + + +# ScoreSdeVpScheduler + +`ScoreSdeVpScheduler` is a variance preserving stochastic differential equation (SDE) scheduler. It was introduced in the [Score-Based Generative Modeling through Stochastic Differential Equations](https://huggingface.co/papers/2011.13456) paper by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole. + +The abstract from the paper is: + +*Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\aka, score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.* + +> [!WARNING] +> 🚧 This scheduler is under construction! + +## ScoreSdeVpScheduler +[[autodoc]] schedulers.deprecated.scheduling_sde_vp.ScoreSdeVpScheduler diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/singlestep_dpm_solver.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/singlestep_dpm_solver.md new file mode 100644 index 0000000000000000000000000000000000000000..5ab14569e23fd1565d90a7ae48674cab6182ea39 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/singlestep_dpm_solver.md @@ -0,0 +1,35 @@ + + +# DPMSolverSinglestepScheduler + +`DPMSolverSinglestepScheduler` is a single step scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. + +DPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality +samples, and it can generate quite good samples even in 10 steps. + +The original implementation can be found at [LuChengTHU/dpm-solver](https://github.com/LuChengTHU/dpm-solver). + +## Tips + +It is recommended to set `solver_order` to 2 for guide sampling, and `solver_order=3` for unconditional sampling. + +Dynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space +diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use dynamic +thresholding. This thresholding method is unsuitable for latent-space diffusion models such as +Stable Diffusion. + +## DPMSolverSinglestepScheduler +[[autodoc]] DPMSolverSinglestepScheduler + +## SchedulerOutput +[[autodoc]] schedulers.scheduling_utils.SchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/stochastic_karras_ve.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/stochastic_karras_ve.md new file mode 100644 index 0000000000000000000000000000000000000000..8d6a0f0f30d59fbd7fae90ee9c7a799319c1c8f3 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/stochastic_karras_ve.md @@ -0,0 +1,21 @@ + + +# KarrasVeScheduler + +`KarrasVeScheduler` is a stochastic sampler tailored to variance-expanding (VE) models. It is based on the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) and [Score-based generative modeling through stochastic differential equations](https://huggingface.co/papers/2011.13456) papers. + +## KarrasVeScheduler +[[autodoc]] KarrasVeScheduler + +## KarrasVeOutput +[[autodoc]] schedulers.deprecated.scheduling_karras_ve.KarrasVeOutput \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/tcd.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/tcd.md new file mode 100644 index 0000000000000000000000000000000000000000..951bc2705592f7ca021455c4f20b3c840b4cca1a --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/tcd.md @@ -0,0 +1,29 @@ + + +# TCDScheduler + +[Trajectory Consistency Distillation](https://huggingface.co/papers/2402.19159) by Jianbin Zheng, Minghui Hu, Zhongyi Fan, Chaoyue Wang, Changxing Ding, Dacheng Tao and Tat-Jen Cham introduced a Strategic Stochastic Sampling (Algorithm 4) that is capable of generating good samples in a small number of steps. Distinguishing it as an advanced iteration of the multistep scheduler (Algorithm 1) in the [Consistency Models](https://huggingface.co/papers/2303.01469), Strategic Stochastic Sampling specifically tailored for the trajectory consistency function. + +The abstract from the paper is: + +*Latent Consistency Model (LCM) extends the Consistency Model to the latent space and leverages the guided consistency distillation technique to achieve impressive performance in accelerating text-to-image synthesis. However, we observed that LCM struggles to generate images with both clarity and detailed intricacy. To address this limitation, we initially delve into and elucidate the underlying causes. Our investigation identifies that the primary issue stems from errors in three distinct areas. Consequently, we introduce Trajectory Consistency Distillation (TCD), which encompasses trajectory consistency function and strategic stochastic sampling. The trajectory consistency function diminishes the distillation errors by broadening the scope of the self-consistency boundary condition and endowing the TCD with the ability to accurately trace the entire trajectory of the Probability Flow ODE. Additionally, strategic stochastic sampling is specifically designed to circumvent the accumulated errors inherent in multi-step consistency sampling, which is meticulously tailored to complement the TCD model. Experiments demonstrate that TCD not only significantly enhances image quality at low NFEs but also yields more detailed results compared to the teacher model at high NFEs.* + +The original codebase can be found at [jabir-zheng/TCD](https://github.com/jabir-zheng/TCD). + +## TCDScheduler +[[autodoc]] TCDScheduler + + +## TCDSchedulerOutput +[[autodoc]] schedulers.scheduling_tcd.TCDSchedulerOutput + diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/unipc.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/unipc.md new file mode 100644 index 0000000000000000000000000000000000000000..54c32222ba4f2c72de0efe001ddd47ad238e5d29 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/unipc.md @@ -0,0 +1,35 @@ + + +# UniPCMultistepScheduler + +`UniPCMultistepScheduler` is a training-free framework designed for fast sampling of diffusion models. It was introduced in [UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models](https://huggingface.co/papers/2302.04867) by Wenliang Zhao, Lujia Bai, Yongming Rao, Jie Zhou, Jiwen Lu. + +It consists of a corrector (UniC) and a predictor (UniP) that share a unified analytical form and support arbitrary orders. +UniPC is by design model-agnostic, supporting pixel-space/latent-space DPMs on unconditional/conditional sampling. It can also be applied to both noise prediction and data prediction models. The corrector UniC can be also applied after any off-the-shelf solvers to increase the order of accuracy. + +The abstract from the paper is: + +*Diffusion probabilistic models (DPMs) have demonstrated a very promising ability in high-resolution image synthesis. However, sampling from a pre-trained DPM is time-consuming due to the multiple evaluations of the denoising network, making it more and more important to accelerate the sampling of DPMs. Despite recent progress in designing fast samplers, existing methods still cannot generate satisfying images in many applications where fewer steps (e.g., <10) are favored. In this paper, we develop a unified corrector (UniC) that can be applied after any existing DPM sampler to increase the order of accuracy without extra model evaluations, and derive a unified predictor (UniP) that supports arbitrary order as a byproduct. Combining UniP and UniC, we propose a unified predictor-corrector framework called UniPC for the fast sampling of DPMs, which has a unified analytical form for any order and can significantly improve the sampling quality over previous methods, especially in extremely few steps. We evaluate our methods through extensive experiments including both unconditional and conditional sampling using pixel-space and latent-space DPMs. Our UniPC can achieve 3.87 FID on CIFAR10 (unconditional) and 7.51 FID on ImageNet 256×256 (conditional) with only 10 function evaluations. Code is available at [this https URL](https://github.com/wl-zhao/UniPC).* + +## Tips + +It is recommended to set `solver_order` to 2 for guide sampling, and `solver_order=3` for unconditional sampling. + +Dynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space +diffusion models, you can set both `predict_x0=True` and `thresholding=True` to use dynamic thresholding. This thresholding method is unsuitable for latent-space diffusion models such as Stable Diffusion. + +## UniPCMultistepScheduler +[[autodoc]] UniPCMultistepScheduler + +## SchedulerOutput +[[autodoc]] schedulers.scheduling_utils.SchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/vq_diffusion.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/vq_diffusion.md new file mode 100644 index 0000000000000000000000000000000000000000..0539c83f886c5507492c20ea39cfd62a1e4953c9 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/schedulers/vq_diffusion.md @@ -0,0 +1,25 @@ + + +# VQDiffusionScheduler + +`VQDiffusionScheduler` converts the transformer model's output into a sample for the unnoised image at the previous diffusion timestep. It was introduced in [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://huggingface.co/papers/2111.14822) by Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, Baining Guo. + +The abstract from the paper is: + +*We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation. This method is based on a vector quantized variational autoencoder (VQ-VAE) whose latent space is modeled by a conditional variant of the recently developed Denoising Diffusion Probabilistic Model (DDPM). We find that this latent-space method is well-suited for text-to-image generation tasks because it not only eliminates the unidirectional bias with existing methods but also allows us to incorporate a mask-and-replace diffusion strategy to avoid the accumulation of errors, which is a serious problem with existing methods. Our experiments show that the VQ-Diffusion produces significantly better text-to-image generation results when compared with conventional autoregressive (AR) models with similar numbers of parameters. Compared with previous GAN-based text-to-image methods, our VQ-Diffusion can handle more complex scenes and improve the synthesized image quality by a large margin. Finally, we show that the image generation computation in our method can be made highly efficient by reparameterization. With traditional AR methods, the text-to-image generation time increases linearly with the output image resolution and hence is quite time consuming even for normal size images. The VQ-Diffusion allows us to achieve a better trade-off between quality and speed. Our experiments indicate that the VQ-Diffusion model with the reparameterization is fifteen times faster than traditional AR methods while achieving a better image quality.* + +## VQDiffusionScheduler +[[autodoc]] VQDiffusionScheduler + +## VQDiffusionSchedulerOutput +[[autodoc]] schedulers.scheduling_vq_diffusion.VQDiffusionSchedulerOutput diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/utilities.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/utilities.md new file mode 100644 index 0000000000000000000000000000000000000000..45a3c5372a1b79a8dd18586a2b3d00b82a40a55e --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/utilities.md @@ -0,0 +1,55 @@ + + +# Utilities + +Utility and helper functions for working with 🤗 Diffusers. + +## numpy_to_pil + +[[autodoc]] utils.numpy_to_pil + +## pt_to_pil + +[[autodoc]] utils.pt_to_pil + +## load_image + +[[autodoc]] utils.load_image + +## load_video + +[[autodoc]] utils.load_video + +## export_to_gif + +[[autodoc]] utils.export_to_gif + +## export_to_video + +[[autodoc]] utils.export_to_video + +## make_image_grid + +[[autodoc]] utils.make_image_grid + +## randn_tensor + +[[autodoc]] utils.torch_utils.randn_tensor + +## apply_layerwise_casting + +[[autodoc]] hooks.layerwise_casting.apply_layerwise_casting + +## apply_group_offloading + +[[autodoc]] hooks.group_offloading.apply_group_offloading diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/video_processor.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/video_processor.md new file mode 100644 index 0000000000000000000000000000000000000000..67dafd439d9dd9aa72a8c8b34ad5da91c373f2cd --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/api/video_processor.md @@ -0,0 +1,21 @@ + + +# Video Processor + +The [`VideoProcessor`] provides a unified API for video pipelines to prepare inputs for VAE encoding and post-processing outputs once they're decoded. The class inherits [`VaeImageProcessor`] so it includes transformations such as resizing, normalization, and conversion between PIL Image, PyTorch, and NumPy arrays. + +## VideoProcessor + +[[autodoc]] video_processor.VideoProcessor.preprocess_video + +[[autodoc]] video_processor.VideoProcessor.postprocess_video diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/community_projects.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/community_projects.md new file mode 100644 index 0000000000000000000000000000000000000000..339e538da9e25899fd9e3a3dd54926f006dfdb78 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/community_projects.md @@ -0,0 +1,90 @@ + + +# Community Projects + +Welcome to Community Projects. This space is dedicated to showcasing the incredible work and innovative applications created by our vibrant community using the `diffusers` library. + +This section aims to: + +- Highlight diverse and inspiring projects built with `diffusers` +- Foster knowledge sharing within our community +- Provide real-world examples of how `diffusers` can be leveraged + +Happy exploring, and thank you for being part of the Diffusers community! + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Project NameDescription
dream-textures Stable Diffusion built-in to Blender
HiDiffusion Increases the resolution and speed of your diffusion model by only adding a single line of code
IC-Light IC-Light is a project to manipulate the illumination of images
InstantID InstantID : Zero-shot Identity-Preserving Generation in Seconds
IOPaint Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.
Kohya Gradio GUI for Kohya's Stable Diffusion trainers
MagicAnimate MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model
OOTDiffusion Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on
SD.Next SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
stable-dreamfusion Text-to-3D & Image-to-3D & Mesh Exportation with NeRF + Diffusion
StoryDiffusion StoryDiffusion can create a magic story by generating consistent images and videos.
StreamDiffusion A Pipeline-Level Solution for Real-Time Interactive Generation
Stable Diffusion Server A server configured for Inpainting/Generation/img2img with one stable diffusion model
Model Search Search models on Civitai and Hugging Face
Skrample Fully modular scheduler functions with 1st class diffusers integration.
diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/conceptual/contribution.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/conceptual/contribution.md new file mode 100644 index 0000000000000000000000000000000000000000..e39a6434f095d5456bc38ed2e53755ec9bb398c6 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/conceptual/contribution.md @@ -0,0 +1,568 @@ + + +# How to contribute to Diffusers 🧨 + +We ❤️ contributions from the open-source community! Everyone is welcome, and all types of participation –not just code– are valued and appreciated. Answering questions, helping others, reaching out, and improving the documentation are all immensely valuable to the community, so don't be afraid and get involved if you're up for it! + +Everyone is encouraged to start by saying 👋 in our public Discord channel. We discuss the latest trends in diffusion models, ask questions, show off personal projects, help each other with contributions, or just hang out ☕. Join us on Discord + +Whichever way you choose to contribute, we strive to be part of an open, welcoming, and kind community. Please, read our [code of conduct](https://github.com/huggingface/diffusers/blob/main/CODE_OF_CONDUCT.md) and be mindful to respect it during your interactions. We also recommend you become familiar with the [ethical guidelines](https://huggingface.co/docs/diffusers/conceptual/ethical_guidelines) that guide our project and ask you to adhere to the same principles of transparency and responsibility. + +We enormously value feedback from the community, so please do not be afraid to speak up if you believe you have valuable feedback that can help improve the library - every message, comment, issue, and pull request (PR) is read and considered. + +## Overview + +You can contribute in many ways ranging from answering questions on issues and discussions to adding new diffusion models to the core library. + +In the following, we give an overview of different ways to contribute, ranked by difficulty in ascending order. All of them are valuable to the community. + +* 1. Asking and answering questions on [the Diffusers discussion forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers) or on [Discord](https://discord.gg/G7tWnz98XR). +* 2. Opening new issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues/new/choose) or new discussions on [the GitHub Discussions tab](https://github.com/huggingface/diffusers/discussions/new/choose). +* 3. Answering issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues) or discussions on [the GitHub Discussions tab](https://github.com/huggingface/diffusers/discussions). +* 4. Fix a simple issue, marked by the "Good first issue" label, see [here](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22). +* 5. Contribute to the [documentation](https://github.com/huggingface/diffusers/tree/main/docs/source). +* 6. Contribute a [Community Pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3Acommunity-examples). +* 7. Contribute to the [examples](https://github.com/huggingface/diffusers/tree/main/examples). +* 8. Fix a more difficult issue, marked by the "Good second issue" label, see [here](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22). +* 9. Add a new pipeline, model, or scheduler, see ["New Pipeline/Model"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) and ["New scheduler"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22) issues. For this contribution, please have a look at [Design Philosophy](https://github.com/huggingface/diffusers/blob/main/PHILOSOPHY.md). + +As said before, **all contributions are valuable to the community**. +In the following, we will explain each contribution a bit more in detail. + +For all contributions 4 - 9, you will need to open a PR. It is explained in detail how to do so in [Opening a pull request](#how-to-open-a-pr). + +### 1. Asking and answering questions on the Diffusers discussion forum or on the Diffusers Discord + +Any question or comment related to the Diffusers library can be asked on the [discussion forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/) or on [Discord](https://discord.gg/G7tWnz98XR). Such questions and comments include (but are not limited to): +- Reports of training or inference experiments in an attempt to share knowledge +- Presentation of personal projects +- Questions to non-official training examples +- Project proposals +- General feedback +- Paper summaries +- Asking for help on personal projects that build on top of the Diffusers library +- General questions +- Ethical questions regarding diffusion models +- ... + +Every question that is asked on the forum or on Discord actively encourages the community to publicly +share knowledge and might very well help a beginner in the future who has the same question you're +having. Please do pose any questions you might have. +In the same spirit, you are of immense help to the community by answering such questions because this way you are publicly documenting knowledge for everybody to learn from. + +**Please** keep in mind that the more effort you put into asking or answering a question, the higher +the quality of the publicly documented knowledge. In the same way, well-posed and well-answered questions create a high-quality knowledge database accessible to everybody, while badly posed questions or answers reduce the overall quality of the public knowledge database. +In short, a high quality question or answer is *precise*, *concise*, *relevant*, *easy-to-understand*, *accessible*, and *well-formatted/well-posed*. For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section. + +**NOTE about channels**: +[*The forum*](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) is much better indexed by search engines, such as Google. Posts are ranked by popularity rather than chronologically. Hence, it's easier to look up questions and answers that we posted some time ago. +In addition, questions and answers posted in the forum can easily be linked to. +In contrast, *Discord* has a chat-like format that invites fast back-and-forth communication. +While it will most likely take less time for you to get an answer to your question on Discord, your +question won't be visible anymore over time. Also, it's much harder to find information that was posted a while back on Discord. We therefore strongly recommend using the forum for high-quality questions and answers in an attempt to create long-lasting knowledge for the community. If discussions on Discord lead to very interesting answers and conclusions, we recommend posting the results on the forum to make the information more available for future readers. + +### 2. Opening new issues on the GitHub issues tab + +The 🧨 Diffusers library is robust and reliable thanks to the users who notify us of +the problems they encounter. So thank you for reporting an issue. + +Remember, GitHub issues are reserved for technical questions directly related to the Diffusers library, bug reports, feature requests, or feedback on the library design. + +In a nutshell, this means that everything that is **not** related to the **code of the Diffusers library** (including the documentation) should **not** be asked on GitHub, but rather on either the [forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) or [Discord](https://discord.gg/G7tWnz98XR). + +**Please consider the following guidelines when opening a new issue**: +- Make sure you have searched whether your issue has already been asked before (use the search bar on GitHub under Issues). +- Please never report a new issue on another (related) issue. If another issue is highly related, please +open a new issue nevertheless and link to the related issue. +- Make sure your issue is written in English. Please use one of the great, free online translation services, such as [DeepL](https://www.deepl.com/translator) to translate from your native language to English if you are not comfortable in English. +- Check whether your issue might be solved by updating to the newest Diffusers version. Before posting your issue, please make sure that `python -c "import diffusers; print(diffusers.__version__)"` is higher or matches the latest Diffusers version. +- Remember that the more effort you put into opening a new issue, the higher the quality of your answer will be and the better the overall quality of the Diffusers issues. + +New issues usually include the following. + +#### 2.1. Reproducible, minimal bug reports + +A bug report should always have a reproducible code snippet and be as minimal and concise as possible. +This means in more detail: +- Narrow the bug down as much as you can, **do not just dump your whole code file**. +- Format your code. +- Do not include any external libraries except for Diffusers depending on them. +- **Always** provide all necessary information about your environment; for this, you can run: `diffusers-cli env` in your shell and copy-paste the displayed information to the issue. +- Explain the issue. If the reader doesn't know what the issue is and why it is an issue, (s)he cannot solve it. +- **Always** make sure the reader can reproduce your issue with as little effort as possible. If your code snippet cannot be run because of missing libraries or undefined variables, the reader cannot help you. Make sure your reproducible code snippet is as minimal as possible and can be copy-pasted into a simple Python shell. +- If in order to reproduce your issue a model and/or dataset is required, make sure the reader has access to that model or dataset. You can always upload your model or dataset to the [Hub](https://huggingface.co) to make it easily downloadable. Try to keep your model and dataset as small as possible, to make the reproduction of your issue as effortless as possible. + +For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section. + +You can open a bug report [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&projects=&template=bug-report.yml). + +#### 2.2. Feature requests + +A world-class feature request addresses the following points: + +1. Motivation first: +* Is it related to a problem/frustration with the library? If so, please explain +why. Providing a code snippet that demonstrates the problem is best. +* Is it related to something you would need for a project? We'd love to hear +about it! +* Is it something you worked on and think could benefit the community? +Awesome! Tell us what problem it solved for you. +2. Write a *full paragraph* describing the feature; +3. Provide a **code snippet** that demonstrates its future use; +4. In case this is related to a paper, please attach a link; +5. Attach any additional information (drawings, screenshots, etc.) you think may help. + +You can open a feature request [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=). + +#### 2.3 Feedback + +Feedback about the library design and why it is good or not good helps the core maintainers immensely to build a user-friendly library. To understand the philosophy behind the current design philosophy, please have a look [here](https://huggingface.co/docs/diffusers/conceptual/philosophy). If you feel like a certain design choice does not fit with the current design philosophy, please explain why and how it should be changed. If a certain design choice follows the design philosophy too much, hence restricting use cases, explain why and how it should be changed. +If a certain design choice is very useful for you, please also leave a note as this is great feedback for future design decisions. + +You can open an issue about feedback [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=). + +#### 2.4 Technical questions + +Technical questions are mainly about why certain code of the library was written in a certain way, or what a certain part of the code does. Please make sure to link to the code in question and please provide details on +why this part of the code is difficult to understand. + +You can open an issue about a technical question [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&template=bug-report.yml). + +#### 2.5 Proposal to add a new model, scheduler, or pipeline + +If the diffusion model community released a new model, pipeline, or scheduler that you would like to see in the Diffusers library, please provide the following information: + +* Short description of the diffusion pipeline, model, or scheduler and link to the paper or public release. +* Link to any of its open-source implementation(s). +* Link to the model weights if they are available. + +If you are willing to contribute to the model yourself, let us know so we can best guide you. Also, don't forget +to tag the original author of the component (model, scheduler, pipeline, etc.) by GitHub handle if you can find it. + +You can open a request for a model/pipeline/scheduler [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=New+model%2Fpipeline%2Fscheduler&template=new-model-addition.yml). + +### 3. Answering issues on the GitHub issues tab + +Answering issues on GitHub might require some technical knowledge of Diffusers, but we encourage everybody to give it a try even if you are not 100% certain that your answer is correct. +Some tips to give a high-quality answer to an issue: +- Be as concise and minimal as possible. +- Stay on topic. An answer to the issue should concern the issue and only the issue. +- Provide links to code, papers, or other sources that prove or encourage your point. +- Answer in code. If a simple code snippet is the answer to the issue or shows how the issue can be solved, please provide a fully reproducible code snippet. + +Also, many issues tend to be simply off-topic, duplicates of other issues, or irrelevant. It is of great +help to the maintainers if you can answer such issues, encouraging the author of the issue to be +more precise, provide the link to a duplicated issue or redirect them to [the forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) or [Discord](https://discord.gg/G7tWnz98XR). + +If you have verified that the issued bug report is correct and requires a correction in the source code, +please have a look at the next sections. + +For all of the following contributions, you will need to open a PR. It is explained in detail how to do so in the [Opening a pull request](#how-to-open-a-pr) section. + +### 4. Fixing a "Good first issue" + +*Good first issues* are marked by the [Good first issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) label. Usually, the issue already +explains how a potential solution should look so that it is easier to fix. +If the issue hasn't been closed and you would like to try to fix this issue, you can just leave a message "I would like to try this issue.". There are usually three scenarios: +- a.) The issue description already proposes a fix. In this case and if the solution makes sense to you, you can open a PR or draft PR to fix it. +- b.) The issue description does not propose a fix. In this case, you can ask what a proposed fix could look like and someone from the Diffusers team should answer shortly. If you have a good idea of how to fix it, feel free to directly open a PR. +- c.) There is already an open PR to fix the issue, but the issue hasn't been closed yet. If the PR has gone stale, you can simply open a new PR and link to the stale PR. PRs often go stale if the original contributor who wanted to fix the issue suddenly cannot find the time anymore to proceed. This often happens in open-source and is very normal. In this case, the community will be very happy if you give it a new try and leverage the knowledge of the existing PR. If there is already a PR and it is active, you can help the author by giving suggestions, reviewing the PR or even asking whether you can contribute to the PR. + + +### 5. Contribute to the documentation + +A good library **always** has good documentation! The official documentation is often one of the first points of contact for new users of the library, and therefore contributing to the documentation is a **highly +valuable contribution**. + +Contributing to the library can have many forms: + +- Correcting spelling or grammatical errors. +- Correct incorrect formatting of the docstring. If you see that the official documentation is weirdly displayed or a link is broken, we would be very happy if you take some time to correct it. +- Correct the shape or dimensions of a docstring input or output tensor. +- Clarify documentation that is hard to understand or incorrect. +- Update outdated code examples. +- Translating the documentation to another language. + +Anything displayed on [the official Diffusers doc page](https://huggingface.co/docs/diffusers/index) is part of the official documentation and can be corrected, adjusted in the respective [documentation source](https://github.com/huggingface/diffusers/tree/main/docs/source). + +Please have a look at [this page](https://github.com/huggingface/diffusers/tree/main/docs) on how to verify changes made to the documentation locally. + +### 6. Contribute a community pipeline + +> [!TIP] +> Read the [Community pipelines](../using-diffusers/custom_pipeline_overview#community-pipelines) guide to learn more about the difference between a GitHub and Hugging Face Hub community pipeline. If you're interested in why we have community pipelines, take a look at GitHub Issue [#841](https://github.com/huggingface/diffusers/issues/841) (basically, we can't maintain all the possible ways diffusion models can be used for inference but we also don't want to prevent the community from building them). + +Contributing a community pipeline is a great way to share your creativity and work with the community. It lets you build on top of the [`DiffusionPipeline`] so that anyone can load and use it by setting the `custom_pipeline` parameter. This section will walk you through how to create a simple pipeline where the UNet only does a single forward pass and calls the scheduler once (a "one-step" pipeline). + +1. Create a one_step_unet.py file for your community pipeline. This file can contain whatever package you want to use as long as it's installed by the user. Make sure you only have one pipeline class that inherits from [`DiffusionPipeline`] to load model weights and the scheduler configuration from the Hub. Add a UNet and scheduler to the `__init__` function. + + You should also add the `register_modules` function to ensure your pipeline and its components can be saved with [`~DiffusionPipeline.save_pretrained`]. + +```py +from diffusers import DiffusionPipeline +import torch + +class UnetSchedulerOneForwardPipeline(DiffusionPipeline): + def __init__(self, unet, scheduler): + super().__init__() + + self.register_modules(unet=unet, scheduler=scheduler) +``` + +1. In the forward pass (which we recommend defining as `__call__`), you can add any feature you'd like. For the "one-step" pipeline, create a random image and call the UNet and scheduler once by setting `timestep=1`. + +```py + from diffusers import DiffusionPipeline + import torch + + class UnetSchedulerOneForwardPipeline(DiffusionPipeline): + def __init__(self, unet, scheduler): + super().__init__() + + self.register_modules(unet=unet, scheduler=scheduler) + + def __call__(self): + image = torch.randn( + (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size), + ) + timestep = 1 + + model_output = self.unet(image, timestep).sample + scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample + + return scheduler_output +``` + +Now you can run the pipeline by passing a UNet and scheduler to it or load pretrained weights if the pipeline structure is identical. + +```py +from diffusers import DDPMScheduler, UNet2DModel + +scheduler = DDPMScheduler() +unet = UNet2DModel() + +pipeline = UnetSchedulerOneForwardPipeline(unet=unet, scheduler=scheduler) +output = pipeline() +# load pretrained weights +pipeline = UnetSchedulerOneForwardPipeline.from_pretrained("google/ddpm-cifar10-32", use_safetensors=True) +output = pipeline() +``` + +You can either share your pipeline as a GitHub community pipeline or Hub community pipeline. + + + + +Share your GitHub pipeline by opening a pull request on the Diffusers [repository](https://github.com/huggingface/diffusers) and add the one_step_unet.py file to the [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) subfolder. + + + + +Share your Hub pipeline by creating a model repository on the Hub and uploading the one_step_unet.py file to it. + + + + +### 7. Contribute to training examples + +Diffusers examples are a collection of training scripts that reside in [examples](https://github.com/huggingface/diffusers/tree/main/examples). + +We support two types of training examples: + +- Official training examples +- Research training examples + +Research training examples are located in [examples/research_projects](https://github.com/huggingface/diffusers/tree/main/examples/research_projects) whereas official training examples include all folders under [examples](https://github.com/huggingface/diffusers/tree/main/examples) except the `research_projects` and `community` folders. +The official training examples are maintained by the Diffusers' core maintainers whereas the research training examples are maintained by the community. +This is because of the same reasons put forward in [6. Contribute a community pipeline](#6-contribute-a-community-pipeline) for official pipelines vs. community pipelines: It is not feasible for the core maintainers to maintain all possible training methods for diffusion models. +If the Diffusers core maintainers and the community consider a certain training paradigm to be too experimental or not popular enough, the corresponding training code should be put in the `research_projects` folder and maintained by the author. + +Both official training and research examples consist of a directory that contains one or more training scripts, a `requirements.txt` file, and a `README.md` file. In order for the user to make use of the +training examples, it is required to clone the repository: + +```bash +git clone https://github.com/huggingface/diffusers +``` + +as well as to install all additional dependencies required for training: + +```bash +cd diffusers +pip install -r examples//requirements.txt +``` + +Therefore when adding an example, the `requirements.txt` file shall define all pip dependencies required for your training example so that once all those are installed, the user can run the example's training script. See, for example, the [DreamBooth `requirements.txt` file](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/requirements.txt). + +Training examples of the Diffusers library should adhere to the following philosophy: +- All the code necessary to run the examples should be found in a single Python file. +- One should be able to run the example from the command line with `python .py --args`. +- Examples should be kept simple and serve as **an example** on how to use Diffusers for training. The purpose of example scripts is **not** to create state-of-the-art diffusion models, but rather to reproduce known training schemes without adding too much custom logic. As a byproduct of this point, our examples also strive to serve as good educational materials. + +To contribute an example, it is highly recommended to look at already existing examples such as [dreambooth](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) to get an idea of how they should look like. +We strongly advise contributors to make use of the [Accelerate library](https://github.com/huggingface/accelerate) as it's tightly integrated +with Diffusers. +Once an example script works, please make sure to add a comprehensive `README.md` that states how to use the example exactly. This README should include: +- An example command on how to run the example script as shown [here](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth#running-locally-with-pytorch). +- A link to some training results (logs, models, etc.) that show what the user can expect as shown [here](https://api.wandb.ai/report/patrickvonplaten/xm6cd5q5). +- If you are adding a non-official/research training example, **please don't forget** to add a sentence that you are maintaining this training example which includes your git handle as shown [here](https://github.com/huggingface/diffusers/tree/main/examples/research_projects/intel_opts#diffusers-examples-with-intel-optimizations). + +If you are contributing to the official training examples, please also make sure to add a test to its folder such as [examples/dreambooth/test_dreambooth.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/test_dreambooth.py). This is not necessary for non-official training examples. + +### 8. Fixing a "Good second issue" + +*Good second issues* are marked by the [Good second issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22) label. Good second issues are +usually more complicated to solve than [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22). +The issue description usually gives less guidance on how to fix the issue and requires +a decent understanding of the library by the interested contributor. +If you are interested in tackling a good second issue, feel free to open a PR to fix it and link the PR to the issue. If you see that a PR has already been opened for this issue but did not get merged, have a look to understand why it wasn't merged and try to open an improved PR. +Good second issues are usually more difficult to get merged compared to good first issues, so don't hesitate to ask for help from the core maintainers. If your PR is almost finished the core maintainers can also jump into your PR and commit to it in order to get it merged. + +### 9. Adding pipelines, models, schedulers + +Pipelines, models, and schedulers are the most important pieces of the Diffusers library. +They provide easy access to state-of-the-art diffusion technologies and thus allow the community to +build powerful generative AI applications. + +By adding a new model, pipeline, or scheduler you might enable a new powerful use case for any of the user interfaces relying on Diffusers which can be of immense value for the whole generative AI ecosystem. + +Diffusers has a couple of open feature requests for all three components - feel free to gloss over them +if you don't know yet what specific component you would like to add: +- [Model or pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) +- [Scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22) + +Before adding any of the three components, it is strongly recommended that you give the [Philosophy guide](philosophy) a read to better understand the design of any of the three components. Please be aware that we cannot merge model, scheduler, or pipeline additions that strongly diverge from our design philosophy +as it will lead to API inconsistencies. If you fundamentally disagree with a design choice, please open a [Feedback issue](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=) instead so that it can be discussed whether a certain design pattern/design choice shall be changed everywhere in the library and whether we shall update our design philosophy. Consistency across the library is very important for us. + +Please make sure to add links to the original codebase/paper to the PR and ideally also ping the original author directly on the PR so that they can follow the progress and potentially help with questions. + +If you are unsure or stuck in the PR, don't hesitate to leave a message to ask for a first review or help. + +#### Copied from mechanism + +A unique and important feature to understand when adding any pipeline, model or scheduler code is the `# Copied from` mechanism. You'll see this all over the Diffusers codebase, and the reason we use it is to keep the codebase easy to understand and maintain. Marking code with the `# Copied from` mechanism forces the marked code to be identical to the code it was copied from. This makes it easy to update and propagate changes across many files whenever you run `make fix-copies`. + +For example, in the code example below, [`~diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is the original code and `AltDiffusionPipelineOutput` uses the `# Copied from` mechanism to copy it. The only difference is changing the class prefix from `Stable` to `Alt`. + +```py +# Copied from diffusers.pipelines.stable_diffusion.pipeline_output.StableDiffusionPipelineOutput with Stable->Alt +class AltDiffusionPipelineOutput(BaseOutput): + """ + Output class for Alt Diffusion pipelines. + + Args: + images (`List[PIL.Image.Image]` or `np.ndarray`) + List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width, + num_channels)`. + nsfw_content_detected (`List[bool]`) + List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or + `None` if safety checking could not be performed. + """ +``` + +To learn more, read this section of the [~Don't~ Repeat Yourself*](https://huggingface.co/blog/transformers-design-philosophy#4-machine-learning-models-are-static) blog post. + +## How to write a good issue + +**The better your issue is written, the higher the chances that it will be quickly resolved.** + +1. Make sure that you've used the correct template for your issue. You can pick between *Bug Report*, *Feature Request*, *Feedback about API Design*, *New model/pipeline/scheduler addition*, *Forum*, or a blank issue. Make sure to pick the correct one when opening [a new issue](https://github.com/huggingface/diffusers/issues/new/choose). +2. **Be precise**: Give your issue a fitting title. Try to formulate your issue description as simple as possible. The more precise you are when submitting an issue, the less time it takes to understand the issue and potentially solve it. Make sure to open an issue for one issue only and not for multiple issues. If you found multiple issues, simply open multiple issues. If your issue is a bug, try to be as precise as possible about what bug it is - you should not just write "Error in diffusers". +3. **Reproducibility**: No reproducible code snippet == no solution. If you encounter a bug, maintainers **have to be able to reproduce** it. Make sure that you include a code snippet that can be copy-pasted into a Python interpreter to reproduce the issue. Make sure that your code snippet works, *i.e.* that there are no missing imports or missing links to images, ... Your issue should contain an error message **and** a code snippet that can be copy-pasted without any changes to reproduce the exact same error message. If your issue is using local model weights or local data that cannot be accessed by the reader, the issue cannot be solved. If you cannot share your data or model, try to make a dummy model or dummy data. +4. **Minimalistic**: Try to help the reader as much as you can to understand the issue as quickly as possible by staying as concise as possible. Remove all code / all information that is irrelevant to the issue. If you have found a bug, try to create the easiest code example you can to demonstrate your issue, do not just dump your whole workflow into the issue as soon as you have found a bug. E.g., if you train a model and get an error at some point during the training, you should first try to understand what part of the training code is responsible for the error and try to reproduce it with a couple of lines. Try to use dummy data instead of full datasets. +5. Add links. If you are referring to a certain naming, method, or model make sure to provide a link so that the reader can better understand what you mean. If you are referring to a specific PR or issue, make sure to link it to your issue. Do not assume that the reader knows what you are talking about. The more links you add to your issue the better. +6. Formatting. Make sure to nicely format your issue by formatting code into Python code syntax, and error messages into normal code syntax. See the [official GitHub formatting docs](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) for more information. +7. Think of your issue not as a ticket to be solved, but rather as a beautiful entry to a well-written encyclopedia. Every added issue is a contribution to publicly available knowledge. By adding a nicely written issue you not only make it easier for maintainers to solve your issue, but you are helping the whole community to better understand a certain aspect of the library. + +## How to write a good PR + +1. Be a chameleon. Understand existing design patterns and syntax and make sure your code additions flow seamlessly into the existing code base. Pull requests that significantly diverge from existing design patterns or user interfaces will not be merged. +2. Be laser focused. A pull request should solve one problem and one problem only. Make sure to not fall into the trap of "also fixing another problem while we're adding it". It is much more difficult to review pull requests that solve multiple, unrelated problems at once. +3. If helpful, try to add a code snippet that displays an example of how your addition can be used. +4. The title of your pull request should be a summary of its contribution. +5. If your pull request addresses an issue, please mention the issue number in +the pull request description to make sure they are linked (and people +consulting the issue know you are working on it); +6. To indicate a work in progress please prefix the title with `[WIP]`. These +are useful to avoid duplicated work, and to differentiate it from PRs ready +to be merged; +7. Try to formulate and format your text as explained in [How to write a good issue](#how-to-write-a-good-issue). +8. Make sure existing tests pass; +9. Add high-coverage tests. No quality testing = no merge. +- If you are adding new `@slow` tests, make sure they pass using +`RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`. +CircleCI does not run the slow tests, but GitHub Actions does every night! +10. All public methods must have informative docstrings that work nicely with markdown. See [`pipeline_latent_diffusion.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py) for an example. +11. Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like +[`hf-internal-testing`](https://huggingface.co/hf-internal-testing) or [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images) to place these files. +If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images +to this dataset. + +## How to open a PR + +Before writing code, we strongly advise you to search through the existing PRs or +issues to make sure that nobody is already working on the same thing. If you are +unsure, it is always a good idea to open an issue to get some feedback. + +You will need basic `git` proficiency to be able to contribute to +🧨 Diffusers. `git` is not the easiest tool to use but it has the greatest +manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro +Git](https://git-scm.com/book/en/v2) is a very good reference. + +Follow these steps to start contributing ([supported Python versions](https://github.com/huggingface/diffusers/blob/83bc6c94eaeb6f7704a2a428931cf2d9ad973ae9/setup.py#L270)): + +1. Fork the [repository](https://github.com/huggingface/diffusers) by +clicking on the 'Fork' button on the repository's page. This creates a copy of the code +under your GitHub user account. + +2. Clone your fork to your local disk, and add the base repository as a remote: + + ```bash + $ git clone git@github.com:/diffusers.git + $ cd diffusers + $ git remote add upstream https://github.com/huggingface/diffusers.git + ``` + +3. Create a new branch to hold your development changes: + + ```bash + $ git checkout -b a-descriptive-name-for-my-changes + ``` + +**Do not** work on the `main` branch. + +4. Set up a development environment by running the following command in a virtual environment: + + ```bash + $ pip install -e ".[dev]" + ``` + +If you have already cloned the repo, you might need to `git pull` to get the most recent changes in the +library. + +5. Develop the features on your branch. + +As you work on the features, you should make sure that the test suite +passes. You should run the tests impacted by your changes like this: + + ```bash + $ pytest tests/.py + ``` + +Before you run the tests, please make sure you install the dependencies required for testing. You can do so +with this command: + + ```bash + $ pip install -e ".[test]" + ``` + +You can also run the full test suite with the following command, but it takes +a beefy machine to produce a result in a decent amount of time now that +Diffusers has grown a lot. Here is the command for it: + + ```bash + $ make test + ``` + +🧨 Diffusers relies on `black` and `isort` to format its source code +consistently. After you make changes, apply automatic style corrections and code verifications +that can't be automated in one go with: + + ```bash + $ make style + ``` + +🧨 Diffusers also uses `ruff` and a few custom scripts to check for coding mistakes. Quality +control runs in CI, however, you can also run the same checks with: + + ```bash + $ make quality + ``` + +Once you're happy with your changes, add changed files using `git add` and +make a commit with `git commit` to record your changes locally: + + ```bash + $ git add modified_file.py + $ git commit -m "A descriptive message about your changes." + ``` + +It is a good idea to sync your copy of the code with the original +repository regularly. This way you can quickly account for changes: + + ```bash + $ git pull upstream main + ``` + +Push the changes to your account using: + + ```bash + $ git push -u origin a-descriptive-name-for-my-changes + ``` + +6. Once you are satisfied, go to the +webpage of your fork on GitHub. Click on 'Pull request' to send your changes +to the project maintainers for review. + +7. It's OK if maintainers ask you for changes. It happens to core contributors +too! So everyone can see the changes in the Pull request, work in your local +branch and push the changes to your fork. They will automatically appear in +the pull request. + +### Tests + +An extensive test suite is included to test the library behavior and several examples. Library tests can be found in +the [tests folder](https://github.com/huggingface/diffusers/tree/main/tests). + +We like `pytest` and `pytest-xdist` because it's faster. From the root of the +repository, here's how to run tests with `pytest` for the library: + +```bash +$ python -m pytest -n auto --dist=loadfile -s -v ./tests/ +``` + +In fact, that's how `make test` is implemented! + +You can specify a smaller set of tests in order to test only the feature +you're working on. + +By default, slow tests are skipped. Set the `RUN_SLOW` environment variable to +`yes` to run them. This will download many gigabytes of models — make sure you +have enough disk space and a good Internet connection, or a lot of patience! + +```bash +$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/ +``` + +`unittest` is fully supported, here's how to run tests with it: + +```bash +$ python -m unittest discover -s tests -t . -v +$ python -m unittest discover -s examples -t examples -v +``` + +### Syncing forked main with upstream (HuggingFace) main + +To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnecessary notifications to the developers involved in these PRs, +when syncing the main branch of a forked repository, please, follow these steps: +1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main. +2. If a PR is absolutely necessary, use the following steps after checking out your branch: +```bash +$ git checkout -b your-branch-for-syncing +$ git pull --squash --no-commit upstream main +$ git commit -m '' +$ git push --set-upstream origin your-branch-for-syncing +``` + +### Style guide + +For documentation strings, 🧨 Diffusers follows the [Google style](https://google.github.io/styleguide/pyguide.html). \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/conceptual/ethical_guidelines.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/conceptual/ethical_guidelines.md new file mode 100644 index 0000000000000000000000000000000000000000..f14b2f8272f85489fa8d68cd695d6e4826579d33 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/conceptual/ethical_guidelines.md @@ -0,0 +1,63 @@ + + +# 🧨 Diffusers’ Ethical Guidelines + +## Preamble + +[Diffusers](https://huggingface.co/docs/diffusers/index) provides pre-trained diffusion models and serves as a modular toolbox for inference and training. + +Given its real case applications in the world and potential negative impacts on society, we think it is important to provide the project with ethical guidelines to guide the development, users’ contributions, and usage of the Diffusers library. + +The risks associated with using this technology are still being examined, but to name a few: copyrights issues for artists; deep-fake exploitation; sexual content generation in inappropriate contexts; non-consensual impersonation; harmful social biases perpetuating the oppression of marginalized groups. +We will keep tracking risks and adapt the following guidelines based on the community's responsiveness and valuable feedback. + + +## Scope + +The Diffusers community will apply the following ethical guidelines to the project’s development and help coordinate how the community will integrate the contributions, especially concerning sensitive topics related to ethical concerns. + + +## Ethical guidelines + +The following ethical guidelines apply generally, but we will primarily implement them when dealing with ethically sensitive issues while making a technical choice. Furthermore, we commit to adapting those ethical principles over time following emerging harms related to the state of the art of the technology in question. + +- **Transparency**: we are committed to being transparent in managing PRs, explaining our choices to users, and making technical decisions. + +- **Consistency**: we are committed to guaranteeing our users the same level of attention in project management, keeping it technically stable and consistent. + +- **Simplicity**: with a desire to make it easy to use and exploit the Diffusers library, we are committed to keeping the project’s goals lean and coherent. + +- **Accessibility**: the Diffusers project helps lower the entry bar for contributors who can help run it even without technical expertise. Doing so makes research artifacts more accessible to the community. + +- **Reproducibility**: we aim to be transparent about the reproducibility of upstream code, models, and datasets when made available through the Diffusers library. + +- **Responsibility**: as a community and through teamwork, we hold a collective responsibility to our users by anticipating and mitigating this technology's potential risks and dangers. + + +## Examples of implementations: Safety features and Mechanisms + +The team works daily to make the technical and non-technical tools available to deal with the potential ethical and social risks associated with diffusion technology. Moreover, the community's input is invaluable in ensuring these features' implementation and raising awareness with us. + +- [**Community tab**](https://huggingface.co/docs/hub/repositories-pull-requests-discussions): it enables the community to discuss and better collaborate on a project. + +- **Bias exploration and evaluation**: the Hugging Face team provides a [space](https://huggingface.co/spaces/society-ethics/DiffusionBiasExplorer) to demonstrate the biases in Stable Diffusion interactively. In this sense, we support and encourage bias explorers and evaluations. + +- **Encouraging safety in deployment** + + - [**Safe Stable Diffusion**](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_safe): It mitigates the well-known issue that models, like Stable Diffusion, that are trained on unfiltered, web-crawled datasets tend to suffer from inappropriate degeneration. Related paper: [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://huggingface.co/papers/2211.05105). + + - [**Safety Checker**](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py): It checks and compares the class probability of a set of hard-coded harmful concepts in the embedding space against an image after it has been generated. The harmful concepts are intentionally hidden to prevent reverse engineering of the checker. + +- **Staged released on the Hub**: in particularly sensitive situations, access to some repositories should be restricted. This staged release is an intermediary step that allows the repository’s authors to have more control over its use. + +- **Licensing**: [OpenRAILs](https://huggingface.co/blog/open_rail), a new type of licensing, allow us to ensure free access while having a set of restrictions that ensure more responsible use. diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/conceptual/evaluation.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/conceptual/evaluation.md new file mode 100644 index 0000000000000000000000000000000000000000..4af38254bea6ebaa3471b069c0d584033db03550 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/conceptual/evaluation.md @@ -0,0 +1,566 @@ + + +# Evaluating Diffusion Models + + + Open In Colab + + +> [!TIP] +> This document has now grown outdated given the emergence of existing evaluation frameworks for diffusion models for image generation. Please check +> out works like [HEIM](https://crfm.stanford.edu/helm/heim/latest/), [T2I-Compbench](https://huggingface.co/papers/2307.06350), +> [GenEval](https://huggingface.co/papers/2310.11513). + +Evaluation of generative models like [Stable Diffusion](https://huggingface.co/docs/diffusers/stable_diffusion) is subjective in nature. But as practitioners and researchers, we often have to make careful choices amongst many different possibilities. So, when working with different generative models (like GANs, Diffusion, etc.), how do we choose one over the other? + +Qualitative evaluation of such models can be error-prone and might incorrectly influence a decision. +However, quantitative metrics don't necessarily correspond to image quality. So, usually, a combination +of both qualitative and quantitative evaluations provides a stronger signal when choosing one model +over the other. + +In this document, we provide a non-exhaustive overview of qualitative and quantitative methods to evaluate Diffusion models. For quantitative methods, we specifically focus on how to implement them alongside `diffusers`. + +The methods shown in this document can also be used to evaluate different [noise schedulers](https://huggingface.co/docs/diffusers/main/en/api/schedulers/overview) keeping the underlying generation model fixed. + +## Scenarios + +We cover Diffusion models with the following pipelines: + +- Text-guided image generation (such as the [`StableDiffusionPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img)). +- Text-guided image generation, additionally conditioned on an input image (such as the [`StableDiffusionImg2ImgPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/img2img) and [`StableDiffusionInstructPix2PixPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/pix2pix)). +- Class-conditioned image generation models (such as the [`DiTPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/dit)). + +## Qualitative Evaluation + +Qualitative evaluation typically involves human assessment of generated images. Quality is measured across aspects such as compositionality, image-text alignment, and spatial relations. Common prompts provide a degree of uniformity for subjective metrics. +DrawBench and PartiPrompts are prompt datasets used for qualitative benchmarking. DrawBench and PartiPrompts were introduced by [Imagen](https://imagen.research.google/) and [Parti](https://parti.research.google/) respectively. + +From the [official Parti website](https://parti.research.google/): + +> PartiPrompts (P2) is a rich set of over 1600 prompts in English that we release as part of this work. P2 can be used to measure model capabilities across various categories and challenge aspects. + +![parti-prompts](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts.png) + +PartiPrompts has the following columns: + +- Prompt +- Category of the prompt (such as “Abstract”, “World Knowledge”, etc.) +- Challenge reflecting the difficulty (such as “Basic”, “Complex”, “Writing & Symbols”, etc.) + +These benchmarks allow for side-by-side human evaluation of different image generation models. + +For this, the 🧨 Diffusers team has built **Open Parti Prompts**, which is a community-driven qualitative benchmark based on Parti Prompts to compare state-of-the-art open-source diffusion models: +- [Open Parti Prompts Game](https://huggingface.co/spaces/OpenGenAI/open-parti-prompts): For 10 parti prompts, 4 generated images are shown and the user selects the image that suits the prompt best. +- [Open Parti Prompts Leaderboard](https://huggingface.co/spaces/OpenGenAI/parti-prompts-leaderboard): The leaderboard comparing the currently best open-sourced diffusion models to each other. + +To manually compare images, let’s see how we can use `diffusers` on a couple of PartiPrompts. + +Below we show some prompts sampled across different challenges: Basic, Complex, Linguistic Structures, Imagination, and Writing & Symbols. Here we are using PartiPrompts as a [dataset](https://huggingface.co/datasets/nateraw/parti-prompts). + +```python +from datasets import load_dataset + +# prompts = load_dataset("nateraw/parti-prompts", split="train") +# prompts = prompts.shuffle() +# sample_prompts = [prompts[i]["Prompt"] for i in range(5)] + +# Fixing these sample prompts in the interest of reproducibility. +sample_prompts = [ + "a corgi", + "a hot air balloon with a yin-yang symbol, with the moon visible in the daytime sky", + "a car with no windows", + "a cube made of porcupine", + 'The saying "BE EXCELLENT TO EACH OTHER" written on a red brick wall with a graffiti image of a green alien wearing a tuxedo. A yellow fire hydrant is on a sidewalk in the foreground.', +] +``` + +Now we can use these prompts to generate some images using Stable Diffusion ([v1-4 checkpoint](https://huggingface.co/CompVis/stable-diffusion-v1-4)): + +```python +import torch + +seed = 0 +generator = torch.manual_seed(seed) + +images = sd_pipeline(sample_prompts, num_images_per_prompt=1, generator=generator).images +``` + +![parti-prompts-14](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts-14.png) + +We can also set `num_images_per_prompt` accordingly to compare different images for the same prompt. Running the same pipeline but with a different checkpoint ([v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)), yields: + +![parti-prompts-15](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts-15.png) + +Once several images are generated from all the prompts using multiple models (under evaluation), these results are presented to human evaluators for scoring. For +more details on the DrawBench and PartiPrompts benchmarks, refer to their respective papers. + +> [!TIP] +> It is useful to look at some inference samples while a model is training to measure the +> training progress. In our [training scripts](https://github.com/huggingface/diffusers/tree/main/examples/), we support this utility with additional support for +> logging to TensorBoard and Weights & Biases. + +## Quantitative Evaluation + +In this section, we will walk you through how to evaluate three different diffusion pipelines using: + +- CLIP score +- CLIP directional similarity +- FID + +### Text-guided image generation + +[CLIP score](https://huggingface.co/papers/2104.08718) measures the compatibility of image-caption pairs. Higher CLIP scores imply higher compatibility 🔼. The CLIP score is a quantitative measurement of the qualitative concept "compatibility". Image-caption pair compatibility can also be thought of as the semantic similarity between the image and the caption. CLIP score was found to have high correlation with human judgement. + +Let's first load a [`StableDiffusionPipeline`]: + +```python +from diffusers import StableDiffusionPipeline +import torch + +model_ckpt = "CompVis/stable-diffusion-v1-4" +sd_pipeline = StableDiffusionPipeline.from_pretrained(model_ckpt, torch_dtype=torch.float16).to("cuda") +``` + +Generate some images with multiple prompts: + +```python +prompts = [ + "a photo of an astronaut riding a horse on mars", + "A high tech solarpunk utopia in the Amazon rainforest", + "A pikachu fine dining with a view to the Eiffel Tower", + "A mecha robot in a favela in expressionist style", + "an insect robot preparing a delicious meal", + "A small cabin on top of a snowy mountain in the style of Disney, artstation", +] + +images = sd_pipeline(prompts, num_images_per_prompt=1, output_type="np").images + +print(images.shape) +# (6, 512, 512, 3) +``` + +And then, we calculate the CLIP score. + +```python +from torchmetrics.functional.multimodal import clip_score +from functools import partial + +clip_score_fn = partial(clip_score, model_name_or_path="openai/clip-vit-base-patch16") + +def calculate_clip_score(images, prompts): + images_int = (images * 255).astype("uint8") + clip_score = clip_score_fn(torch.from_numpy(images_int).permute(0, 3, 1, 2), prompts).detach() + return round(float(clip_score), 4) + +sd_clip_score = calculate_clip_score(images, prompts) +print(f"CLIP score: {sd_clip_score}") +# CLIP score: 35.7038 +``` + +In the above example, we generated one image per prompt. If we generated multiple images per prompt, we would have to take the average score from the generated images per prompt. + +Now, if we wanted to compare two checkpoints compatible with the [`StableDiffusionPipeline`] we should pass a generator while calling the pipeline. First, we generate images with a +fixed seed with the [v1-4 Stable Diffusion checkpoint](https://huggingface.co/CompVis/stable-diffusion-v1-4): + +```python +seed = 0 +generator = torch.manual_seed(seed) + +images = sd_pipeline(prompts, num_images_per_prompt=1, generator=generator, output_type="np").images +``` + +Then we load the [v1-5 checkpoint](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) to generate images: + +```python +model_ckpt_1_5 = "stable-diffusion-v1-5/stable-diffusion-v1-5" +sd_pipeline_1_5 = StableDiffusionPipeline.from_pretrained(model_ckpt_1_5, torch_dtype=torch.float16).to("cuda") + +images_1_5 = sd_pipeline_1_5(prompts, num_images_per_prompt=1, generator=generator, output_type="np").images +``` + +And finally, we compare their CLIP scores: + +```python +sd_clip_score_1_4 = calculate_clip_score(images, prompts) +print(f"CLIP Score with v-1-4: {sd_clip_score_1_4}") +# CLIP Score with v-1-4: 34.9102 + +sd_clip_score_1_5 = calculate_clip_score(images_1_5, prompts) +print(f"CLIP Score with v-1-5: {sd_clip_score_1_5}") +# CLIP Score with v-1-5: 36.2137 +``` + +It seems like the [v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) checkpoint performs better than its predecessor. Note, however, that the number of prompts we used to compute the CLIP scores is quite low. For a more practical evaluation, this number should be way higher, and the prompts should be diverse. + +> [!WARNING] +> By construction, there are some limitations in this score. The captions in the training dataset +> were crawled from the web and extracted from `alt` and similar tags associated an image on the internet. +> They are not necessarily representative of what a human being would use to describe an image. Hence we +> had to "engineer" some prompts here. + +### Image-conditioned text-to-image generation + +In this case, we condition the generation pipeline with an input image as well as a text prompt. Let's take the [`StableDiffusionInstructPix2PixPipeline`], as an example. It takes an edit instruction as an input prompt and an input image to be edited. + +Here is one example: + +![edit-instruction](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-instruction.png) + +One strategy to evaluate such a model is to measure the consistency of the change between the two images (in [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) space) with the change between the two image captions (as shown in [CLIP-Guided Domain Adaptation of Image Generators](https://huggingface.co/papers/2108.00946)). This is referred to as the "**CLIP directional similarity**". + +- Caption 1 corresponds to the input image (image 1) that is to be edited. +- Caption 2 corresponds to the edited image (image 2). It should reflect the edit instruction. + +Following is a pictorial overview: + +![edit-consistency](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-consistency.png) + +We have prepared a mini dataset to implement this metric. Let's first load the dataset. + +```python +from datasets import load_dataset + +dataset = load_dataset("sayakpaul/instructpix2pix-demo", split="train") +dataset.features +``` + +```bash +{'input': Value(dtype='string', id=None), + 'edit': Value(dtype='string', id=None), + 'output': Value(dtype='string', id=None), + 'image': Image(decode=True, id=None)} +``` + +Here we have: + +- `input` is a caption corresponding to the `image`. +- `edit` denotes the edit instruction. +- `output` denotes the modified caption reflecting the `edit` instruction. + +Let's take a look at a sample. + +```python +idx = 0 +print(f"Original caption: {dataset[idx]['input']}") +print(f"Edit instruction: {dataset[idx]['edit']}") +print(f"Modified caption: {dataset[idx]['output']}") +``` + +```bash +Original caption: 2. FAROE ISLANDS: An archipelago of 18 mountainous isles in the North Atlantic Ocean between Norway and Iceland, the Faroe Islands has 'everything you could hope for', according to Big 7 Travel. It boasts 'crystal clear waterfalls, rocky cliffs that seem to jut out of nowhere and velvety green hills' +Edit instruction: make the isles all white marble +Modified caption: 2. WHITE MARBLE ISLANDS: An archipelago of 18 mountainous white marble isles in the North Atlantic Ocean between Norway and Iceland, the White Marble Islands has 'everything you could hope for', according to Big 7 Travel. It boasts 'crystal clear waterfalls, rocky cliffs that seem to jut out of nowhere and velvety green hills' +``` + +And here is the image: + +```python +dataset[idx]["image"] +``` + +![edit-dataset](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-dataset.png) + +We will first edit the images of our dataset with the edit instruction and compute the directional similarity. + +Let's first load the [`StableDiffusionInstructPix2PixPipeline`]: + +```python +from diffusers import StableDiffusionInstructPix2PixPipeline + +instruct_pix2pix_pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained( + "timbrooks/instruct-pix2pix", torch_dtype=torch.float16 +).to("cuda") +``` + +Now, we perform the edits: + +```python +import numpy as np + + +def edit_image(input_image, instruction): + image = instruct_pix2pix_pipeline( + instruction, + image=input_image, + output_type="np", + generator=generator, + ).images[0] + return image + +input_images = [] +original_captions = [] +modified_captions = [] +edited_images = [] + +for idx in range(len(dataset)): + input_image = dataset[idx]["image"] + edit_instruction = dataset[idx]["edit"] + edited_image = edit_image(input_image, edit_instruction) + + input_images.append(np.array(input_image)) + original_captions.append(dataset[idx]["input"]) + modified_captions.append(dataset[idx]["output"]) + edited_images.append(edited_image) +``` + +To measure the directional similarity, we first load CLIP's image and text encoders: + +```python +from transformers import ( + CLIPTokenizer, + CLIPTextModelWithProjection, + CLIPVisionModelWithProjection, + CLIPImageProcessor, +) + +clip_id = "openai/clip-vit-large-patch14" +tokenizer = CLIPTokenizer.from_pretrained(clip_id) +text_encoder = CLIPTextModelWithProjection.from_pretrained(clip_id).to("cuda") +image_processor = CLIPImageProcessor.from_pretrained(clip_id) +image_encoder = CLIPVisionModelWithProjection.from_pretrained(clip_id).to("cuda") +``` + +Notice that we are using a particular CLIP checkpoint, i.e., `openai/clip-vit-large-patch14`. This is because the Stable Diffusion pre-training was performed with this CLIP variant. For more details, refer to the [documentation](https://huggingface.co/docs/transformers/model_doc/clip). + +Next, we prepare a PyTorch `nn.Module` to compute directional similarity: + +```python +import torch.nn as nn +import torch.nn.functional as F + + +class DirectionalSimilarity(nn.Module): + def __init__(self, tokenizer, text_encoder, image_processor, image_encoder): + super().__init__() + self.tokenizer = tokenizer + self.text_encoder = text_encoder + self.image_processor = image_processor + self.image_encoder = image_encoder + + def preprocess_image(self, image): + image = self.image_processor(image, return_tensors="pt")["pixel_values"] + return {"pixel_values": image.to("cuda")} + + def tokenize_text(self, text): + inputs = self.tokenizer( + text, + max_length=self.tokenizer.model_max_length, + padding="max_length", + truncation=True, + return_tensors="pt", + ) + return {"input_ids": inputs.input_ids.to("cuda")} + + def encode_image(self, image): + preprocessed_image = self.preprocess_image(image) + image_features = self.image_encoder(**preprocessed_image).image_embeds + image_features = image_features / image_features.norm(dim=1, keepdim=True) + return image_features + + def encode_text(self, text): + tokenized_text = self.tokenize_text(text) + text_features = self.text_encoder(**tokenized_text).text_embeds + text_features = text_features / text_features.norm(dim=1, keepdim=True) + return text_features + + def compute_directional_similarity(self, img_feat_one, img_feat_two, text_feat_one, text_feat_two): + sim_direction = F.cosine_similarity(img_feat_two - img_feat_one, text_feat_two - text_feat_one) + return sim_direction + + def forward(self, image_one, image_two, caption_one, caption_two): + img_feat_one = self.encode_image(image_one) + img_feat_two = self.encode_image(image_two) + text_feat_one = self.encode_text(caption_one) + text_feat_two = self.encode_text(caption_two) + directional_similarity = self.compute_directional_similarity( + img_feat_one, img_feat_two, text_feat_one, text_feat_two + ) + return directional_similarity +``` + +Let's put `DirectionalSimilarity` to use now. + +```python +dir_similarity = DirectionalSimilarity(tokenizer, text_encoder, image_processor, image_encoder) +scores = [] + +for i in range(len(input_images)): + original_image = input_images[i] + original_caption = original_captions[i] + edited_image = edited_images[i] + modified_caption = modified_captions[i] + + similarity_score = dir_similarity(original_image, edited_image, original_caption, modified_caption) + scores.append(float(similarity_score.detach().cpu())) + +print(f"CLIP directional similarity: {np.mean(scores)}") +# CLIP directional similarity: 0.0797976553440094 +``` + +Like the CLIP Score, the higher the CLIP directional similarity, the better it is. + +It should be noted that the `StableDiffusionInstructPix2PixPipeline` exposes two arguments, namely, `image_guidance_scale` and `guidance_scale` that let you control the quality of the final edited image. We encourage you to experiment with these two arguments and see the impact of that on the directional similarity. + +We can extend the idea of this metric to measure how similar the original image and edited version are. To do that, we can just do `F.cosine_similarity(img_feat_two, img_feat_one)`. For these kinds of edits, we would still want the primary semantics of the images to be preserved as much as possible, i.e., a high similarity score. + +We can use these metrics for similar pipelines such as the [`StableDiffusionPix2PixZeroPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/pix2pix_zero#diffusers.StableDiffusionPix2PixZeroPipeline). + +> [!TIP] +> Both CLIP score and CLIP direction similarity rely on the CLIP model, which can make the evaluations biased. + +***Extending metrics like IS, FID (discussed later), or KID can be difficult*** when the model under evaluation was pre-trained on a large image-captioning dataset (such as the [LAION-5B dataset](https://laion.ai/blog/laion-5b/)). This is because underlying these metrics is an InceptionNet (pre-trained on the ImageNet-1k dataset) used for extracting intermediate image features. The pre-training dataset of Stable Diffusion may have limited overlap with the pre-training dataset of InceptionNet, so it is not a good candidate here for feature extraction. + +***Using the above metrics helps evaluate models that are class-conditioned. For example, [DiT](https://huggingface.co/docs/diffusers/main/en/api/pipelines/dit). It was pre-trained being conditioned on the ImageNet-1k classes.*** + +### Class-conditioned image generation + +Class-conditioned generative models are usually pre-trained on a class-labeled dataset such as [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k). Popular metrics for evaluating these models include Fréchet Inception Distance (FID), Kernel Inception Distance (KID), and Inception Score (IS). In this document, we focus on FID ([Heusel et al.](https://huggingface.co/papers/1706.08500)). We show how to compute it with the [`DiTPipeline`](https://huggingface.co/docs/diffusers/api/pipelines/dit), which uses the [DiT model](https://huggingface.co/papers/2212.09748) under the hood. + +FID aims to measure how similar are two datasets of images. As per [this resource](https://mmgeneration.readthedocs.io/en/latest/quick_run.html#fid): + +> Fréchet Inception Distance is a measure of similarity between two datasets of images. It was shown to correlate well with the human judgment of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network. + +These two datasets are essentially the dataset of real images and the dataset of fake images (generated images in our case). FID is usually calculated with two large datasets. However, for this document, we will work with two mini datasets. + +Let's first download a few images from the ImageNet-1k training set: + +```python +from zipfile import ZipFile +import requests + + +def download(url, local_filepath): + r = requests.get(url) + with open(local_filepath, "wb") as f: + f.write(r.content) + return local_filepath + +dummy_dataset_url = "https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/sample-imagenet-images.zip" +local_filepath = download(dummy_dataset_url, dummy_dataset_url.split("/")[-1]) + +with ZipFile(local_filepath, "r") as zipper: + zipper.extractall(".") +``` + +```python +from PIL import Image +import os +import numpy as np + +dataset_path = "sample-imagenet-images" +image_paths = sorted([os.path.join(dataset_path, x) for x in os.listdir(dataset_path)]) + +real_images = [np.array(Image.open(path).convert("RGB")) for path in image_paths] +``` + +These are 10 images from the following ImageNet-1k classes: "cassette_player", "chain_saw" (x2), "church", "gas_pump" (x3), "parachute" (x2), and "tench". + +

+ real-images
+ Real images. +

+ +Now that the images are loaded, let's apply some lightweight pre-processing on them to use them for FID calculation. + +```python +from torchvision.transforms import functional as F +import torch + + +def preprocess_image(image): + image = torch.tensor(image).unsqueeze(0) + image = image.permute(0, 3, 1, 2) / 255.0 + return F.center_crop(image, (256, 256)) + +real_images = torch.cat([preprocess_image(image) for image in real_images]) +print(real_images.shape) +# torch.Size([10, 3, 256, 256]) +``` + +We now load the [`DiTPipeline`](https://huggingface.co/docs/diffusers/api/pipelines/dit) to generate images conditioned on the above-mentioned classes. + +```python +from diffusers import DiTPipeline, DPMSolverMultistepScheduler + +dit_pipeline = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256", torch_dtype=torch.float16) +dit_pipeline.scheduler = DPMSolverMultistepScheduler.from_config(dit_pipeline.scheduler.config) +dit_pipeline = dit_pipeline.to("cuda") + +seed = 0 +generator = torch.manual_seed(seed) + + +words = [ + "cassette player", + "chainsaw", + "chainsaw", + "church", + "gas pump", + "gas pump", + "gas pump", + "parachute", + "parachute", + "tench", +] + +class_ids = dit_pipeline.get_label_ids(words) +output = dit_pipeline(class_labels=class_ids, generator=generator, output_type="np") + +fake_images = output.images +fake_images = torch.tensor(fake_images) +fake_images = fake_images.permute(0, 3, 1, 2) +print(fake_images.shape) +# torch.Size([10, 3, 256, 256]) +``` + +Now, we can compute the FID using [`torchmetrics`](https://torchmetrics.readthedocs.io/). + +```python +from torchmetrics.image.fid import FrechetInceptionDistance + +fid = FrechetInceptionDistance(normalize=True) +fid.update(real_images, real=True) +fid.update(fake_images, real=False) + +print(f"FID: {float(fid.compute())}") +# FID: 177.7147216796875 +``` + +The lower the FID, the better it is. Several things can influence FID here: + +- Number of images (both real and fake) +- Randomness induced in the diffusion process +- Number of inference steps in the diffusion process +- The scheduler being used in the diffusion process + +For the last two points, it is, therefore, a good practice to run the evaluation across different seeds and inference steps, and then report an average result. + +> [!WARNING] +> FID results tend to be fragile as they depend on a lot of factors: +> +> * The specific Inception model used during computation. +> * The implementation accuracy of the computation. +> * The image format (not the same if we start from PNGs vs JPGs). +> +> Keeping that in mind, FID is often most useful when comparing similar runs, but it is +> hard to reproduce paper results unless the authors carefully disclose the FID +> measurement code. +> +> These points apply to other related metrics too, such as KID and IS. + +As a final step, let's visually inspect the `fake_images`. + +

+ fake-images
+ Fake images. +

diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/conceptual/philosophy.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/conceptual/philosophy.md new file mode 100644 index 0000000000000000000000000000000000000000..f6080521b325bc6ea299fb203127734ff6135428 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/conceptual/philosophy.md @@ -0,0 +1,110 @@ + + +# Philosophy + +🧨 Diffusers provides **state-of-the-art** pretrained diffusion models across multiple modalities. +Its purpose is to serve as a **modular toolbox** for both inference and training. + +We aim at building a library that stands the test of time and therefore take API design very seriously. + +In a nutshell, Diffusers is built to be a natural extension of PyTorch. Therefore, most of our design choices are based on [PyTorch's Design Principles](https://pytorch.org/docs/stable/community/design.html#pytorch-design-philosophy). Let's go over the most important ones: + +## Usability over Performance + +- While Diffusers has many built-in performance-enhancing features (see [Memory and Speed](https://huggingface.co/docs/diffusers/optimization/fp16)), models are always loaded with the highest precision and lowest optimization. Therefore, by default diffusion pipelines are always instantiated on CPU with float32 precision if not otherwise defined by the user. This ensures usability across different platforms and accelerators and means that no complex installations are required to run the library. +- Diffusers aims to be a **light-weight** package and therefore has very few required dependencies, but many soft dependencies that can improve performance (such as `accelerate`, `safetensors`, `onnx`, etc...). We strive to keep the library as lightweight as possible so that it can be added without much concern as a dependency on other packages. +- Diffusers prefers simple, self-explainable code over condensed, magic code. This means that short-hand code syntaxes such as lambda functions, and advanced PyTorch operators are often not desired. + +## Simple over easy + +As PyTorch states, **explicit is better than implicit** and **simple is better than complex**. This design philosophy is reflected in multiple parts of the library: +- We follow PyTorch's API with methods like [`DiffusionPipeline.to`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.to) to let the user handle device management. +- Raising concise error messages is preferred to silently correct erroneous input. Diffusers aims at teaching the user, rather than making the library as easy to use as possible. +- Complex model vs. scheduler logic is exposed instead of magically handled inside. Schedulers/Samplers are separated from diffusion models with minimal dependencies on each other. This forces the user to write the unrolled denoising loop. However, the separation allows for easier debugging and gives the user more control over adapting the denoising process or switching out diffusion models or schedulers. +- Separately trained components of the diffusion pipeline, *e.g.* the text encoder, the unet, and the variational autoencoder, each have their own model class. This forces the user to handle the interaction between the different model components, and the serialization format separates the model components into different files. However, this allows for easier debugging and customization. DreamBooth or Textual Inversion training +is very simple thanks to Diffusers' ability to separate single components of the diffusion pipeline. + +## Tweakable, contributor-friendly over abstraction + +For large parts of the library, Diffusers adopts an important design principle of the [Transformers library](https://github.com/huggingface/transformers), which is to prefer copy-pasted code over hasty abstractions. This design principle is very opinionated and stands in stark contrast to popular design principles such as [Don't repeat yourself (DRY)](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself). +In short, just like Transformers does for modeling files, Diffusers prefers to keep an extremely low level of abstraction and very self-contained code for pipelines and schedulers. +Functions, long code blocks, and even classes can be copied across multiple files which at first can look like a bad, sloppy design choice that makes the library unmaintainable. +**However**, this design has proven to be extremely successful for Transformers and makes a lot of sense for community-driven, open-source machine learning libraries because: +- Machine Learning is an extremely fast-moving field in which paradigms, model architectures, and algorithms are changing rapidly, which therefore makes it very difficult to define long-lasting code abstractions. +- Machine Learning practitioners like to be able to quickly tweak existing code for ideation and research and therefore prefer self-contained code over one that contains many abstractions. +- Open-source libraries rely on community contributions and therefore must build a library that is easy to contribute to. The more abstract the code, the more dependencies, the harder to read, and the harder to contribute to. Contributors simply stop contributing to very abstract libraries out of fear of breaking vital functionality. If contributing to a library cannot break other fundamental code, not only is it more inviting for potential new contributors, but it is also easier to review and contribute to multiple parts in parallel. + +At Hugging Face, we call this design the **single-file policy** which means that almost all of the code of a certain class should be written in a single, self-contained file. To read more about the philosophy, you can have a look +at [this blog post](https://huggingface.co/blog/transformers-design-philosophy). + +In Diffusers, we follow this philosophy for both pipelines and schedulers, but only partly for diffusion models. The reason we don't follow this design fully for diffusion models is because almost all diffusion pipelines, such +as [DDPM](https://huggingface.co/docs/diffusers/api/pipelines/ddpm), [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview#stable-diffusion-pipelines), [unCLIP (DALL·E 2)](https://huggingface.co/docs/diffusers/api/pipelines/unclip) and [Imagen](https://imagen.research.google/) all rely on the same diffusion model, the [UNet](https://huggingface.co/docs/diffusers/api/models/unet2d-cond). + +Great, now you should have generally understood why 🧨 Diffusers is designed the way it is 🤗. +We try to apply these design principles consistently across the library. Nevertheless, there are some minor exceptions to the philosophy or some unlucky design choices. If you have feedback regarding the design, we would ❤️ to hear it [directly on GitHub](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=). + +## Design Philosophy in Details + +Now, let's look a bit into the nitty-gritty details of the design philosophy. Diffusers essentially consists of three major classes: [pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines), [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models), and [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers). +Let's walk through more in-detail design decisions for each class. + +### Pipelines + +Pipelines are designed to be easy to use (therefore do not follow [*Simple over easy*](#simple-over-easy) 100%), are not feature complete, and should loosely be seen as examples of how to use [models](#models) and [schedulers](#schedulers) for inference. + +The following design principles are followed: +- Pipelines follow the single-file policy. All pipelines can be found in individual directories under src/diffusers/pipelines. One pipeline folder corresponds to one diffusion paper/project/release. Multiple pipeline files can be gathered in one pipeline folder, as it’s done for [`src/diffusers/pipelines/stable-diffusion`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/stable_diffusion). If pipelines share similar functionality, one can make use of the [# Copied from mechanism](https://github.com/huggingface/diffusers/blob/125d783076e5bd9785beb05367a2d2566843a271/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L251). +- Pipelines all inherit from [`DiffusionPipeline`]. +- Every pipeline consists of different model and scheduler components, that are documented in the [`model_index.json` file](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/model_index.json), are accessible under the same name as attributes of the pipeline and can be shared between pipelines with [`DiffusionPipeline.components`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.components) function. +- Every pipeline should be loadable via the [`DiffusionPipeline.from_pretrained`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained) function. +- Pipelines should be used **only** for inference. +- Pipelines should be very readable, self-explanatory, and easy to tweak. +- Pipelines should be designed to build on top of each other and be easy to integrate into higher-level APIs. +- Pipelines are **not** intended to be feature-complete user interfaces. For feature-complete user interfaces one should rather have a look at [InvokeAI](https://github.com/invoke-ai/InvokeAI), [Diffuzers](https://github.com/abhishekkrthakur/diffuzers), and [lama-cleaner](https://github.com/Sanster/lama-cleaner). +- Every pipeline should have one and only one way to run it via a `__call__` method. The naming of the `__call__` arguments should be shared across all pipelines. +- Pipelines should be named after the task they are intended to solve. +- In almost all cases, novel diffusion pipelines shall be implemented in a new pipeline folder/file. + +### Models + +Models are designed as configurable toolboxes that are natural extensions of [PyTorch's Module class](https://pytorch.org/docs/stable/generated/torch.nn.Module.html). They only partly follow the **single-file policy**. + +The following design principles are followed: +- Models correspond to **a type of model architecture**. *E.g.* the [`UNet2DConditionModel`] class is used for all UNet variations that expect 2D image inputs and are conditioned on some context. +- All models can be found in [`src/diffusers/models`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and every model architecture shall be defined in its file, e.g. [`unets/unet_2d_condition.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unets/unet_2d_condition.py), [`transformers/transformer_2d.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_2d.py), etc... +- Models **do not** follow the single-file policy and should make use of smaller model building blocks, such as [`attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py), [`resnet.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py), [`embeddings.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/embeddings.py), etc... **Note**: This is in stark contrast to Transformers' modeling files and shows that models do not really follow the single-file policy. +- Models intend to expose complexity, just like PyTorch's `Module` class, and give clear error messages. +- Models all inherit from `ModelMixin` and `ConfigMixin`. +- Models can be optimized for performance when it doesn’t demand major code changes, keeps backward compatibility, and gives significant memory or compute gain. +- Models should by default have the highest precision and lowest performance setting. +- To integrate new model checkpoints whose general architecture can be classified as an architecture that already exists in Diffusers, the existing model architecture shall be adapted to make it work with the new checkpoint. One should only create a new file if the model architecture is fundamentally different. +- Models should be designed to be easily extendable to future changes. This can be achieved by limiting public function arguments, configuration arguments, and "foreseeing" future changes, *e.g.* it is usually better to add `string` "...type" arguments that can easily be extended to new future types instead of boolean `is_..._type` arguments. Only the minimum amount of changes shall be made to existing architectures to make a new model checkpoint work. +- The model design is a difficult trade-off between keeping code readable and concise and supporting many model checkpoints. For most parts of the modeling code, classes shall be adapted for new model checkpoints, while there are some exceptions where it is preferred to add new classes to make sure the code is kept concise and +readable long-term, such as [UNet blocks](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unets/unet_2d_blocks.py) and [Attention processors](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + +### Schedulers + +Schedulers are responsible to guide the denoising process for inference as well as to define a noise schedule for training. They are designed as individual classes with loadable configuration files and strongly follow the **single-file policy**. + +The following design principles are followed: +- All schedulers are found in [`src/diffusers/schedulers`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers). +- Schedulers are **not** allowed to import from large utils files and shall be kept very self-contained. +- One scheduler Python file corresponds to one scheduler algorithm (as might be defined in a paper). +- If schedulers share similar functionalities, we can make use of the `# Copied from` mechanism. +- Schedulers all inherit from `SchedulerMixin` and `ConfigMixin`. +- Schedulers can be easily swapped out with the [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) method as explained in detail [here](../using-diffusers/schedulers). +- Every scheduler has to have a `set_num_inference_steps`, and a `step` function. `set_num_inference_steps(...)` has to be called before every denoising process, *i.e.* before `step(...)` is called. +- Every scheduler exposes the timesteps to be "looped over" via a `timesteps` attribute, which is an array of timesteps the model will be called upon. +- The `step(...)` function takes a predicted model output and the "current" sample (x_t) and returns the "previous", slightly more denoised sample (x_t-1). +- Given the complexity of diffusion schedulers, the `step` function does not expose all the complexity and can be a bit of a "black box". +- In almost all cases, novel schedulers shall be implemented in a new scheduling file. \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/hybrid_inference/api_reference.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/hybrid_inference/api_reference.md new file mode 100644 index 0000000000000000000000000000000000000000..865aaba5ebb6be99ebe115ed90efeff52d61cce1 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/hybrid_inference/api_reference.md @@ -0,0 +1,9 @@ +# Hybrid Inference API Reference + +## Remote Decode + +[[autodoc]] utils.remote_utils.remote_decode + +## Remote Encode + +[[autodoc]] utils.remote_utils.remote_encode diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/hybrid_inference/overview.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/hybrid_inference/overview.md new file mode 100644 index 0000000000000000000000000000000000000000..7ed1bbb88b3ff5d74cbb6855d0998654febf6520 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/hybrid_inference/overview.md @@ -0,0 +1,60 @@ + + +# Hybrid Inference + +**Empowering local AI builders with Hybrid Inference** + + +> [!TIP] +> Hybrid Inference is an [experimental feature](https://huggingface.co/blog/remote_vae). +> Feedback can be provided [here](https://github.com/huggingface/diffusers/issues/new?template=remote-vae-pilot-feedback.yml). + + + +## Why use Hybrid Inference? + +Hybrid Inference offers a fast and simple way to offload local generation requirements. + +- 🚀 **Reduced Requirements:** Access powerful models without expensive hardware. +- 💎 **Without Compromise:** Achieve the highest quality without sacrificing performance. +- 💰 **Cost Effective:** It's free! 🤑 +- 🎯 **Diverse Use Cases:** Fully compatible with Diffusers 🧨 and the wider community. +- 🔧 **Developer-Friendly:** Simple requests, fast responses. + +--- + +## Available Models + +* **VAE Decode 🖼️:** Quickly decode latent representations into high-quality images without compromising performance or workflow speed. +* **VAE Encode 🔢:** Efficiently encode images into latent representations for generation and training. +* **Text Encoders 📃 (coming soon):** Compute text embeddings for your prompts quickly and accurately, ensuring a smooth and high-quality workflow. + +--- + +## Integrations + +* **[SD.Next](https://github.com/vladmandic/sdnext):** All-in-one UI with direct supports Hybrid Inference. +* **[ComfyUI-HFRemoteVae](https://github.com/kijai/ComfyUI-HFRemoteVae):** ComfyUI node for Hybrid Inference. + +## Changelog + +- March 10 2025: Added VAE encode +- March 2 2025: Initial release with VAE decoding + +## Contents + +The documentation is organized into three sections: + +* **VAE Decode** Learn the basics of how to use VAE Decode with Hybrid Inference. +* **VAE Encode** Learn the basics of how to use VAE Encode with Hybrid Inference. +* **API Reference** Dive into task-specific settings and parameters. diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/hybrid_inference/vae_decode.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/hybrid_inference/vae_decode.md new file mode 100644 index 0000000000000000000000000000000000000000..1457090550c724b223ff9470f224f121a3bad74e --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/hybrid_inference/vae_decode.md @@ -0,0 +1,345 @@ +# Getting Started: VAE Decode with Hybrid Inference + +VAE decode is an essential component of diffusion models - turning latent representations into images or videos. + +## Memory + +These tables demonstrate the VRAM requirements for VAE decode with SD v1 and SD XL on different GPUs. + +For the majority of these GPUs the memory usage % dictates other models (text encoders, UNet/Transformer) must be offloaded, or tiled decoding has to be used which increases time taken and impacts quality. + +
SD v1.5 + +| GPU | Resolution | Time (seconds) | Memory (%) | Tiled Time (secs) | Tiled Memory (%) | +| --- | --- | --- | --- | --- | --- | +| NVIDIA GeForce RTX 4090 | 512x512 | 0.031 | 5.60% | 0.031 (0%) | 5.60% | +| NVIDIA GeForce RTX 4090 | 1024x1024 | 0.148 | 20.00% | 0.301 (+103%) | 5.60% | +| NVIDIA GeForce RTX 4080 | 512x512 | 0.05 | 8.40% | 0.050 (0%) | 8.40% | +| NVIDIA GeForce RTX 4080 | 1024x1024 | 0.224 | 30.00% | 0.356 (+59%) | 8.40% | +| NVIDIA GeForce RTX 4070 Ti | 512x512 | 0.066 | 11.30% | 0.066 (0%) | 11.30% | +| NVIDIA GeForce RTX 4070 Ti | 1024x1024 | 0.284 | 40.50% | 0.454 (+60%) | 11.40% | +| NVIDIA GeForce RTX 3090 | 512x512 | 0.062 | 5.20% | 0.062 (0%) | 5.20% | +| NVIDIA GeForce RTX 3090 | 1024x1024 | 0.253 | 18.50% | 0.464 (+83%) | 5.20% | +| NVIDIA GeForce RTX 3080 | 512x512 | 0.07 | 12.80% | 0.070 (0%) | 12.80% | +| NVIDIA GeForce RTX 3080 | 1024x1024 | 0.286 | 45.30% | 0.466 (+63%) | 12.90% | +| NVIDIA GeForce RTX 3070 | 512x512 | 0.102 | 15.90% | 0.102 (0%) | 15.90% | +| NVIDIA GeForce RTX 3070 | 1024x1024 | 0.421 | 56.30% | 0.746 (+77%) | 16.00% | + +
+ +
SDXL + +| GPU | Resolution | Time (seconds) | Memory Consumed (%) | Tiled Time (seconds) | Tiled Memory (%) | +| --- | --- | --- | --- | --- | --- | +| NVIDIA GeForce RTX 4090 | 512x512 | 0.057 | 10.00% | 0.057 (0%) | 10.00% | +| NVIDIA GeForce RTX 4090 | 1024x1024 | 0.256 | 35.50% | 0.257 (+0.4%) | 35.50% | +| NVIDIA GeForce RTX 4080 | 512x512 | 0.092 | 15.00% | 0.092 (0%) | 15.00% | +| NVIDIA GeForce RTX 4080 | 1024x1024 | 0.406 | 53.30% | 0.406 (0%) | 53.30% | +| NVIDIA GeForce RTX 4070 Ti | 512x512 | 0.121 | 20.20% | 0.120 (-0.8%) | 20.20% | +| NVIDIA GeForce RTX 4070 Ti | 1024x1024 | 0.519 | 72.00% | 0.519 (0%) | 72.00% | +| NVIDIA GeForce RTX 3090 | 512x512 | 0.107 | 10.50% | 0.107 (0%) | 10.50% | +| NVIDIA GeForce RTX 3090 | 1024x1024 | 0.459 | 38.00% | 0.460 (+0.2%) | 38.00% | +| NVIDIA GeForce RTX 3080 | 512x512 | 0.121 | 25.60% | 0.121 (0%) | 25.60% | +| NVIDIA GeForce RTX 3080 | 1024x1024 | 0.524 | 93.00% | 0.524 (0%) | 93.00% | +| NVIDIA GeForce RTX 3070 | 512x512 | 0.183 | 31.80% | 0.183 (0%) | 31.80% | +| NVIDIA GeForce RTX 3070 | 1024x1024 | 0.794 | 96.40% | 0.794 (0%) | 96.40% | + +
+ +## Available VAEs + +| | **Endpoint** | **Model** | +|:-:|:-----------:|:--------:| +| **Stable Diffusion v1** | [https://q1bj3bpq6kzilnsu.us-east-1.aws.endpoints.huggingface.cloud](https://q1bj3bpq6kzilnsu.us-east-1.aws.endpoints.huggingface.cloud) | [`stabilityai/sd-vae-ft-mse`](https://hf.co/stabilityai/sd-vae-ft-mse) | +| **Stable Diffusion XL** | [https://x2dmsqunjd6k9prw.us-east-1.aws.endpoints.huggingface.cloud](https://x2dmsqunjd6k9prw.us-east-1.aws.endpoints.huggingface.cloud) | [`madebyollin/sdxl-vae-fp16-fix`](https://hf.co/madebyollin/sdxl-vae-fp16-fix) | +| **Flux** | [https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud](https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud) | [`black-forest-labs/FLUX.1-schnell`](https://hf.co/black-forest-labs/FLUX.1-schnell) | +| **HunyuanVideo** | [https://o7ywnmrahorts457.us-east-1.aws.endpoints.huggingface.cloud](https://o7ywnmrahorts457.us-east-1.aws.endpoints.huggingface.cloud) | [`hunyuanvideo-community/HunyuanVideo`](https://hf.co/hunyuanvideo-community/HunyuanVideo) | + + +> [!TIP] +> Model support can be requested [here](https://github.com/huggingface/diffusers/issues/new?template=remote-vae-pilot-feedback.yml). + + +## Code + +> [!TIP] +> Install `diffusers` from `main` to run the code: `pip install git+https://github.com/huggingface/diffusers@main` + + +A helper method simplifies interacting with Hybrid Inference. + +```python +from diffusers.utils.remote_utils import remote_decode +``` + +### Basic example + +Here, we show how to use the remote VAE on random tensors. + +
Code + +```python +image = remote_decode( + endpoint="https://q1bj3bpq6kzilnsu.us-east-1.aws.endpoints.huggingface.cloud/", + tensor=torch.randn([1, 4, 64, 64], dtype=torch.float16), + scaling_factor=0.18215, +) +``` + +
+ +
+ +
+ +Usage for Flux is slightly different. Flux latents are packed so we need to send the `height` and `width`. + +
Code + +```python +image = remote_decode( + endpoint="https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud/", + tensor=torch.randn([1, 4096, 64], dtype=torch.float16), + height=1024, + width=1024, + scaling_factor=0.3611, + shift_factor=0.1159, +) +``` + +
+ +
+ +
+ +Finally, an example for HunyuanVideo. + +
Code + +```python +video = remote_decode( + endpoint="https://o7ywnmrahorts457.us-east-1.aws.endpoints.huggingface.cloud/", + tensor=torch.randn([1, 16, 3, 40, 64], dtype=torch.float16), + output_type="mp4", +) +with open("video.mp4", "wb") as f: + f.write(video) +``` + +
+ +
+ +
+ + +### Generation + +But we want to use the VAE on an actual pipeline to get an actual image, not random noise. The example below shows how to do it with SD v1.5. + +
Code + +```python +from diffusers import StableDiffusionPipeline + +pipe = StableDiffusionPipeline.from_pretrained( + "stable-diffusion-v1-5/stable-diffusion-v1-5", + torch_dtype=torch.float16, + variant="fp16", + vae=None, +).to("cuda") + +prompt = "Strawberry ice cream, in a stylish modern glass, coconut, splashing milk cream and honey, in a gradient purple background, fluid motion, dynamic movement, cinematic lighting, Mysterious" + +latent = pipe( + prompt=prompt, + output_type="latent", +).images +image = remote_decode( + endpoint="https://q1bj3bpq6kzilnsu.us-east-1.aws.endpoints.huggingface.cloud/", + tensor=latent, + scaling_factor=0.18215, +) +image.save("test.jpg") +``` + +
+ +
+ +
+ +Here’s another example with Flux. + +
Code + +```python +from diffusers import FluxPipeline + +pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-schnell", + torch_dtype=torch.bfloat16, + vae=None, +).to("cuda") + +prompt = "Strawberry ice cream, in a stylish modern glass, coconut, splashing milk cream and honey, in a gradient purple background, fluid motion, dynamic movement, cinematic lighting, Mysterious" + +latent = pipe( + prompt=prompt, + guidance_scale=0.0, + num_inference_steps=4, + output_type="latent", +).images +image = remote_decode( + endpoint="https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud/", + tensor=latent, + height=1024, + width=1024, + scaling_factor=0.3611, + shift_factor=0.1159, +) +image.save("test.jpg") +``` + +
+ +
+ +
+ +Here’s an example with HunyuanVideo. + +
Code + +```python +from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel + +model_id = "hunyuanvideo-community/HunyuanVideo" +transformer = HunyuanVideoTransformer3DModel.from_pretrained( + model_id, subfolder="transformer", torch_dtype=torch.bfloat16 +) +pipe = HunyuanVideoPipeline.from_pretrained( + model_id, transformer=transformer, vae=None, torch_dtype=torch.float16 +).to("cuda") + +latent = pipe( + prompt="A cat walks on the grass, realistic", + height=320, + width=512, + num_frames=61, + num_inference_steps=30, + output_type="latent", +).frames + +video = remote_decode( + endpoint="https://o7ywnmrahorts457.us-east-1.aws.endpoints.huggingface.cloud/", + tensor=latent, + output_type="mp4", +) + +if isinstance(video, bytes): + with open("video.mp4", "wb") as f: + f.write(video) +``` + +
+ +
+ +
+ + +### Queueing + +One of the great benefits of using a remote VAE is that we can queue multiple generation requests. While the current latent is being processed for decoding, we can already queue another one. This helps improve concurrency. + + +
Code + +```python +import queue +import threading +from IPython.display import display +from diffusers import StableDiffusionPipeline + +def decode_worker(q: queue.Queue): + while True: + item = q.get() + if item is None: + break + image = remote_decode( + endpoint="https://q1bj3bpq6kzilnsu.us-east-1.aws.endpoints.huggingface.cloud/", + tensor=item, + scaling_factor=0.18215, + ) + display(image) + q.task_done() + +q = queue.Queue() +thread = threading.Thread(target=decode_worker, args=(q,), daemon=True) +thread.start() + +def decode(latent: torch.Tensor): + q.put(latent) + +prompts = [ + "Blueberry ice cream, in a stylish modern glass , ice cubes, nuts, mint leaves, splashing milk cream, in a gradient purple background, fluid motion, dynamic movement, cinematic lighting, Mysterious", + "Lemonade in a glass, mint leaves, in an aqua and white background, flowers, ice cubes, halo, fluid motion, dynamic movement, soft lighting, digital painting, rule of thirds composition, Art by Greg rutkowski, Coby whitmore", + "Comic book art, beautiful, vintage, pastel neon colors, extremely detailed pupils, delicate features, light on face, slight smile, Artgerm, Mary Blair, Edmund Dulac, long dark locks, bangs, glowing, fashionable style, fairytale ambience, hot pink.", + "Masterpiece, vanilla cone ice cream garnished with chocolate syrup, crushed nuts, choco flakes, in a brown background, gold, cinematic lighting, Art by WLOP", + "A bowl of milk, falling cornflakes, berries, blueberries, in a white background, soft lighting, intricate details, rule of thirds, octane render, volumetric lighting", + "Cold Coffee with cream, crushed almonds, in a glass, choco flakes, ice cubes, wet, in a wooden background, cinematic lighting, hyper realistic painting, art by Carne Griffiths, octane render, volumetric lighting, fluid motion, dynamic movement, muted colors,", +] + +pipe = StableDiffusionPipeline.from_pretrained( + "Lykon/dreamshaper-8", + torch_dtype=torch.float16, + vae=None, +).to("cuda") + +pipe.unet = pipe.unet.to(memory_format=torch.channels_last) +pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) + +_ = pipe( + prompt=prompts[0], + output_type="latent", +) + +for prompt in prompts: + latent = pipe( + prompt=prompt, + output_type="latent", + ).images + decode(latent) + +q.put(None) +thread.join() +``` + +
+ + +
+ +
+ +## Integrations + +* **[SD.Next](https://github.com/vladmandic/sdnext):** All-in-one UI with direct supports Hybrid Inference. +* **[ComfyUI-HFRemoteVae](https://github.com/kijai/ComfyUI-HFRemoteVae):** ComfyUI node for Hybrid Inference. diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/hybrid_inference/vae_encode.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/hybrid_inference/vae_encode.md new file mode 100644 index 0000000000000000000000000000000000000000..dd285fa25c03339e417934e320fabff61107a308 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/hybrid_inference/vae_encode.md @@ -0,0 +1,183 @@ +# Getting Started: VAE Encode with Hybrid Inference + +VAE encode is used for training, image-to-image and image-to-video - turning into images or videos into latent representations. + +## Memory + +These tables demonstrate the VRAM requirements for VAE encode with SD v1 and SD XL on different GPUs. + +For the majority of these GPUs the memory usage % dictates other models (text encoders, UNet/Transformer) must be offloaded, or tiled encoding has to be used which increases time taken and impacts quality. + +
SD v1.5 + +| GPU | Resolution | Time (seconds) | Memory (%) | Tiled Time (secs) | Tiled Memory (%) | +|:------------------------------|:-------------|-----------------:|-------------:|--------------------:|-------------------:| +| NVIDIA GeForce RTX 4090 | 512x512 | 0.015 | 3.51901 | 0.015 | 3.51901 | +| NVIDIA GeForce RTX 4090 | 256x256 | 0.004 | 1.3154 | 0.005 | 1.3154 | +| NVIDIA GeForce RTX 4090 | 2048x2048 | 0.402 | 47.1852 | 0.496 | 3.51901 | +| NVIDIA GeForce RTX 4090 | 1024x1024 | 0.078 | 12.2658 | 0.094 | 3.51901 | +| NVIDIA GeForce RTX 4080 SUPER | 512x512 | 0.023 | 5.30105 | 0.023 | 5.30105 | +| NVIDIA GeForce RTX 4080 SUPER | 256x256 | 0.006 | 1.98152 | 0.006 | 1.98152 | +| NVIDIA GeForce RTX 4080 SUPER | 2048x2048 | 0.574 | 71.08 | 0.656 | 5.30105 | +| NVIDIA GeForce RTX 4080 SUPER | 1024x1024 | 0.111 | 18.4772 | 0.14 | 5.30105 | +| NVIDIA GeForce RTX 3090 | 512x512 | 0.032 | 3.52782 | 0.032 | 3.52782 | +| NVIDIA GeForce RTX 3090 | 256x256 | 0.01 | 1.31869 | 0.009 | 1.31869 | +| NVIDIA GeForce RTX 3090 | 2048x2048 | 0.742 | 47.3033 | 0.954 | 3.52782 | +| NVIDIA GeForce RTX 3090 | 1024x1024 | 0.136 | 12.2965 | 0.207 | 3.52782 | +| NVIDIA GeForce RTX 3080 | 512x512 | 0.036 | 8.51761 | 0.036 | 8.51761 | +| NVIDIA GeForce RTX 3080 | 256x256 | 0.01 | 3.18387 | 0.01 | 3.18387 | +| NVIDIA GeForce RTX 3080 | 2048x2048 | 0.863 | 86.7424 | 1.191 | 8.51761 | +| NVIDIA GeForce RTX 3080 | 1024x1024 | 0.157 | 29.6888 | 0.227 | 8.51761 | +| NVIDIA GeForce RTX 3070 | 512x512 | 0.051 | 10.6941 | 0.051 | 10.6941 | +| NVIDIA GeForce RTX 3070 | 256x256 | 0.015 | 3.99743 | 0.015 | 3.99743 | +| NVIDIA GeForce RTX 3070 | 2048x2048 | 1.217 | 96.054 | 1.482 | 10.6941 | +| NVIDIA GeForce RTX 3070 | 1024x1024 | 0.223 | 37.2751 | 0.327 | 10.6941 | + + +
+ +
SDXL + +| GPU | Resolution | Time (seconds) | Memory Consumed (%) | Tiled Time (seconds) | Tiled Memory (%) | +|:------------------------------|:-------------|-----------------:|----------------------:|-----------------------:|-------------------:| +| NVIDIA GeForce RTX 4090 | 512x512 | 0.029 | 4.95707 | 0.029 | 4.95707 | +| NVIDIA GeForce RTX 4090 | 256x256 | 0.007 | 2.29666 | 0.007 | 2.29666 | +| NVIDIA GeForce RTX 4090 | 2048x2048 | 0.873 | 66.3452 | 0.863 | 15.5649 | +| NVIDIA GeForce RTX 4090 | 1024x1024 | 0.142 | 15.5479 | 0.143 | 15.5479 | +| NVIDIA GeForce RTX 4080 SUPER | 512x512 | 0.044 | 7.46735 | 0.044 | 7.46735 | +| NVIDIA GeForce RTX 4080 SUPER | 256x256 | 0.01 | 3.4597 | 0.01 | 3.4597 | +| NVIDIA GeForce RTX 4080 SUPER | 2048x2048 | 1.317 | 87.1615 | 1.291 | 23.447 | +| NVIDIA GeForce RTX 4080 SUPER | 1024x1024 | 0.213 | 23.4215 | 0.214 | 23.4215 | +| NVIDIA GeForce RTX 3090 | 512x512 | 0.058 | 5.65638 | 0.058 | 5.65638 | +| NVIDIA GeForce RTX 3090 | 256x256 | 0.016 | 2.45081 | 0.016 | 2.45081 | +| NVIDIA GeForce RTX 3090 | 2048x2048 | 1.755 | 77.8239 | 1.614 | 18.4193 | +| NVIDIA GeForce RTX 3090 | 1024x1024 | 0.265 | 18.4023 | 0.265 | 18.4023 | +| NVIDIA GeForce RTX 3080 | 512x512 | 0.064 | 13.6568 | 0.064 | 13.6568 | +| NVIDIA GeForce RTX 3080 | 256x256 | 0.018 | 5.91728 | 0.018 | 5.91728 | +| NVIDIA GeForce RTX 3080 | 2048x2048 | OOM | OOM | 1.866 | 44.4717 | +| NVIDIA GeForce RTX 3080 | 1024x1024 | 0.302 | 44.4308 | 0.302 | 44.4308 | +| NVIDIA GeForce RTX 3070 | 512x512 | 0.093 | 17.1465 | 0.093 | 17.1465 | +| NVIDIA GeForce RTX 3070 | 256x256 | 0.025 | 7.42931 | 0.026 | 7.42931 | +| NVIDIA GeForce RTX 3070 | 2048x2048 | OOM | OOM | 2.674 | 55.8355 | +| NVIDIA GeForce RTX 3070 | 1024x1024 | 0.443 | 55.7841 | 0.443 | 55.7841 | + +
+ +## Available VAEs + +| | **Endpoint** | **Model** | +|:-:|:-----------:|:--------:| +| **Stable Diffusion v1** | [https://qc6479g0aac6qwy9.us-east-1.aws.endpoints.huggingface.cloud](https://qc6479g0aac6qwy9.us-east-1.aws.endpoints.huggingface.cloud) | [`stabilityai/sd-vae-ft-mse`](https://hf.co/stabilityai/sd-vae-ft-mse) | +| **Stable Diffusion XL** | [https://xjqqhmyn62rog84g.us-east-1.aws.endpoints.huggingface.cloud](https://xjqqhmyn62rog84g.us-east-1.aws.endpoints.huggingface.cloud) | [`madebyollin/sdxl-vae-fp16-fix`](https://hf.co/madebyollin/sdxl-vae-fp16-fix) | +| **Flux** | [https://ptccx55jz97f9zgo.us-east-1.aws.endpoints.huggingface.cloud](https://ptccx55jz97f9zgo.us-east-1.aws.endpoints.huggingface.cloud) | [`black-forest-labs/FLUX.1-schnell`](https://hf.co/black-forest-labs/FLUX.1-schnell) | + + +> [!TIP] +> Model support can be requested [here](https://github.com/huggingface/diffusers/issues/new?template=remote-vae-pilot-feedback.yml). + + +## Code + +> [!TIP] +> Install `diffusers` from `main` to run the code: `pip install git+https://github.com/huggingface/diffusers@main` + + +A helper method simplifies interacting with Hybrid Inference. + +```python +from diffusers.utils.remote_utils import remote_encode +``` + +### Basic example + +Let's encode an image, then decode it to demonstrate. + +
+ +
+ +
Code + +```python +from diffusers.utils import load_image +from diffusers.utils.remote_utils import remote_decode + +image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg?download=true") + +latent = remote_encode( + endpoint="https://ptccx55jz97f9zgo.us-east-1.aws.endpoints.huggingface.cloud/", + scaling_factor=0.3611, + shift_factor=0.1159, +) + +decoded = remote_decode( + endpoint="https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud/", + tensor=latent, + scaling_factor=0.3611, + shift_factor=0.1159, +) +``` + +
+ +
+ +
+ + +### Generation + +Now let's look at a generation example, we'll encode the image, generate then remotely decode too! + +
Code + +```python +import torch +from diffusers import StableDiffusionImg2ImgPipeline +from diffusers.utils import load_image +from diffusers.utils.remote_utils import remote_decode, remote_encode + +pipe = StableDiffusionImg2ImgPipeline.from_pretrained( + "stable-diffusion-v1-5/stable-diffusion-v1-5", + torch_dtype=torch.float16, + variant="fp16", + vae=None, +).to("cuda") + +init_image = load_image( + "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" +) +init_image = init_image.resize((768, 512)) + +init_latent = remote_encode( + endpoint="https://qc6479g0aac6qwy9.us-east-1.aws.endpoints.huggingface.cloud/", + image=init_image, + scaling_factor=0.18215, +) + +prompt = "A fantasy landscape, trending on artstation" +latent = pipe( + prompt=prompt, + image=init_latent, + strength=0.75, + output_type="latent", +).images + +image = remote_decode( + endpoint="https://q1bj3bpq6kzilnsu.us-east-1.aws.endpoints.huggingface.cloud/", + tensor=latent, + scaling_factor=0.18215, +) +image.save("fantasy_landscape.jpg") +``` + +
+ +
+ +
+ +## Integrations + +* **[SD.Next](https://github.com/vladmandic/sdnext):** All-in-one UI with direct supports Hybrid Inference. +* **[ComfyUI-HFRemoteVae](https://github.com/kijai/ComfyUI-HFRemoteVae):** ComfyUI node for Hybrid Inference. diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/imgs/access_request.png b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/imgs/access_request.png new file mode 100644 index 0000000000000000000000000000000000000000..1a19908c64bd08dcba67f10375813d2821bf6f66 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/imgs/access_request.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9688dabf75e180590251cd1f75d18966f9c94d5d6584bc7d0278b698c175c61f +size 104814 diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/imgs/diffusers_library.jpg b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/imgs/diffusers_library.jpg new file mode 100644 index 0000000000000000000000000000000000000000..c2f8c529a69d4e01f4601bfc435ae90b24659fca --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/imgs/diffusers_library.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:be2485d6656bec11b85f469b2bc04736a8de8270fa2f3779d9d40bfab3966950 +size 14061 diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/index.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/index.md new file mode 100644 index 0000000000000000000000000000000000000000..0aca1d22c142b29eadd0e417680e9930f0ebc59b --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/index.md @@ -0,0 +1,35 @@ + + +

+
+ +
+

+ +# Diffusers + +Diffusers is a library of state-of-the-art pretrained diffusion models for generating videos, images, and audio. + +The library revolves around the [`DiffusionPipeline`], an API designed for: + +- easy inference with only a few lines of code +- flexibility to mix-and-match pipeline components (models, schedulers) +- loading and using adapters like LoRA + +Diffusers also comes with optimizations - such as offloading and quantization - to ensure even the largest models are accessible on memory-constrained devices. If memory is not an issue, Diffusers supports torch.compile to boost inference speed. + +Get started right away with a Diffusers model on the [Hub](https://huggingface.co/models?library=diffusers&sort=trending) today! + +## Learn + +If you're a beginner, we recommend starting with the [Hugging Face Diffusion Models Course](https://huggingface.co/learn/diffusion-course/unit0/1). You'll learn the theory behind diffusion models, and learn how to use the Diffusers library to generate images, fine-tune your own models, and more. diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/installation.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/installation.md new file mode 100644 index 0000000000000000000000000000000000000000..abde3251de2782e1a77619a6bcd3bf4847e20f5d --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/installation.md @@ -0,0 +1,146 @@ + + +# Installation + +Diffusers is tested on Python 3.8+ and PyTorch 1.4+. Install [PyTorch](https://pytorch.org/get-started/locally/) according to your system and setup. + +Create a [virtual environment](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) for easier management of separate projects and to avoid compatibility issues between dependencies. Use [uv](https://docs.astral.sh/uv/), a Rust-based Python package and project manager, to create a virtual environment and install Diffusers. + +```bash +uv venv my-env +source my-env/bin/activate +``` + +Install Diffusers with one of the following methods. + + + + +PyTorch only supports Python 3.8 - 3.11 on Windows. + +```bash +uv pip install diffusers["torch"] transformers +``` + + + + +```bash +conda install -c conda-forge diffusers +``` + + + + +A source install installs the `main` version instead of the latest `stable` version. The `main` version is useful for staying updated with the latest changes but it may not always be stable. If you run into a problem, open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) and we will try to resolve it as soon as possible. + +Make sure [Accelerate](https://huggingface.co/docs/accelerate/index) is installed. + +```bash +uv pip install accelerate +``` + +Install Diffusers from source with the command below. + +```bash +uv pip install git+https://github.com/huggingface/diffusers +``` + + + + +## Editable install + +An editable install is recommended for development workflows or if you're using the `main` version of the source code. A special link is created between the cloned repository and the Python library paths. This avoids reinstalling a package after every change. + +Clone the repository and install Diffusers with the following commands. + +```bash +git clone https://github.com/huggingface/diffusers.git +cd diffusers +uv pip install -e ".[torch]" +``` + +> [!WARNING] +> You must keep the `diffusers` folder if you want to keep using the library with the editable install. + +Update your cloned repository to the latest version of Diffusers with the command below. + +```bash +cd ~/diffusers/ +git pull +``` + +## Cache + +Model weights and files are downloaded from the Hub to a cache, which is usually your home directory. Change the cache location with the [HF_HOME](https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfhome) or [HF_HUB_CACHE](https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfhubcache) environment variables or configuring the `cache_dir` parameter in methods like [`~DiffusionPipeline.from_pretrained`]. + + + + +```bash +export HF_HOME="/path/to/your/cache" +export HF_HUB_CACHE="/path/to/your/hub/cache" +``` + + + + +```py +from diffusers import DiffusionPipeline + +pipeline = DiffusionPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + cache_dir="/path/to/your/cache" +) +``` + + + + +Cached files allow you to use Diffusers offline. Set the [HF_HUB_OFFLINE](https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfhuboffline) environment variable to `1` to prevent Diffusers from connecting to the internet. + +```shell +export HF_HUB_OFFLINE=1 +``` + +For more details about managing and cleaning the cache, take a look at the [Understand caching](https://huggingface.co/docs/huggingface_hub/guides/manage-cache) guide. + +## Telemetry logging + +Diffusers gathers telemetry information during [`~DiffusionPipeline.from_pretrained`] requests. +The data gathered includes the Diffusers and PyTorch version, the requested model or pipeline class, +and the path to a pretrained checkpoint if it is hosted on the Hub. + +This usage data helps us debug issues and prioritize new features. +Telemetry is only sent when loading models and pipelines from the Hub, +and it is not collected if you're loading local files. + +Opt-out and disable telemetry collection with the [HF_HUB_DISABLE_TELEMETRY](https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfhubdisabletelemetry) environment variable. + + + + +```bash +export HF_HUB_DISABLE_TELEMETRY=1 +``` + + + + +```bash +set HF_HUB_DISABLE_TELEMETRY=1 +``` + + + diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/auto_pipeline_blocks.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/auto_pipeline_blocks.md new file mode 100644 index 0000000000000000000000000000000000000000..2d4d82c735bd1b7ad29cbbce39ab44570e63b646 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/auto_pipeline_blocks.md @@ -0,0 +1,156 @@ + + +# AutoPipelineBlocks + +[`~modular_pipelines.AutoPipelineBlocks`] are a multi-block type containing blocks that support different workflows. It automatically selects which sub-blocks to run based on the input provided at runtime. This is typically used to package multiple workflows - text-to-image, image-to-image, inpaint - into a single pipeline for convenience. + +This guide shows how to create [`~modular_pipelines.AutoPipelineBlocks`]. + +Create three [`~modular_pipelines.ModularPipelineBlocks`] for text-to-image, image-to-image, and inpainting. These represent the different workflows available in the pipeline. + + + + +```py +import torch +from diffusers.modular_pipelines import ModularPipelineBlocks, InputParam, OutputParam + +class TextToImageBlock(ModularPipelineBlocks): + model_name = "text2img" + + @property + def inputs(self): + return [InputParam(name="prompt")] + + @property + def intermediate_outputs(self): + return [] + + @property + def description(self): + return "I'm a text-to-image workflow!" + + def __call__(self, components, state): + block_state = self.get_block_state(state) + print("running the text-to-image workflow") + # Add your text-to-image logic here + # For example: generate image from prompt + self.set_block_state(state, block_state) + return components, state +``` + + + + + +```py +class ImageToImageBlock(ModularPipelineBlocks): + model_name = "img2img" + + @property + def inputs(self): + return [InputParam(name="prompt"), InputParam(name="image")] + + @property + def intermediate_outputs(self): + return [] + + @property + def description(self): + return "I'm an image-to-image workflow!" + + def __call__(self, components, state): + block_state = self.get_block_state(state) + print("running the image-to-image workflow") + # Add your image-to-image logic here + # For example: transform input image based on prompt + self.set_block_state(state, block_state) + return components, state +``` + + + + + +```py +class InpaintBlock(ModularPipelineBlocks): + model_name = "inpaint" + + @property + def inputs(self): + return [InputParam(name="prompt"), InputParam(name="image"), InputParam(name="mask")] + + @property + def intermediate_outputs(self): + return [] + + @property + def description(self): + return "I'm an inpaint workflow!" + + def __call__(self, components, state): + block_state = self.get_block_state(state) + print("running the inpaint workflow") + # Add your inpainting logic here + # For example: fill masked areas based on prompt + self.set_block_state(state, block_state) + return components, state +``` + + + + +Create an [`~modular_pipelines.AutoPipelineBlocks`] class that includes a list of the sub-block classes and their corresponding block names. + +You also need to include `block_trigger_inputs`, a list of input names that trigger the corresponding block. If a trigger input is provided at runtime, then that block is selected to run. Use `None` to specify the default block to run if no trigger inputs are detected. + +Lastly, it is important to include a `description` that clearly explains which inputs trigger which workflow. This helps users understand how to run specific workflows. + +```py +from diffusers.modular_pipelines import AutoPipelineBlocks + +class AutoImageBlocks(AutoPipelineBlocks): + # List of sub-block classes to choose from + block_classes = [block_inpaint_cls, block_i2i_cls, block_t2i_cls] + # Names for each block in the same order + block_names = ["inpaint", "img2img", "text2img"] + # Trigger inputs that determine which block to run + # - "mask" triggers inpaint workflow + # - "image" triggers img2img workflow (but only if mask is not provided) + # - if none of above, runs the text2img workflow (default) + block_trigger_inputs = ["mask", "image", None] + # Description is extremely important for AutoPipelineBlocks + + def description(self): + return ( + "Pipeline generates images given different types of conditions!\n" + + "This is an auto pipeline block that works for text2img, img2img and inpainting tasks.\n" + + " - inpaint workflow is run when `mask` is provided.\n" + + " - img2img workflow is run when `image` is provided (but only when `mask` is not provided).\n" + + " - text2img workflow is run when neither `image` nor `mask` is provided.\n" + ) +``` + +It is **very** important to include a `description` to avoid any confusion over how to run a block and what inputs are required. While [`~modular_pipelines.AutoPipelineBlocks`] are convenient, it's conditional logic may be difficult to figure out if it isn't properly explained. + +Create an instance of `AutoImageBlocks`. + +```py +auto_blocks = AutoImageBlocks() +``` + +For more complex compositions, such as nested [`~modular_pipelines.AutoPipelineBlocks`] blocks when they're used as sub-blocks in larger pipelines, use the [`~modular_pipelines.SequentialPipelineBlocks.get_execution_blocks`] method to extract the a block that is actually run based on your input. + +```py +auto_blocks.get_execution_blocks("mask") +``` \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/components_manager.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/components_manager.md new file mode 100644 index 0000000000000000000000000000000000000000..af53411b9533a577ad65302212feed4d19c24584 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/components_manager.md @@ -0,0 +1,190 @@ + + +# ComponentsManager + +The [`ComponentsManager`] is a model registry and management system for Modular Diffusers. It adds and tracks models, stores useful metadata (model size, device placement, adapters), prevents duplicate model instances, and supports offloading. + +This guide will show you how to use [`ComponentsManager`] to manage components and device memory. + +## Add a component + +The [`ComponentsManager`] should be created alongside a [`ModularPipeline`] in either [`~ModularPipeline.from_pretrained`] or [`~ModularPipelineBlocks.init_pipeline`]. + +> [!TIP] +> The `collection` parameter is optional but makes it easier to organize and manage components. + + + + +```py +from diffusers import ModularPipeline, ComponentsManager + +comp = ComponentsManager() +pipe = ModularPipeline.from_pretrained("YiYiXu/modular-demo-auto", components_manager=comp, collection="test1") +``` + + + + +```py +from diffusers import ComponentsManager +from diffusers.modular_pipelines import SequentialPipelineBlocks +from diffusers.modular_pipelines.stable_diffusion_xl import TEXT2IMAGE_BLOCKS + +t2i_blocks = SequentialPipelineBlocks.from_blocks_dict(TEXT2IMAGE_BLOCKS) + +modular_repo_id = "YiYiXu/modular-loader-t2i-0704" +components = ComponentsManager() +t2i_pipeline = t2i_blocks.init_pipeline(modular_repo_id, components_manager=components) +``` + + + + +Components are only loaded and registered when using [`~ModularPipeline.load_components`] or [`~ModularPipeline.load_components`]. The example below uses [`~ModularPipeline.load_components`] to create a second pipeline that reuses all the components from the first one, and assigns it to a different collection + +```py +pipe.load_components() +pipe2 = ModularPipeline.from_pretrained("YiYiXu/modular-demo-auto", components_manager=comp, collection="test2") +``` + +Use the [`~ModularPipeline.null_component_names`] property to identify any components that need to be loaded, retrieve them with [`~ComponentsManager.get_components_by_names`], and then call [`~ModularPipeline.update_components`] to add the missing components. + +```py +pipe2.null_component_names +['text_encoder', 'text_encoder_2', 'tokenizer', 'tokenizer_2', 'image_encoder', 'unet', 'vae', 'scheduler', 'controlnet'] + +comp_dict = comp.get_components_by_names(names=pipe2.null_component_names) +pipe2.update_components(**comp_dict) +``` + +To add individual components, use the [`~ComponentsManager.add`] method. This registers a component with a unique id. + +```py +from diffusers import AutoModel + +text_encoder = AutoModel.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder") +component_id = comp.add("text_encoder", text_encoder) +comp +``` + +Use [`~ComponentsManager.remove`] to remove a component using their id. + +```py +comp.remove("text_encoder_139917733042864") +``` + +## Retrieve a component + +The [`ComponentsManager`] provides several methods to retrieve registered components. + +### get_one + +The [`~ComponentsManager.get_one`] method returns a single component and supports pattern matching for the `name` parameter. If multiple components match, [`~ComponentsManager.get_one`] returns an error. + +| Pattern | Example | Description | +|-------------|----------------------------------|-------------------------------------------| +| exact | `comp.get_one(name="unet")` | exact name match | +| wildcard | `comp.get_one(name="unet*")` | names starting with "unet" | +| exclusion | `comp.get_one(name="!unet")` | exclude components named "unet" | +| or | `comp.get_one(name="unet|vae")` | name is "unet" or "vae" | + +[`~ComponentsManager.get_one`] also filters components by the `collection` argument or `load_id` argument. + +```py +comp.get_one(name="unet", collection="sdxl") +``` + +### get_components_by_names + +The [`~ComponentsManager.get_components_by_names`] method accepts a list of names and returns a dictionary mapping names to components. This is especially useful with [`ModularPipeline`] since they provide lists of required component names and the returned dictionary can be passed directly to [`~ModularPipeline.update_components`]. + +```py +component_dict = comp.get_components_by_names(names=["text_encoder", "unet", "vae"]) +{"text_encoder": component1, "unet": component2, "vae": component3} +``` + +## Duplicate detection + +It is recommended to load model components with [`ComponentSpec`] to assign components with a unique id that encodes their loading parameters. This allows [`ComponentsManager`] to automatically detect and prevent duplicate model instances even when different objects represent the same underlying checkpoint. + +```py +from diffusers import ComponentSpec, ComponentsManager +from transformers import CLIPTextModel + +comp = ComponentsManager() + +# Create ComponentSpec for the first text encoder +spec = ComponentSpec(name="text_encoder", repo="stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder", type_hint=AutoModel) +# Create ComponentSpec for a duplicate text encoder (it is same checkpoint, from the same repo/subfolder) +spec_duplicated = ComponentSpec(name="text_encoder_duplicated", repo="stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder", type_hint=CLIPTextModel) + +# Load and add both components - the manager will detect they're the same model +comp.add("text_encoder", spec.load()) +comp.add("text_encoder_duplicated", spec_duplicated.load()) +``` + +This returns a warning with instructions for removing the duplicate. + +```py +ComponentsManager: adding component 'text_encoder_duplicated_139917580682672', but it has duplicate load_id 'stabilityai/stable-diffusion-xl-base-1.0|text_encoder|null|null' with existing components: text_encoder_139918506246832. To remove a duplicate, call `components_manager.remove('')`. +'text_encoder_duplicated_139917580682672' +``` + +You could also add a component without using [`ComponentSpec`] and duplicate detection still works in most cases even if you're adding the same component under a different name. + +However, [`ComponentManager`] can't detect duplicates when you load the same component into different objects. In this case, you should load a model with [`ComponentSpec`]. + +```py +text_encoder_2 = AutoModel.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder") +comp.add("text_encoder", text_encoder_2) +'text_encoder_139917732983664' +``` + +## Collections + +Collections are labels assigned to components for better organization and management. Add a component to a collection with the `collection` argument in [`~ComponentsManager.add`]. + +Only one component per name is allowed in each collection. Adding a second component with the same name automatically removes the first component. + +```py +from diffusers import ComponentSpec, ComponentsManager + +comp = ComponentsManager() +# Create ComponentSpec for the first UNet +spec = ComponentSpec(name="unet", repo="stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet", type_hint=AutoModel) +# Create ComponentSpec for a different UNet +spec2 = ComponentSpec(name="unet", repo="RunDiffusion/Juggernaut-XL-v9", subfolder="unet", type_hint=AutoModel, variant="fp16") + +# Add both UNets to the same collection - the second one will replace the first +comp.add("unet", spec.load(), collection="sdxl") +comp.add("unet", spec2.load(), collection="sdxl") +``` + +This makes it convenient to work with node-based systems because you can: + +- Mark all models as loaded from one node with the `collection` label. +- Automatically replace models when new checkpoints are loaded under the same name. +- Batch delete all models in a collection when a node is removed. + +## Offloading + +The [`~ComponentsManager.enable_auto_cpu_offload`] method is a global offloading strategy that works across all models regardless of which pipeline is using them. Once enabled, you don't need to worry about device placement if you add or remove components. + +```py +comp.enable_auto_cpu_offload(device="cuda") +``` + +All models begin on the CPU and [`ComponentsManager`] moves them to the appropriate device right before they're needed, and moves other models back to the CPU when GPU memory is low. + +You can set your own rules for which models to offload first. diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/custom_blocks.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/custom_blocks.md new file mode 100644 index 0000000000000000000000000000000000000000..1c311582264e2f142f5890a95d76c39a9492769f --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/custom_blocks.md @@ -0,0 +1,492 @@ + + + +# Building Custom Blocks + +[ModularPipelineBlocks](./pipeline_block) are the fundamental building blocks of a [`ModularPipeline`]. You can create custom blocks by defining their inputs, outputs, and computation logic. This guide demonstrates how to create and use a custom block. + +> [!TIP] +> Explore the [Modular Diffusers Custom Blocks](https://huggingface.co/collections/diffusers/modular-diffusers-custom-blocks) collection for official custom modular blocks like Nano Banana. + +## Project Structure + +Your custom block project should use the following structure: + +```shell +. +├── block.py +└── modular_config.json +``` + +- `block.py` contains the custom block implementation +- `modular_config.json` contains the metadata needed to load the block + +## Example: Florence 2 Inpainting Block + +In this example we will create a custom block that uses the [Florence 2](https://huggingface.co/docs/transformers/model_doc/florence2) model to process an input image and generate a mask for inpainting. + +The first step is to define the components that the block will use. In this case, we will need to use the `Florence2ForConditionalGeneration` model and its corresponding processor `AutoProcessor`. When defining components, we must specify the name of the component within our pipeline, model class via `type_hint`, and provide a `pretrained_model_name_or_path` for the component if we intend to load the model weights from a specific repository on the Hub. + +```py +# Inside block.py +from diffusers.modular_pipelines import ( + ModularPipelineBlocks, + ComponentSpec, +) +from transformers import AutoProcessor, Florence2ForConditionalGeneration + + +class Florence2ImageAnnotatorBlock(ModularPipelineBlocks): + + @property + def expected_components(self): + return [ + ComponentSpec( + name="image_annotator", + type_hint=Florence2ForConditionalGeneration, + pretrained_model_name_or_path="florence-community/Florence-2-base-ft", + ), + ComponentSpec( + name="image_annotator_processor", + type_hint=AutoProcessor, + pretrained_model_name_or_path="florence-community/Florence-2-base-ft", + ), + ] +``` + +Next, we define the inputs and outputs of the block. The inputs include the image to be annotated, the annotation task, and the annotation prompt. The outputs include the generated mask image and annotations. + +```py +from typing import List, Union +from PIL import Image, ImageDraw +import torch +import numpy as np + +from diffusers.modular_pipelines import ( + PipelineState, + ModularPipelineBlocks, + InputParam, + ComponentSpec, + OutputParam, +) +from transformers import AutoProcessor, Florence2ForConditionalGeneration + + +class Florence2ImageAnnotatorBlock(ModularPipelineBlocks): + + @property + def expected_components(self): + return [ + ComponentSpec( + name="image_annotator", + type_hint=Florence2ForConditionalGeneration, + pretrained_model_name_or_path="florence-community/Florence-2-base-ft", + ), + ComponentSpec( + name="image_annotator_processor", + type_hint=AutoProcessor, + pretrained_model_name_or_path="florence-community/Florence-2-base-ft", + ), + ] + + @property + def inputs(self) -> List[InputParam]: + return [ + InputParam( + "image", + type_hint=Union[Image.Image, List[Image.Image]], + required=True, + description="Image(s) to annotate", + ), + InputParam( + "annotation_task", + type_hint=Union[str, List[str]], + required=True, + default="", + description="""Annotation Task to perform on the image. + Supported Tasks: + + + + + + + + + + + """, + ), + InputParam( + "annotation_prompt", + type_hint=Union[str, List[str]], + required=True, + description="""Annotation Prompt to provide more context to the task. + Can be used to detect or segment out specific elements in the image + """, + ), + InputParam( + "annotation_output_type", + type_hint=str, + required=True, + default="mask_image", + description="""Output type from annotation predictions. Availabe options are + mask_image: + -black and white mask image for the given image based on the task type + mask_overlay: + - mask overlayed on the original image + bounding_box: + - bounding boxes drawn on the original image + """, + ), + InputParam( + "annotation_overlay", + type_hint=bool, + required=True, + default=False, + description="", + ), + ] + + @property + def intermediate_outputs(self) -> List[OutputParam]: + return [ + OutputParam( + "mask_image", + type_hint=Image, + description="Inpainting Mask for input Image(s)", + ), + OutputParam( + "annotations", + type_hint=dict, + description="Annotations Predictions for input Image(s)", + ), + OutputParam( + "image", + type_hint=Image, + description="Annotated input Image(s)", + ), + ] + +``` + +Now we implement the `__call__` method, which contains the logic for processing the input image and generating the mask. + +```py +from typing import List, Union +from PIL import Image, ImageDraw +import torch +import numpy as np + +from diffusers.modular_pipelines import ( + PipelineState, + ModularPipelineBlocks, + InputParam, + ComponentSpec, + OutputParam, +) +from transformers import AutoProcessor, Florence2ForConditionalGeneration + + +class Florence2ImageAnnotatorBlock(ModularPipelineBlocks): + + @property + def expected_components(self): + return [ + ComponentSpec( + name="image_annotator", + type_hint=Florence2ForConditionalGeneration, + pretrained_model_name_or_path="florence-community/Florence-2-base-ft", + ), + ComponentSpec( + name="image_annotator_processor", + type_hint=AutoProcessor, + pretrained_model_name_or_path="florence-community/Florence-2-base-ft", + ), + ] + + @property + def inputs(self) -> List[InputParam]: + return [ + InputParam( + "image", + type_hint=Union[Image.Image, List[Image.Image]], + required=True, + description="Image(s) to annotate", + ), + InputParam( + "annotation_task", + type_hint=Union[str, List[str]], + required=True, + default="", + description="""Annotation Task to perform on the image. + Supported Tasks: + + + + + + + + + + + """, + ), + InputParam( + "annotation_prompt", + type_hint=Union[str, List[str]], + required=True, + description="""Annotation Prompt to provide more context to the task. + Can be used to detect or segment out specific elements in the image + """, + ), + InputParam( + "annotation_output_type", + type_hint=str, + required=True, + default="mask_image", + description="""Output type from annotation predictions. Availabe options are + mask_image: + -black and white mask image for the given image based on the task type + mask_overlay: + - mask overlayed on the original image + bounding_box: + - bounding boxes drawn on the original image + """, + ), + InputParam( + "annotation_overlay", + type_hint=bool, + required=True, + default=False, + description="", + ), + ] + + @property + def intermediate_outputs(self) -> List[OutputParam]: + return [ + OutputParam( + "mask_image", + type_hint=Image, + description="Inpainting Mask for input Image(s)", + ), + OutputParam( + "annotations", + type_hint=dict, + description="Annotations Predictions for input Image(s)", + ), + OutputParam( + "image", + type_hint=Image, + description="Annotated input Image(s)", + ), + ] + + def get_annotations(self, components, images, prompts, task): + task_prompts = [task + prompt for prompt in prompts] + + inputs = components.image_annotator_processor( + text=task_prompts, images=images, return_tensors="pt" + ).to(components.image_annotator.device, components.image_annotator.dtype) + + generated_ids = components.image_annotator.generate( + input_ids=inputs["input_ids"], + pixel_values=inputs["pixel_values"], + max_new_tokens=1024, + early_stopping=False, + do_sample=False, + num_beams=3, + ) + annotations = components.image_annotator_processor.batch_decode( + generated_ids, skip_special_tokens=False + ) + outputs = [] + for image, annotation in zip(images, annotations): + outputs.append( + components.image_annotator_processor.post_process_generation( + annotation, task=task, image_size=(image.width, image.height) + ) + ) + return outputs + + def prepare_mask(self, images, annotations, overlay=False, fill="white"): + masks = [] + for image, annotation in zip(images, annotations): + mask_image = image.copy() if overlay else Image.new("L", image.size, 0) + draw = ImageDraw.Draw(mask_image) + + for _, _annotation in annotation.items(): + if "polygons" in _annotation: + for polygon in _annotation["polygons"]: + polygon = np.array(polygon).reshape(-1, 2) + if len(polygon) < 3: + continue + polygon = polygon.reshape(-1).tolist() + draw.polygon(polygon, fill=fill) + + elif "bbox" in _annotation: + bbox = _annotation["bbox"] + draw.rectangle(bbox, fill="white") + + masks.append(mask_image) + + return masks + + def prepare_bounding_boxes(self, images, annotations): + outputs = [] + for image, annotation in zip(images, annotations): + image_copy = image.copy() + draw = ImageDraw.Draw(image_copy) + for _, _annotation in annotation.items(): + bbox = _annotation["bbox"] + label = _annotation["label"] + + draw.rectangle(bbox, outline="red", width=3) + draw.text((bbox[0], bbox[1] - 20), label, fill="red") + + outputs.append(image_copy) + + return outputs + + def prepare_inputs(self, images, prompts): + prompts = prompts or "" + + if isinstance(images, Image.Image): + images = [images] + if isinstance(prompts, str): + prompts = [prompts] + + if len(images) != len(prompts): + raise ValueError("Number of images and annotation prompts must match.") + + return images, prompts + + @torch.no_grad() + def __call__(self, components, state: PipelineState) -> PipelineState: + block_state = self.get_block_state(state) + images, annotation_task_prompt = self.prepare_inputs( + block_state.image, block_state.annotation_prompt + ) + task = block_state.annotation_task + fill = block_state.fill + + annotations = self.get_annotations( + components, images, annotation_task_prompt, task + ) + block_state.annotations = annotations + if block_state.annotation_output_type == "mask_image": + block_state.mask_image = self.prepare_mask(images, annotations) + else: + block_state.mask_image = None + + if block_state.annotation_output_type == "mask_overlay": + block_state.image = self.prepare_mask(images, annotations, overlay=True, fill=fill) + + elif block_state.annotation_output_type == "bounding_box": + block_state.image = self.prepare_bounding_boxes(images, annotations) + + self.set_block_state(state, block_state) + + return components, state + +``` + +Once we have defined our custom block, we can save it to the Hub, using either the CLI or the [`push_to_hub`] method. This will make it easy to share and reuse our custom block with other pipelines. + + + + +```shell +# In the folder with the `block.py` file, run: +diffusers-cli custom_block +``` + +Then upload the block to the Hub: + +```shell +hf upload . . +``` + + + +```py +from block import Florence2ImageAnnotatorBlock +block = Florence2ImageAnnotatorBlock() +block.push_to_hub("") +``` + + + + +## Using Custom Blocks + +Load the custom block with [`~ModularPipelineBlocks.from_pretrained`] and set `trust_remote_code=True`. + +```py +import torch +from diffusers.modular_pipelines import ModularPipelineBlocks, SequentialPipelineBlocks +from diffusers.modular_pipelines.stable_diffusion_xl import INPAINT_BLOCKS +from diffusers.utils import load_image + +# Fetch the Florence2 image annotator block that will create our mask +image_annotator_block = ModularPipelineBlocks.from_pretrained("diffusers/florence-2-custom-block", trust_remote_code=True) + +my_blocks = INPAINT_BLOCKS.copy() +# insert the annotation block before the image encoding step +my_blocks.insert("image_annotator", image_annotator_block, 1) + +# Create our initial set of inpainting blocks +blocks = SequentialPipelineBlocks.from_blocks_dict(my_blocks) + +repo_id = "diffusers/modular-stable-diffusion-xl-base-1.0" +pipe = blocks.init_pipeline(repo_id) +pipe.load_components(torch_dtype=torch.float16, device_map="cuda", trust_remote_code=True) + +image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true") +image = image.resize((1024, 1024)) + +prompt = ["A red car"] +annotation_task = "" +annotation_prompt = ["the car"] + +output = pipe( + prompt=prompt, + image=image, + annotation_task=annotation_task, + annotation_prompt=annotation_prompt, + annotation_output_type="mask_image", + num_inference_steps=35, + guidance_scale=7.5, + strength=0.95, + output="images" +) +output[0].save("florence-inpainting.png") +``` + +## Editing Custom Blocks + +By default, custom blocks are saved in your cache directory. Use the `local_dir` argument to download and edit a custom block in a specific folder. + +```py +import torch +from diffusers.modular_pipelines import ModularPipelineBlocks, SequentialPipelineBlocks +from diffusers.modular_pipelines.stable_diffusion_xl import INPAINT_BLOCKS +from diffusers.utils import load_image + +# Fetch the Florence2 image annotator block that will create our mask +image_annotator_block = ModularPipelineBlocks.from_pretrained("diffusers/florence-2-custom-block", trust_remote_code=True, local_dir="/my-local-folder") +``` + +Any changes made to the block files in this folder will be reflected when you load the block again. diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/guiders.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/guiders.md new file mode 100644 index 0000000000000000000000000000000000000000..fd0d27844205a9ee766c10d86ec74a67fa2e46e3 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/guiders.md @@ -0,0 +1,175 @@ + + +# Guiders + +[Classifier-free guidance](https://huggingface.co/papers/2207.12598) steers model generation that better match a prompt and is commonly used to improve generation quality, control, and adherence to prompts. There are different types of guidance methods, and in Diffusers, they are known as *guiders*. Like blocks, it is easy to switch and use different guiders for different use cases without rewriting the pipeline. + +This guide will show you how to switch guiders, adjust guider parameters, and load and share them to the Hub. + +## Switching guiders + +[`ClassifierFreeGuidance`] is the default guider and created when a pipeline is initialized with [`~ModularPipelineBlocks.init_pipeline`]. It is created by `from_config` which means it doesn't require loading specifications from a modular repository. A guider won't be listed in `modular_model_index.json`. + +Use [`~ModularPipeline.get_component_spec`] to inspect a guider. + +```py +t2i_pipeline.get_component_spec("guider") +ComponentSpec(name='guider', type_hint=, description=None, config=FrozenDict([('guidance_scale', 7.5), ('guidance_rescale', 0.0), ('use_original_formulation', False), ('start', 0.0), ('stop', 1.0), ('_use_default_values', ['start', 'guidance_rescale', 'stop', 'use_original_formulation'])]), repo=None, subfolder=None, variant=None, revision=None, default_creation_method='from_config') +``` + +Switch to a different guider by passing the new guider to [`~ModularPipeline.update_components`]. + +> [!TIP] +> Changing guiders will return text letting you know you're changing the guider type. +> ```bash +> ModularPipeline.update_components: adding guider with new type: PerturbedAttentionGuidance, previous type: ClassifierFreeGuidance +> ``` + +```py +from diffusers import LayerSkipConfig, PerturbedAttentionGuidance + +config = LayerSkipConfig(indices=[2, 9], fqn="mid_block.attentions.0.transformer_blocks", skip_attention=False, skip_attention_scores=True, skip_ff=False) +guider = PerturbedAttentionGuidance( + guidance_scale=5.0, perturbed_guidance_scale=2.5, perturbed_guidance_config=config +) +t2i_pipeline.update_components(guider=guider) +``` + +Use [`~ModularPipeline.get_component_spec`] again to verify the guider type is different. + +```py +t2i_pipeline.get_component_spec("guider") +ComponentSpec(name='guider', type_hint=, description=None, config=FrozenDict([('guidance_scale', 5.0), ('perturbed_guidance_scale', 2.5), ('perturbed_guidance_start', 0.01), ('perturbed_guidance_stop', 0.2), ('perturbed_guidance_layers', None), ('perturbed_guidance_config', LayerSkipConfig(indices=[2, 9], fqn='mid_block.attentions.0.transformer_blocks', skip_attention=False, skip_attention_scores=True, skip_ff=False, dropout=1.0)), ('guidance_rescale', 0.0), ('use_original_formulation', False), ('start', 0.0), ('stop', 1.0), ('_use_default_values', ['perturbed_guidance_start', 'use_original_formulation', 'perturbed_guidance_layers', 'stop', 'start', 'guidance_rescale', 'perturbed_guidance_stop']), ('_class_name', 'PerturbedAttentionGuidance'), ('_diffusers_version', '0.35.0.dev0')]), repo=None, subfolder=None, variant=None, revision=None, default_creation_method='from_config') +``` + +## Loading custom guiders + +Guiders that are already saved on the Hub with a `modular_model_index.json` file are considered a `from_pretrained` component now instead of a `from_config` component. + +```json +{ + "guider": [ + null, + null, + { + "repo": "YiYiXu/modular-loader-t2i-guider", + "revision": null, + "subfolder": "pag_guider", + "type_hint": [ + "diffusers", + "PerturbedAttentionGuidance" + ], + "variant": null + } + ] +} +``` + +The guider is only created after calling [`~ModularPipeline.load_components`] based on the loading specification in `modular_model_index.json`. + +```py +t2i_pipeline = t2i_blocks.init_pipeline("YiYiXu/modular-doc-guider") +# not created during init +assert t2i_pipeline.guider is None +t2i_pipeline.load_components() +# loaded as PAG guider +t2i_pipeline.guider +``` + + +## Changing guider parameters + +The guider parameters can be adjusted with either the [`~ComponentSpec.create`] method or with [`~ModularPipeline.update_components`]. The example below changes the `guidance_scale` value. + + + + +```py +guider_spec = t2i_pipeline.get_component_spec("guider") +guider = guider_spec.create(guidance_scale=10) +t2i_pipeline.update_components(guider=guider) +``` + + + + +```py +guider_spec = t2i_pipeline.get_component_spec("guider") +guider_spec.config["guidance_scale"] = 10 +t2i_pipeline.update_components(guider=guider_spec) +``` + + + + +## Uploading custom guiders + +Call the [`~utils.PushToHubMixin.push_to_hub`] method on a custom guider to share it to the Hub. + +```py +guider.push_to_hub("YiYiXu/modular-loader-t2i-guider", subfolder="pag_guider") +``` + +To make this guider available to the pipeline, either modify the `modular_model_index.json` file or use the [`~ModularPipeline.update_components`] method. + + + + +Edit the `modular_model_index.json` file and add a loading specification for the guider by pointing to a folder containing the guider config. + +```json +{ + "guider": [ + "diffusers", + "PerturbedAttentionGuidance", + { + "repo": "YiYiXu/modular-loader-t2i-guider", + "revision": null, + "subfolder": "pag_guider", + "type_hint": [ + "diffusers", + "PerturbedAttentionGuidance" + ], + "variant": null + } + ], +``` + + + + +Change the [`~ComponentSpec.default_creation_method`] to `from_pretrained` and use [`~ModularPipeline.update_components`] to update the guider and component specifications as well as the pipeline config. + +> [!TIP] +> Changing the creation method will return text letting you know you're changing the creation type to `from_pretrained`. +> ```bash +> ModularPipeline.update_components: changing the default_creation_method of guider from from_config to from_pretrained. +> ``` + +```py +guider_spec = t2i_pipeline.get_component_spec("guider") +guider_spec.default_creation_method="from_pretrained" +guider_spec.repo="YiYiXu/modular-loader-t2i-guider" +guider_spec.subfolder="pag_guider" +pag_guider = guider_spec.load() +t2i_pipeline.update_components(guider=pag_guider) +``` + +To make it the default guider for a pipeline, call [`~utils.PushToHubMixin.push_to_hub`]. This is an optional step and not necessary if you are only experimenting locally. + +```py +t2i_pipeline.push_to_hub("YiYiXu/modular-doc-guider") +``` + + + diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/loop_sequential_pipeline_blocks.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/loop_sequential_pipeline_blocks.md new file mode 100644 index 0000000000000000000000000000000000000000..a80309de19a61756af9bedc8d5c992ab8b13cc1e --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/loop_sequential_pipeline_blocks.md @@ -0,0 +1,92 @@ + + +# LoopSequentialPipelineBlocks + +[`~modular_pipelines.LoopSequentialPipelineBlocks`] are a multi-block type that composes other [`~modular_pipelines.ModularPipelineBlocks`] together in a loop. Data flows circularly, using `inputs` and `intermediate_outputs`, and each block is run iteratively. This is typically used to create a denoising loop which is iterative by default. + +This guide shows you how to create [`~modular_pipelines.LoopSequentialPipelineBlocks`]. + +## Loop wrapper + +[`~modular_pipelines.LoopSequentialPipelineBlocks`], is also known as the *loop wrapper* because it defines the loop structure, iteration variables, and configuration. Within the loop wrapper, you need the following variables. + +- `loop_inputs` are user provided values and equivalent to [`~modular_pipelines.ModularPipelineBlocks.inputs`]. +- `loop_intermediate_outputs` are new intermediate variables created by the block and added to the [`~modular_pipelines.PipelineState`]. It is equivalent to [`~modular_pipelines.ModularPipelineBlocks.intermediate_outputs`]. +- `__call__` method defines the loop structure and iteration logic. + +```py +import torch +from diffusers.modular_pipelines import LoopSequentialPipelineBlocks, ModularPipelineBlocks, InputParam, OutputParam + +class LoopWrapper(LoopSequentialPipelineBlocks): + model_name = "test" + @property + def description(self): + return "I'm a loop!!" + @property + def loop_inputs(self): + return [InputParam(name="num_steps")] + @torch.no_grad() + def __call__(self, components, state): + block_state = self.get_block_state(state) + # Loop structure - can be customized to your needs + for i in range(block_state.num_steps): + # loop_step executes all registered blocks in sequence + components, block_state = self.loop_step(components, block_state, i=i) + self.set_block_state(state, block_state) + return components, state +``` + +The loop wrapper can pass additional arguments, like current iteration index, to the loop blocks. + +## Loop blocks + +A loop block is a [`~modular_pipelines.ModularPipelineBlocks`], but the `__call__` method behaves differently. + +- It recieves the iteration variable from the loop wrapper. +- It works directly with the [`~modular_pipelines.BlockState`] instead of the [`~modular_pipelines.PipelineState`]. +- It doesn't require retrieving or updating the [`~modular_pipelines.BlockState`]. + +Loop blocks share the same [`~modular_pipelines.BlockState`] to allow values to accumulate and change for each iteration in the loop. + +```py +class LoopBlock(ModularPipelineBlocks): + model_name = "test" + @property + def inputs(self): + return [InputParam(name="x")] + @property + def intermediate_outputs(self): + # outputs produced by this block + return [OutputParam(name="x")] + @property + def description(self): + return "I'm a block used inside the `LoopWrapper` class" + def __call__(self, components, block_state, i: int): + block_state.x += 1 + return components, block_state +``` + +## LoopSequentialPipelineBlocks + +Use the [`~modular_pipelines.LoopSequentialPipelineBlocks.from_blocks_dict`] method to add the loop block to the loop wrapper to create [`~modular_pipelines.LoopSequentialPipelineBlocks`]. + +```py +loop = LoopWrapper.from_blocks_dict({"block1": LoopBlock}) +``` + +Add more loop blocks to run within each iteration with [`~modular_pipelines.LoopSequentialPipelineBlocks.from_blocks_dict`]. This allows you to modify the blocks without changing the loop logic itself. + +```py +loop = LoopWrapper.from_blocks_dict({"block1": LoopBlock(), "block2": LoopBlock}) +``` diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/modular_diffusers_states.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/modular_diffusers_states.md new file mode 100644 index 0000000000000000000000000000000000000000..eb55b524e491e464f831032af9d79fc11377641f --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/modular_diffusers_states.md @@ -0,0 +1,75 @@ + + +# States + +Blocks rely on the [`~modular_pipelines.PipelineState`] and [`~modular_pipelines.BlockState`] data structures for communicating and sharing data. + +| State | Description | +|-------|-------------| +| [`~modular_pipelines.PipelineState`] | Maintains the overall data required for a pipeline's execution and allows blocks to read and update its data. | +| [`~modular_pipelines.BlockState`] | Allows each block to perform its computation with the necessary data from `inputs`| + +This guide explains how states work and how they connect blocks. + +## PipelineState + +The [`~modular_pipelines.PipelineState`] is a global state container for all blocks. It maintains the complete runtime state of the pipeline and provides a structured way for blocks to read from and write to shared data. + +There are two dict's in [`~modular_pipelines.PipelineState`] for structuring data. + +- The `values` dict is a **mutable** state containing a copy of user provided input values and intermediate output values generated by blocks. If a block modifies an `input`, it will be reflected in the `values` dict after calling `set_block_state`. + +```py +PipelineState( + values={ + 'prompt': 'a cat' + 'guidance_scale': 7.0 + 'num_inference_steps': 25 + 'prompt_embeds': Tensor(dtype=torch.float32, shape=torch.Size([1, 1, 1, 1])) + 'negative_prompt_embeds': None + }, +) +``` + +## BlockState + +The [`~modular_pipelines.BlockState`] is a local view of the relevant variables an individual block needs from [`~modular_pipelines.PipelineState`] for performing it's computations. + +Access these variables directly as attributes like `block_state.image`. + +```py +BlockState( + image: +) +``` + +When a block's `__call__` method is executed, it retrieves the [`BlockState`] with `self.get_block_state(state)`, performs it's operations, and updates [`~modular_pipelines.PipelineState`] with `self.set_block_state(state, block_state)`. + +```py +def __call__(self, components, state): + # retrieve BlockState + block_state = self.get_block_state(state) + + # computation logic on inputs + + # update PipelineState + self.set_block_state(state, block_state) + return components, state +``` + +## State interaction + +[`~modular_pipelines.PipelineState`] and [`~modular_pipelines.BlockState`] interaction is defined by a block's `inputs`, and `intermediate_outputs`. + +- `inputs`, a block can modify an input - like `block_state.image` - and this change can be propagated globally to [`~modular_pipelines.PipelineState`] by calling `set_block_state`. +- `intermediate_outputs`, is a new variable that a block creates. It is added to the [`~modular_pipelines.PipelineState`]'s `values` dict and is available as for subsequent blocks or accessed by users as a final output from the pipeline. diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/modular_pipeline.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/modular_pipeline.md new file mode 100644 index 0000000000000000000000000000000000000000..0e0a7bd75d51e96c9aa822ca6612047999988076 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/modular_pipeline.md @@ -0,0 +1,358 @@ + + +# ModularPipeline + +[`ModularPipeline`] converts [`~modular_pipelines.ModularPipelineBlocks`]'s into an executable pipeline that loads models and performs the computation steps defined in the block. It is the main interface for running a pipeline and it is very similar to the [`DiffusionPipeline`] API. + +The main difference is to include an expected `output` argument in the pipeline. + + + + +```py +import torch +from diffusers.modular_pipelines import SequentialPipelineBlocks +from diffusers.modular_pipelines.stable_diffusion_xl import TEXT2IMAGE_BLOCKS + +blocks = SequentialPipelineBlocks.from_blocks_dict(TEXT2IMAGE_BLOCKS) + +modular_repo_id = "YiYiXu/modular-loader-t2i-0704" +pipeline = blocks.init_pipeline(modular_repo_id) + +pipeline.load_components(torch_dtype=torch.float16) +pipeline.to("cuda") + +image = pipeline(prompt="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", output="images")[0] +image.save("modular_t2i_out.png") +``` + + + + +```py +import torch +from diffusers.modular_pipelines import SequentialPipelineBlocks +from diffusers.modular_pipelines.stable_diffusion_xl import IMAGE2IMAGE_BLOCKS + +blocks = SequentialPipelineBlocks.from_blocks_dict(IMAGE2IMAGE_BLOCKS) + +modular_repo_id = "YiYiXu/modular-loader-t2i-0704" +pipeline = blocks.init_pipeline(modular_repo_id) + +pipeline.load_components(torch_dtype=torch.float16) +pipeline.to("cuda") + +url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png" +init_image = load_image(url) +prompt = "a dog catching a frisbee in the jungle" +image = pipeline(prompt=prompt, image=init_image, strength=0.8, output="images")[0] +image.save("modular_i2i_out.png") +``` + + + + +```py +import torch +from diffusers.modular_pipelines import SequentialPipelineBlocks +from diffusers.modular_pipelines.stable_diffusion_xl import INPAINT_BLOCKS +from diffusers.utils import load_image + +blocks = SequentialPipelineBlocks.from_blocks_dict(INPAINT_BLOCKS) + +modular_repo_id = "YiYiXu/modular-loader-t2i-0704" +pipeline = blocks.init_pipeline(modular_repo_id) + +pipeline.load_components(torch_dtype=torch.float16) +pipeline.to("cuda") + +img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png" +mask_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-inpaint-mask.png" + +init_image = load_image(img_url) +mask_image = load_image(mask_url) + +prompt = "A deep sea diver floating" +image = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, strength=0.85, output="images")[0] +image.save("moduar_inpaint_out.png") +``` + + + + +This guide will show you how to create a [`ModularPipeline`] and manage the components in it. + +## Adding blocks + +Blocks are [`InsertableDict`] objects that can be inserted at specific positions, providing a flexible way to mix-and-match blocks. + +Use [`~modular_pipelines.modular_pipeline_utils.InsertableDict.insert`] on either the block class or `sub_blocks` attribute to add a block. + +```py +# BLOCKS is dict of block classes, you need to add class to it +BLOCKS.insert("block_name", BlockClass, index) +# sub_blocks attribute contains instance, add a block instance to the attribute +t2i_blocks.sub_blocks.insert("block_name", block_instance, index) +``` + +Use [`~modular_pipelines.modular_pipeline_utils.InsertableDict.pop`] on either the block class or `sub_blocks` attribute to remove a block. + +```py +# remove a block class from preset +BLOCKS.pop("text_encoder") +# split out a block instance on its own +text_encoder_block = t2i_blocks.sub_blocks.pop("text_encoder") +``` + +Swap blocks by setting the existing block to the new block. + +```py +# Replace block class in preset +BLOCKS["prepare_latents"] = CustomPrepareLatents +# Replace in sub_blocks attribute using an block instance +t2i_blocks.sub_blocks["prepare_latents"] = CustomPrepareLatents() +``` + +## Creating a pipeline + +There are two ways to create a [`ModularPipeline`]. Assemble and create a pipeline from [`ModularPipelineBlocks`] or load an existing pipeline with [`~ModularPipeline.from_pretrained`]. + +You should also initialize a [`ComponentsManager`] to handle device placement and memory and component management. + +> [!TIP] +> Refer to the [ComponentsManager](./components_manager) doc for more details about how it can help manage components across different workflows. + + + + +Use the [`~ModularPipelineBlocks.init_pipeline`] method to create a [`ModularPipeline`] from the component and configuration specifications. This method loads the *specifications* from a `modular_model_index.json` file, but it doesn't load the *models* yet. + +```py +from diffusers import ComponentsManager +from diffusers.modular_pipelines import SequentialPipelineBlocks +from diffusers.modular_pipelines.stable_diffusion_xl import TEXT2IMAGE_BLOCKS + +t2i_blocks = SequentialPipelineBlocks.from_blocks_dict(TEXT2IMAGE_BLOCKS) + +modular_repo_id = "YiYiXu/modular-loader-t2i-0704" +components = ComponentsManager() +t2i_pipeline = t2i_blocks.init_pipeline(modular_repo_id, components_manager=components) +``` + + + + +The [`~ModularPipeline.from_pretrained`] method creates a [`ModularPipeline`] from a modular repository on the Hub. + +```py +from diffusers import ModularPipeline, ComponentsManager + +components = ComponentsManager() +pipeline = ModularPipeline.from_pretrained("YiYiXu/modular-loader-t2i-0704", components_manager=components) +``` + +Add the `trust_remote_code` argument to load a custom [`ModularPipeline`]. + +```py +from diffusers import ModularPipeline, ComponentsManager + +components = ComponentsManager() +modular_repo_id = "YiYiXu/modular-diffdiff-0704" +diffdiff_pipeline = ModularPipeline.from_pretrained(modular_repo_id, trust_remote_code=True, components_manager=components) +``` + + + + +## Loading components + +A [`ModularPipeline`] doesn't automatically instantiate with components. It only loads the configuration and component specifications. You can load all components with [`~ModularPipeline.load_components`] or only load specific components with [`~ModularPipeline.load_components`]. + + + + +```py +import torch + +t2i_pipeline.load_components(torch_dtype=torch.float16) +t2i_pipeline.to("cuda") +``` + + + + +The example below only loads the UNet and VAE. + +```py +import torch + +t2i_pipeline.load_components(names=["unet", "vae"], torch_dtype=torch.float16) +``` + + + + +Print the pipeline to inspect the loaded pretrained components. + +```py +t2i_pipeline +``` + +This should match the `modular_model_index.json` file from the modular repository a pipeline is initialized from. If a pipeline doesn't need a component, it won't be included even if it exists in the modular repository. + +To modify where components are loaded from, edit the `modular_model_index.json` file in the repository and change it to your desired loading path. The example below loads a UNet from a different repository. + +```json +# original +"unet": [ + null, null, + { + "repo": "stabilityai/stable-diffusion-xl-base-1.0", + "subfolder": "unet", + "variant": "fp16" + } +] + +# modified +"unet": [ + null, null, + { + "repo": "RunDiffusion/Juggernaut-XL-v9", + "subfolder": "unet", + "variant": "fp16" + } +] +``` + +### Component loading status + +The pipeline properties below provide more information about which components are loaded. + +Use `component_names` to return all expected components. + +```py +t2i_pipeline.component_names +['text_encoder', 'text_encoder_2', 'tokenizer', 'tokenizer_2', 'guider', 'scheduler', 'unet', 'vae', 'image_processor'] +``` + +Use `null_component_names` to return components that aren't loaded yet. Load these components with [`~ModularPipeline.from_pretrained`]. + +```py +t2i_pipeline.null_component_names +['text_encoder', 'text_encoder_2', 'tokenizer', 'tokenizer_2', 'scheduler'] +``` + +Use `pretrained_component_names` to return components that will be loaded from pretrained models. + +```py +t2i_pipeline.pretrained_component_names +['text_encoder', 'text_encoder_2', 'tokenizer', 'tokenizer_2', 'scheduler', 'unet', 'vae'] +``` + +Use `config_component_names` to return components that are created with the default config (not loaded from a modular repository). Components from a config aren't included because they are already initialized during pipeline creation. This is why they aren't listed in `null_component_names`. + +```py +t2i_pipeline.config_component_names +['guider', 'image_processor'] +``` + +## Updating components + +Components may be updated depending on whether it is a *pretrained component* or a *config component*. + +> [!WARNING] +> A component may change from pretrained to config when updating a component. The component type is initially defined in a block's `expected_components` field. + +A pretrained component is updated with [`ComponentSpec`] whereas a config component is updated by eihter passing the object directly or with [`ComponentSpec`]. + +The [`ComponentSpec`] shows `default_creation_method="from_pretrained"` for a pretrained component shows `default_creation_method="from_config` for a config component. + +To update a pretrained component, create a [`ComponentSpec`] with the name of the component and where to load it from. Use the [`~ComponentSpec.load`] method to load the component. + +```py +from diffusers import ComponentSpec, UNet2DConditionModel + +unet_spec = ComponentSpec(name="unet",type_hint=UNet2DConditionModel, repo="stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet", variant="fp16") +unet = unet_spec.load(torch_dtype=torch.float16) +``` + +The [`~ModularPipeline.update_components`] method replaces the component with a new one. + +```py +t2i_pipeline.update_components(unet=unet2) +``` + +When a component is updated, the loading specifications are also updated in the pipeline config. + +### Component extraction and modification + +When you use [`~ComponentSpec.load`], the new component maintains its loading specifications. This makes it possible to extract the specification and recreate the component. + +```py +spec = ComponentSpec.from_component("unet", unet2) +spec +ComponentSpec(name='unet', type_hint=, description=None, config=None, repo='stabilityai/stable-diffusion-xl-base-1.0', subfolder='unet', variant='fp16', revision=None, default_creation_method='from_pretrained') +unet2_recreated = spec.load(torch_dtype=torch.float16) +``` + +The [`~ModularPipeline.get_component_spec`] method gets a copy of the current component specification to modify or update. + +```py +unet_spec = t2i_pipeline.get_component_spec("unet") +unet_spec +ComponentSpec( + name='unet', + type_hint=, + repo='RunDiffusion/Juggernaut-XL-v9', + subfolder='unet', + variant='fp16', + default_creation_method='from_pretrained' +) + +# modify to load from a different repository +unet_spec.repo = "stabilityai/stable-diffusion-xl-base-1.0" + +# load component with modified spec +unet = unet_spec.load(torch_dtype=torch.float16) +``` + +## Modular repository + +A repository is required if the pipeline blocks use *pretrained components*. The repository supplies loading specifications and metadata. + +[`ModularPipeline`] specifically requires *modular repositories* (see [example repository](https://huggingface.co/YiYiXu/modular-diffdiff)) which are more flexible than a typical repository. It contains a `modular_model_index.json` file containing the following 3 elements. + +- `library` and `class` shows which library the component was loaded from and it's class. If `null`, the component hasn't been loaded yet. +- `loading_specs_dict` contains the information required to load the component such as the repository and subfolder it is loaded from. + +Unlike standard repositories, a modular repository can fetch components from different repositories based on the `loading_specs_dict`. Components don't need to exist in the same repository. + +A modular repository may contain custom code for loading a [`ModularPipeline`]. This allows you to use specialized blocks that aren't native to Diffusers. + +``` +modular-diffdiff-0704/ +├── block.py # Custom pipeline blocks implementation +├── config.json # Pipeline configuration and auto_map +└── modular_model_index.json # Component loading specifications +``` + +The [config.json](https://huggingface.co/YiYiXu/modular-diffdiff-0704/blob/main/config.json) file contains an `auto_map` key that points to where a custom block is defined in `block.py`. + +```json +{ + "_class_name": "DiffDiffBlocks", + "auto_map": { + "ModularPipelineBlocks": "block.DiffDiffBlocks" + } +} +``` diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/overview.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/overview.md new file mode 100644 index 0000000000000000000000000000000000000000..7d07c4b734347e1fdfb7ad6eba5ef4ecdb8ecb45 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/overview.md @@ -0,0 +1,41 @@ + + +# Overview + +> [!WARNING] +> Modular Diffusers is under active development and it's API may change. + +Modular Diffusers is a unified pipeline system that simplifies your workflow with *pipeline blocks*. + +- Blocks are reusable and you only need to create new blocks that are unique to your pipeline. +- Blocks can be mixed and matched to adapt to or create a pipeline for a specific workflow or multiple workflows. + +The Modular Diffusers docs are organized as shown below. + +## Quickstart + +- A [quickstart](./quickstart) demonstrating how to implement an example workflow with Modular Diffusers. + +## ModularPipelineBlocks + +- [States](./modular_diffusers_states) explains how data is shared and communicated between blocks and [`ModularPipeline`]. +- [ModularPipelineBlocks](./pipeline_block) is the most basic unit of a [`ModularPipeline`] and this guide shows you how to create one. +- [SequentialPipelineBlocks](./sequential_pipeline_blocks) is a type of block that chains multiple blocks so they run one after another, passing data along the chain. This guide shows you how to create [`~modular_pipelines.SequentialPipelineBlocks`] and how they connect and work together. +- [LoopSequentialPipelineBlocks](./loop_sequential_pipeline_blocks) is a type of block that runs a series of blocks in a loop. This guide shows you how to create [`~modular_pipelines.LoopSequentialPipelineBlocks`]. +- [AutoPipelineBlocks](./auto_pipeline_blocks) is a type of block that automatically chooses which blocks to run based on the input. This guide shows you how to create [`~modular_pipelines.AutoPipelineBlocks`]. + +## ModularPipeline + +- [ModularPipeline](./modular_pipeline) shows you how to create and convert pipeline blocks into an executable [`ModularPipeline`]. +- [ComponentsManager](./components_manager) shows you how to manage and reuse components across multiple pipelines. +- [Guiders](./guiders) shows you how to use different guidance methods in the pipeline. \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/pipeline_block.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/pipeline_block.md new file mode 100644 index 0000000000000000000000000000000000000000..06c115e1fb5218594f1f3dfe8fdd60bc9d909a7f --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/pipeline_block.md @@ -0,0 +1,105 @@ + + +# ModularPipelineBlocks + +[`~modular_pipelines.ModularPipelineBlocks`] is the basic block for building a [`ModularPipeline`]. It defines what components, inputs/outputs, and computation a block should perform for a specific step in a pipeline. A [`~modular_pipelines.ModularPipelineBlocks`] connects with other blocks, using [state](./modular_diffusers_states), to enable the modular construction of workflows. + +A [`~modular_pipelines.ModularPipelineBlocks`] on it's own can't be executed. It is a blueprint for what a step should do in a pipeline. To actually run and execute a pipeline, the [`~modular_pipelines.ModularPipelineBlocks`] needs to be converted into a [`ModularPipeline`]. + +This guide will show you how to create a [`~modular_pipelines.ModularPipelineBlocks`]. + +## Inputs and outputs + +> [!TIP] +> Refer to the [States](./modular_diffusers_states) guide if you aren't familiar with how state works in Modular Diffusers. + +A [`~modular_pipelines.ModularPipelineBlocks`] requires `inputs`, and `intermediate_outputs`. + +- `inputs` are values provided by a user and retrieved from the [`~modular_pipelines.PipelineState`]. This is useful because some workflows resize an image, but the original image is still required. The [`~modular_pipelines.PipelineState`] maintains the original image. + + Use `InputParam` to define `inputs`. + + ```py + from diffusers.modular_pipelines import InputParam + + user_inputs = [ + InputParam(name="image", type_hint="PIL.Image", description="raw input image to process") + ] + ``` + +- `intermediate_outputs` are new values created by a block and added to the [`~modular_pipelines.PipelineState`]. The `intermediate_outputs` are available as `inputs` for subsequent blocks or available as the final output from running the pipeline. + + Use `OutputParam` to define `intermediate_outputs`. + + ```py + from diffusers.modular_pipelines import OutputParam + + user_intermediate_outputs = [ + OutputParam(name="image_latents", description="latents representing the image") + ] + ``` + +The intermediate inputs and outputs share data to connect blocks. They are accessible at any point, allowing you to track the workflow's progress. + +## Computation logic + +The computation a block performs is defined in the `__call__` method and it follows a specific structure. + +1. Retrieve the [`~modular_pipelines.BlockState`] to get a local view of the `inputs` +2. Implement the computation logic on the `inputs`. +3. Update [`~modular_pipelines.PipelineState`] to push changes from the local [`~modular_pipelines.BlockState`] back to the global [`~modular_pipelines.PipelineState`]. +4. Return the components and state which becomes available to the next block. + +```py +def __call__(self, components, state): + # Get a local view of the state variables this block needs + block_state = self.get_block_state(state) + + # Your computation logic here + # block_state contains all your inputs + # Access them like: block_state.image, block_state.processed_image + + # Update the pipeline state with your updated block_states + self.set_block_state(state, block_state) + return components, state +``` + +### Components and configs + +The components and pipeline-level configs a block needs are specified in [`ComponentSpec`] and [`~modular_pipelines.ConfigSpec`]. + +- [`ComponentSpec`] contains the expected components used by a block. You need the `name` of the component and ideally a `type_hint` that specifies exactly what the component is. +- [`~modular_pipelines.ConfigSpec`] contains pipeline-level settings that control behavior across all blocks. + +```py +from diffusers import ComponentSpec, ConfigSpec + +expected_components = [ + ComponentSpec(name="unet", type_hint=UNet2DConditionModel), + ComponentSpec(name="scheduler", type_hint=EulerDiscreteScheduler) +] + +expected_config = [ + ConfigSpec("force_zeros_for_empty_prompt", True) +] +``` + +When the blocks are converted into a pipeline, the components become available to the block as the first argument in `__call__`. + +```py +def __call__(self, components, state): + # Access components using dot notation + unet = components.unet + vae = components.vae + scheduler = components.scheduler +``` diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/quickstart.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/quickstart.md new file mode 100644 index 0000000000000000000000000000000000000000..32d14d84e2433bd59cebab90bebc12616b92a473 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/quickstart.md @@ -0,0 +1,344 @@ + + +# Quickstart + +Modular Diffusers is a framework for quickly building flexible and customizable pipelines. At the core of Modular Diffusers are [`ModularPipelineBlocks`] that can be combined with other blocks to adapt to new workflows. The blocks are converted into a [`ModularPipeline`], a friendly user-facing interface developers can use. + +This doc will show you how to implement a [Differential Diffusion](https://differential-diffusion.github.io/) pipeline with the modular framework. + +## ModularPipelineBlocks + +[`ModularPipelineBlocks`] are *definitions* that specify the components, inputs, outputs, and computation logic for a single step in a pipeline. There are four types of blocks. + +- [`ModularPipelineBlocks`] is the most basic block for a single step. +- [`SequentialPipelineBlocks`] is a multi-block that composes other blocks linearly. The outputs of one block are the inputs to the next block. +- [`LoopSequentialPipelineBlocks`] is a multi-block that runs iteratively and is designed for iterative workflows. +- [`AutoPipelineBlocks`] is a collection of blocks for different workflows and it selects which block to run based on the input. It is designed to conveniently package multiple workflows into a single pipeline. + +[Differential Diffusion](https://differential-diffusion.github.io/) is an image-to-image workflow. Start with the `IMAGE2IMAGE_BLOCKS` preset, a collection of `ModularPipelineBlocks` for image-to-image generation. + +```py +from diffusers.modular_pipelines.stable_diffusion_xl import IMAGE2IMAGE_BLOCKS +IMAGE2IMAGE_BLOCKS = InsertableDict([ + ("text_encoder", StableDiffusionXLTextEncoderStep), + ("image_encoder", StableDiffusionXLVaeEncoderStep), + ("input", StableDiffusionXLInputStep), + ("set_timesteps", StableDiffusionXLImg2ImgSetTimestepsStep), + ("prepare_latents", StableDiffusionXLImg2ImgPrepareLatentsStep), + ("prepare_add_cond", StableDiffusionXLImg2ImgPrepareAdditionalConditioningStep), + ("denoise", StableDiffusionXLDenoiseStep), + ("decode", StableDiffusionXLDecodeStep) +]) +``` + +## Pipeline and block states + +Modular Diffusers uses *state* to communicate data between blocks. There are two types of states. + +- [`PipelineState`] is a global state that can be used to track all inputs and outputs across all blocks. +- [`BlockState`] is a local view of relevant variables from [`PipelineState`] for an individual block. + +## Customizing blocks + +[Differential Diffusion](https://differential-diffusion.github.io/) differs from standard image-to-image in its `prepare_latents` and `denoise` blocks. All the other blocks can be reused, but you'll need to modify these two. + +Create placeholder `ModularPipelineBlocks` for `prepare_latents` and `denoise` by copying and modifying the existing ones. + +Print the `denoise` block to see that it is composed of [`LoopSequentialPipelineBlocks`] with three sub-blocks, `before_denoiser`, `denoiser`, and `after_denoiser`. Only the `before_denoiser` sub-block needs to be modified to prepare the latent input for the denoiser based on the change map. + +```py +denoise_blocks = IMAGE2IMAGE_BLOCKS["denoise"]() +print(denoise_blocks) +``` + +Replace the `StableDiffusionXLLoopBeforeDenoiser` sub-block with the new `SDXLDiffDiffLoopBeforeDenoiser` block. + +```py +# Copy existing blocks as placeholders +class SDXLDiffDiffPrepareLatentsStep(ModularPipelineBlocks): + """Copied from StableDiffusionXLImg2ImgPrepareLatentsStep - will modify later""" + # ... same implementation as StableDiffusionXLImg2ImgPrepareLatentsStep + +class SDXLDiffDiffDenoiseStep(StableDiffusionXLDenoiseLoopWrapper): + block_classes = [SDXLDiffDiffLoopBeforeDenoiser, StableDiffusionXLLoopDenoiser, StableDiffusionXLLoopAfterDenoiser] + block_names = ["before_denoiser", "denoiser", "after_denoiser"] +``` + +### prepare_latents + +The `prepare_latents` block requires the following changes. + +- a processor to process the change map +- a new `inputs` to accept the user-provided change map, `timestep` for precomputing all the latents and `num_inference_steps` to create the mask for updating the image regions +- update the computation in the `__call__` method for processing the change map and creating the masks, and storing it in the [`BlockState`] + +```diff +class SDXLDiffDiffPrepareLatentsStep(ModularPipelineBlocks): + @property + def expected_components(self) -> List[ComponentSpec]: + return [ + ComponentSpec("vae", AutoencoderKL), + ComponentSpec("scheduler", EulerDiscreteScheduler), ++ ComponentSpec("mask_processor", VaeImageProcessor, config=FrozenDict({"do_normalize": False, "do_convert_grayscale": True})) + ] + @property + def inputs(self) -> List[Tuple[str, Any]]: + return [ + InputParam("generator"), ++ InputParam("diffdiff_map", required=True), +- InputParam("latent_timestep", required=True, type_hint=torch.Tensor), ++ InputParam("timesteps", type_hint=torch.Tensor), ++ InputParam("num_inference_steps", type_hint=int), + ] + + @property + def intermediate_outputs(self) -> List[OutputParam]: + return [ ++ OutputParam("original_latents", type_hint=torch.Tensor), ++ OutputParam("diffdiff_masks", type_hint=torch.Tensor), + ] + def __call__(self, components, state: PipelineState): + # ... existing logic ... ++ # Process change map and create masks ++ diffdiff_map = components.mask_processor.preprocess(block_state.diffdiff_map, height=latent_height, width=latent_width) ++ thresholds = torch.arange(block_state.num_inference_steps, dtype=diffdiff_map.dtype) / block_state.num_inference_steps ++ block_state.diffdiff_masks = diffdiff_map > (thresholds + (block_state.denoising_start or 0)) ++ block_state.original_latents = block_state.latents +``` + +### denoise + +The `before_denoiser` sub-block requires the following changes. + +- a new `inputs` to accept a `denoising_start` parameter, `original_latents` and `diffdiff_masks` from the `prepare_latents` block +- update the computation in the `__call__` method for applying Differential Diffusion + +```diff +class SDXLDiffDiffLoopBeforeDenoiser(ModularPipelineBlocks): + @property + def description(self) -> str: + return ( + "Step within the denoising loop for differential diffusion that prepare the latent input for the denoiser" + ) + + @property + def inputs(self) -> List[str]: + return [ + InputParam("latents", required=True, type_hint=torch.Tensor), ++ InputParam("denoising_start"), ++ InputParam("original_latents", type_hint=torch.Tensor), ++ InputParam("diffdiff_masks", type_hint=torch.Tensor), + ] + + def __call__(self, components, block_state, i, t): ++ # Apply differential diffusion logic ++ if i == 0 and block_state.denoising_start is None: ++ block_state.latents = block_state.original_latents[:1] ++ else: ++ block_state.mask = block_state.diffdiff_masks[i].unsqueeze(0).unsqueeze(1) ++ block_state.latents = block_state.original_latents[i] * block_state.mask + block_state.latents * (1 - block_state.mask) + + # ... rest of existing logic ... +``` + +## Assembling the blocks + +You should have all the blocks you need at this point to create a [`ModularPipeline`]. + +Copy the existing `IMAGE2IMAGE_BLOCKS` preset and for the `set_timesteps` block, use the `set_timesteps` from the `TEXT2IMAGE_BLOCKS` because Differential Diffusion doesn't require a `strength` parameter. + +Set the `prepare_latents` and `denoise` blocks to the `SDXLDiffDiffPrepareLatentsStep` and `SDXLDiffDiffDenoiseStep` blocks you just modified. + +Call [`SequentialPipelineBlocks.from_blocks_dict`] on the blocks to create a `SequentialPipelineBlocks`. + +```py +DIFFDIFF_BLOCKS = IMAGE2IMAGE_BLOCKS.copy() +DIFFDIFF_BLOCKS["set_timesteps"] = TEXT2IMAGE_BLOCKS["set_timesteps"] +DIFFDIFF_BLOCKS["prepare_latents"] = SDXLDiffDiffPrepareLatentsStep +DIFFDIFF_BLOCKS["denoise"] = SDXLDiffDiffDenoiseStep + +dd_blocks = SequentialPipelineBlocks.from_blocks_dict(DIFFDIFF_BLOCKS) +print(dd_blocks) +``` + +## ModularPipeline + +Convert the [`SequentialPipelineBlocks`] into a [`ModularPipeline`] with the [`ModularPipeline.init_pipeline`] method. This initializes the expected components to load from a `modular_model_index.json` file. Explicitly load the components by calling [`ModularPipeline.load_components`]. + +It is a good idea to initialize the [`ComponentManager`] with the pipeline to help manage the different components. Once you call [`~ModularPipeline.load_components`], the components are registered to the [`ComponentManager`] and can be shared between workflows. The example below uses the `collection` argument to assign the components a `"diffdiff"` label for better organization. + +```py +from diffusers.modular_pipelines import ComponentsManager + +components = ComponentManager() + +dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", components_manager=components, collection="diffdiff") +dd_pipeline.load_componenets(torch_dtype=torch.float16) +dd_pipeline.to("cuda") +``` + +## Adding workflows + +Other workflows can be added to the [`ModularPipeline`] to support additional features without rewriting the entire pipeline from scratch. + +This section demonstrates how to add an IP-Adapter or ControlNet. + +### IP-Adapter + +Stable Diffusion XL already has a preset IP-Adapter block that you can use and doesn't require any changes to the existing Differential Diffusion pipeline. + +```py +from diffusers.modular_pipelines.stable_diffusion_xl.encoders import StableDiffusionXLAutoIPAdapterStep + +ip_adapter_block = StableDiffusionXLAutoIPAdapterStep() +``` + +Use the [`sub_blocks.insert`] method to insert it into the [`ModularPipeline`]. The example below inserts the `ip_adapter_block` at position `0`. Print the pipeline to see that the `ip_adapter_block` is added and it requires an `ip_adapter_image`. This also added two components to the pipeline, the `image_encoder` and `feature_extractor`. + +```py +dd_blocks.sub_blocks.insert("ip_adapter", ip_adapter_block, 0) +``` + +Call [`~ModularPipeline.init_pipeline`] to initialize a [`ModularPipeline`] and use [`~ModularPipeline.load_components`] to load the model components. Load and set the IP-Adapter to run the pipeline. + +```py +dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff") +dd_pipeline.load_components(torch_dtype=torch.float16) +dd_pipeline.loader.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin") +dd_pipeline.loader.set_ip_adapter_scale(0.6) +dd_pipeline = dd_pipeline.to(device) + +ip_adapter_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/diffdiff_orange.jpeg") +image = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/20240329211129_4024911930.png?download=true") +mask = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/gradient_mask.png?download=true") + +prompt = "a green pear" +negative_prompt = "blurry" +generator = torch.Generator(device=device).manual_seed(42) + +image = dd_pipeline( + prompt=prompt, + negative_prompt=negative_prompt, + num_inference_steps=25, + generator=generator, + ip_adapter_image=ip_adapter_image, + diffdiff_map=mask, + image=image, + output="images" +)[0] +``` + +### ControlNet + +Stable Diffusion XL already has a preset ControlNet block that can readily be used. + +```py +from diffusers.modular_pipelines.stable_diffusion_xl.modular_blocks import StableDiffusionXLAutoControlNetInputStep + +control_input_block = StableDiffusionXLAutoControlNetInputStep() +``` + +However, it requires modifying the `denoise` block because that's where the ControlNet injects the control information into the UNet. + +Modify the `denoise` block by replacing the `StableDiffusionXLLoopDenoiser` sub-block with the `StableDiffusionXLControlNetLoopDenoiser`. + +```py +class SDXLDiffDiffControlNetDenoiseStep(StableDiffusionXLDenoiseLoopWrapper): + block_classes = [SDXLDiffDiffLoopBeforeDenoiser, StableDiffusionXLControlNetLoopDenoiser, StableDiffusionXLDenoiseLoopAfterDenoiser] + block_names = ["before_denoiser", "denoiser", "after_denoiser"] + +controlnet_denoise_block = SDXLDiffDiffControlNetDenoiseStep() +``` + +Insert the `controlnet_input` block and replace the `denoise` block with the new `controlnet_denoise_block`. Initialize a [`ModularPipeline`] and [`~ModularPipeline.load_components`] into it. + +```py +dd_blocks.sub_blocks.insert("controlnet_input", control_input_block, 7) +dd_blocks.sub_blocks["denoise"] = controlnet_denoise_block + +dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff") +dd_pipeline.load_components(torch_dtype=torch.float16) +dd_pipeline = dd_pipeline.to(device) + +control_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/diffdiff_tomato_canny.jpeg") +image = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/20240329211129_4024911930.png?download=true") +mask = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/gradient_mask.png?download=true") + +prompt = "a green pear" +negative_prompt = "blurry" +generator = torch.Generator(device=device).manual_seed(42) + +image = dd_pipeline( + prompt=prompt, + negative_prompt=negative_prompt, + num_inference_steps=25, + generator=generator, + control_image=control_image, + controlnet_conditioning_scale=0.5, + diffdiff_map=mask, + image=image, + output="images" +)[0] +``` + +### AutoPipelineBlocks + +The Differential Diffusion, IP-Adapter, and ControlNet workflows can be bundled into a single [`ModularPipeline`] by using [`AutoPipelineBlocks`]. This allows automatically selecting which sub-blocks to run based on the inputs like `control_image` or `ip_adapter_image`. If none of these inputs are passed, then it defaults to the Differential Diffusion. + +Use `block_trigger_inputs` to only run the `SDXLDiffDiffControlNetDenoiseStep` block if a `control_image` input is provided. Otherwise, the `SDXLDiffDiffDenoiseStep` is used. + +```py +class SDXLDiffDiffAutoDenoiseStep(AutoPipelineBlocks): + block_classes = [SDXLDiffDiffControlNetDenoiseStep, SDXLDiffDiffDenoiseStep] + block_names = ["controlnet_denoise", "denoise"] + block_trigger_inputs = ["controlnet_cond", None] +``` + +Add the `ip_adapter` and `controlnet_input` blocks. + +```py +DIFFDIFF_AUTO_BLOCKS = IMAGE2IMAGE_BLOCKS.copy() +DIFFDIFF_AUTO_BLOCKS["prepare_latents"] = SDXLDiffDiffPrepareLatentsStep +DIFFDIFF_AUTO_BLOCKS["set_timesteps"] = TEXT2IMAGE_BLOCKS["set_timesteps"] +DIFFDIFF_AUTO_BLOCKS["denoise"] = SDXLDiffDiffAutoDenoiseStep +DIFFDIFF_AUTO_BLOCKS.insert("ip_adapter", StableDiffusionXLAutoIPAdapterStep, 0) +DIFFDIFF_AUTO_BLOCKS.insert("controlnet_input",StableDiffusionXLControlNetAutoInput, 7) +``` + +Call [`SequentialPipelineBlocks.from_blocks_dict`] to create a [`SequentialPipelineBlocks`] and create a [`ModularPipeline`] and load in the model components to run. + +```py +dd_auto_blocks = SequentialPipelineBlocks.from_blocks_dict(DIFFDIFF_AUTO_BLOCKS) +dd_pipeline = dd_auto_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff") +dd_pipeline.load_components(torch_dtype=torch.float16) +``` + +## Share + +Add your [`ModularPipeline`] to the Hub with [`~ModularPipeline.save_pretrained`] and set `push_to_hub` argument to `True`. + +```py +dd_pipeline.save_pretrained("YiYiXu/test_modular_doc", push_to_hub=True) +``` + +Other users can load the [`ModularPipeline`] with [`~ModularPipeline.from_pretrained`]. + +```py +import torch +from diffusers.modular_pipelines import ModularPipeline, ComponentsManager + +components = ComponentsManager() + +diffdiff_pipeline = ModularPipeline.from_pretrained("YiYiXu/modular-diffdiff-0704", trust_remote_code=True, components_manager=components, collection="diffdiff") +diffdiff_pipeline.load_components(torch_dtype=torch.float16) +``` diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/sequential_pipeline_blocks.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/sequential_pipeline_blocks.md new file mode 100644 index 0000000000000000000000000000000000000000..f1549a26b86f5e356d680491eaca6c95527aa849 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/modular_diffusers/sequential_pipeline_blocks.md @@ -0,0 +1,113 @@ + + +# SequentialPipelineBlocks + +[`~modular_pipelines.SequentialPipelineBlocks`] are a multi-block type that composes other [`~modular_pipelines.ModularPipelineBlocks`] together in a sequence. Data flows linearly from one block to the next using `inputs` and `intermediate_outputs`. Each block in [`~modular_pipelines.SequentialPipelineBlocks`] usually represents a step in the pipeline, and by combining them, you gradually build a pipeline. + +This guide shows you how to connect two blocks into a [`~modular_pipelines.SequentialPipelineBlocks`]. + +Create two [`~modular_pipelines.ModularPipelineBlocks`]. The first block, `InputBlock`, outputs a `batch_size` value and the second block, `ImageEncoderBlock` uses `batch_size` as `inputs`. + + + + +```py +from diffusers.modular_pipelines import ModularPipelineBlocks, InputParam, OutputParam + +class InputBlock(ModularPipelineBlocks): + + @property + def inputs(self): + return [ + InputParam(name="prompt", type_hint=list, description="list of text prompts"), + InputParam(name="num_images_per_prompt", type_hint=int, description="number of images per prompt"), + ] + + @property + def intermediate_outputs(self): + return [ + OutputParam(name="batch_size", description="calculated batch size"), + ] + + @property + def description(self): + return "A block that determines batch_size based on the number of prompts and num_images_per_prompt argument." + + def __call__(self, components, state): + block_state = self.get_block_state(state) + batch_size = len(block_state.prompt) + block_state.batch_size = batch_size * block_state.num_images_per_prompt + self.set_block_state(state, block_state) + return components, state +``` + + + + +```py +import torch +from diffusers.modular_pipelines import ModularPipelineBlocks, InputParam, OutputParam + +class ImageEncoderBlock(ModularPipelineBlocks): + + @property + def inputs(self): + return [ + InputParam(name="image", type_hint="PIL.Image", description="raw input image to process"), + InputParam(name="batch_size", type_hint=int), + ] + + @property + def intermediate_outputs(self): + return [ + OutputParam(name="image_latents", description="latents representing the image"), + ] + + @property + def description(self): + return "Encode raw image into its latent presentation" + + def __call__(self, components, state): + block_state = self.get_block_state(state) + # Simulate processing the image + # This will change the state of the image from a PIL image to a tensor for all blocks + block_state.image = torch.randn(1, 3, 512, 512) + block_state.batch_size = block_state.batch_size * 2 + block_state.image_latents = torch.randn(1, 4, 64, 64) + self.set_block_state(state, block_state) + return components, state +``` + + + + +Connect the two blocks by defining an [`InsertableDict`] to map the block names to the block instances. Blocks are executed in the order they're registered in `blocks_dict`. + +Use [`~modular_pipelines.SequentialPipelineBlocks.from_blocks_dict`] to create a [`~modular_pipelines.SequentialPipelineBlocks`]. + +```py +from diffusers.modular_pipelines import SequentialPipelineBlocks, InsertableDict + +blocks_dict = InsertableDict() +blocks_dict["input"] = input_block +blocks_dict["image_encoder"] = image_encoder_block + +blocks = SequentialPipelineBlocks.from_blocks_dict(blocks_dict) +``` + +Inspect the sub-blocks in [`~modular_pipelines.SequentialPipelineBlocks`] by calling `blocks`, and for more details about the inputs and outputs, access the `docs` attribute. + +```py +print(blocks) +print(blocks.doc) +``` diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/attention_backends.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/attention_backends.md new file mode 100644 index 0000000000000000000000000000000000000000..bce5a8adaee945f35f36c90b2215025d7658b9d3 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/attention_backends.md @@ -0,0 +1,157 @@ + + +# Attention backends + +> [!NOTE] +> The attention dispatcher is an experimental feature. Please open an issue if you have any feedback or encounter any problems. + +Diffusers provides several optimized attention algorithms that are more memory and computationally efficient through it's *attention dispatcher*. The dispatcher acts as a router for managing and switching between different attention implementations and provides a unified interface for interacting with them. + +Refer to the table below for an overview of the available attention families and to the [Available backends](#available-backends) section for a more complete list. + +| attention family | main feature | +|---|---| +| FlashAttention | minimizes memory reads/writes through tiling and recomputation | +| AI Tensor Engine for ROCm | FlashAttention implementation optimized for AMD ROCm accelerators | +| SageAttention | quantizes attention to int8 | +| PyTorch native | built-in PyTorch implementation using [scaled_dot_product_attention](./fp16#scaled-dot-product-attention) | +| xFormers | memory-efficient attention with support for various attention kernels | + +This guide will show you how to set and use the different attention backends. + +## set_attention_backend + +The [`~ModelMixin.set_attention_backend`] method iterates through all the modules in the model and sets the appropriate attention backend to use. The attention backend setting persists until [`~ModelMixin.reset_attention_backend`] is called. + +The example below demonstrates how to enable the `_flash_3_hub` implementation for FlashAttention-3 from the [kernel](https://github.com/huggingface/kernels) library, which allows you to instantly use optimized compute kernels from the Hub without requiring any setup. + +> [!NOTE] +> FlashAttention-3 is not supported for non-Hopper architectures, in which case, use FlashAttention with `set_attention_backend("flash")`. + +```py +import torch +from diffusers import QwenImagePipeline + +pipeline = QwenImagePipeline.from_pretrained( + "Qwen/Qwen-Image", torch_dtype=torch.bfloat16, device_map="cuda" +) +pipeline.transformer.set_attention_backend("_flash_3_hub") + +prompt = """ +cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California +highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain +""" +pipeline(prompt).images[0] +``` + +To restore the default attention backend, call [`~ModelMixin.reset_attention_backend`]. + +```py +pipeline.transformer.reset_attention_backend() +``` + +## attention_backend context manager + +The [attention_backend](https://github.com/huggingface/diffusers/blob/5e181eddfe7e44c1444a2511b0d8e21d177850a0/src/diffusers/models/attention_dispatch.py#L225) context manager temporarily sets an attention backend for a model within the context. Outside the context, the default attention (PyTorch's native scaled dot product attention) is used. This is useful if you want to use different backends for different parts of a pipeline or if you want to test the different backends. + +```py +import torch +from diffusers import QwenImagePipeline + +pipeline = QwenImagePipeline.from_pretrained( + "Qwen/Qwen-Image", torch_dtype=torch.bfloat16, device_map="cuda" +) +prompt = """ +cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California +highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain +""" + +with attention_backend("_flash_3_hub"): + image = pipeline(prompt).images[0] +``` + +> [!TIP] +> Most attention backends support `torch.compile` without graph breaks and can be used to further speed up inference. + +## Checks + +The attention dispatcher includes debugging checks that catch common errors before they cause problems. + +1. Device checks verify that query, key, and value tensors live on the same device. +2. Data type checks confirm tensors have matching dtypes and use either bfloat16 or float16. +3. Shape checks validate tensor dimensions and prevent mixing attention masks with causal flags. + +Enable these checks by setting the `DIFFUSERS_ATTN_CHECKS` environment variable. Checks add overhead to every attention operation, so they're disabled by default. + +```bash +export DIFFUSERS_ATTN_CHECKS=yes +``` + +The checks are run now before every attention operation. + +```py +import torch + +query = torch.randn(1, 10, 8, 64, dtype=torch.bfloat16, device="cuda") +key = torch.randn(1, 10, 8, 64, dtype=torch.bfloat16, device="cuda") +value = torch.randn(1, 10, 8, 64, dtype=torch.bfloat16, device="cuda") + +try: + with attention_backend("flash"): + output = dispatch_attention_fn(query, key, value) + print("✓ Flash Attention works with checks enabled") +except Exception as e: + print(f"✗ Flash Attention failed: {e}") +``` + +You can also configure the registry directly. + +```py +from diffusers.models.attention_dispatch import _AttentionBackendRegistry + +_AttentionBackendRegistry._checks_enabled = True +``` + +## Available backends + +Refer to the table below for a complete list of available attention backends and their variants. + +
+Expand + +| Backend Name | Family | Description | +|--------------|--------|-------------| +| `native` | [PyTorch native](https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend) | Default backend using PyTorch's scaled_dot_product_attention | +| `flex` | [FlexAttention](https://docs.pytorch.org/docs/stable/nn.attention.flex_attention.html#module-torch.nn.attention.flex_attention) | PyTorch FlexAttention implementation | +| `_native_cudnn` | [PyTorch native](https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend) | CuDNN-optimized attention | +| `_native_efficient` | [PyTorch native](https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend) | Memory-efficient attention | +| `_native_flash` | [PyTorch native](https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend) | PyTorch's FlashAttention | +| `_native_math` | [PyTorch native](https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend) | Math-based attention (fallback) | +| `_native_npu` | [PyTorch native](https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend) | NPU-optimized attention | +| `_native_xla` | [PyTorch native](https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend) | XLA-optimized attention | +| `flash` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-2 | +| `flash_hub` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-2 from kernels | +| `flash_varlen` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention | +| `aiter` | [AI Tensor Engine for ROCm](https://github.com/ROCm/aiter) | FlashAttention for AMD ROCm | +| `_flash_3` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-3 | +| `_flash_varlen_3` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention-3 | +| `_flash_3_hub` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-3 from kernels | +| `sage` | [SageAttention](https://github.com/thu-ml/SageAttention) | Quantized attention (INT8 QK) | +| `sage_hub` | [SageAttention](https://github.com/thu-ml/SageAttention) | Quantized attention (INT8 QK) from kernels | +| `sage_varlen` | [SageAttention](https://github.com/thu-ml/SageAttention) | Variable length SageAttention | +| `_sage_qk_int8_pv_fp8_cuda` | [SageAttention](https://github.com/thu-ml/SageAttention) | INT8 QK + FP8 PV (CUDA) | +| `_sage_qk_int8_pv_fp8_cuda_sm90` | [SageAttention](https://github.com/thu-ml/SageAttention) | INT8 QK + FP8 PV (SM90) | +| `_sage_qk_int8_pv_fp16_cuda` | [SageAttention](https://github.com/thu-ml/SageAttention) | INT8 QK + FP16 PV (CUDA) | +| `_sage_qk_int8_pv_fp16_triton` | [SageAttention](https://github.com/thu-ml/SageAttention) | INT8 QK + FP16 PV (Triton) | +| `xformers` | [xFormers](https://github.com/facebookresearch/xformers) | Memory-efficient attention | + +
\ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/cache.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/cache.md new file mode 100644 index 0000000000000000000000000000000000000000..881529b27ff1b6782f5e824dcf5193339cf5b007 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/cache.md @@ -0,0 +1,69 @@ + + +# Caching + +Caching accelerates inference by storing and reusing intermediate outputs of different layers, such as attention and feedforward layers, instead of performing the entire computation at each inference step. It significantly improves generation speed at the expense of more memory and doesn't require additional training. + +This guide shows you how to use the caching methods supported in Diffusers. + +## Pyramid Attention Broadcast + +[Pyramid Attention Broadcast (PAB)](https://huggingface.co/papers/2408.12588) is based on the observation that attention outputs aren't that different between successive timesteps of the generation process. The attention differences are smallest in the cross attention layers and are generally cached over a longer timestep range. This is followed by temporal attention and spatial attention layers. + +> [!TIP] +> Not all video models have three types of attention (cross, temporal, and spatial)! + +PAB can be combined with other techniques like sequence parallelism and classifier-free guidance parallelism (data parallelism) for near real-time video generation. + +Set up and pass a [`PyramidAttentionBroadcastConfig`] to a pipeline's transformer to enable it. The `spatial_attention_block_skip_range` controls how often to skip attention calculations in the spatial attention blocks and the `spatial_attention_timestep_skip_range` is the range of timesteps to skip. Take care to choose an appropriate range because a smaller interval can lead to slower inference speeds and a larger interval can result in lower generation quality. + +```python +import torch +from diffusers import CogVideoXPipeline, PyramidAttentionBroadcastConfig + +pipeline = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16) +pipeline.to("cuda") + +config = PyramidAttentionBroadcastConfig( + spatial_attention_block_skip_range=2, + spatial_attention_timestep_skip_range=(100, 800), + current_timestep_callback=lambda: pipe.current_timestep, +) +pipeline.transformer.enable_cache(config) +``` + +## FasterCache + +[FasterCache](https://huggingface.co/papers/2410.19355) caches and reuses attention features similar to [PAB](#pyramid-attention-broadcast) since output differences are small for each successive timestep. + +This method may also choose to skip the unconditional branch prediction, when using classifier-free guidance for sampling (common in most base models), and estimate it from the conditional branch prediction if there is significant redundancy in the predicted latent outputs between successive timesteps. + +Set up and pass a [`FasterCacheConfig`] to a pipeline's transformer to enable it. + +```python +import torch +from diffusers import CogVideoXPipeline, FasterCacheConfig + +pipe line= CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16) +pipeline.to("cuda") + +config = FasterCacheConfig( + spatial_attention_block_skip_range=2, + spatial_attention_timestep_skip_range=(-1, 681), + current_timestep_callback=lambda: pipe.current_timestep, + attention_weight_callback=lambda _: 0.3, + unconditional_batch_skip_range=5, + unconditional_batch_timestep_skip_range=(-1, 781), + tensor_format="BFCHW", +) +pipeline.transformer.enable_cache(config) +``` \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/cache_dit.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/cache_dit.md new file mode 100644 index 0000000000000000000000000000000000000000..1261423212490d2400c57466c87691383331242d --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/cache_dit.md @@ -0,0 +1,270 @@ +## CacheDiT + +CacheDiT is a unified, flexible, and training-free cache acceleration framework designed to support nearly all Diffusers' DiT-based pipelines. It provides a unified cache API that supports automatic block adapter, DBCache, and more. + +To learn more, refer to the [CacheDiT](https://github.com/vipshop/cache-dit) repository. + +Install a stable release of CacheDiT from PyPI or you can install the latest version from GitHub. + + + + +```bash +pip3 install -U cache-dit +``` + + + + +```bash +pip3 install git+https://github.com/vipshop/cache-dit.git +``` + + + + +Run the command below to view supported DiT pipelines. + +```python +>>> import cache_dit +>>> cache_dit.supported_pipelines() +(30, ['Flux*', 'Mochi*', 'CogVideoX*', 'Wan*', 'HunyuanVideo*', 'QwenImage*', 'LTX*', 'Allegro*', +'CogView3Plus*', 'CogView4*', 'Cosmos*', 'EasyAnimate*', 'SkyReelsV2*', 'StableDiffusion3*', +'ConsisID*', 'DiT*', 'Amused*', 'Bria*', 'Lumina*', 'OmniGen*', 'PixArt*', 'Sana*', 'StableAudio*', +'VisualCloze*', 'AuraFlow*', 'Chroma*', 'ShapE*', 'HiDream*', 'HunyuanDiT*', 'HunyuanDiTPAG*']) +``` + +For a complete benchmark, please refer to [Benchmarks](https://github.com/vipshop/cache-dit/blob/main/bench/). + + +## Unified Cache API + +CacheDiT works by matching specific input/output patterns as shown below. + +![](https://github.com/vipshop/cache-dit/raw/main/assets/patterns-v1.png) + +Call the `enable_cache()` function on a pipeline to enable cache acceleration. This function is the entry point to many of CacheDiT's features. + +```python +import cache_dit +from diffusers import DiffusionPipeline + +# Can be any diffusion pipeline +pipe = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image") + +# One-line code with default cache options. +cache_dit.enable_cache(pipe) + +# Just call the pipe as normal. +output = pipe(...) + +# Disable cache and run original pipe. +cache_dit.disable_cache(pipe) +``` + +## Automatic Block Adapter + +For custom or modified pipelines or transformers not included in Diffusers, use the `BlockAdapter` in `auto` mode or via manual configuration. Please check the [BlockAdapter](https://github.com/vipshop/cache-dit/blob/main/docs/User_Guide.md#automatic-block-adapter) docs for more details. Refer to [Qwen-Image w/ BlockAdapter](https://github.com/vipshop/cache-dit/blob/main/examples/adapter/run_qwen_image_adapter.py) as an example. + + +```python +from cache_dit import ForwardPattern, BlockAdapter + +# Use 🔥BlockAdapter with `auto` mode. +cache_dit.enable_cache( + BlockAdapter( + # Any DiffusionPipeline, Qwen-Image, etc. + pipe=pipe, auto=True, + # Check `📚Forward Pattern Matching` documentation and hack the code of + # of Qwen-Image, you will find that it has satisfied `FORWARD_PATTERN_1`. + forward_pattern=ForwardPattern.Pattern_1, + ), +) + +# Or, manually setup transformer configurations. +cache_dit.enable_cache( + BlockAdapter( + pipe=pipe, # Qwen-Image, etc. + transformer=pipe.transformer, + blocks=pipe.transformer.transformer_blocks, + forward_pattern=ForwardPattern.Pattern_1, + ), +) +``` + +Sometimes, a Transformer class will contain more than one transformer `blocks`. For example, FLUX.1 (HiDream, Chroma, etc) contains `transformer_blocks` and `single_transformer_blocks` (with different forward patterns). The BlockAdapter is able to detect this hybrid pattern type as well. +Refer to [FLUX.1](https://github.com/vipshop/cache-dit/blob/main/examples/adapter/run_flux_adapter.py) as an example. + +```python +# For diffusers <= 0.34.0, FLUX.1 transformer_blocks and +# single_transformer_blocks have different forward patterns. +cache_dit.enable_cache( + BlockAdapter( + pipe=pipe, # FLUX.1, etc. + transformer=pipe.transformer, + blocks=[ + pipe.transformer.transformer_blocks, + pipe.transformer.single_transformer_blocks, + ], + forward_pattern=[ + ForwardPattern.Pattern_1, + ForwardPattern.Pattern_3, + ], + ), +) +``` + +This also works if there is more than one transformer (namely `transformer` and `transformer_2`) in its structure. Refer to [Wan 2.2 MoE](https://github.com/vipshop/cache-dit/blob/main/examples/pipeline/run_wan_2.2.py) as an example. + +## Patch Functor + +For any pattern not included in CacheDiT, use the Patch Functor to convert the pattern into a known pattern. You need to subclass the Patch Functor and may also need to fuse the operations within the blocks for loop into block `forward`. After implementing a Patch Functor, set the `patch_functor` property in `BlockAdapter`. + +![](https://github.com/vipshop/cache-dit/raw/main/assets/patch-functor.png) + +Some Patch Functors are already provided in CacheDiT, [HiDreamPatchFunctor](https://github.com/vipshop/cache-dit/blob/main/src/cache_dit/cache_factory/patch_functors/functor_hidream.py), [ChromaPatchFunctor](https://github.com/vipshop/cache-dit/blob/main/src/cache_dit/cache_factory/patch_functors/functor_chroma.py), etc. + +```python +@BlockAdapterRegistry.register("HiDream") +def hidream_adapter(pipe, **kwargs) -> BlockAdapter: + from diffusers import HiDreamImageTransformer2DModel + from cache_dit.cache_factory.patch_functors import HiDreamPatchFunctor + + assert isinstance(pipe.transformer, HiDreamImageTransformer2DModel) + return BlockAdapter( + pipe=pipe, + transformer=pipe.transformer, + blocks=[ + pipe.transformer.double_stream_blocks, + pipe.transformer.single_stream_blocks, + ], + forward_pattern=[ + ForwardPattern.Pattern_0, + ForwardPattern.Pattern_3, + ], + # NOTE: Setup your custom patch functor here. + patch_functor=HiDreamPatchFunctor(), + **kwargs, + ) +``` + +Finally, you can call the `cache_dit.summary()` function on a pipeline after its completed inference to get the cache acceleration details. + +```python +stats = cache_dit.summary(pipe) +``` + +```python +⚡️Cache Steps and Residual Diffs Statistics: QwenImagePipeline + +| Cache Steps | Diffs Min | Diffs P25 | Diffs P50 | Diffs P75 | Diffs P95 | Diffs Max | +|-------------|-----------|-----------|-----------|-----------|-----------|-----------| +| 23 | 0.045 | 0.084 | 0.114 | 0.147 | 0.241 | 0.297 | +``` + +## DBCache: Dual Block Cache + +![](https://github.com/vipshop/cache-dit/raw/main/assets/dbcache-v1.png) + +DBCache (Dual Block Caching) supports different configurations of compute blocks (F8B12, etc.) to enable a balanced trade-off between performance and precision. +- Fn_compute_blocks: Specifies that DBCache uses the **first n** Transformer blocks to fit the information at time step t, enabling the calculation of a more stable L1 diff and delivering more accurate information to subsequent blocks. +- Bn_compute_blocks: Further fuses approximate information in the **last n** Transformer blocks to enhance prediction accuracy. These blocks act as an auto-scaler for approximate hidden states that use residual cache. + + +```python +import cache_dit +from diffusers import FluxPipeline + +pipe_or_adapter = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=torch.bfloat16, +).to("cuda") + +# Default options, F8B0, 8 warmup steps, and unlimited cached +# steps for good balance between performance and precision +cache_dit.enable_cache(pipe_or_adapter) + +# Custom options, F8B8, higher precision +from cache_dit import BasicCacheConfig + +cache_dit.enable_cache( + pipe_or_adapter, + cache_config=BasicCacheConfig( + max_warmup_steps=8, # steps do not cache + max_cached_steps=-1, # -1 means no limit + Fn_compute_blocks=8, # Fn, F8, etc. + Bn_compute_blocks=8, # Bn, B8, etc. + residual_diff_threshold=0.12, + ), +) +``` +Check the [DBCache](https://github.com/vipshop/cache-dit/blob/main/docs/DBCache.md) and [User Guide](https://github.com/vipshop/cache-dit/blob/main/docs/User_Guide.md#dbcache) docs for more design details. + +## TaylorSeer Calibrator + +The [TaylorSeers](https://huggingface.co/papers/2503.06923) algorithm further improves the precision of DBCache in cases where the cached steps are large (Hybrid TaylorSeer + DBCache). At timesteps with significant intervals, the feature similarity in diffusion models decreases substantially, significantly harming the generation quality. + +TaylorSeer employs a differential method to approximate the higher-order derivatives of features and predict features in future timesteps with Taylor series expansion. The TaylorSeer implemented in CacheDiT supports both hidden states and residual cache types. F_pred can be a residual cache or a hidden-state cache. + +```python +from cache_dit import BasicCacheConfig, TaylorSeerCalibratorConfig + +cache_dit.enable_cache( + pipe_or_adapter, + # Basic DBCache w/ FnBn configurations + cache_config=BasicCacheConfig( + max_warmup_steps=8, # steps do not cache + max_cached_steps=-1, # -1 means no limit + Fn_compute_blocks=8, # Fn, F8, etc. + Bn_compute_blocks=8, # Bn, B8, etc. + residual_diff_threshold=0.12, + ), + # Then, you can use the TaylorSeer Calibrator to approximate + # the values in cached steps, taylorseer_order default is 1. + calibrator_config=TaylorSeerCalibratorConfig( + taylorseer_order=1, + ), +) +``` + +> [!TIP] +> The `Bn_compute_blocks` parameter of DBCache can be set to `0` if you use TaylorSeer as the calibrator for approximate hidden states. DBCache's `Bn_compute_blocks` also acts as a calibrator, so you can choose either `Bn_compute_blocks` > 0 or TaylorSeer. We recommend using the configuration scheme of TaylorSeer + DBCache FnB0. + +## Hybrid Cache CFG + +CacheDiT supports caching for CFG (classifier-free guidance). For models that fuse CFG and non-CFG into a single forward step, or models that do not include CFG in the forward step, please set `enable_separate_cfg` parameter to `False (default, None)`. Otherwise, set it to `True`. + +```python +from cache_dit import BasicCacheConfig + +cache_dit.enable_cache( + pipe_or_adapter, + cache_config=BasicCacheConfig( + ..., + # For example, set it as True for Wan 2.1, Qwen-Image + # and set it as False for FLUX.1, HunyuanVideo, etc. + enable_separate_cfg=True, + ), +) +``` + +## torch.compile + +CacheDiT is designed to work with torch.compile for even better performance. Call `torch.compile` after enabling the cache. + + +```python +cache_dit.enable_cache(pipe) + +# Compile the Transformer module +pipe.transformer = torch.compile(pipe.transformer) +``` + +If you're using CacheDiT with dynamic input shapes, consider increasing the `recompile_limit` of `torch._dynamo`. Otherwise, the `recompile_limit` error may be triggered, causing the module to fall back to eager mode. + +```python +torch._dynamo.config.recompile_limit = 96 # default is 8 +torch._dynamo.config.accumulated_recompile_limit = 2048 # default is 256 +``` + +Please check [perf.py](https://github.com/vipshop/cache-dit/blob/main/bench/perf.py) for more details. diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/coreml.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/coreml.md new file mode 100644 index 0000000000000000000000000000000000000000..71da1e3dc1fe8518523b5f8bdac2b3e28592f71c --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/coreml.md @@ -0,0 +1,161 @@ + + +# How to run Stable Diffusion with Core ML + +[Core ML](https://developer.apple.com/documentation/coreml) is the model format and machine learning library supported by Apple frameworks. If you are interested in running Stable Diffusion models inside your macOS or iOS/iPadOS apps, this guide will show you how to convert existing PyTorch checkpoints into the Core ML format and use them for inference with Python or Swift. + +Core ML models can leverage all the compute engines available in Apple devices: the CPU, the GPU, and the Apple Neural Engine (or ANE, a tensor-optimized accelerator available in Apple Silicon Macs and modern iPhones/iPads). Depending on the model and the device it's running on, Core ML can mix and match compute engines too, so some portions of the model may run on the CPU while others run on GPU, for example. + +> [!TIP] +> You can also run the `diffusers` Python codebase on Apple Silicon Macs using the `mps` accelerator built into PyTorch. This approach is explained in depth in [the mps guide](mps), but it is not compatible with native apps. + +## Stable Diffusion Core ML Checkpoints + +Stable Diffusion weights (or checkpoints) are stored in the PyTorch format, so you need to convert them to the Core ML format before we can use them inside native apps. + +Thankfully, Apple engineers developed [a conversion tool](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml) based on `diffusers` to convert the PyTorch checkpoints to Core ML. + +Before you convert a model, though, take a moment to explore the Hugging Face Hub – chances are the model you're interested in is already available in Core ML format: + +- the [Apple](https://huggingface.co/apple) organization includes Stable Diffusion versions 1.4, 1.5, 2.0 base, and 2.1 base +- [coreml community](https://huggingface.co/coreml-community) includes custom finetuned models +- use this [filter](https://huggingface.co/models?pipeline_tag=text-to-image&library=coreml&p=2&sort=likes) to return all available Core ML checkpoints + +If you can't find the model you're interested in, we recommend you follow the instructions for [Converting Models to Core ML](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml) by Apple. + +## Selecting the Core ML Variant to Use + +Stable Diffusion models can be converted to different Core ML variants intended for different purposes: + +- The type of attention blocks used. The attention operation is used to "pay attention" to the relationship between different areas in the image representations and to understand how the image and text representations are related. Attention is compute- and memory-intensive, so different implementations exist that consider the hardware characteristics of different devices. For Core ML Stable Diffusion models, there are two attention variants: + * `split_einsum` ([introduced by Apple](https://machinelearning.apple.com/research/neural-engine-transformers)) is optimized for ANE devices, which is available in modern iPhones, iPads and M-series computers. + * The "original" attention (the base implementation used in `diffusers`) is only compatible with CPU/GPU and not ANE. It can be *faster* to run your model on CPU + GPU using `original` attention than ANE. See [this performance benchmark](https://huggingface.co/blog/fast-mac-diffusers#performance-benchmarks) as well as some [additional measures provided by the community](https://github.com/huggingface/swift-coreml-diffusers/issues/31) for additional details. + +- The supported inference framework. + * `packages` are suitable for Python inference. This can be used to test converted Core ML models before attempting to integrate them inside native apps, or if you want to explore Core ML performance but don't need to support native apps. For example, an application with a web UI could perfectly use a Python Core ML backend. + * `compiled` models are required for Swift code. The `compiled` models in the Hub split the large UNet model weights into several files for compatibility with iOS and iPadOS devices. This corresponds to the [`--chunk-unet` conversion option](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml). If you want to support native apps, then you need to select the `compiled` variant. + +The official Core ML Stable Diffusion [models](https://huggingface.co/apple/coreml-stable-diffusion-v1-4/tree/main) include these variants, but the community ones may vary: + +``` +coreml-stable-diffusion-v1-4 +├── README.md +├── original +│ ├── compiled +│ └── packages +└── split_einsum + ├── compiled + └── packages +``` + +You can download and use the variant you need as shown below. + +## Core ML Inference in Python + +Install the following libraries to run Core ML inference in Python: + +```bash +pip install huggingface_hub +pip install git+https://github.com/apple/ml-stable-diffusion +``` + +### Download the Model Checkpoints + +To run inference in Python, use one of the versions stored in the `packages` folders because the `compiled` ones are only compatible with Swift. You may choose whether you want to use `original` or `split_einsum` attention. + +This is how you'd download the `original` attention variant from the Hub to a directory called `models`: + +```Python +from huggingface_hub import snapshot_download +from pathlib import Path + +repo_id = "apple/coreml-stable-diffusion-v1-4" +variant = "original/packages" + +model_path = Path("./models") / (repo_id.split("/")[-1] + "_" + variant.replace("/", "_")) +snapshot_download(repo_id, allow_patterns=f"{variant}/*", local_dir=model_path, local_dir_use_symlinks=False) +print(f"Model downloaded at {model_path}") +``` + +### Inference[[python-inference]] + +Once you have downloaded a snapshot of the model, you can test it using Apple's Python script. + +```shell +python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astronaut riding a horse on mars" -i ./models/coreml-stable-diffusion-v1-4_original_packages/original/packages -o --compute-unit CPU_AND_GPU --seed 93 +``` + +Pass the path of the downloaded checkpoint with `-i` flag to the script. `--compute-unit` indicates the hardware you want to allow for inference. It must be one of the following options: `ALL`, `CPU_AND_GPU`, `CPU_ONLY`, `CPU_AND_NE`. You may also provide an optional output path, and a seed for reproducibility. + +The inference script assumes you're using the original version of the Stable Diffusion model, `CompVis/stable-diffusion-v1-4`. If you use another model, you *have* to specify its Hub id in the inference command line, using the `--model-version` option. This works for models already supported and custom models you trained or fine-tuned yourself. + +For example, if you want to use [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5): + +```shell +python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astronaut riding a horse on mars" --compute-unit ALL -o output --seed 93 -i models/coreml-stable-diffusion-v1-5_original_packages --model-version stable-diffusion-v1-5/stable-diffusion-v1-5 +``` + +## Core ML inference in Swift + +Running inference in Swift is slightly faster than in Python because the models are already compiled in the `mlmodelc` format. This is noticeable on app startup when the model is loaded but shouldn’t be noticeable if you run several generations afterward. + +### Download + +To run inference in Swift on your Mac, you need one of the `compiled` checkpoint versions. We recommend you download them locally using Python code similar to the previous example, but with one of the `compiled` variants: + +```Python +from huggingface_hub import snapshot_download +from pathlib import Path + +repo_id = "apple/coreml-stable-diffusion-v1-4" +variant = "original/compiled" + +model_path = Path("./models") / (repo_id.split("/")[-1] + "_" + variant.replace("/", "_")) +snapshot_download(repo_id, allow_patterns=f"{variant}/*", local_dir=model_path, local_dir_use_symlinks=False) +print(f"Model downloaded at {model_path}") +``` + +### Inference[[swift-inference]] + +To run inference, please clone Apple's repo: + +```bash +git clone https://github.com/apple/ml-stable-diffusion +cd ml-stable-diffusion +``` + +And then use Apple's command line tool, [Swift Package Manager](https://www.swift.org/package-manager/#): + +```bash +swift run StableDiffusionSample --resource-path models/coreml-stable-diffusion-v1-4_original_compiled --compute-units all "a photo of an astronaut riding a horse on mars" +``` + +You have to specify in `--resource-path` one of the checkpoints downloaded in the previous step, so please make sure it contains compiled Core ML bundles with the extension `.mlmodelc`. The `--compute-units` has to be one of these values: `all`, `cpuOnly`, `cpuAndGPU`, `cpuAndNeuralEngine`. + +For more details, please refer to the [instructions in Apple's repo](https://github.com/apple/ml-stable-diffusion). + +## Supported Diffusers Features + +The Core ML models and inference code don't support many of the features, options, and flexibility of 🧨 Diffusers. These are some of the limitations to keep in mind: + +- Core ML models are only suitable for inference. They can't be used for training or fine-tuning. +- Only two schedulers have been ported to Swift, the default one used by Stable Diffusion and `DPMSolverMultistepScheduler`, which we ported to Swift from our `diffusers` implementation. We recommend you use `DPMSolverMultistepScheduler`, since it produces the same quality in about half the steps. +- Negative prompts, classifier-free guidance scale, and image-to-image tasks are available in the inference code. Advanced features such as depth guidance, ControlNet, and latent upscalers are not available yet. + +Apple's [conversion and inference repo](https://github.com/apple/ml-stable-diffusion) and our own [swift-coreml-diffusers](https://github.com/huggingface/swift-coreml-diffusers) repos are intended as technology demonstrators to enable other developers to build upon. + +If you feel strongly about any missing features, please feel free to open a feature request or, better yet, a contribution PR 🙂. + +## Native Diffusers Swift app + +One easy way to run Stable Diffusion on your own Apple hardware is to use [our open-source Swift repo](https://github.com/huggingface/swift-coreml-diffusers), based on `diffusers` and Apple's conversion and inference repo. You can study the code, compile it with [Xcode](https://developer.apple.com/xcode/) and adapt it for your own needs. For your convenience, there's also a [standalone Mac app in the App Store](https://apps.apple.com/app/diffusers/id1666309574), so you can play with it without having to deal with the code or IDE. If you are a developer and have determined that Core ML is the best solution to build your Stable Diffusion app, then you can use the rest of this guide to get started with your project. We can't wait to see what you'll build 🙂. diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/deepcache.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/deepcache.md new file mode 100644 index 0000000000000000000000000000000000000000..bba22d72635ae7e937309d66127521ec36be7fe6 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/deepcache.md @@ -0,0 +1,62 @@ + + +# DeepCache +[DeepCache](https://huggingface.co/papers/2312.00858) accelerates [`StableDiffusionPipeline`] and [`StableDiffusionXLPipeline`] by strategically caching and reusing high-level features while efficiently updating low-level features by taking advantage of the U-Net architecture. + +Start by installing [DeepCache](https://github.com/horseee/DeepCache): +```bash +pip install DeepCache +``` + +Then load and enable the [`DeepCacheSDHelper`](https://github.com/horseee/DeepCache#usage): + +```diff + import torch + from diffusers import StableDiffusionPipeline + pipe = StableDiffusionPipeline.from_pretrained('stable-diffusion-v1-5/stable-diffusion-v1-5', torch_dtype=torch.float16).to("cuda") + ++ from DeepCache import DeepCacheSDHelper ++ helper = DeepCacheSDHelper(pipe=pipe) ++ helper.set_params( ++ cache_interval=3, ++ cache_branch_id=0, ++ ) ++ helper.enable() + + image = pipe("a photo of an astronaut on a moon").images[0] +``` + +The `set_params` method accepts two arguments: `cache_interval` and `cache_branch_id`. `cache_interval` means the frequency of feature caching, specified as the number of steps between each cache operation. `cache_branch_id` identifies which branch of the network (ordered from the shallowest to the deepest layer) is responsible for executing the caching processes. +Opting for a lower `cache_branch_id` or a larger `cache_interval` can lead to faster inference speed at the expense of reduced image quality (ablation experiments of these two hyperparameters can be found in the [paper](https://huggingface.co/papers/2312.00858)). Once those arguments are set, use the `enable` or `disable` methods to activate or deactivate the `DeepCacheSDHelper`. + +
+ +
+ +You can find more generated samples (original pipeline vs DeepCache) and the corresponding inference latency in the [WandB report](https://wandb.ai/horseee/DeepCache/runs/jwlsqqgt?workspace=user-horseee). The prompts are randomly selected from the [MS-COCO 2017](https://cocodataset.org/#home) dataset. + +## Benchmark + +We tested how much faster DeepCache accelerates [Stable Diffusion v2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) with 50 inference steps on an NVIDIA RTX A5000, using different configurations for resolution, batch size, cache interval (I), and cache branch (B). + +| **Resolution** | **Batch size** | **Original** | **DeepCache(I=3, B=0)** | **DeepCache(I=5, B=0)** | **DeepCache(I=5, B=1)** | +|----------------|----------------|--------------|-------------------------|-------------------------|-------------------------| +| 512| 8| 15.96| 6.88(2.32x)| 5.03(3.18x)| 7.27(2.20x)| +| | 4| 8.39| 3.60(2.33x)| 2.62(3.21x)| 3.75(2.24x)| +| | 1| 2.61| 1.12(2.33x)| 0.81(3.24x)| 1.11(2.35x)| +| 768| 8| 43.58| 18.99(2.29x)| 13.96(3.12x)| 21.27(2.05x)| +| | 4| 22.24| 9.67(2.30x)| 7.10(3.13x)| 10.74(2.07x)| +| | 1| 6.33| 2.72(2.33x)| 1.97(3.21x)| 2.98(2.12x)| +| 1024| 8| 101.95| 45.57(2.24x)| 33.72(3.02x)| 53.00(1.92x)| +| | 4| 49.25| 21.86(2.25x)| 16.19(3.04x)| 25.78(1.91x)| +| | 1| 13.83| 6.07(2.28x)| 4.43(3.12x)| 7.15(1.93x)| diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/fp16.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/fp16.md new file mode 100644 index 0000000000000000000000000000000000000000..941f53604cec9f3c687020c513bf11a8df2d908c --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/fp16.md @@ -0,0 +1,306 @@ + + +# Accelerate inference + +Diffusion models are slow at inference because generation is an iterative process where noise is gradually refined into an image or video over a certain number of "steps". To speedup this process, you can try experimenting with different [schedulers](../api/schedulers/overview), reduce the precision of the model weights for faster computations, use more memory-efficient attention mechanisms, and more. + +Combine and use these techniques together to make inference faster than using any single technique on its own. + +This guide will go over how to accelerate inference. + +## Model data type + +The precision and data type of the model weights affect inference speed because a higher precision requires more memory to load and more time to perform the computations. PyTorch loads model weights in float32 or full precision by default, so changing the data type is a simple way to quickly get faster inference. + + + + +bfloat16 is similar to float16 but it is more robust to numerical errors. Hardware support for bfloat16 varies, but most modern GPUs are capable of supporting bfloat16. + +```py +import torch +from diffusers import StableDiffusionXLPipeline + +pipeline = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16 +).to("cuda") + +prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" +pipeline(prompt, num_inference_steps=30).images[0] +``` + + + + +float16 is similar to bfloat16 but may be more prone to numerical errors. + +```py +import torch +from diffusers import StableDiffusionXLPipeline + +pipeline = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 +).to("cuda") + +prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" +pipeline(prompt, num_inference_steps=30).images[0] +``` + + + + +[TensorFloat-32 (tf32)](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) mode is supported on NVIDIA Ampere GPUs and it computes the convolution and matrix multiplication operations in tf32. Storage and other operations are kept in float32. This enables significantly faster computations when combined with bfloat16 or float16. + +PyTorch only enables tf32 mode for convolutions by default and you'll need to explicitly enable it for matrix multiplications. + +```py +import torch +from diffusers import StableDiffusionXLPipeline + +torch.backends.cuda.matmul.allow_tf32 = True + +pipeline = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16 +).to("cuda") + +prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" +pipeline(prompt, num_inference_steps=30).images[0] +``` + +Refer to the [mixed precision training](https://huggingface.co/docs/transformers/en/perf_train_gpu_one#mixed-precision) docs for more details. + + + + +## Scaled dot product attention + +> [!TIP] +> Memory-efficient attention optimizes for inference speed *and* [memory usage](./memory#memory-efficient-attention)! + +[Scaled dot product attention (SDPA)](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) implements several attention backends, [FlashAttention](https://github.com/Dao-AILab/flash-attention), [xFormers](https://github.com/facebookresearch/xformers), and a native C++ implementation. It automatically selects the most optimal backend for your hardware. + +SDPA is enabled by default if you're using PyTorch >= 2.0 and no additional changes are required to your code. You could try experimenting with other attention backends though if you'd like to choose your own. The example below uses the [torch.nn.attention.sdpa_kernel](https://pytorch.org/docs/stable/generated/torch.nn.attention.sdpa_kernel.html) context manager to enable efficient attention. + +```py +from torch.nn.attention import SDPBackend, sdpa_kernel +import torch +from diffusers import StableDiffusionXLPipeline + +pipeline = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16 +).to("cuda") + +prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" + +with sdpa_kernel(SDPBackend.EFFICIENT_ATTENTION): + image = pipeline(prompt, num_inference_steps=30).images[0] +``` + +## torch.compile + +[torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) accelerates inference by compiling PyTorch code and operations into optimized kernels. Diffusers typically compiles the more compute-intensive models like the UNet, transformer, or VAE. + +Enable the following compiler settings for maximum speed (refer to the [full list](https://github.com/pytorch/pytorch/blob/main/torch/_inductor/config.py) for more options). + +```py +import torch +from diffusers import StableDiffusionXLPipeline + +torch._inductor.config.conv_1x1_as_mm = True +torch._inductor.config.coordinate_descent_tuning = True +torch._inductor.config.epilogue_fusion = False +torch._inductor.config.coordinate_descent_check_all_directions = True +``` + +Load and compile the UNet and VAE. There are several different modes you can choose from, but `"max-autotune"` optimizes for the fastest speed by compiling to a CUDA graph. CUDA graphs effectively reduces the overhead by launching multiple GPU operations through a single CPU operation. + +> [!TIP] +> With PyTorch 2.3.1, you can control the caching behavior of torch.compile. This is particularly beneficial for compilation modes like `"max-autotune"` which performs a grid-search over several compilation flags to find the optimal configuration. Learn more in the [Compile Time Caching in torch.compile](https://pytorch.org/tutorials/recipes/torch_compile_caching_tutorial.html) tutorial. + +Changing the memory layout to [channels_last](./memory#torchchannels_last) also optimizes memory and inference speed. + +```py +pipeline = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 +).to("cuda") +pipeline.unet.to(memory_format=torch.channels_last) +pipeline.vae.to(memory_format=torch.channels_last) +pipeline.unet = torch.compile( + pipeline.unet, mode="max-autotune", fullgraph=True +) +pipeline.vae.decode = torch.compile( + pipeline.vae.decode, + mode="max-autotune", + fullgraph=True +) + +prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" +pipeline(prompt, num_inference_steps=30).images[0] +``` + +Compilation is slow the first time, but once compiled, it is significantly faster. Try to only use the compiled pipeline on the same type of inference operations. Calling the compiled pipeline on a different image size retriggers compilation which is slow and inefficient. + +### Dynamic shape compilation + +> [!TIP] +> Make sure to always use the nightly version of PyTorch for better support. + +`torch.compile` keeps track of input shapes and conditions, and if these are different, it recompiles the model. For example, if a model is compiled on a 1024x1024 resolution image and used on an image with a different resolution, it triggers recompilation. + +To avoid recompilation, add `dynamic=True` to try and generate a more dynamic kernel to avoid recompilation when conditions change. + +```diff ++ torch.fx.experimental._config.use_duck_shape = False ++ pipeline.unet = torch.compile( + pipeline.unet, fullgraph=True, dynamic=True +) +``` + +Specifying `use_duck_shape=False` instructs the compiler if it should use the same symbolic variable to represent input sizes that are the same. For more details, check out this [comment](https://github.com/huggingface/diffusers/pull/11327#discussion_r2047659790). + +Not all models may benefit from dynamic compilation out of the box and may require changes. Refer to this [PR](https://github.com/huggingface/diffusers/pull/11297/) that improved the [`AuraFlowPipeline`] implementation to benefit from dynamic compilation. + +Feel free to open an issue if dynamic compilation doesn't work as expected for a Diffusers model. + +### Regional compilation + +[Regional compilation](https://docs.pytorch.org/tutorials/recipes/regional_compilation.html) trims cold-start latency by only compiling the *small and frequently-repeated block(s)* of a model - typically a transformer layer - and enables reusing compiled artifacts for every subsequent occurrence. +For many diffusion architectures, this delivers the same runtime speedups as full-graph compilation and reduces compile time by 8–10x. + +Use the [`~ModelMixin.compile_repeated_blocks`] method, a helper that wraps `torch.compile`, on any component such as the transformer model as shown below. + +```py +# pip install -U diffusers +import torch +from diffusers import StableDiffusionXLPipeline + +pipeline = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", + torch_dtype=torch.float16, +).to("cuda") + +# compile only the repeated transformer layers inside the UNet +pipeline.unet.compile_repeated_blocks(fullgraph=True) +``` + +To enable regional compilation for a new model, add a `_repeated_blocks` attribute to a model class containing the class names (as strings) of the blocks you want to compile. + +```py +class MyUNet(ModelMixin): + _repeated_blocks = ("Transformer2DModel",) # ← compiled by default +``` + +> [!TIP] +> For more regional compilation examples, see the reference [PR](https://github.com/huggingface/diffusers/pull/11705). + +There is also a [compile_regions](https://github.com/huggingface/accelerate/blob/273799c85d849a1954a4f2e65767216eb37fa089/src/accelerate/utils/other.py#L78) method in [Accelerate](https://huggingface.co/docs/accelerate/index) that automatically selects candidate blocks in a model to compile. The remaining graph is compiled separately. This is useful for quick experiments because there aren't as many options for you to set which blocks to compile or adjust compilation flags. + +```py +# pip install -U accelerate +import torch +from diffusers import StableDiffusionXLPipeline +from accelerate.utils import compile_regions + +pipeline = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 +).to("cuda") +pipeline.unet = compile_regions(pipeline.unet, mode="reduce-overhead", fullgraph=True) +``` + +[`~ModelMixin.compile_repeated_blocks`] is intentionally explicit. List the blocks to repeat in `_repeated_blocks` and the helper only compiles those blocks. It offers predictable behavior and easy reasoning about cache reuse in one line of code. + +### Graph breaks + +It is important to specify `fullgraph=True` in torch.compile to ensure there are no graph breaks in the underlying model. This allows you to take advantage of torch.compile without any performance degradation. For the UNet and VAE, this changes how you access the return variables. + +```diff +- latents = unet( +- latents, timestep=timestep, encoder_hidden_states=prompt_embeds +-).sample + ++ latents = unet( ++ latents, timestep=timestep, encoder_hidden_states=prompt_embeds, return_dict=False ++)[0] +``` + +### GPU sync + +The `step()` function is [called](https://github.com/huggingface/diffusers/blob/1d686bac8146037e97f3fd8c56e4063230f71751/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py#L1228) on the scheduler each time after the denoiser makes a prediction, and the `sigmas` variable is [indexed](https://github.com/huggingface/diffusers/blob/1d686bac8146037e97f3fd8c56e4063230f71751/src/diffusers/schedulers/scheduling_euler_discrete.py#L476). When placed on the GPU, it introduces latency because of the communication sync between the CPU and GPU. It becomes more evident when the denoiser has already been compiled. + +In general, the `sigmas` should [stay on the CPU](https://github.com/huggingface/diffusers/blob/35a969d297cba69110d175ee79c59312b9f49e1e/src/diffusers/schedulers/scheduling_euler_discrete.py#L240) to avoid the communication sync and latency. + +> [!TIP] +> Refer to the [torch.compile and Diffusers: A Hands-On Guide to Peak Performance](https://pytorch.org/blog/torch-compile-and-diffusers-a-hands-on-guide-to-peak-performance/) blog post for maximizing performance with `torch.compile` for diffusion models. + +### Benchmarks + +Refer to the [diffusers/benchmarks](https://huggingface.co/datasets/diffusers/benchmarks) dataset to see inference latency and memory usage data for compiled pipelines. + +The [diffusers-torchao](https://github.com/sayakpaul/diffusers-torchao#benchmarking-results) repository also contains benchmarking results for compiled versions of Flux and CogVideoX. + +## Dynamic quantization + +[Dynamic quantization](https://pytorch.org/tutorials/recipes/recipes/dynamic_quantization.html) improves inference speed by reducing precision to enable faster math operations. This particular type of quantization determines how to scale the activations based on the data at runtime rather than using a fixed scaling factor. As a result, the scaling factor is more accurately aligned with the data. + +The example below applies [dynamic int8 quantization](https://pytorch.org/tutorials/recipes/recipes/dynamic_quantization.html) to the UNet and VAE with the [torchao](../quantization/torchao) library. + +> [!TIP] +> Refer to our [torchao](../quantization/torchao) docs to learn more about how to use the Diffusers torchao integration. + +Configure the compiler tags for maximum speed. + +```py +import torch +from torchao import apply_dynamic_quant +from diffusers import StableDiffusionXLPipeline + +torch._inductor.config.conv_1x1_as_mm = True +torch._inductor.config.coordinate_descent_tuning = True +torch._inductor.config.epilogue_fusion = False +torch._inductor.config.coordinate_descent_check_all_directions = True +torch._inductor.config.force_fuse_int_mm_with_mul = True +torch._inductor.config.use_mixed_mm = True +``` + +Filter out some linear layers in the UNet and VAE which don't benefit from dynamic quantization with the [dynamic_quant_filter_fn](https://github.com/huggingface/diffusion-fast/blob/0f169640b1db106fe6a479f78c1ed3bfaeba3386/utils/pipeline_utils.py#L16). + +```py +pipeline = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16 +).to("cuda") + +apply_dynamic_quant(pipeline.unet, dynamic_quant_filter_fn) +apply_dynamic_quant(pipeline.vae, dynamic_quant_filter_fn) + +prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" +pipeline(prompt, num_inference_steps=30).images[0] +``` + +## Fused projection matrices + +> [!WARNING] +> The [fuse_qkv_projections](https://github.com/huggingface/diffusers/blob/58431f102cf39c3c8a569f32d71b2ea8caa461e1/src/diffusers/pipelines/pipeline_utils.py#L2034) method is experimental and support is limited to mostly Stable Diffusion pipelines. Take a look at this [PR](https://github.com/huggingface/diffusers/pull/6179) to learn more about how to enable it for other pipelines + +An input is projected into three subspaces, represented by the projection matrices Q, K, and V, in an attention block. These projections are typically calculated separately, but you can horizontally combine these into a single matrix and perform the projection in a single step. It increases the size of the matrix multiplications of the input projections and also improves the impact of quantization. + +```py +pipeline.fuse_qkv_projections() +``` + +## Resources + +- Read the [Presenting Flux Fast: Making Flux go brrr on H100s](https://pytorch.org/blog/presenting-flux-fast-making-flux-go-brrr-on-h100s/) blog post to learn more about how you can combine all of these optimizations with [TorchInductor](https://docs.pytorch.org/docs/stable/torch.compiler.html) and [AOTInductor](https://docs.pytorch.org/docs/stable/torch.compiler_aot_inductor.html) for a ~2.5x speedup using recipes from [flux-fast](https://github.com/huggingface/flux-fast). + + These recipes support AMD hardware and [Flux.1 Kontext Dev](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev). +- Read the [torch.compile and Diffusers: A Hands-On Guide to Peak Performance](https://pytorch.org/blog/torch-compile-and-diffusers-a-hands-on-guide-to-peak-performance/) blog post +to maximize performance when using `torch.compile`. \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/habana.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/habana.md new file mode 100644 index 0000000000000000000000000000000000000000..1e5563ae101ed7f498da1c4021fbbf11bb9ff710 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/habana.md @@ -0,0 +1,31 @@ + + +# Intel Gaudi + +The Intel Gaudi AI accelerator family includes [Intel Gaudi 1](https://habana.ai/products/gaudi/), [Intel Gaudi 2](https://habana.ai/products/gaudi2/), and [Intel Gaudi 3](https://habana.ai/products/gaudi3/). Each server is equipped with 8 devices, known as Habana Processing Units (HPUs), providing 128GB of memory on Gaudi 3, 96GB on Gaudi 2, and 32GB on the first-gen Gaudi. For more details on the underlying hardware architecture, check out the [Gaudi Architecture](https://docs.habana.ai/en/latest/Gaudi_Overview/Gaudi_Architecture.html) overview. + +Diffusers pipelines can take advantage of HPU acceleration, even if a pipeline hasn't been added to [Optimum for Intel Gaudi](https://huggingface.co/docs/optimum/main/en/habana/index) yet, with the [GPU Migration Toolkit](https://docs.habana.ai/en/latest/PyTorch/PyTorch_Model_Porting/GPU_Migration_Toolkit/GPU_Migration_Toolkit.html). + +Call `.to("hpu")` on your pipeline to move it to a HPU device as shown below for Flux: +```py +import torch +from diffusers import DiffusionPipeline + +pipeline = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16) +pipeline.to("hpu") + +image = pipeline("An image of a squirrel in Picasso style").images[0] +``` + +> [!TIP] +> For Gaudi-optimized diffusion pipeline implementations, we recommend using [Optimum for Intel Gaudi](https://huggingface.co/docs/optimum/main/en/habana/index). diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/memory.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/memory.md new file mode 100644 index 0000000000000000000000000000000000000000..611e07ec765555df1f78adaee0e46538943c68a1 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/memory.md @@ -0,0 +1,625 @@ + + +# Reduce memory usage + +Modern diffusion models like [Flux](../api/pipelines/flux) and [Wan](../api/pipelines/wan) have billions of parameters that take up a lot of memory on your hardware for inference. This is challenging because common GPUs often don't have sufficient memory. To overcome the memory limitations, you can use more than one GPU (if available), offload some of the pipeline components to the CPU, and more. + +This guide will show you how to reduce your memory usage. + +> [!TIP] +> Keep in mind these techniques may need to be adjusted depending on the model. For example, a transformer-based diffusion model may not benefit equally from these memory optimizations as a UNet-based model. + +## Multiple GPUs + +If you have access to more than one GPU, there a few options for efficiently loading and distributing a large model across your hardware. These features are supported by the [Accelerate](https://huggingface.co/docs/accelerate/index) library, so make sure it is installed first. + +```bash +pip install -U accelerate +``` + +### Sharded checkpoints + +Loading large checkpoints in several shards in useful because the shards are loaded one at a time. This keeps memory usage low, only requiring enough memory for the model size and the largest shard size. We recommend sharding when the fp32 checkpoint is greater than 5GB. The default shard size is 5GB. + +Shard a checkpoint in [`~DiffusionPipeline.save_pretrained`] with the `max_shard_size` parameter. + +```py +from diffusers import AutoModel + +unet = AutoModel.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet" +) +unet.save_pretrained("sdxl-unet-sharded", max_shard_size="5GB") +``` + +Now you can use the sharded checkpoint, instead of the regular checkpoint, to save memory. + +```py +import torch +from diffusers import AutoModel, StableDiffusionXLPipeline + +unet = AutoModel.from_pretrained( + "username/sdxl-unet-sharded", torch_dtype=torch.float16 +) +pipeline = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", + unet=unet, + torch_dtype=torch.float16 +).to("cuda") +``` + +### Device placement + +> [!WARNING] +> Device placement is an experimental feature and the API may change. Only the `balanced` strategy is supported at the moment. We plan to support additional mapping strategies in the future. + +The `device_map` parameter controls how the model components in a pipeline or the layers in an individual model are distributed across devices. + + + + +The `balanced` device placement strategy evenly splits the pipeline across all available devices. + +```py +import torch +from diffusers import AutoModel, StableDiffusionXLPipeline + +pipeline = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", + torch_dtype=torch.float16, + device_map="balanced" +) +``` + +You can inspect a pipeline's device map with `hf_device_map`. + +```py +print(pipeline.hf_device_map) +{'unet': 1, 'vae': 1, 'safety_checker': 0, 'text_encoder': 0} +``` + + + + +The `device_map` is useful for loading large models, such as the Flux diffusion transformer which has 12.5B parameters. Set it to `"auto"` to automatically distribute a model across the fastest device first before moving to slower devices. Refer to the [Model sharding](../training/distributed_inference#model-sharding) docs for more details. + +```py +import torch +from diffusers import AutoModel + +transformer = AutoModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="transformer", + device_map="auto", + torch_dtype=torch.bfloat16 +) +``` + +You can inspect a model's device map with `hf_device_map`. + +```py +print(transformer.hf_device_map) +``` + + + + +When designing your own `device_map`, it should be a dictionary of a model's specific module name or layer and a device identifier (an integer for GPUs, `cpu` for CPUs, and `disk` for disk). + +Call `hf_device_map` on a model to see how model layers are distributed and then design your own. + +```py +print(transformer.hf_device_map) +{'pos_embed': 0, 'time_text_embed': 0, 'context_embedder': 0, 'x_embedder': 0, 'transformer_blocks': 0, 'single_transformer_blocks.0': 0, 'single_transformer_blocks.1': 0, 'single_transformer_blocks.2': 0, 'single_transformer_blocks.3': 0, 'single_transformer_blocks.4': 0, 'single_transformer_blocks.5': 0, 'single_transformer_blocks.6': 0, 'single_transformer_blocks.7': 0, 'single_transformer_blocks.8': 0, 'single_transformer_blocks.9': 0, 'single_transformer_blocks.10': 'cpu', 'single_transformer_blocks.11': 'cpu', 'single_transformer_blocks.12': 'cpu', 'single_transformer_blocks.13': 'cpu', 'single_transformer_blocks.14': 'cpu', 'single_transformer_blocks.15': 'cpu', 'single_transformer_blocks.16': 'cpu', 'single_transformer_blocks.17': 'cpu', 'single_transformer_blocks.18': 'cpu', 'single_transformer_blocks.19': 'cpu', 'single_transformer_blocks.20': 'cpu', 'single_transformer_blocks.21': 'cpu', 'single_transformer_blocks.22': 'cpu', 'single_transformer_blocks.23': 'cpu', 'single_transformer_blocks.24': 'cpu', 'single_transformer_blocks.25': 'cpu', 'single_transformer_blocks.26': 'cpu', 'single_transformer_blocks.27': 'cpu', 'single_transformer_blocks.28': 'cpu', 'single_transformer_blocks.29': 'cpu', 'single_transformer_blocks.30': 'cpu', 'single_transformer_blocks.31': 'cpu', 'single_transformer_blocks.32': 'cpu', 'single_transformer_blocks.33': 'cpu', 'single_transformer_blocks.34': 'cpu', 'single_transformer_blocks.35': 'cpu', 'single_transformer_blocks.36': 'cpu', 'single_transformer_blocks.37': 'cpu', 'norm_out': 'cpu', 'proj_out': 'cpu'} +``` + +For example, the `device_map` below places `single_transformer_blocks.10` through `single_transformer_blocks.20` on a second GPU (`1`). + +```py +import torch +from diffusers import AutoModel + +device_map = { + 'pos_embed': 0, 'time_text_embed': 0, 'context_embedder': 0, 'x_embedder': 0, 'transformer_blocks': 0, 'single_transformer_blocks.0': 0, 'single_transformer_blocks.1': 0, 'single_transformer_blocks.2': 0, 'single_transformer_blocks.3': 0, 'single_transformer_blocks.4': 0, 'single_transformer_blocks.5': 0, 'single_transformer_blocks.6': 0, 'single_transformer_blocks.7': 0, 'single_transformer_blocks.8': 0, 'single_transformer_blocks.9': 0, 'single_transformer_blocks.10': 1, 'single_transformer_blocks.11': 1, 'single_transformer_blocks.12': 1, 'single_transformer_blocks.13': 1, 'single_transformer_blocks.14': 1, 'single_transformer_blocks.15': 1, 'single_transformer_blocks.16': 1, 'single_transformer_blocks.17': 1, 'single_transformer_blocks.18': 1, 'single_transformer_blocks.19': 1, 'single_transformer_blocks.20': 1, 'single_transformer_blocks.21': 'cpu', 'single_transformer_blocks.22': 'cpu', 'single_transformer_blocks.23': 'cpu', 'single_transformer_blocks.24': 'cpu', 'single_transformer_blocks.25': 'cpu', 'single_transformer_blocks.26': 'cpu', 'single_transformer_blocks.27': 'cpu', 'single_transformer_blocks.28': 'cpu', 'single_transformer_blocks.29': 'cpu', 'single_transformer_blocks.30': 'cpu', 'single_transformer_blocks.31': 'cpu', 'single_transformer_blocks.32': 'cpu', 'single_transformer_blocks.33': 'cpu', 'single_transformer_blocks.34': 'cpu', 'single_transformer_blocks.35': 'cpu', 'single_transformer_blocks.36': 'cpu', 'single_transformer_blocks.37': 'cpu', 'norm_out': 'cpu', 'proj_out': 'cpu' +} + +transformer = AutoModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="transformer", + device_map=device_map, + torch_dtype=torch.bfloat16 +) +``` + +Pass a dictionary mapping maximum memory usage to each device to enforce a limit. If a device is not in `max_memory`, it is ignored and pipeline components won't be distributed to it. + +```py +import torch +from diffusers import AutoModel, StableDiffusionXLPipeline + +max_memory = {0:"1GB", 1:"1GB"} +pipeline = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", + torch_dtype=torch.float16, + device_map="balanced", + max_memory=max_memory +) +``` + +Diffusers uses the maxmium memory of all devices by default, but if they don't fit on the GPUs, then you'll need to use a single GPU and offload to the CPU with the methods below. + +- [`~DiffusionPipeline.enable_model_cpu_offload`] only works on a single GPU but a very large model may not fit on it +- [`~DiffusionPipeline.enable_sequential_cpu_offload`] may work but it is extremely slow and also limited to a single GPU + +Use the [`~DiffusionPipeline.reset_device_map`] method to reset the `device_map`. This is necessary if you want to use methods like `.to()`, [`~DiffusionPipeline.enable_sequential_cpu_offload`], and [`~DiffusionPipeline.enable_model_cpu_offload`] on a pipeline that was device-mapped. + +```py +pipeline.reset_device_map() +``` + +## VAE slicing + +VAE slicing saves memory by splitting large batches of inputs into a single batch of data and separately processing them. This method works best when generating more than one image at a time. + +For example, if you're generating 4 images at once, decoding would increase peak activation memory by 4x. VAE slicing reduces this by only decoding 1 image at a time instead of all 4 images at once. + +Call [`~StableDiffusionPipeline.enable_vae_slicing`] to enable sliced VAE. You can expect a small increase in performance when decoding multi-image batches and no performance impact for single-image batches. + +```py +import torch +from diffusers import AutoModel, StableDiffusionXLPipeline + +pipeline = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", + torch_dtype=torch.float16, +).to("cuda") +pipeline.enable_vae_slicing() +pipeline(["An astronaut riding a horse on Mars"]*32).images[0] +print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB") +``` + +> [!WARNING] +> The [`AutoencoderKLWan`] and [`AsymmetricAutoencoderKL`] classes don't support slicing. + +## VAE tiling + +VAE tiling saves memory by dividing an image into smaller overlapping tiles instead of processing the entire image at once. This also reduces peak memory usage because the GPU is only processing a tile at a time. + +Call [`~StableDiffusionPipeline.enable_vae_tiling`] to enable VAE tiling. The generated image may have some tone variation from tile-to-tile because they're decoded separately, but there shouldn't be any obvious seams between the tiles. Tiling is disabled for resolutions lower than a pre-specified (but configurable) limit. For example, this limit is 512x512 for the VAE in [`StableDiffusionPipeline`]. + +```py +import torch +from diffusers import AutoPipelineForImage2Image +from diffusers.utils import load_image + +pipeline = AutoPipelineForImage2Image.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 +).to("cuda") +pipeline.enable_vae_tiling() + +init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-sdxl-init.png") +prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" +pipeline(prompt, image=init_image, strength=0.5).images[0] +print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB") +``` + +> [!WARNING] +> [`AutoencoderKLWan`] and [`AsymmetricAutoencoderKL`] don't support tiling. + +## Offloading + +Offloading strategies move not currently active layers or models to the CPU to avoid increasing GPU memory. These strategies can be combined with quantization and torch.compile to balance inference speed and memory usage. + +Refer to the [Compile and offloading quantized models](./speed-memory-optims) guide for more details. + +### CPU offloading + +CPU offloading selectively moves weights from the GPU to the CPU. When a component is required, it is transferred to the GPU and when it isn't required, it is moved to the CPU. This method works on submodules rather than whole models. It saves memory by avoiding storing the entire model on the GPU. + +CPU offloading dramatically reduces memory usage, but it is also **extremely slow** because submodules are passed back and forth multiple times between devices. It can often be impractical due to how slow it is. + +> [!WARNING] +> Don't move the pipeline to CUDA before calling [`~DiffusionPipeline.enable_sequential_cpu_offload`], otherwise the amount of memory saved is only minimal (refer to this [issue](https://github.com/huggingface/diffusers/issues/1934) for more details). This is a stateful operation that installs hooks on the model. + +Call [`~DiffusionPipeline.enable_sequential_cpu_offload`] to enable it on a pipeline. + +```py +import torch +from diffusers import DiffusionPipeline + +pipeline = DiffusionPipeline.from_pretrained( + "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16 +) +pipeline.enable_sequential_cpu_offload() + +pipeline( + prompt="An astronaut riding a horse on Mars", + guidance_scale=0., + height=768, + width=1360, + num_inference_steps=4, + max_sequence_length=256, +).images[0] +print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB") +``` + +### Model offloading + +Model offloading moves entire models to the GPU instead of selectively moving *some* layers or model components. One of the main pipeline models, usually the text encoder, UNet, and VAE, is placed on the GPU while the other components are held on the CPU. Components like the UNet that run multiple times stays on the GPU until its completely finished and no longer needed. This eliminates the communication overhead of [CPU offloading](#cpu-offloading) and makes model offloading a faster alternative. The tradeoff is memory savings won't be as large. + +> [!WARNING] +> Keep in mind that if models are reused outside the pipeline after hookes have been installed (see [Removing Hooks](https://huggingface.co/docs/accelerate/en/package_reference/big_modeling#accelerate.hooks.remove_hook_from_module) for more details), you need to run the entire pipeline and models in the expected order to properly offload them. This is a stateful operation that installs hooks on the model. + +Call [`~DiffusionPipeline.enable_model_cpu_offload`] to enable it on a pipeline. + +```py +import torch +from diffusers import DiffusionPipeline + +pipeline = DiffusionPipeline.from_pretrained( + "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16 +) +pipeline.enable_model_cpu_offload() + +pipeline( + prompt="An astronaut riding a horse on Mars", + guidance_scale=0., + height=768, + width=1360, + num_inference_steps=4, + max_sequence_length=256, +).images[0] +print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB") +``` + +[`~DiffusionPipeline.enable_model_cpu_offload`] also helps when you're using the [`~StableDiffusionXLPipeline.encode_prompt`] method on its own to generate the text encoders hidden state. + +### Group offloading + +Group offloading moves groups of internal layers ([torch.nn.ModuleList](https://pytorch.org/docs/stable/generated/torch.nn.ModuleList.html) or [torch.nn.Sequential](https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html)) to the CPU. It uses less memory than [model offloading](#model-offloading) and it is faster than [CPU offloading](#cpu-offloading) because it reduces communication overhead. + +> [!WARNING] +> Group offloading may not work with all models if the forward implementation contains weight-dependent device casting of inputs because it may clash with group offloading's device casting mechanism. + +Enable group offloading by configuring the `offload_type` parameter to `block_level` or `leaf_level`. + +- `block_level` offloads groups of layers based on the `num_blocks_per_group` parameter. For example, if `num_blocks_per_group=2` on a model with 40 layers, 2 layers are onloaded and offloaded at a time (20 total onloads/offloads). This drastically reduces memory requirements. +- `leaf_level` offloads individual layers at the lowest level and is equivalent to [CPU offloading](#cpu-offloading). But it can be made faster if you use streams without giving up inference speed. + +Group offloading is supported for entire pipelines or individual models. Applying group offloading to the entire pipeline is the easiest option while selectively applying it to individual models gives users more flexibility to use different offloading techniques for different models. + + + + +Call [`~DiffusionPipeline.enable_group_offload`] on a pipeline. + +```py +import torch +from diffusers import CogVideoXPipeline +from diffusers.hooks import apply_group_offloading +from diffusers.utils import export_to_video + +onload_device = torch.device("cuda") +offload_device = torch.device("cpu") + +pipeline = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16) +pipeline.enable_group_offload( + onload_device=onload_device, + offload_device=offload_device, + offload_type="leaf_level", + use_stream=True +) + +prompt = ( + "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. " + "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other " + "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, " + "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. " + "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical " + "atmosphere of this unique musical performance." +) +video = pipeline(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0] +print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB") +export_to_video(video, "output.mp4", fps=8) +``` + + + + +Call [`~ModelMixin.enable_group_offload`] on standard Diffusers model components that inherit from [`ModelMixin`]. For other model components that don't inherit from [`ModelMixin`], such as a generic [torch.nn.Module](https://pytorch.org/docs/stable/generated/torch.nn.Module.html), use [`~hooks.apply_group_offloading`] instead. + +```py +import torch +from diffusers import CogVideoXPipeline +from diffusers.hooks import apply_group_offloading +from diffusers.utils import export_to_video + +onload_device = torch.device("cuda") +offload_device = torch.device("cpu") +pipeline = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16) + +# Use the enable_group_offload method for Diffusers model implementations +pipeline.transformer.enable_group_offload(onload_device=onload_device, offload_device=offload_device, offload_type="leaf_level") +pipeline.vae.enable_group_offload(onload_device=onload_device, offload_type="leaf_level") + +# Use the apply_group_offloading method for other model components +apply_group_offloading(pipeline.text_encoder, onload_device=onload_device, offload_type="block_level", num_blocks_per_group=2) + +prompt = ( + "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. " + "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other " + "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, " + "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. " + "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical " + "atmosphere of this unique musical performance." +) +video = pipeline(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0] +print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB") +export_to_video(video, "output.mp4", fps=8) +``` + + + + +#### CUDA stream + +The `use_stream` parameter can be activated for CUDA devices that support asynchronous data transfer streams to reduce overall execution time compared to [CPU offloading](#cpu-offloading). It overlaps data transfer and computation by using layer prefetching. The next layer to be executed is loaded onto the GPU while the current layer is still being executed. It can increase CPU memory significantly so ensure you have 2x the amount of memory as the model size. + +Set `record_stream=True` for more of a speedup at the cost of slightly increased memory usage. Refer to the [torch.Tensor.record_stream](https://pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html) docs to learn more. + +> [!TIP] +> When `use_stream=True` on VAEs with tiling enabled, make sure to do a dummy forward pass (possible with dummy inputs as well) before inference to avoid device mismatch errors. This may not work on all implementations, so feel free to open an issue if you encounter any problems. + +If you're using `block_level` group offloading with `use_stream` enabled, the `num_blocks_per_group` parameter should be set to `1`, otherwise a warning will be raised. + +```py +pipeline.transformer.enable_group_offload(onload_device=onload_device, offload_device=offload_device, offload_type="leaf_level", use_stream=True, record_stream=True) +``` + +The `low_cpu_mem_usage` parameter can be set to `True` to reduce CPU memory usage when using streams during group offloading. It is best for `leaf_level` offloading and when CPU memory is bottlenecked. Memory is saved by creating pinned tensors on the fly instead of pre-pinning them. However, this may increase overall execution time. + +#### Offloading to disk + +Group offloading can consume significant system memory depending on the model size. On systems with limited memory, try group offloading onto the disk as a secondary memory. + +Set the `offload_to_disk_path` argument in either [`~ModelMixin.enable_group_offload`] or [`~hooks.apply_group_offloading`] to offload the model to the disk. + +```py +pipeline.transformer.enable_group_offload(onload_device=onload_device, offload_device=offload_device, offload_type="leaf_level", offload_to_disk_path="path/to/disk") + +apply_group_offloading(pipeline.text_encoder, onload_device=onload_device, offload_type="block_level", num_blocks_per_group=2, offload_to_disk_path="path/to/disk") +``` + +Refer to these [two](https://github.com/huggingface/diffusers/pull/11682#issue-3129365363) [tables](https://github.com/huggingface/diffusers/pull/11682#issuecomment-2955715126) to compare the speed and memory trade-offs. + +## Layerwise casting + +> [!TIP] +> Combine layerwise casting with [group offloading](#group-offloading) for even more memory savings. + +Layerwise casting stores weights in a smaller data format (for example, `torch.float8_e4m3fn` and `torch.float8_e5m2`) to use less memory and upcasts those weights to a higher precision like `torch.float16` or `torch.bfloat16` for computation. Certain layers (normalization and modulation related weights) are skipped because storing them in fp8 can degrade generation quality. + +> [!WARNING] +> Layerwise casting may not work with all models if the forward implementation contains internal typecasting of weights. The current implementation of layerwise casting assumes the forward pass is independent of the weight precision and the input datatypes are always specified in `compute_dtype` (see [here](https://github.com/huggingface/transformers/blob/7f5077e53682ca855afc826162b204ebf809f1f9/src/transformers/models/t5/modeling_t5.py#L294-L299) for an incompatible implementation). +> +> Layerwise casting may also fail on custom modeling implementations with [PEFT](https://huggingface.co/docs/peft/index) layers. There are some checks available but they are not extensively tested or guaranteed to work in all cases. + +Call [`~ModelMixin.enable_layerwise_casting`] to set the storage and computation datatypes. + +```py +import torch +from diffusers import CogVideoXPipeline, CogVideoXTransformer3DModel +from diffusers.utils import export_to_video + +transformer = CogVideoXTransformer3DModel.from_pretrained( + "THUDM/CogVideoX-5b", + subfolder="transformer", + torch_dtype=torch.bfloat16 +) +transformer.enable_layerwise_casting(storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.bfloat16) + +pipeline = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", + transformer=transformer, + torch_dtype=torch.bfloat16 +).to("cuda") +prompt = ( + "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. " + "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other " + "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, " + "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. " + "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical " + "atmosphere of this unique musical performance." +) +video = pipeline(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0] +print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB") +export_to_video(video, "output.mp4", fps=8) +``` + +The [`~hooks.apply_layerwise_casting`] method can also be used if you need more control and flexibility. It can be partially applied to model layers by calling it on specific internal modules. Use the `skip_modules_pattern` or `skip_modules_classes` parameters to specify modules to avoid, such as the normalization and modulation layers. + +```python +import torch +from diffusers import CogVideoXTransformer3DModel +from diffusers.hooks import apply_layerwise_casting + +transformer = CogVideoXTransformer3DModel.from_pretrained( + "THUDM/CogVideoX-5b", + subfolder="transformer", + torch_dtype=torch.bfloat16 +) + +# skip the normalization layer +apply_layerwise_casting( + transformer, + storage_dtype=torch.float8_e4m3fn, + compute_dtype=torch.bfloat16, + skip_modules_classes=["norm"], + non_blocking=True, +) +``` + +## torch.channels_last + +[torch.channels_last](https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html) flips how tensors are stored from `(batch size, channels, height, width)` to `(batch size, heigh, width, channels)`. This aligns the tensors with how the hardware sequentially accesses the tensors stored in memory and avoids skipping around in memory to access the pixel values. + +Not all operators currently support the channels-last format and may result in worst performance, but it is still worth trying. + +```py +print(pipeline.unet.conv_out.state_dict()["weight"].stride()) # (2880, 9, 3, 1) +pipeline.unet.to(memory_format=torch.channels_last) # in-place operation +print( + pipeline.unet.conv_out.state_dict()["weight"].stride() +) # (2880, 1, 960, 320) having a stride of 1 for the 2nd dimension proves that it works +``` + +## torch.jit.trace + +[torch.jit.trace](https://pytorch.org/docs/stable/generated/torch.jit.trace.html) records the operations a model performs on a sample input and creates a new, optimized representation of the model based on the recorded execution path. During tracing, the model is optimized to reduce overhead from Python and dynamic control flows and operations are fused together for more efficiency. The returned executable or [ScriptFunction](https://pytorch.org/docs/stable/generated/torch.jit.ScriptFunction.html) can be compiled. + +```py +import time +import torch +from diffusers import StableDiffusionPipeline +import functools + +# torch disable grad +torch.set_grad_enabled(False) + +# set variables +n_experiments = 2 +unet_runs_per_experiment = 50 + +# load sample inputs +def generate_inputs(): + sample = torch.randn((2, 4, 64, 64), device="cuda", dtype=torch.float16) + timestep = torch.rand(1, device="cuda", dtype=torch.float16) * 999 + encoder_hidden_states = torch.randn((2, 77, 768), device="cuda", dtype=torch.float16) + return sample, timestep, encoder_hidden_states + + +pipeline = StableDiffusionPipeline.from_pretrained( + "stable-diffusion-v1-5/stable-diffusion-v1-5", + torch_dtype=torch.float16, + use_safetensors=True, +).to("cuda") +unet = pipeline.unet +unet.eval() +unet.to(memory_format=torch.channels_last) # use channels_last memory format +unet.forward = functools.partial(unet.forward, return_dict=False) # set return_dict=False as default + +# warmup +for _ in range(3): + with torch.inference_mode(): + inputs = generate_inputs() + orig_output = unet(*inputs) + +# trace +print("tracing..") +unet_traced = torch.jit.trace(unet, inputs) +unet_traced.eval() +print("done tracing") + +# warmup and optimize graph +for _ in range(5): + with torch.inference_mode(): + inputs = generate_inputs() + orig_output = unet_traced(*inputs) + +# benchmarking +with torch.inference_mode(): + for _ in range(n_experiments): + torch.cuda.synchronize() + start_time = time.time() + for _ in range(unet_runs_per_experiment): + orig_output = unet_traced(*inputs) + torch.cuda.synchronize() + print(f"unet traced inference took {time.time() - start_time:.2f} seconds") + for _ in range(n_experiments): + torch.cuda.synchronize() + start_time = time.time() + for _ in range(unet_runs_per_experiment): + orig_output = unet(*inputs) + torch.cuda.synchronize() + print(f"unet inference took {time.time() - start_time:.2f} seconds") + +# save the model +unet_traced.save("unet_traced.pt") +``` + +Replace the pipeline's UNet with the traced version. + +```py +import torch +from diffusers import StableDiffusionPipeline +from dataclasses import dataclass + +@dataclass +class UNet2DConditionOutput: + sample: torch.Tensor + +pipeline = StableDiffusionPipeline.from_pretrained( + "stable-diffusion-v1-5/stable-diffusion-v1-5", + torch_dtype=torch.float16, + use_safetensors=True, +).to("cuda") + +# use jitted unet +unet_traced = torch.jit.load("unet_traced.pt") + +# del pipeline.unet +class TracedUNet(torch.nn.Module): + def __init__(self): + super().__init__() + self.in_channels = pipe.unet.config.in_channels + self.device = pipe.unet.device + + def forward(self, latent_model_input, t, encoder_hidden_states): + sample = unet_traced(latent_model_input, t, encoder_hidden_states)[0] + return UNet2DConditionOutput(sample=sample) + +pipeline.unet = TracedUNet() + +with torch.inference_mode(): + image = pipe([prompt] * 1, num_inference_steps=50).images[0] +``` + +## Memory-efficient attention + +> [!TIP] +> Memory-efficient attention optimizes for memory usage *and* [inference speed](./fp16#scaled-dot-product-attention)! + +The Transformers attention mechanism is memory-intensive, especially for long sequences, so you can try using different and more memory-efficient attention types. + +By default, if PyTorch >= 2.0 is installed, [scaled dot-product attention (SDPA)](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) is used. You don't need to make any additional changes to your code. + +SDPA supports [FlashAttention](https://github.com/Dao-AILab/flash-attention) and [xFormers](https://github.com/facebookresearch/xformers) as well as a native C++ PyTorch implementation. It automatically selects the most optimal implementation based on your input. + +You can explicitly use xFormers with the [`~ModelMixin.enable_xformers_memory_efficient_attention`] method. + +```py +# pip install xformers +import torch +from diffusers import StableDiffusionXLPipeline + +pipeline = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", + torch_dtype=torch.float16, +).to("cuda") +pipeline.enable_xformers_memory_efficient_attention() +``` + +Call [`~ModelMixin.disable_xformers_memory_efficient_attention`] to disable it. + +```py +pipeline.disable_xformers_memory_efficient_attention() +``` \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/mps.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/mps.md new file mode 100644 index 0000000000000000000000000000000000000000..b5afa25b2fda23d419fbcdf2f11311d49e6e824b --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/mps.md @@ -0,0 +1,82 @@ + + +# Metal Performance Shaders (MPS) + +> [!TIP] +> Pipelines with a MPS badge indicate a model can take advantage of the MPS backend on Apple silicon devices for faster inference. Feel free to open a [Pull Request](https://github.com/huggingface/diffusers/compare) to add this badge to pipelines that are missing it. + +🤗 Diffusers is compatible with Apple silicon (M1/M2 chips) using the PyTorch [`mps`](https://pytorch.org/docs/stable/notes/mps.html) device, which uses the Metal framework to leverage the GPU on MacOS devices. You'll need to have: + +- macOS computer with Apple silicon (M1/M2) hardware +- macOS 12.6 or later (13.0 or later recommended) +- arm64 version of Python +- [PyTorch 2.0](https://pytorch.org/get-started/locally/) (recommended) or 1.13 (minimum version supported for `mps`) + +The `mps` backend uses PyTorch's `.to()` interface to move the Stable Diffusion pipeline on to your M1 or M2 device: + +```python +from diffusers import DiffusionPipeline + +pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5") +pipe = pipe.to("mps") + +# Recommended if your computer has < 64 GB of RAM +pipe.enable_attention_slicing() + +prompt = "a photo of an astronaut riding a horse on mars" +image = pipe(prompt).images[0] +image +``` + +> [!WARNING] +> The PyTorch [mps](https://pytorch.org/docs/stable/notes/mps.html) backend does not support NDArray sizes greater than `2**32`. Please open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) if you encounter this problem so we can investigate. + +If you're using **PyTorch 1.13**, you need to "prime" the pipeline with an additional one-time pass through it. This is a temporary workaround for an issue where the first inference pass produces slightly different results than subsequent ones. You only need to do this pass once, and after just one inference step you can discard the result. + +```diff + from diffusers import DiffusionPipeline + + pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5").to("mps") + pipe.enable_attention_slicing() + + prompt = "a photo of an astronaut riding a horse on mars" + # First-time "warmup" pass if PyTorch version is 1.13 ++ _ = pipe(prompt, num_inference_steps=1) + + # Results match those from the CPU device after the warmup pass. + image = pipe(prompt).images[0] +``` + +## Troubleshoot + +This section lists some common issues with using the `mps` backend and how to solve them. + +### Attention slicing + +M1/M2 performance is very sensitive to memory pressure. When this occurs, the system automatically swaps if it needs to which significantly degrades performance. + +To prevent this from happening, we recommend *attention slicing* to reduce memory pressure during inference and prevent swapping. This is especially relevant if your computer has less than 64GB of system RAM, or if you generate images at non-standard resolutions larger than 512×512 pixels. Call the [`~DiffusionPipeline.enable_attention_slicing`] function on your pipeline: + +```py +from diffusers import DiffusionPipeline +import torch + +pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True).to("mps") +pipeline.enable_attention_slicing() +``` + +Attention slicing performs the costly attention operation in multiple steps instead of all at once. It usually improves performance by ~20% in computers without universal memory, but we've observed *better performance* in most Apple silicon computers unless you have 64GB of RAM or more. + +### Batch inference + +Generating multiple prompts in a batch can crash or fail to work reliably. If this is the case, try iterating instead of batching. \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/neuron.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/neuron.md new file mode 100644 index 0000000000000000000000000000000000000000..6a45bd0563bbf7d2a6d93d3ecdb1c510334d4d6b --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/neuron.md @@ -0,0 +1,58 @@ + + +# AWS Neuron + +Diffusers functionalities are available on [AWS Inf2 instances](https://aws.amazon.com/ec2/instance-types/inf2/), which are EC2 instances powered by [Neuron machine learning accelerators](https://aws.amazon.com/machine-learning/inferentia/). These instances aim to provide better compute performance (higher throughput, lower latency) with good cost-efficiency, making them good candidates for AWS users to deploy diffusion models to production. + +[Optimum Neuron](https://huggingface.co/docs/optimum-neuron/en/index) is the interface between Hugging Face libraries and AWS Accelerators, including AWS [Trainium](https://aws.amazon.com/machine-learning/trainium/) and AWS [Inferentia](https://aws.amazon.com/machine-learning/inferentia/). It supports many of the features in Diffusers with similar APIs, so it is easier to learn if you're already familiar with Diffusers. Once you have created an AWS Inf2 instance, install Optimum Neuron. + +```bash +python -m pip install --upgrade-strategy eager optimum[neuronx] +``` + +> [!TIP] +> We provide pre-built [Hugging Face Neuron Deep Learning AMI](https://aws.amazon.com/marketplace/pp/prodview-gr3e6yiscria2) (DLAMI) and Optimum Neuron containers for Amazon SageMaker. It's recommended to correctly set up your environment. + +The example below demonstrates how to generate images with the Stable Diffusion XL model on an inf2.8xlarge instance (you can switch to cheaper inf2.xlarge instances once the model is compiled). To generate some images, use the [`~optimum.neuron.NeuronStableDiffusionXLPipeline`] class, which is similar to the [`StableDiffusionXLPipeline`] class in Diffusers. + +Unlike Diffusers, you need to compile models in the pipeline to the Neuron format, `.neuron`. Launch the following command to export the model to the `.neuron` format. + +```bash +optimum-cli export neuron --model stabilityai/stable-diffusion-xl-base-1.0 \ + --batch_size 1 \ + --height 1024 `# height in pixels of generated image, eg. 768, 1024` \ + --width 1024 `# width in pixels of generated image, eg. 768, 1024` \ + --num_images_per_prompt 1 `# number of images to generate per prompt, defaults to 1` \ + --auto_cast matmul `# cast only matrix multiplication operations` \ + --auto_cast_type bf16 `# cast operations from FP32 to BF16` \ + sd_neuron_xl/ +``` + +Now generate some images with the pre-compiled SDXL model. + +```python +>>> from optimum.neuron import NeuronStableDiffusionXLPipeline + +>>> stable_diffusion_xl = NeuronStableDiffusionXLPipeline.from_pretrained("sd_neuron_xl/") +>>> prompt = "a pig with wings flying in floating US dollar banknotes in the air, skyscrapers behind, warm color palette, muted colors, detailed, 8k" +>>> image = stable_diffusion_xl(prompt).images[0] +``` + +peggy generated by sdxl on inf2 + +Feel free to check out more guides and examples on different use cases from the Optimum Neuron [documentation](https://huggingface.co/docs/optimum-neuron/en/inference_tutorials/stable_diffusion#generate-images-with-stable-diffusion-models-on-aws-inferentia)! diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/onnx.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/onnx.md new file mode 100644 index 0000000000000000000000000000000000000000..620f2af994b36266de67ea8f8b8e584142da2723 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/onnx.md @@ -0,0 +1,83 @@ + + +# ONNX Runtime + +🤗 [Optimum](https://github.com/huggingface/optimum) provides a Stable Diffusion pipeline compatible with ONNX Runtime. You'll need to install 🤗 Optimum with the following command for ONNX Runtime support: + +```bash +pip install -q optimum["onnxruntime"] +``` + +This guide will show you how to use the Stable Diffusion and Stable Diffusion XL (SDXL) pipelines with ONNX Runtime. + +## Stable Diffusion + +To load and run inference, use the [`~optimum.onnxruntime.ORTStableDiffusionPipeline`]. If you want to load a PyTorch model and convert it to the ONNX format on-the-fly, set `export=True`: + +```python +from optimum.onnxruntime import ORTStableDiffusionPipeline + +model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5" +pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id, export=True) +prompt = "sailing ship in storm by Leonardo da Vinci" +image = pipeline(prompt).images[0] +pipeline.save_pretrained("./onnx-stable-diffusion-v1-5") +``` + +> [!WARNING] +> Generating multiple prompts in a batch seems to take too much memory. While we look into it, you may need to iterate instead of batching. + +To export the pipeline in the ONNX format offline and use it later for inference, +use the [`optimum-cli export`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) command: + +```bash +optimum-cli export onnx --model stable-diffusion-v1-5/stable-diffusion-v1-5 sd_v15_onnx/ +``` + +Then to perform inference (you don't have to specify `export=True` again): + +```python +from optimum.onnxruntime import ORTStableDiffusionPipeline + +model_id = "sd_v15_onnx" +pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id) +prompt = "sailing ship in storm by Leonardo da Vinci" +image = pipeline(prompt).images[0] +``` + +
+ +
+ +You can find more examples in 🤗 Optimum [documentation](https://huggingface.co/docs/optimum/), and Stable Diffusion is supported for text-to-image, image-to-image, and inpainting. + +## Stable Diffusion XL + +To load and run inference with SDXL, use the [`~optimum.onnxruntime.ORTStableDiffusionXLPipeline`]: + +```python +from optimum.onnxruntime import ORTStableDiffusionXLPipeline + +model_id = "stabilityai/stable-diffusion-xl-base-1.0" +pipeline = ORTStableDiffusionXLPipeline.from_pretrained(model_id) +prompt = "sailing ship in storm by Leonardo da Vinci" +image = pipeline(prompt).images[0] +``` + +To export the pipeline in the ONNX format and use it later for inference, use the [`optimum-cli export`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) command: + +```bash +optimum-cli export onnx --model stabilityai/stable-diffusion-xl-base-1.0 --task stable-diffusion-xl sd_xl_onnx/ +``` + +SDXL in the ONNX format is supported for text-to-image and image-to-image. diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/open_vino.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/open_vino.md new file mode 100644 index 0000000000000000000000000000000000000000..d91b17e4958ce3d4c02bd152883a24a40920b074 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/open_vino.md @@ -0,0 +1,80 @@ + + +# OpenVINO + +🤗 [Optimum](https://github.com/huggingface/optimum-intel) provides Stable Diffusion pipelines compatible with OpenVINO to perform inference on a variety of Intel processors (see the [full list](https://docs.openvino.ai/latest/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html) of supported devices). + +You'll need to install 🤗 Optimum Intel with the `--upgrade-strategy eager` option to ensure [`optimum-intel`](https://github.com/huggingface/optimum-intel) is using the latest version: + +```bash +pip install --upgrade-strategy eager optimum["openvino"] +``` + +This guide will show you how to use the Stable Diffusion and Stable Diffusion XL (SDXL) pipelines with OpenVINO. + +## Stable Diffusion + +To load and run inference, use the [`~optimum.intel.OVStableDiffusionPipeline`]. If you want to load a PyTorch model and convert it to the OpenVINO format on-the-fly, set `export=True`: + +```python +from optimum.intel import OVStableDiffusionPipeline + +model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5" +pipeline = OVStableDiffusionPipeline.from_pretrained(model_id, export=True) +prompt = "sailing ship in storm by Rembrandt" +image = pipeline(prompt).images[0] + +# Don't forget to save the exported model +pipeline.save_pretrained("openvino-sd-v1-5") +``` + +To further speed-up inference, statically reshape the model. If you change any parameters such as the outputs height or width, you’ll need to statically reshape your model again. + +```python +# Define the shapes related to the inputs and desired outputs +batch_size, num_images, height, width = 1, 1, 512, 512 + +# Statically reshape the model +pipeline.reshape(batch_size, height, width, num_images) +# Compile the model before inference +pipeline.compile() + +image = pipeline( + prompt, + height=height, + width=width, + num_images_per_prompt=num_images, +).images[0] +``` +
+ +
+ +You can find more examples in the 🤗 Optimum [documentation](https://huggingface.co/docs/optimum/intel/inference#stable-diffusion), and Stable Diffusion is supported for text-to-image, image-to-image, and inpainting. + +## Stable Diffusion XL + +To load and run inference with SDXL, use the [`~optimum.intel.OVStableDiffusionXLPipeline`]: + +```python +from optimum.intel import OVStableDiffusionXLPipeline + +model_id = "stabilityai/stable-diffusion-xl-base-1.0" +pipeline = OVStableDiffusionXLPipeline.from_pretrained(model_id) +prompt = "sailing ship in storm by Rembrandt" +image = pipeline(prompt).images[0] +``` + +To further speed-up inference, [statically reshape](#stable-diffusion) the model as shown in the Stable Diffusion section. + +You can find more examples in the 🤗 Optimum [documentation](https://huggingface.co/docs/optimum/intel/inference#stable-diffusion-xl), and running SDXL in OpenVINO is supported for text-to-image and image-to-image. diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/para_attn.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/para_attn.md new file mode 100644 index 0000000000000000000000000000000000000000..94b0d5ce3af4c570fef6806df306a2bbb3c15d00 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/para_attn.md @@ -0,0 +1,497 @@ +# ParaAttention + +
+ +
+
+ +
+ + +Large image and video generation models, such as [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) and [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo), can be an inference challenge for real-time applications and deployment because of their size. + +[ParaAttention](https://github.com/chengzeyi/ParaAttention) is a library that implements **context parallelism** and **first block cache**, and can be combined with other techniques (torch.compile, fp8 dynamic quantization), to accelerate inference. + +This guide will show you how to apply ParaAttention to FLUX.1-dev and HunyuanVideo on NVIDIA L20 GPUs. +No optimizations are applied for our baseline benchmark, except for HunyuanVideo to avoid out-of-memory errors. + +Our baseline benchmark shows that FLUX.1-dev is able to generate a 1024x1024 resolution image in 28 steps in 26.36 seconds, and HunyuanVideo is able to generate 129 frames at 720p resolution in 30 steps in 3675.71 seconds. + +> [!TIP] +> For even faster inference with context parallelism, try using NVIDIA A100 or H100 GPUs (if available) with NVLink support, especially when there is a large number of GPUs. + +## First Block Cache + +Caching the output of the transformers blocks in the model and reusing them in the next inference steps reduces the computation cost and makes inference faster. + +However, it is hard to decide when to reuse the cache to ensure quality generated images or videos. ParaAttention directly uses the **residual difference of the first transformer block output** to approximate the difference among model outputs. When the difference is small enough, the residual difference of previous inference steps is reused. In other words, the denoising step is skipped. + +This achieves a 2x speedup on FLUX.1-dev and HunyuanVideo inference with very good quality. + +
+ Cache in Diffusion Transformer +
How AdaCache works, First Block Cache is a variant of it
+
+ + + + +To apply first block cache on FLUX.1-dev, call `apply_cache_on_pipe` as shown below. 0.08 is the default residual difference value for FLUX models. + +```python +import time +import torch +from diffusers import FluxPipeline + +pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=torch.bfloat16, +).to("cuda") + +from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe + +apply_cache_on_pipe(pipe, residual_diff_threshold=0.08) + +# Enable memory savings +# pipe.enable_model_cpu_offload() +# pipe.enable_sequential_cpu_offload() + +begin = time.time() +image = pipe( + "A cat holding a sign that says hello world", + num_inference_steps=28, +).images[0] +end = time.time() +print(f"Time: {end - begin:.2f}s") + +print("Saving image to flux.png") +image.save("flux.png") +``` + +| Optimizations | Original | FBCache rdt=0.06 | FBCache rdt=0.08 | FBCache rdt=0.10 | FBCache rdt=0.12 | +| - | - | - | - | - | - | +| Preview | ![Original](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-original.png) | ![FBCache rdt=0.06](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-fbc-0.06.png) | ![FBCache rdt=0.08](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-fbc-0.08.png) | ![FBCache rdt=0.10](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-fbc-0.10.png) | ![FBCache rdt=0.12](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-fbc-0.12.png) | +| Wall Time (s) | 26.36 | 21.83 | 17.01 | 16.00 | 13.78 | + +First Block Cache reduced the inference speed to 17.01 seconds compared to the baseline, or 1.55x faster, while maintaining nearly zero quality loss. + + + + +To apply First Block Cache on HunyuanVideo, `apply_cache_on_pipe` as shown below. 0.06 is the default residual difference value for HunyuanVideo models. + +```python +import time +import torch +from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel +from diffusers.utils import export_to_video + +model_id = "tencent/HunyuanVideo" +transformer = HunyuanVideoTransformer3DModel.from_pretrained( + model_id, + subfolder="transformer", + torch_dtype=torch.bfloat16, + revision="refs/pr/18", +) +pipe = HunyuanVideoPipeline.from_pretrained( + model_id, + transformer=transformer, + torch_dtype=torch.float16, + revision="refs/pr/18", +).to("cuda") + +from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe + +apply_cache_on_pipe(pipe, residual_diff_threshold=0.6) + +pipe.vae.enable_tiling() + +begin = time.time() +output = pipe( + prompt="A cat walks on the grass, realistic", + height=720, + width=1280, + num_frames=129, + num_inference_steps=30, +).frames[0] +end = time.time() +print(f"Time: {end - begin:.2f}s") + +print("Saving video to hunyuan_video.mp4") +export_to_video(output, "hunyuan_video.mp4", fps=15) +``` + + + + HunyuanVideo without FBCache + + + + HunyuanVideo with FBCache + +First Block Cache reduced the inference speed to 2271.06 seconds compared to the baseline, or 1.62x faster, while maintaining nearly zero quality loss. + + + + +## fp8 quantization + +fp8 with dynamic quantization further speeds up inference and reduces memory usage. Both the activations and weights must be quantized in order to use the 8-bit [NVIDIA Tensor Cores](https://www.nvidia.com/en-us/data-center/tensor-cores/). + +Use `float8_weight_only` and `float8_dynamic_activation_float8_weight` to quantize the text encoder and transformer model. + +The default quantization method is per tensor quantization, but if your GPU supports row-wise quantization, you can also try it for better accuracy. + +Install [torchao](https://github.com/pytorch/ao/tree/main) with the command below. + +```bash +pip3 install -U torch torchao +``` + +[torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) with `mode="max-autotune-no-cudagraphs"` or `mode="max-autotune"` selects the best kernel for performance. Compilation can take a long time if it's the first time the model is called, but it is worth it once the model has been compiled. + +This example only quantizes the transformer model, but you can also quantize the text encoder to reduce memory usage even more. + +> [!TIP] +> Dynamic quantization can significantly change the distribution of the model output, so you need to change the `residual_diff_threshold` to a larger value for it to take effect. + + + + +```python +import time +import torch +from diffusers import FluxPipeline + +pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=torch.bfloat16, +).to("cuda") + +from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe + +apply_cache_on_pipe( + pipe, + residual_diff_threshold=0.12, # Use a larger value to make the cache take effect +) + +from torchao.quantization import quantize_, float8_dynamic_activation_float8_weight, float8_weight_only + +quantize_(pipe.text_encoder, float8_weight_only()) +quantize_(pipe.transformer, float8_dynamic_activation_float8_weight()) +pipe.transformer = torch.compile( + pipe.transformer, mode="max-autotune-no-cudagraphs", +) + +# Enable memory savings +# pipe.enable_model_cpu_offload() +# pipe.enable_sequential_cpu_offload() + +for i in range(2): + begin = time.time() + image = pipe( + "A cat holding a sign that says hello world", + num_inference_steps=28, + ).images[0] + end = time.time() + if i == 0: + print(f"Warm up time: {end - begin:.2f}s") + else: + print(f"Time: {end - begin:.2f}s") + +print("Saving image to flux.png") +image.save("flux.png") +``` + +fp8 dynamic quantization and torch.compile reduced the inference speed to 7.56 seconds compared to the baseline, or 3.48x faster. + + + + +```python +import time +import torch +from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel +from diffusers.utils import export_to_video + +model_id = "tencent/HunyuanVideo" +transformer = HunyuanVideoTransformer3DModel.from_pretrained( + model_id, + subfolder="transformer", + torch_dtype=torch.bfloat16, + revision="refs/pr/18", +) +pipe = HunyuanVideoPipeline.from_pretrained( + model_id, + transformer=transformer, + torch_dtype=torch.float16, + revision="refs/pr/18", +).to("cuda") + +from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe + +apply_cache_on_pipe(pipe) + +from torchao.quantization import quantize_, float8_dynamic_activation_float8_weight, float8_weight_only + +quantize_(pipe.text_encoder, float8_weight_only()) +quantize_(pipe.transformer, float8_dynamic_activation_float8_weight()) +pipe.transformer = torch.compile( + pipe.transformer, mode="max-autotune-no-cudagraphs", +) + +# Enable memory savings +pipe.vae.enable_tiling() +# pipe.enable_model_cpu_offload() +# pipe.enable_sequential_cpu_offload() + +for i in range(2): + begin = time.time() + output = pipe( + prompt="A cat walks on the grass, realistic", + height=720, + width=1280, + num_frames=129, + num_inference_steps=1 if i == 0 else 30, + ).frames[0] + end = time.time() + if i == 0: + print(f"Warm up time: {end - begin:.2f}s") + else: + print(f"Time: {end - begin:.2f}s") + +print("Saving video to hunyuan_video.mp4") +export_to_video(output, "hunyuan_video.mp4", fps=15) +``` + +A NVIDIA L20 GPU only has 48GB memory and could face out-of-memory (OOM) errors after compilation and if `enable_model_cpu_offload` isn't called because HunyuanVideo has very large activation tensors when running with high resolution and large number of frames. For GPUs with less than 80GB of memory, you can try reducing the resolution and number of frames to avoid OOM errors. + +Large video generation models are usually bottlenecked by the attention computations rather than the fully connected layers. These models don't significantly benefit from quantization and torch.compile. + + + + +## Context Parallelism + +Context Parallelism parallelizes inference and scales with multiple GPUs. The ParaAttention compositional design allows you to combine Context Parallelism with First Block Cache and dynamic quantization. + +> [!TIP] +> Refer to the [ParaAttention](https://github.com/chengzeyi/ParaAttention/tree/main) repository for detailed instructions and examples of how to scale inference with multiple GPUs. + +If the inference process needs to be persistent and serviceable, it is suggested to use [torch.multiprocessing](https://pytorch.org/docs/stable/multiprocessing.html) to write your own inference processor. This can eliminate the overhead of launching the process and loading and recompiling the model. + + + + +The code sample below combines First Block Cache, fp8 dynamic quantization, torch.compile, and Context Parallelism for the fastest inference speed. + +```python +import time +import torch +import torch.distributed as dist +from diffusers import FluxPipeline + +dist.init_process_group() + +torch.cuda.set_device(dist.get_rank()) + +pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=torch.bfloat16, +).to("cuda") + +from para_attn.context_parallel import init_context_parallel_mesh +from para_attn.context_parallel.diffusers_adapters import parallelize_pipe +from para_attn.parallel_vae.diffusers_adapters import parallelize_vae + +mesh = init_context_parallel_mesh( + pipe.device.type, + max_ring_dim_size=2, +) +parallelize_pipe( + pipe, + mesh=mesh, +) +parallelize_vae(pipe.vae, mesh=mesh._flatten()) + +from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe + +apply_cache_on_pipe( + pipe, + residual_diff_threshold=0.12, # Use a larger value to make the cache take effect +) + +from torchao.quantization import quantize_, float8_dynamic_activation_float8_weight, float8_weight_only + +quantize_(pipe.text_encoder, float8_weight_only()) +quantize_(pipe.transformer, float8_dynamic_activation_float8_weight()) +torch._inductor.config.reorder_for_compute_comm_overlap = True +pipe.transformer = torch.compile( + pipe.transformer, mode="max-autotune-no-cudagraphs", +) + +# Enable memory savings +# pipe.enable_model_cpu_offload(gpu_id=dist.get_rank()) +# pipe.enable_sequential_cpu_offload(gpu_id=dist.get_rank()) + +for i in range(2): + begin = time.time() + image = pipe( + "A cat holding a sign that says hello world", + num_inference_steps=28, + output_type="pil" if dist.get_rank() == 0 else "pt", + ).images[0] + end = time.time() + if dist.get_rank() == 0: + if i == 0: + print(f"Warm up time: {end - begin:.2f}s") + else: + print(f"Time: {end - begin:.2f}s") + +if dist.get_rank() == 0: + print("Saving image to flux.png") + image.save("flux.png") + +dist.destroy_process_group() +``` + +Save to `run_flux.py` and launch it with [torchrun](https://pytorch.org/docs/stable/elastic/run.html). + +```bash +# Use --nproc_per_node to specify the number of GPUs +torchrun --nproc_per_node=2 run_flux.py +``` + +Inference speed is reduced to 8.20 seconds compared to the baseline, or 3.21x faster, with 2 NVIDIA L20 GPUs. On 4 L20s, inference speed is 3.90 seconds, or 6.75x faster. + + + + +The code sample below combines First Block Cache and Context Parallelism for the fastest inference speed. + +```python +import time +import torch +import torch.distributed as dist +from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel +from diffusers.utils import export_to_video + +dist.init_process_group() + +torch.cuda.set_device(dist.get_rank()) + +model_id = "tencent/HunyuanVideo" +transformer = HunyuanVideoTransformer3DModel.from_pretrained( + model_id, + subfolder="transformer", + torch_dtype=torch.bfloat16, + revision="refs/pr/18", +) +pipe = HunyuanVideoPipeline.from_pretrained( + model_id, + transformer=transformer, + torch_dtype=torch.float16, + revision="refs/pr/18", +).to("cuda") + +from para_attn.context_parallel import init_context_parallel_mesh +from para_attn.context_parallel.diffusers_adapters import parallelize_pipe +from para_attn.parallel_vae.diffusers_adapters import parallelize_vae + +mesh = init_context_parallel_mesh( + pipe.device.type, +) +parallelize_pipe( + pipe, + mesh=mesh, +) +parallelize_vae(pipe.vae, mesh=mesh._flatten()) + +from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe + +apply_cache_on_pipe(pipe) + +# from torchao.quantization import quantize_, float8_dynamic_activation_float8_weight, float8_weight_only +# +# torch._inductor.config.reorder_for_compute_comm_overlap = True +# +# quantize_(pipe.text_encoder, float8_weight_only()) +# quantize_(pipe.transformer, float8_dynamic_activation_float8_weight()) +# pipe.transformer = torch.compile( +# pipe.transformer, mode="max-autotune-no-cudagraphs", +# ) + +# Enable memory savings +pipe.vae.enable_tiling() +# pipe.enable_model_cpu_offload(gpu_id=dist.get_rank()) +# pipe.enable_sequential_cpu_offload(gpu_id=dist.get_rank()) + +for i in range(2): + begin = time.time() + output = pipe( + prompt="A cat walks on the grass, realistic", + height=720, + width=1280, + num_frames=129, + num_inference_steps=1 if i == 0 else 30, + output_type="pil" if dist.get_rank() == 0 else "pt", + ).frames[0] + end = time.time() + if dist.get_rank() == 0: + if i == 0: + print(f"Warm up time: {end - begin:.2f}s") + else: + print(f"Time: {end - begin:.2f}s") + +if dist.get_rank() == 0: + print("Saving video to hunyuan_video.mp4") + export_to_video(output, "hunyuan_video.mp4", fps=15) + +dist.destroy_process_group() +``` + +Save to `run_hunyuan_video.py` and launch it with [torchrun](https://pytorch.org/docs/stable/elastic/run.html). + +```bash +# Use --nproc_per_node to specify the number of GPUs +torchrun --nproc_per_node=8 run_hunyuan_video.py +``` + +Inference speed is reduced to 649.23 seconds compared to the baseline, or 5.66x faster, with 8 NVIDIA L20 GPUs. + + + + +## Benchmarks + + + + +| GPU Type | Number of GPUs | Optimizations | Wall Time (s) | Speedup | +| - | - | - | - | - | +| NVIDIA L20 | 1 | Baseline | 26.36 | 1.00x | +| NVIDIA L20 | 1 | FBCache (rdt=0.08) | 17.01 | 1.55x | +| NVIDIA L20 | 1 | FP8 DQ | 13.40 | 1.96x | +| NVIDIA L20 | 1 | FBCache (rdt=0.12) + FP8 DQ | 7.56 | 3.48x | +| NVIDIA L20 | 2 | FBCache (rdt=0.12) + FP8 DQ + CP | 4.92 | 5.35x | +| NVIDIA L20 | 4 | FBCache (rdt=0.12) + FP8 DQ + CP | 3.90 | 6.75x | + + + + +| GPU Type | Number of GPUs | Optimizations | Wall Time (s) | Speedup | +| - | - | - | - | - | +| NVIDIA L20 | 1 | Baseline | 3675.71 | 1.00x | +| NVIDIA L20 | 1 | FBCache | 2271.06 | 1.62x | +| NVIDIA L20 | 2 | FBCache + CP | 1132.90 | 3.24x | +| NVIDIA L20 | 4 | FBCache + CP | 718.15 | 5.12x | +| NVIDIA L20 | 8 | FBCache + CP | 649.23 | 5.66x | + + + diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/pruna.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/pruna.md new file mode 100644 index 0000000000000000000000000000000000000000..56c1f3af59577e899bb3c5cafd7c63d91e49e2bb --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/pruna.md @@ -0,0 +1,187 @@ +# Pruna + +[Pruna](https://github.com/PrunaAI/pruna) is a model optimization framework that offers various optimization methods - quantization, pruning, caching, compilation - for accelerating inference and reducing memory usage. A general overview of the optimization methods are shown below. + + +| Technique | Description | Speed | Memory | Quality | +|--------------|-----------------------------------------------------------------------------------------------|:-----:|:------:|:-------:| +| `batcher` | Groups multiple inputs together to be processed simultaneously, improving computational efficiency and reducing processing time. | ✅ | ❌ | ➖ | +| `cacher` | Stores intermediate results of computations to speed up subsequent operations. | ✅ | ➖ | ➖ | +| `compiler` | Optimises the model with instructions for specific hardware. | ✅ | ➖ | ➖ | +| `distiller` | Trains a smaller, simpler model to mimic a larger, more complex model. | ✅ | ✅ | ❌ | +| `quantizer` | Reduces the precision of weights and activations, lowering memory requirements. | ✅ | ✅ | ❌ | +| `pruner` | Removes less important or redundant connections and neurons, resulting in a sparser, more efficient network. | ✅ | ✅ | ❌ | +| `recoverer` | Restores the performance of a model after compression. | ➖ | ➖ | ✅ | +| `factorizer` | Factorization batches several small matrix multiplications into one large fused operation. | ✅ | ➖ | ➖ | +| `enhancer` | Enhances the model output by applying post-processing algorithms such as denoising or upscaling. | ❌ | - | ✅ | + +✅ (improves), ➖ (approx. the same), ❌ (worsens) + +Explore the full range of optimization methods in the [Pruna documentation](https://docs.pruna.ai/en/stable/docs_pruna/user_manual/configure.html#configure-algorithms). + +## Installation + +Install Pruna with the following command. + +```bash +pip install pruna +``` + + +## Optimize Diffusers models + +A broad range of optimization algorithms are supported for Diffusers models as shown below. + +
+ Overview of the supported optimization algorithms for diffusers models +
+ +The example below optimizes [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) +with a combination of factorizer, compiler, and cacher algorithms. This combination accelerates inference by up to 4.2x and cuts peak GPU memory usage from 34.7GB to 28.0GB, all while maintaining virtually the same output quality. + +> [!TIP] +> Refer to the [Pruna optimization](https://docs.pruna.ai/en/stable/docs_pruna/user_manual/configure.html) docs to learn more about the optimization techniques used in this example. + +
+ Optimization techniques used for FLUX.1-dev showing the combination of factorizer, compiler, and cacher algorithms +
+ +Start by defining a `SmashConfig` with the optimization algorithms to use. To optimize the model, wrap the pipeline and the `SmashConfig` with `smash` and then use the pipeline as normal for inference. + +```python +import torch +from diffusers import FluxPipeline + +from pruna import PrunaModel, SmashConfig, smash + +# load the model +# Try segmind/Segmind-Vega or black-forest-labs/FLUX.1-schnell with a small GPU memory +pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=torch.bfloat16 +).to("cuda") + +# define the configuration +smash_config = SmashConfig() +smash_config["factorizer"] = "qkv_diffusers" +smash_config["compiler"] = "torch_compile" +smash_config["torch_compile_target"] = "module_list" +smash_config["cacher"] = "fora" +smash_config["fora_interval"] = 2 + +# for the best results in terms of speed you can add these configs +# however they will increase your warmup time from 1.5 min to 10 min +# smash_config["torch_compile_mode"] = "max-autotune-no-cudagraphs" +# smash_config["quantizer"] = "torchao" +# smash_config["torchao_quant_type"] = "fp8dq" +# smash_config["torchao_excluded_modules"] = "norm+embedding" + +# optimize the model +smashed_pipe = smash(pipe, smash_config) + +# run the model +smashed_pipe("a knitted purple prune").images[0] +``` + +
+ +
+ +After optimization, we can share and load the optimized model using the Hugging Face Hub. + +```python +# save the model +smashed_pipe.save_to_hub("/FLUX.1-dev-smashed") + +# load the model +smashed_pipe = PrunaModel.from_hub("/FLUX.1-dev-smashed") +``` + +## Evaluate and benchmark Diffusers models + +Pruna provides the [EvaluationAgent](https://docs.pruna.ai/en/stable/docs_pruna/user_manual/evaluate.html) to evaluate the quality of your optimized models. + +We can metrics we care about, such as total time and throughput, and the dataset to evaluate on. We can define a model and pass it to the `EvaluationAgent`. + + + + +We can load and evaluate an optimized model by using the `EvaluationAgent` and pass it to the `Task`. + +```python +import torch +from diffusers import FluxPipeline + +from pruna import PrunaModel +from pruna.data.pruna_datamodule import PrunaDataModule +from pruna.evaluation.evaluation_agent import EvaluationAgent +from pruna.evaluation.metrics import ( + ThroughputMetric, + TorchMetricWrapper, + TotalTimeMetric, +) +from pruna.evaluation.task import Task + +# define the device +device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" + +# load the model +# Try PrunaAI/Segmind-Vega-smashed or PrunaAI/FLUX.1-dev-smashed with a small GPU memory +smashed_pipe = PrunaModel.from_hub("PrunaAI/FLUX.1-dev-smashed") + +# Define the metrics +metrics = [ + TotalTimeMetric(n_iterations=20, n_warmup_iterations=5), + ThroughputMetric(n_iterations=20, n_warmup_iterations=5), + TorchMetricWrapper("clip"), +] + +# Define the datamodule +datamodule = PrunaDataModule.from_string("LAION256") +datamodule.limit_datasets(10) + +# Define the task and evaluation agent +task = Task(metrics, datamodule=datamodule, device=device) +eval_agent = EvaluationAgent(task) + +# Evaluate smashed model and offload it to CPU +smashed_pipe.move_to_device(device) +smashed_pipe_results = eval_agent.evaluate(smashed_pipe) +smashed_pipe.move_to_device("cpu") +``` + + + + +Instead of comparing the optimized model to the base model, you can also evaluate the standalone `diffusers` model. This is useful if you want to evaluate the performance of the model without the optimization. We can do so by using the `PrunaModel` wrapper and run the `EvaluationAgent` on it. + +```python +import torch +from diffusers import FluxPipeline + +from pruna import PrunaModel + +# load the model +# Try PrunaAI/Segmind-Vega-smashed or PrunaAI/FLUX.1-dev-smashed with a small GPU memory +pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=torch.bfloat16 +).to("cpu") +wrapped_pipe = PrunaModel(model=pipe) +``` + + + + +Now that you have seen how to optimize and evaluate your models, you can start using Pruna to optimize your own models. Luckily, we have many examples to help you get started. + +> [!TIP] +> For more details about benchmarking Flux, check out the [Announcing FLUX-Juiced: The Fastest Image Generation Endpoint (2.6 times faster)!](https://huggingface.co/blog/PrunaAI/flux-fastest-image-generation-endpoint) blog post and the [InferBench](https://huggingface.co/spaces/PrunaAI/InferBench) Space. + +## Reference + +- [Pruna](https://github.com/pruna-ai/pruna) +- [Pruna optimization](https://docs.pruna.ai/en/stable/docs_pruna/user_manual/configure.html#configure-algorithms) +- [Pruna evaluation](https://docs.pruna.ai/en/stable/docs_pruna/user_manual/evaluate.html) +- [Pruna tutorials](https://docs.pruna.ai/en/stable/docs_pruna/tutorials/index.html) + diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/speed-memory-optims.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/speed-memory-optims.md new file mode 100644 index 0000000000000000000000000000000000000000..80c6c79a3c83325abf917d741323d5f692cc2074 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/speed-memory-optims.md @@ -0,0 +1,203 @@ + + +# Compiling and offloading quantized models + +Optimizing models often involves trade-offs between [inference speed](./fp16) and [memory-usage](./memory). For instance, while [caching](./cache) can boost inference speed, it also increases memory consumption since it needs to store the outputs of intermediate attention layers. A more balanced optimization strategy combines quantizing a model, [torch.compile](./fp16#torchcompile) and various [offloading methods](./memory#offloading). + +> [!TIP] +> Check the [torch.compile](./fp16#torchcompile) guide to learn more about compilation and how they can be applied here. For example, regional compilation can significantly reduce compilation time without giving up any speedups. + +For image generation, combining quantization and [model offloading](./memory#model-offloading) can often give the best trade-off between quality, speed, and memory. Group offloading is not as effective for image generation because it is usually not possible to *fully* overlap data transfer if the compute kernel finishes faster. This results in some communication overhead between the CPU and GPU. + +For video generation, combining quantization and [group-offloading](./memory#group-offloading) tends to be better because video models are more compute-bound. + +The table below provides a comparison of optimization strategy combinations and their impact on latency and memory-usage for Flux. + +| combination | latency (s) | memory-usage (GB) | +|---|---|---| +| quantization | 32.602 | 14.9453 | +| quantization, torch.compile | 25.847 | 14.9448 | +| quantization, torch.compile, model CPU offloading | 32.312 | 12.2369 | + +These results are benchmarked on Flux with a RTX 4090. The transformer and text_encoder components are quantized. Refer to the benchmarking script if you're interested in evaluating your own model. + +This guide will show you how to compile and offload a quantized model with [bitsandbytes](../quantization/bitsandbytes#torchcompile). Make sure you are using [PyTorch nightly](https://pytorch.org/get-started/locally/) and the latest version of bitsandbytes. + +```bash +pip install -U bitsandbytes +``` + +## Quantization and torch.compile + +Start by [quantizing](../quantization/overview) a model to reduce the memory required for storage and [compiling](./fp16#torchcompile) it to accelerate inference. + +Configure the [Dynamo](https://docs.pytorch.org/docs/stable/torch.compiler_dynamo_overview.html) `capture_dynamic_output_shape_ops = True` to handle dynamic outputs when compiling bitsandbytes models. + +```py +import torch +from diffusers import DiffusionPipeline +from diffusers.quantizers import PipelineQuantizationConfig + +torch._dynamo.config.capture_dynamic_output_shape_ops = True + +# quantize +pipeline_quant_config = PipelineQuantizationConfig( + quant_backend="bitsandbytes_4bit", + quant_kwargs={"load_in_4bit": True, "bnb_4bit_quant_type": "nf4", "bnb_4bit_compute_dtype": torch.bfloat16}, + components_to_quantize=["transformer", "text_encoder_2"], +) +pipeline = DiffusionPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + quantization_config=pipeline_quant_config, + torch_dtype=torch.bfloat16, +).to("cuda") + +# compile +pipeline.transformer.to(memory_format=torch.channels_last) +pipeline.transformer.compile(mode="max-autotune", fullgraph=True) +pipeline(""" + cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California + highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain +""" +).images[0] +``` + +## Quantization, torch.compile, and offloading + +In addition to quantization and torch.compile, try offloading if you need to reduce memory-usage further. Offloading moves various layers or model components from the CPU to the GPU as needed for computations. + +Configure the [Dynamo](https://docs.pytorch.org/docs/stable/torch.compiler_dynamo_overview.html) `cache_size_limit` during offloading to avoid excessive recompilation and set `capture_dynamic_output_shape_ops = True` to handle dynamic outputs when compiling bitsandbytes models. + + + + +[Model CPU offloading](./memory#model-offloading) moves an individual pipeline component, like the transformer model, to the GPU when it is needed for computation. Otherwise, it is offloaded to the CPU. + +```py +import torch +from diffusers import DiffusionPipeline +from diffusers.quantizers import PipelineQuantizationConfig + +torch._dynamo.config.cache_size_limit = 1000 +torch._dynamo.config.capture_dynamic_output_shape_ops = True + +# quantize +pipeline_quant_config = PipelineQuantizationConfig( + quant_backend="bitsandbytes_4bit", + quant_kwargs={"load_in_4bit": True, "bnb_4bit_quant_type": "nf4", "bnb_4bit_compute_dtype": torch.bfloat16}, + components_to_quantize=["transformer", "text_encoder_2"], +) +pipeline = DiffusionPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + quantization_config=pipeline_quant_config, + torch_dtype=torch.bfloat16, +).to("cuda") + +# model CPU offloading +pipeline.enable_model_cpu_offload() + +# compile +pipeline.transformer.compile() +pipeline( + "cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain" +).images[0] +``` + + + + +[Group offloading](./memory#group-offloading) moves the internal layers of an individual pipeline component, like the transformer model, to the GPU for computation and offloads it when it's not required. At the same time, it uses the [CUDA stream](./memory#cuda-stream) feature to prefetch the next layer for execution. + +By overlapping computation and data transfer, it is faster than model CPU offloading while also saving memory. + +```py +# pip install ftfy +import torch +from diffusers import AutoModel, DiffusionPipeline +from diffusers.hooks import apply_group_offloading +from diffusers.utils import export_to_video +from diffusers.quantizers import PipelineQuantizationConfig +from transformers import UMT5EncoderModel + +torch._dynamo.config.cache_size_limit = 1000 +torch._dynamo.config.capture_dynamic_output_shape_ops = True + +# quantize +pipeline_quant_config = PipelineQuantizationConfig( + quant_backend="bitsandbytes_4bit", + quant_kwargs={"load_in_4bit": True, "bnb_4bit_quant_type": "nf4", "bnb_4bit_compute_dtype": torch.bfloat16}, + components_to_quantize=["transformer", "text_encoder"], +) + +text_encoder = UMT5EncoderModel.from_pretrained( + "Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="text_encoder", torch_dtype=torch.bfloat16 +) +pipeline = DiffusionPipeline.from_pretrained( + "Wan-AI/Wan2.1-T2V-14B-Diffusers", + quantization_config=pipeline_quant_config, + torch_dtype=torch.bfloat16, +).to("cuda") + +# group offloading +onload_device = torch.device("cuda") +offload_device = torch.device("cpu") + +pipeline.transformer.enable_group_offload( + onload_device=onload_device, + offload_device=offload_device, + offload_type="leaf_level", + use_stream=True, + non_blocking=True +) +pipeline.vae.enable_group_offload( + onload_device=onload_device, + offload_device=offload_device, + offload_type="leaf_level", + use_stream=True, + non_blocking=True +) +apply_group_offloading( + pipeline.text_encoder, + onload_device=onload_device, + offload_type="leaf_level", + use_stream=True, + non_blocking=True +) + +# compile +pipeline.transformer.compile() + +prompt = """ +The camera rushes from far to near in a low-angle shot, +revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in +for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground. +Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic +shadows and warm highlights. Medium composition, front view, low angle, with depth of field. +""" +negative_prompt = """ +Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, +low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, +misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards +""" + +output = pipeline( + prompt=prompt, + negative_prompt=negative_prompt, + num_frames=81, + guidance_scale=5.0, +).frames[0] +export_to_video(output, "output.mp4", fps=16) +``` + + + \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/tgate.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/tgate.md new file mode 100644 index 0000000000000000000000000000000000000000..90e0bc32f71b085244ce60b43bb7538c767724ef --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/tgate.md @@ -0,0 +1,182 @@ +# T-GATE + +[T-GATE](https://github.com/HaozheLiu-ST/T-GATE/tree/main) accelerates inference for [Stable Diffusion](../api/pipelines/stable_diffusion/overview), [PixArt](../api/pipelines/pixart), and [Latency Consistency Model](../api/pipelines/latent_consistency_models.md) pipelines by skipping the cross-attention calculation once it converges. This method doesn't require any additional training and it can speed up inference from 10-50%. T-GATE is also compatible with other optimization methods like [DeepCache](./deepcache). + +Before you begin, make sure you install T-GATE. + +```bash +pip install tgate +pip install -U torch diffusers transformers accelerate DeepCache +``` + + +To use T-GATE with a pipeline, you need to use its corresponding loader. + +| Pipeline | T-GATE Loader | +|---|---| +| PixArt | TgatePixArtLoader | +| Stable Diffusion XL | TgateSDXLLoader | +| Stable Diffusion XL + DeepCache | TgateSDXLDeepCacheLoader | +| Stable Diffusion | TgateSDLoader | +| Stable Diffusion + DeepCache | TgateSDDeepCacheLoader | + +Next, create a `TgateLoader` with a pipeline, the gate step (the time step to stop calculating the cross attention), and the number of inference steps. Then call the `tgate` method on the pipeline with a prompt, gate step, and the number of inference steps. + +Let's see how to enable this for several different pipelines. + + + + +Accelerate `PixArtAlphaPipeline` with T-GATE: + +```py +import torch +from diffusers import PixArtAlphaPipeline +from tgate import TgatePixArtLoader + +pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", torch_dtype=torch.float16) + +gate_step = 8 +inference_step = 25 +pipe = TgatePixArtLoader( + pipe, + gate_step=gate_step, + num_inference_steps=inference_step, +).to("cuda") + +image = pipe.tgate( + "An alpaca made of colorful building blocks, cyberpunk.", + gate_step=gate_step, + num_inference_steps=inference_step, +).images[0] +``` + + + +Accelerate `StableDiffusionXLPipeline` with T-GATE: + +```py +import torch +from diffusers import StableDiffusionXLPipeline +from diffusers import DPMSolverMultistepScheduler +from tgate import TgateSDXLLoader + +pipe = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", + torch_dtype=torch.float16, + variant="fp16", + use_safetensors=True, +) +pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) + +gate_step = 10 +inference_step = 25 +pipe = TgateSDXLLoader( + pipe, + gate_step=gate_step, + num_inference_steps=inference_step, +).to("cuda") + +image = pipe.tgate( + "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.", + gate_step=gate_step, + num_inference_steps=inference_step +).images[0] +``` + + + +Accelerate `StableDiffusionXLPipeline` with [DeepCache](https://github.com/horseee/DeepCache) and T-GATE: + +```py +import torch +from diffusers import StableDiffusionXLPipeline +from diffusers import DPMSolverMultistepScheduler +from tgate import TgateSDXLDeepCacheLoader + +pipe = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", + torch_dtype=torch.float16, + variant="fp16", + use_safetensors=True, +) +pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) + +gate_step = 10 +inference_step = 25 +pipe = TgateSDXLDeepCacheLoader( + pipe, + cache_interval=3, + cache_branch_id=0, +).to("cuda") + +image = pipe.tgate( + "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.", + gate_step=gate_step, + num_inference_steps=inference_step +).images[0] +``` + + + +Accelerate `latent-consistency/lcm-sdxl` with T-GATE: + +```py +import torch +from diffusers import StableDiffusionXLPipeline +from diffusers import UNet2DConditionModel, LCMScheduler +from diffusers import DPMSolverMultistepScheduler +from tgate import TgateSDXLLoader + +unet = UNet2DConditionModel.from_pretrained( + "latent-consistency/lcm-sdxl", + torch_dtype=torch.float16, + variant="fp16", +) +pipe = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", + unet=unet, + torch_dtype=torch.float16, + variant="fp16", +) +pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) + +gate_step = 1 +inference_step = 4 +pipe = TgateSDXLLoader( + pipe, + gate_step=gate_step, + num_inference_steps=inference_step, + lcm=True +).to("cuda") + +image = pipe.tgate( + "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.", + gate_step=gate_step, + num_inference_steps=inference_step +).images[0] +``` + + + +T-GATE also supports [`StableDiffusionPipeline`] and [PixArt-alpha/PixArt-LCM-XL-2-1024-MS](https://hf.co/PixArt-alpha/PixArt-LCM-XL-2-1024-MS). + +## Benchmarks +| Model | MACs | Param | Latency | Zero-shot 10K-FID on MS-COCO | +|-----------------------|----------|-----------|---------|---------------------------| +| SD-1.5 | 16.938T | 859.520M | 7.032s | 23.927 | +| SD-1.5 w/ T-GATE | 9.875T | 815.557M | 4.313s | 20.789 | +| SD-2.1 | 38.041T | 865.785M | 16.121s | 22.609 | +| SD-2.1 w/ T-GATE | 22.208T | 815.433 M | 9.878s | 19.940 | +| SD-XL | 149.438T | 2.570B | 53.187s | 24.628 | +| SD-XL w/ T-GATE | 84.438T | 2.024B | 27.932s | 22.738 | +| Pixart-Alpha | 107.031T | 611.350M | 61.502s | 38.669 | +| Pixart-Alpha w/ T-GATE | 65.318T | 462.585M | 37.867s | 35.825 | +| DeepCache (SD-XL) | 57.888T | - | 19.931s | 23.755 | +| DeepCache w/ T-GATE | 43.868T | - | 14.666s | 23.999 | +| LCM (SD-XL) | 11.955T | 2.570B | 3.805s | 25.044 | +| LCM w/ T-GATE | 11.171T | 2.024B | 3.533s | 25.028 | +| LCM (Pixart-Alpha) | 8.563T | 611.350M | 4.733s | 36.086 | +| LCM w/ T-GATE | 7.623T | 462.585M | 4.543s | 37.048 | + +The latency is tested on an NVIDIA 1080TI, MACs and Params are calculated with [calflops](https://github.com/MrYxJ/calculate-flops.pytorch), and the FID is calculated with [PytorchFID](https://github.com/mseitzer/pytorch-fid). diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/tome.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/tome.md new file mode 100644 index 0000000000000000000000000000000000000000..ab368c9ccbb99c1ececf15e0af734d765ad2c6e8 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/tome.md @@ -0,0 +1,96 @@ + + +# Token merging + +[Token merging](https://huggingface.co/papers/2303.17604) (ToMe) merges redundant tokens/patches progressively in the forward pass of a Transformer-based network which can speed-up the inference latency of [`StableDiffusionPipeline`]. + +Install ToMe from `pip`: + +```bash +pip install tomesd +``` + +You can use ToMe from the [`tomesd`](https://github.com/dbolya/tomesd) library with the [`apply_patch`](https://github.com/dbolya/tomesd?tab=readme-ov-file#usage) function: + +```diff + from diffusers import StableDiffusionPipeline + import torch + import tomesd + + pipeline = StableDiffusionPipeline.from_pretrained( + "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True, + ).to("cuda") ++ tomesd.apply_patch(pipeline, ratio=0.5) + + image = pipeline("a photo of an astronaut riding a horse on mars").images[0] +``` + +The `apply_patch` function exposes a number of [arguments](https://github.com/dbolya/tomesd#usage) to help strike a balance between pipeline inference speed and the quality of the generated tokens. The most important argument is `ratio` which controls the number of tokens that are merged during the forward pass. + +As reported in the [paper](https://huggingface.co/papers/2303.17604), ToMe can greatly preserve the quality of the generated images while boosting inference speed. By increasing the `ratio`, you can speed-up inference even further, but at the cost of some degraded image quality. + +To test the quality of the generated images, we sampled a few prompts from [Parti Prompts](https://parti.research.google/) and performed inference with the [`StableDiffusionPipeline`] with the following settings: + +
+ +
+ +We didn’t notice any significant decrease in the quality of the generated samples, and you can check out the generated samples in this [WandB report](https://wandb.ai/sayakpaul/tomesd-results/runs/23j4bj3i?workspace=). If you're interested in reproducing this experiment, use this [script](https://gist.github.com/sayakpaul/8cac98d7f22399085a060992f411ecbd). + +## Benchmarks + +We also benchmarked the impact of `tomesd` on the [`StableDiffusionPipeline`] with [xFormers](https://huggingface.co/docs/diffusers/optimization/xformers) enabled across several image resolutions. The results are obtained from A100 and V100 GPUs in the following development environment: + +```bash +- `diffusers` version: 0.15.1 +- Python version: 3.8.16 +- PyTorch version (GPU?): 1.13.1+cu116 (True) +- Huggingface_hub version: 0.13.2 +- Transformers version: 4.27.2 +- Accelerate version: 0.18.0 +- xFormers version: 0.0.16 +- tomesd version: 0.1.2 +``` + +To reproduce this benchmark, feel free to use this [script](https://gist.github.com/sayakpaul/27aec6bca7eb7b0e0aa4112205850335). The results are reported in seconds, and where applicable we report the speed-up percentage over the vanilla pipeline when using ToMe and ToMe + xFormers. + +| **GPU** | **Resolution** | **Batch size** | **Vanilla** | **ToMe** | **ToMe + xFormers** | +|----------|----------------|----------------|-------------|----------------|---------------------| +| **A100** | 512 | 10 | 6.88 | 5.26 (+23.55%) | 4.69 (+31.83%) | +| | 768 | 10 | OOM | 14.71 | 11 | +| | | 8 | OOM | 11.56 | 8.84 | +| | | 4 | OOM | 5.98 | 4.66 | +| | | 2 | 4.99 | 3.24 (+35.07%) | 2.1 (+37.88%) | +| | | 1 | 3.29 | 2.24 (+31.91%) | 2.03 (+38.3%) | +| | 1024 | 10 | OOM | OOM | OOM | +| | | 8 | OOM | OOM | OOM | +| | | 4 | OOM | 12.51 | 9.09 | +| | | 2 | OOM | 6.52 | 4.96 | +| | | 1 | 6.4 | 3.61 (+43.59%) | 2.81 (+56.09%) | +| **V100** | 512 | 10 | OOM | 10.03 | 9.29 | +| | | 8 | OOM | 8.05 | 7.47 | +| | | 4 | 5.7 | 4.3 (+24.56%) | 3.98 (+30.18%) | +| | | 2 | 3.14 | 2.43 (+22.61%) | 2.27 (+27.71%) | +| | | 1 | 1.88 | 1.57 (+16.49%) | 1.57 (+16.49%) | +| | 768 | 10 | OOM | OOM | 23.67 | +| | | 8 | OOM | OOM | 18.81 | +| | | 4 | OOM | 11.81 | 9.7 | +| | | 2 | OOM | 6.27 | 5.2 | +| | | 1 | 5.43 | 3.38 (+37.75%) | 2.82 (+48.07%) | +| | 1024 | 10 | OOM | OOM | OOM | +| | | 8 | OOM | OOM | OOM | +| | | 4 | OOM | OOM | 19.35 | +| | | 2 | OOM | 13 | 10.78 | +| | | 1 | OOM | 6.66 | 5.54 | + +As seen in the tables above, the speed-up from `tomesd` becomes more pronounced for larger image resolutions. It is also interesting to note that with `tomesd`, it is possible to run the pipeline on a higher resolution like 1024x1024. You may be able to speed-up inference even more with [`torch.compile`](fp16#torchcompile). diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/xdit.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/xdit.md new file mode 100644 index 0000000000000000000000000000000000000000..ecf45635684ac0f106833a421e4d573ce1897423 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/xdit.md @@ -0,0 +1,121 @@ +# xDiT + +[xDiT](https://github.com/xdit-project/xDiT) is an inference engine designed for the large scale parallel deployment of Diffusion Transformers (DiTs). xDiT provides a suite of efficient parallel approaches for Diffusion Models, as well as GPU kernel accelerations. + +There are four parallel methods supported in xDiT, including [Unified Sequence Parallelism](https://huggingface.co/papers/2405.07719), [PipeFusion](https://huggingface.co/papers/2405.14430), CFG parallelism and data parallelism. The four parallel methods in xDiT can be configured in a hybrid manner, optimizing communication patterns to best suit the underlying network hardware. + +Optimization orthogonal to parallelization focuses on accelerating single GPU performance. In addition to utilizing well-known Attention optimization libraries, we leverage compilation acceleration technologies such as torch.compile and onediff. + +The overview of xDiT is shown as follows. + +
+ +
+You can install xDiT using the following command: + + +```bash +pip install xfuser +``` + +Here's an example of using xDiT to accelerate inference of a Diffusers model. + +```diff + import torch + from diffusers import StableDiffusion3Pipeline + + from xfuser import xFuserArgs, xDiTParallel + from xfuser.config import FlexibleArgumentParser + from xfuser.core.distributed import get_world_group + + def main(): ++ parser = FlexibleArgumentParser(description="xFuser Arguments") ++ args = xFuserArgs.add_cli_args(parser).parse_args() ++ engine_args = xFuserArgs.from_cli_args(args) ++ engine_config, input_config = engine_args.create_config() + + local_rank = get_world_group().local_rank + pipe = StableDiffusion3Pipeline.from_pretrained( + pretrained_model_name_or_path=engine_config.model_config.model, + torch_dtype=torch.float16, + ).to(f"cuda:{local_rank}") + +# do anything you want with pipeline here + ++ pipe = xDiTParallel(pipe, engine_config, input_config) + + pipe( + height=input_config.height, + width=input_config.height, + prompt=input_config.prompt, + num_inference_steps=input_config.num_inference_steps, + output_type=input_config.output_type, + generator=torch.Generator(device="cuda").manual_seed(input_config.seed), + ) + ++ if input_config.output_type == "pil": ++ pipe.save("results", "stable_diffusion_3") + +if __name__ == "__main__": + main() + +``` + +As you can see, we only need to use xFuserArgs from xDiT to get configuration parameters, and pass these parameters along with the pipeline object from the Diffusers library into xDiTParallel to complete the parallelization of a specific pipeline in Diffusers. + +xDiT runtime parameters can be viewed in the command line using `-h`, and you can refer to this [usage](https://github.com/xdit-project/xDiT?tab=readme-ov-file#2-usage) example for more details. + +xDiT needs to be launched using torchrun to support its multi-node, multi-GPU parallel capabilities. For example, the following command can be used for 8-GPU parallel inference: + +```bash +torchrun --nproc_per_node=8 ./inference.py --model models/FLUX.1-dev --data_parallel_degree 2 --ulysses_degree 2 --ring_degree 2 --prompt "A snowy mountain" "A small dog" --num_inference_steps 50 +``` + +## Supported models + +A subset of Diffusers models are supported in xDiT, such as Flux.1, Stable Diffusion 3, etc. The latest supported models can be found [here](https://github.com/xdit-project/xDiT?tab=readme-ov-file#-supported-dits). + +## Benchmark +We tested different models on various machines, and here is some of the benchmark data. + +### Flux.1-schnell +
+ +
+ + +
+ +
+ +### Stable Diffusion 3 +
+ +
+ +
+ +
+ +### HunyuanDiT +
+ +
+ +
+ +
+ +
+ +
+ +More detailed performance metric can be found on our [github page](https://github.com/xdit-project/xDiT?tab=readme-ov-file#perf). + +## Reference + +[xDiT-project](https://github.com/xdit-project/xDiT) + +[USP: A Unified Sequence Parallelism Approach for Long Context Generative AI](https://huggingface.co/papers/2405.07719) + +[PipeFusion: Displaced Patch Pipeline Parallelism for Inference of Diffusion Transformer Models](https://huggingface.co/papers/2405.14430) \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/xformers.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/xformers.md new file mode 100644 index 0000000000000000000000000000000000000000..523e81559547bda8b11c0a2f0ceec373a4dacc20 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/optimization/xformers.md @@ -0,0 +1,29 @@ + + +# xFormers + +We recommend [xFormers](https://github.com/facebookresearch/xformers) for both inference and training. In our tests, the optimizations performed in the attention blocks allow for both faster speed and reduced memory consumption. + +Install xFormers from `pip`: + +```bash +pip install xformers +``` + +> [!TIP] +> The xFormers `pip` package requires the latest version of PyTorch. If you need to use a previous version of PyTorch, then we recommend [installing xFormers from the source](https://github.com/facebookresearch/xformers#installing-xformers). + +After xFormers is installed, you can use `enable_xformers_memory_efficient_attention()` for faster inference and reduced memory consumption as shown in this [section](memory#memory-efficient-attention). + +> [!WARNING] +> According to this [issue](https://github.com/huggingface/diffusers/issues/2234#issuecomment-1416931212), xFormers `v0.0.16` cannot be used for training (fine-tune or DreamBooth) in some GPUs. If you observe this problem, please install a development version as indicated in the issue comments. diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/bitsandbytes.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/bitsandbytes.md new file mode 100644 index 0000000000000000000000000000000000000000..0729472744630df7ecb1513745ec6bcf504849ec --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/bitsandbytes.md @@ -0,0 +1,452 @@ + + +# bitsandbytes + +[bitsandbytes](https://huggingface.co/docs/bitsandbytes/index) is the easiest option for quantizing a model to 8 and 4-bit. 8-bit quantization multiplies outliers in fp16 with non-outliers in int8, converts the non-outlier values back to fp16, and then adds them together to return the weights in fp16. This reduces the degradative effect outlier values have on a model's performance. + +4-bit quantization compresses a model even further, and it is commonly used with [QLoRA](https://hf.co/papers/2305.14314) to finetune quantized LLMs. + +This guide demonstrates how quantization can enable running +[FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) +on less than 16GB of VRAM and even on a free Google +Colab instance. + +![comparison image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/quant-bnb/comparison.png) + +To use bitsandbytes, make sure you have the following libraries installed: + +```bash +pip install diffusers transformers accelerate bitsandbytes -U +``` + +Now you can quantize a model by passing a [`BitsAndBytesConfig`] to [`~ModelMixin.from_pretrained`]. This works for any model in any modality, as long as it supports loading with [Accelerate](https://hf.co/docs/accelerate/index) and contains `torch.nn.Linear` layers. + + + + +Quantizing a model in 8-bit halves the memory-usage: + +bitsandbytes is supported in both Transformers and Diffusers, so you can quantize both the +[`FluxTransformer2DModel`] and [`~transformers.T5EncoderModel`]. + +For Ada and higher-series GPUs. we recommend changing `torch_dtype` to `torch.bfloat16`. + +> [!TIP] +> The [`CLIPTextModel`] and [`AutoencoderKL`] aren't quantized because they're already small in size and because [`AutoencoderKL`] only has a few `torch.nn.Linear` layers. + +```py +from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig +from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig +import torch +from diffusers import AutoModel +from transformers import T5EncoderModel + +quant_config = TransformersBitsAndBytesConfig(load_in_8bit=True,) + +text_encoder_2_8bit = T5EncoderModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="text_encoder_2", + quantization_config=quant_config, + torch_dtype=torch.float16, +) + +quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True,) + +transformer_8bit = AutoModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="transformer", + quantization_config=quant_config, + torch_dtype=torch.float16, +) +``` + +By default, all the other modules such as `torch.nn.LayerNorm` are converted to `torch.float16`. You can change the data type of these modules with the `torch_dtype` parameter. + +```diff +transformer_8bit = AutoModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="transformer", + quantization_config=quant_config, ++ torch_dtype=torch.float32, +) +``` + +Let's generate an image using our quantized models. + +Setting `device_map="auto"` automatically fills all available space on the GPU(s) first, then the +CPU, and finally, the hard drive (the absolute slowest option) if there is still not enough memory. + +```py +from diffusers import FluxPipeline + +pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + transformer=transformer_8bit, + text_encoder_2=text_encoder_2_8bit, + torch_dtype=torch.float16, + device_map="auto", +) + +pipe_kwargs = { + "prompt": "A cat holding a sign that says hello world", + "height": 1024, + "width": 1024, + "guidance_scale": 3.5, + "num_inference_steps": 50, + "max_sequence_length": 512, +} + +image = pipe(**pipe_kwargs, generator=torch.manual_seed(0),).images[0] +``` + +
+ +
+ +When there is enough memory, you can also directly move the pipeline to the GPU with `.to("cuda")` and apply [`~DiffusionPipeline.enable_model_cpu_offload`] to optimize GPU memory usage. + +Once a model is quantized, you can push the model to the Hub with the [`~ModelMixin.push_to_hub`] method. The quantization `config.json` file is pushed first, followed by the quantized model weights. You can also save the serialized 8-bit models locally with [`~ModelMixin.save_pretrained`]. + + + + +Quantizing a model in 4-bit reduces your memory-usage by 4x: + +bitsandbytes is supported in both Transformers and Diffusers, so you can can quantize both the +[`FluxTransformer2DModel`] and [`~transformers.T5EncoderModel`]. + +For Ada and higher-series GPUs. we recommend changing `torch_dtype` to `torch.bfloat16`. + +> [!TIP] +> The [`CLIPTextModel`] and [`AutoencoderKL`] aren't quantized because they're already small in size and because [`AutoencoderKL`] only has a few `torch.nn.Linear` layers. + +```py +from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig +from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig +import torch +from diffusers import AutoModel +from transformers import T5EncoderModel + +quant_config = TransformersBitsAndBytesConfig(load_in_4bit=True,) + +text_encoder_2_4bit = T5EncoderModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="text_encoder_2", + quantization_config=quant_config, + torch_dtype=torch.float16, +) + +quant_config = DiffusersBitsAndBytesConfig(load_in_4bit=True,) + +transformer_4bit = AutoModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="transformer", + quantization_config=quant_config, + torch_dtype=torch.float16, +) +``` + +By default, all the other modules such as `torch.nn.LayerNorm` are converted to `torch.float16`. You can change the data type of these modules with the `torch_dtype` parameter. + +```diff +transformer_4bit = AutoModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="transformer", + quantization_config=quant_config, ++ torch_dtype=torch.float32, +) +``` + +Let's generate an image using our quantized models. + +Setting `device_map="auto"` automatically fills all available space on the GPU(s) first, then the CPU, and finally, the hard drive (the absolute slowest option) if there is still not enough memory. + +```py +from diffusers import FluxPipeline + +pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + transformer=transformer_4bit, + text_encoder_2=text_encoder_2_4bit, + torch_dtype=torch.float16, + device_map="auto", +) + +pipe_kwargs = { + "prompt": "A cat holding a sign that says hello world", + "height": 1024, + "width": 1024, + "guidance_scale": 3.5, + "num_inference_steps": 50, + "max_sequence_length": 512, +} + +image = pipe(**pipe_kwargs, generator=torch.manual_seed(0),).images[0] +``` + +
+ +
+ +When there is enough memory, you can also directly move the pipeline to the GPU with `.to("cuda")` and apply [`~DiffusionPipeline.enable_model_cpu_offload`] to optimize GPU memory usage. + +Once a model is quantized, you can push the model to the Hub with the [`~ModelMixin.push_to_hub`] method. The quantization `config.json` file is pushed first, followed by the quantized model weights. You can also save the serialized 4-bit models locally with [`~ModelMixin.save_pretrained`]. + + + + +> [!WARNING] +> Training with 8-bit and 4-bit weights are only supported for training *extra* parameters. + +Check your memory footprint with the `get_memory_footprint` method: + +```py +print(model.get_memory_footprint()) +``` + +Note that this only tells you the memory footprint of the model params and does _not_ estimate the inference memory requirements. + +Quantized models can be loaded from the [`~ModelMixin.from_pretrained`] method without needing to specify the `quantization_config` parameters: + +```py +from diffusers import AutoModel, BitsAndBytesConfig + +quantization_config = BitsAndBytesConfig(load_in_4bit=True) + +model_4bit = AutoModel.from_pretrained( + "hf-internal-testing/flux.1-dev-nf4-pkg", subfolder="transformer" +) +``` + +## 8-bit (LLM.int8() algorithm) + +> [!TIP] +> Learn more about the details of 8-bit quantization in this [blog post](https://huggingface.co/blog/hf-bitsandbytes-integration)! + +This section explores some of the specific features of 8-bit models, such as outlier thresholds and skipping module conversion. + +### Outlier threshold + +An "outlier" is a hidden state value greater than a certain threshold, and these values are computed in fp16. While the values are usually normally distributed ([-3.5, 3.5]), this distribution can be very different for large models ([-60, 6] or [6, 60]). 8-bit quantization works well for values ~5, but beyond that, there is a significant performance penalty. A good default threshold value is 6, but a lower threshold may be needed for more unstable models (small models or finetuning). + +To find the best threshold for your model, we recommend experimenting with the `llm_int8_threshold` parameter in [`BitsAndBytesConfig`]: + +```py +from diffusers import AutoModel, BitsAndBytesConfig + +quantization_config = BitsAndBytesConfig( + load_in_8bit=True, llm_int8_threshold=10, +) + +model_8bit = AutoModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="transformer", + quantization_config=quantization_config, +) +``` + +### Skip module conversion + +For some models, you don't need to quantize every module to 8-bit which can actually cause instability. For example, for diffusion models like [Stable Diffusion 3](../api/pipelines/stable_diffusion/stable_diffusion_3), the `proj_out` module can be skipped using the `llm_int8_skip_modules` parameter in [`BitsAndBytesConfig`]: + +```py +from diffusers import SD3Transformer2DModel, BitsAndBytesConfig + +quantization_config = BitsAndBytesConfig( + load_in_8bit=True, llm_int8_skip_modules=["proj_out"], +) + +model_8bit = SD3Transformer2DModel.from_pretrained( + "stabilityai/stable-diffusion-3-medium-diffusers", + subfolder="transformer", + quantization_config=quantization_config, +) +``` + + +## 4-bit (QLoRA algorithm) + +> [!TIP] +> Learn more about its details in this [blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes). + +This section explores some of the specific features of 4-bit models, such as changing the compute data type, using the Normal Float 4 (NF4) data type, and using nested quantization. + + +### Compute data type + +To speedup computation, you can change the data type from float32 (the default value) to bf16 using the `bnb_4bit_compute_dtype` parameter in [`BitsAndBytesConfig`]: + +```py +import torch +from diffusers import BitsAndBytesConfig + +quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16) +``` + +### Normal Float 4 (NF4) + +NF4 is a 4-bit data type from the [QLoRA](https://hf.co/papers/2305.14314) paper, adapted for weights initialized from a normal distribution. You should use NF4 for training 4-bit base models. This can be configured with the `bnb_4bit_quant_type` parameter in the [`BitsAndBytesConfig`]: + +```py +from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig +from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig + +from diffusers import AutoModel +from transformers import T5EncoderModel + +quant_config = TransformersBitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4", +) + +text_encoder_2_4bit = T5EncoderModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="text_encoder_2", + quantization_config=quant_config, + torch_dtype=torch.float16, +) + +quant_config = DiffusersBitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4", +) + +transformer_4bit = AutoModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="transformer", + quantization_config=quant_config, + torch_dtype=torch.float16, +) +``` + +For inference, the `bnb_4bit_quant_type` does not have a huge impact on performance. However, to remain consistent with the model weights, you should use the `bnb_4bit_compute_dtype` and `torch_dtype` values. + +### Nested quantization + +Nested quantization is a technique that can save additional memory at no additional performance cost. This feature performs a second quantization of the already quantized weights to save an additional 0.4 bits/parameter. + +```py +from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig +from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig + +from diffusers import AutoModel +from transformers import T5EncoderModel + +quant_config = TransformersBitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_use_double_quant=True, +) + +text_encoder_2_4bit = T5EncoderModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="text_encoder_2", + quantization_config=quant_config, + torch_dtype=torch.float16, +) + +quant_config = DiffusersBitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_use_double_quant=True, +) + +transformer_4bit = AutoModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="transformer", + quantization_config=quant_config, + torch_dtype=torch.float16, +) +``` + +## Dequantizing `bitsandbytes` models + +Once quantized, you can dequantize a model to its original precision, but this might result in a small loss of quality. Make sure you have enough GPU RAM to fit the dequantized model. + +```python +from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig +from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig + +from diffusers import AutoModel +from transformers import T5EncoderModel + +quant_config = TransformersBitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_use_double_quant=True, +) + +text_encoder_2_4bit = T5EncoderModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="text_encoder_2", + quantization_config=quant_config, + torch_dtype=torch.float16, +) + +quant_config = DiffusersBitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_use_double_quant=True, +) + +transformer_4bit = AutoModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="transformer", + quantization_config=quant_config, + torch_dtype=torch.float16, +) + +text_encoder_2_4bit.dequantize() +transformer_4bit.dequantize() +``` + +## torch.compile + +Speed up inference with `torch.compile`. Make sure you have the latest `bitsandbytes` installed and we also recommend installing [PyTorch nightly](https://pytorch.org/get-started/locally/). + + + +```py +torch._dynamo.config.capture_dynamic_output_shape_ops = True + +quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True) +transformer_4bit = AutoModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="transformer", + quantization_config=quant_config, + torch_dtype=torch.float16, +) +transformer_4bit.compile(fullgraph=True) +``` + + + + +```py +quant_config = DiffusersBitsAndBytesConfig(load_in_4bit=True) +transformer_4bit = AutoModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="transformer", + quantization_config=quant_config, + torch_dtype=torch.float16, +) +transformer_4bit.compile(fullgraph=True) +``` + + + +On an RTX 4090 with compilation, 4-bit Flux generation completed in 25.809 seconds versus 32.570 seconds without. + +Check out the [benchmarking script](https://gist.github.com/sayakpaul/0db9d8eeeb3d2a0e5ed7cf0d9ca19b7d) for more details. + +## Resources + +* [End-to-end notebook showing Flux.1 Dev inference in a free-tier Colab](https://gist.github.com/sayakpaul/c76bd845b48759e11687ac550b99d8b4) +* [Training](https://github.com/huggingface/diffusers/blob/8c661ea586bf11cb2440da740dd3c4cf84679b85/examples/dreambooth/README_hidream.md#using-quantization) \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/gguf.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/gguf.md new file mode 100644 index 0000000000000000000000000000000000000000..47804c102da237f6b3fc2806e070f27241ca8707 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/gguf.md @@ -0,0 +1,120 @@ + + +# GGUF + +The GGUF file format is typically used to store models for inference with [GGML](https://github.com/ggerganov/ggml) and supports a variety of block wise quantization options. Diffusers supports loading checkpoints prequantized and saved in the GGUF format via `from_single_file` loading with Model classes. Loading GGUF checkpoints via Pipelines is currently not supported. + +The following example will load the [FLUX.1 DEV](https://huggingface.co/black-forest-labs/FLUX.1-dev) transformer model using the GGUF Q2_K quantization variant. + +Before starting please install gguf in your environment + +```shell +pip install -U gguf +``` + +Since GGUF is a single file format, use [`~FromSingleFileMixin.from_single_file`] to load the model and pass in the [`GGUFQuantizationConfig`]. + +When using GGUF checkpoints, the quantized weights remain in a low memory `dtype`(typically `torch.uint8`) and are dynamically dequantized and cast to the configured `compute_dtype` during each module's forward pass through the model. The `GGUFQuantizationConfig` allows you to set the `compute_dtype`. + +The functions used for dynamic dequantizatation are based on the great work done by [city96](https://github.com/city96/ComfyUI-GGUF), who created the Pytorch ports of the original [`numpy`](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/gguf/quants.py) implementation by [compilade](https://github.com/compilade). + +```python +import torch + +from diffusers import FluxPipeline, FluxTransformer2DModel, GGUFQuantizationConfig + +ckpt_path = ( + "https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q2_K.gguf" +) +transformer = FluxTransformer2DModel.from_single_file( + ckpt_path, + quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16), + torch_dtype=torch.bfloat16, +) +pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + transformer=transformer, + torch_dtype=torch.bfloat16, +) +pipe.enable_model_cpu_offload() +prompt = "A cat holding a sign that says hello world" +image = pipe(prompt, generator=torch.manual_seed(0)).images[0] +image.save("flux-gguf.png") +``` + +## Using Optimized CUDA Kernels with GGUF + +Optimized CUDA kernels can accelerate GGUF quantized model inference by approximately 10%. This functionality requires a compatible GPU with `torch.cuda.get_device_capability` greater than 7 and the kernels library: + +```shell +pip install -U kernels +``` + +Once installed, set `DIFFUSERS_GGUF_CUDA_KERNELS=true` to use optimized kernels when available. Note that CUDA kernels may introduce minor numerical differences compared to the original GGUF implementation, potentially causing subtle visual variations in generated images. To disable CUDA kernel usage, set the environment variable `DIFFUSERS_GGUF_CUDA_KERNELS=false`. + +## Supported Quantization Types + +- BF16 +- Q4_0 +- Q4_1 +- Q5_0 +- Q5_1 +- Q8_0 +- Q2_K +- Q3_K +- Q4_K +- Q5_K +- Q6_K + +## Convert to GGUF + +Use the Space below to convert a Diffusers checkpoint into the GGUF format for inference. +run conversion: + + + + +```py +import torch + +from diffusers import FluxPipeline, FluxTransformer2DModel, GGUFQuantizationConfig + +ckpt_path = ( + "https://huggingface.co/sayakpaul/different-lora-from-civitai/blob/main/flux_dev_diffusers-q4_0.gguf" +) +transformer = FluxTransformer2DModel.from_single_file( + ckpt_path, + quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16), + config="black-forest-labs/FLUX.1-dev", + subfolder="transformer", + torch_dtype=torch.bfloat16, +) +pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + transformer=transformer, + torch_dtype=torch.bfloat16, +) +pipe.enable_model_cpu_offload() +prompt = "A cat holding a sign that says hello world" +image = pipe(prompt, generator=torch.manual_seed(0)).images[0] +image.save("flux-gguf.png") +``` + +When using Diffusers format GGUF checkpoints, it's a must to provide the model `config` path. If the +model config resides in a `subfolder`, that needs to be specified, too. \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/modelopt.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/modelopt.md new file mode 100644 index 0000000000000000000000000000000000000000..06933d47c2219e0a310af68c2846ad00cf0df4fe --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/modelopt.md @@ -0,0 +1,141 @@ + + +# NVIDIA ModelOpt + +[NVIDIA-ModelOpt](https://github.com/NVIDIA/TensorRT-Model-Optimizer) is a unified library of state-of-the-art model optimization techniques like quantization, pruning, distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed. + +Before you begin, make sure you have nvidia_modelopt installed. + +```bash +pip install -U "nvidia_modelopt[hf]" +``` + +Quantize a model by passing [`NVIDIAModelOptConfig`] to [`~ModelMixin.from_pretrained`] (you can also load pre-quantized models). This works for any model in any modality, as long as it supports loading with [Accelerate](https://hf.co/docs/accelerate/index) and contains `torch.nn.Linear` layers. + +The example below only quantizes the weights to FP8. + +```python +import torch +from diffusers import AutoModel, SanaPipeline, NVIDIAModelOptConfig + +model_id = "Efficient-Large-Model/Sana_600M_1024px_diffusers" +dtype = torch.bfloat16 + +quantization_config = NVIDIAModelOptConfig(quant_type="FP8", quant_method="modelopt") +transformer = AutoModel.from_pretrained( + model_id, + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=dtype, +) +pipe = SanaPipeline.from_pretrained( + model_id, + transformer=transformer, + torch_dtype=dtype, +) +pipe.to("cuda") + +print(f"Pipeline memory usage: {torch.cuda.max_memory_reserved() / 1024**3:.3f} GB") + +prompt = "A cat holding a sign that says hello world" +image = pipe( + prompt, num_inference_steps=50, guidance_scale=4.5, max_sequence_length=512 +).images[0] +image.save("output.png") +``` + +> **Note:** +> +> The quantization methods in NVIDIA-ModelOpt are designed to reduce the memory footprint of model weights using various QAT (Quantization-Aware Training) and PTQ (Post-Training Quantization) techniques while maintaining model performance. However, the actual performance gain during inference depends on the deployment framework (e.g., TRT-LLM, TensorRT) and the specific hardware configuration. +> +> More details can be found [here](https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples). + +## NVIDIAModelOptConfig + +The `NVIDIAModelOptConfig` class accepts three parameters: +- `quant_type`: A string value mentioning one of the quantization types below. +- `modules_to_not_convert`: A list of module full/partial module names for which quantization should not be performed. For example, to not perform any quantization of the [`SD3Transformer2DModel`]'s pos_embed projection blocks, one would specify: `modules_to_not_convert=["pos_embed.proj.weight"]`. +- `disable_conv_quantization`: A boolean value which when set to `True` disables quantization for all convolutional layers in the model. This is useful as channel and block quantization generally don't work well with convolutional layers (used with INT4, NF4, NVFP4). If you want to disable quantization for specific convolutional layers, use `modules_to_not_convert` instead. +- `algorithm`: The algorithm to use for determining scale, defaults to `"max"`. You can check modelopt documentation for more algorithms and details. +- `forward_loop`: The forward loop function to use for calibrating activation during quantization. If not provided, it relies on static scale values computed using the weights only. +- `kwargs`: A dict of keyword arguments to pass to the underlying quantization method which will be invoked based on `quant_type`. + +## Supported quantization types + +ModelOpt supports weight-only, channel and block quantization int8, fp8, int4, nf4, and nvfp4. The quantization methods are designed to reduce the memory footprint of the model weights while maintaining the performance of the model during inference. + +Weight-only quantization stores the model weights in a specific low-bit data type but performs computation with a higher-precision data type, like `bfloat16`. This lowers the memory requirements from model weights but retains the memory peaks for activation computation. + +The quantization methods supported are as follows: + +| **Quantization Type** | **Supported Schemes** | **Required Kwargs** | **Additional Notes** | +|-----------------------|-----------------------|---------------------|----------------------| +| **INT8** | `int8 weight only`, `int8 channel quantization`, `int8 block quantization` | `quant_type`, `quant_type + channel_quantize`, `quant_type + channel_quantize + block_quantize` | +| **FP8** | `fp8 weight only`, `fp8 channel quantization`, `fp8 block quantization` | `quant_type`, `quant_type + channel_quantize`, `quant_type + channel_quantize + block_quantize` | +| **INT4** | `int4 weight only`, `int4 block quantization` | `quant_type`, `quant_type + channel_quantize + block_quantize` | `channel_quantize = -1 is only supported for now`| +| **NF4** | `nf4 weight only`, `nf4 double block quantization` | `quant_type`, `quant_type + channel_quantize + block_quantize + scale_channel_quantize` + `scale_block_quantize` | `channel_quantize = -1 and scale_channel_quantize = -1 are only supported for now` | +| **NVFP4** | `nvfp4 weight only`, `nvfp4 block quantization` | `quant_type`, `quant_type + channel_quantize + block_quantize` | `channel_quantize = -1 is only supported for now`| + + +Refer to the [official modelopt documentation](https://nvidia.github.io/TensorRT-Model-Optimizer/) for a better understanding of the available quantization methods and the exhaustive list of configuration options available. + +## Serializing and Deserializing quantized models + +To serialize a quantized model in a given dtype, first load the model with the desired quantization dtype and then save it using the [`~ModelMixin.save_pretrained`] method. + +```python +import torch +from diffusers import AutoModel, NVIDIAModelOptConfig +from modelopt.torch.opt import enable_huggingface_checkpointing + +enable_huggingface_checkpointing() + +model_id = "Efficient-Large-Model/Sana_600M_1024px_diffusers" +quant_config_fp8 = {"quant_type": "FP8", "quant_method": "modelopt"} +quant_config_fp8 = NVIDIAModelOptConfig(**quant_config_fp8) +model = AutoModel.from_pretrained( + model_id, + subfolder="transformer", + quantization_config=quant_config_fp8, + torch_dtype=torch.bfloat16, +) +model.save_pretrained('path/to/sana_fp8', safe_serialization=False) +``` + +To load a serialized quantized model, use the [`~ModelMixin.from_pretrained`] method. + +```python +import torch +from diffusers import AutoModel, NVIDIAModelOptConfig, SanaPipeline +from modelopt.torch.opt import enable_huggingface_checkpointing + +enable_huggingface_checkpointing() + +quantization_config = NVIDIAModelOptConfig(quant_type="FP8", quant_method="modelopt") +transformer = AutoModel.from_pretrained( + "path/to/sana_fp8", + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, +) +pipe = SanaPipeline.from_pretrained( + "Efficient-Large-Model/Sana_600M_1024px_diffusers", + transformer=transformer, + torch_dtype=torch.bfloat16, +) +pipe.to("cuda") +prompt = "A cat holding a sign that says hello world" +image = pipe( + prompt, num_inference_steps=50, guidance_scale=4.5, max_sequence_length=512 +).images[0] +image.save("output.png") +``` diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/overview.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/overview.md new file mode 100644 index 0000000000000000000000000000000000000000..38abeeac6d4ddc3f1043ea947f54829ec043b374 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/overview.md @@ -0,0 +1,141 @@ + + +# Getting started + +Quantization focuses on representing data with fewer bits while also trying to preserve the precision of the original data. This often means converting a data type to represent the same information with fewer bits. For example, if your model weights are stored as 32-bit floating points and they're quantized to 16-bit floating points, this halves the model size which makes it easier to store and reduces memory usage. Lower precision can also speedup inference because it takes less time to perform calculations with fewer bits. + +Diffusers supports multiple quantization backends to make large diffusion models like [Flux](../api/pipelines/flux) more accessible. This guide shows how to use the [`~quantizers.PipelineQuantizationConfig`] class to quantize a pipeline during its initialization from a pretrained or non-quantized checkpoint. + +## Pipeline-level quantization + +There are two ways to use [`~quantizers.PipelineQuantizationConfig`] depending on how much customization you want to apply to the quantization configuration. + +- for basic use cases, define the `quant_backend`, `quant_kwargs`, and `components_to_quantize` arguments +- for granular quantization control, define a `quant_mapping` that provides the quantization configuration for individual model components + +### Basic quantization + +Initialize [`~quantizers.PipelineQuantizationConfig`] with the following parameters. + +- `quant_backend` specifies which quantization backend to use. Currently supported backends include: `bitsandbytes_4bit`, `bitsandbytes_8bit`, `gguf`, `quanto`, and `torchao`. +- `quant_kwargs` specifies the quantization arguments to use. + +> [!TIP] +> These `quant_kwargs` arguments are different for each backend. Refer to the [Quantization API](../api/quantization) docs to view the arguments for each backend. + +- `components_to_quantize` specifies which component(s) of the pipeline to quantize. Typically, you should quantize the most compute intensive components like the transformer. The text encoder is another component to consider quantizing if a pipeline has more than one such as [`FluxPipeline`]. The example below quantizes the T5 text encoder in [`FluxPipeline`] while keeping the CLIP model intact. + + `components_to_quantize` accepts either a list for multiple models or a string for a single model. + +The example below loads the bitsandbytes backend with the following arguments from [`~quantizers.quantization_config.BitsAndBytesConfig`], `load_in_4bit`, `bnb_4bit_quant_type`, and `bnb_4bit_compute_dtype`. + +```py +import torch +from diffusers import DiffusionPipeline +from diffusers.quantizers import PipelineQuantizationConfig + +pipeline_quant_config = PipelineQuantizationConfig( + quant_backend="bitsandbytes_4bit", + quant_kwargs={"load_in_4bit": True, "bnb_4bit_quant_type": "nf4", "bnb_4bit_compute_dtype": torch.bfloat16}, + components_to_quantize=["transformer", "text_encoder_2"], +) +``` + +Pass the `pipeline_quant_config` to [`~DiffusionPipeline.from_pretrained`] to quantize the pipeline. + +```py +pipe = DiffusionPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + quantization_config=pipeline_quant_config, + torch_dtype=torch.bfloat16, +).to("cuda") + +image = pipe("photo of a cute dog").images[0] +``` + + +### Advanced quantization + +The `quant_mapping` argument provides more options for how to quantize each individual component in a pipeline, like combining different quantization backends. + +Initialize [`~quantizers.PipelineQuantizationConfig`] and pass a `quant_mapping` to it. The `quant_mapping` allows you to specify the quantization options for each component in the pipeline such as the transformer and text encoder. + +The example below uses two quantization backends, [`~quantizers.quantization_config.QuantoConfig`] and [`transformers.BitsAndBytesConfig`], for the transformer and text encoder. + +```py +import torch +from diffusers import DiffusionPipeline +from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig +from diffusers.quantizers.quantization_config import QuantoConfig +from diffusers.quantizers import PipelineQuantizationConfig +from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig + +pipeline_quant_config = PipelineQuantizationConfig( + quant_mapping={ + "transformer": QuantoConfig(weights_dtype="int8"), + "text_encoder_2": TransformersBitsAndBytesConfig( + load_in_4bit=True, compute_dtype=torch.bfloat16 + ), + } +) +``` + +There is a separate bitsandbytes backend in [Transformers](https://huggingface.co/docs/transformers/main_classes/quantization#transformers.BitsAndBytesConfig). You need to import and use [`transformers.BitsAndBytesConfig`] for components that come from Transformers. For example, `text_encoder_2` in [`FluxPipeline`] is a [`~transformers.T5EncoderModel`] from Transformers so you need to use [`transformers.BitsAndBytesConfig`] instead of [`diffusers.BitsAndBytesConfig`]. + +> [!TIP] +> Use the [basic quantization](#basic-quantization) method above if you don't want to manage these distinct imports or aren't sure where each pipeline component comes from. + +```py +import torch +from diffusers import DiffusionPipeline +from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig +from diffusers.quantizers import PipelineQuantizationConfig +from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig + +pipeline_quant_config = PipelineQuantizationConfig( + quant_mapping={ + "transformer": DiffusersBitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16), + "text_encoder_2": TransformersBitsAndBytesConfig( + load_in_4bit=True, compute_dtype=torch.bfloat16 + ), + } +) +``` + +Pass the `pipeline_quant_config` to [`~DiffusionPipeline.from_pretrained`] to quantize the pipeline. + +```py +pipe = DiffusionPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + quantization_config=pipeline_quant_config, + torch_dtype=torch.bfloat16, +).to("cuda") + +image = pipe("photo of a cute dog").images[0] +``` + +## Resources + +Check out the resources below to learn more about quantization. + +- If you are new to quantization, we recommend checking out the following beginner-friendly courses in collaboration with DeepLearning.AI. + + - [Quantization Fundamentals with Hugging Face](https://www.deeplearning.ai/short-courses/quantization-fundamentals-with-hugging-face/) + - [Quantization in Depth](https://www.deeplearning.ai/short-courses/quantization-in-depth/) + +- Refer to the [Contribute new quantization method guide](https://huggingface.co/docs/transformers/main/en/quantization/contribute) if you're interested in adding a new quantization method. + +- The Transformers quantization [Overview](https://huggingface.co/docs/transformers/quantization/overview#when-to-use-what) provides an overview of the pros and cons of different quantization backends. + +- Read the [Exploring Quantization Backends in Diffusers](https://huggingface.co/blog/diffusers-quantization) blog post for a brief introduction to each quantization backend, how to choose a backend, and combining quantization with other memory optimizations. diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/quanto.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/quanto.md new file mode 100644 index 0000000000000000000000000000000000000000..d322d76be267dcf73de5c7f81d7f7ea9e3584b6a --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/quanto.md @@ -0,0 +1,148 @@ + + +# Quanto + +[Quanto](https://github.com/huggingface/optimum-quanto) is a PyTorch quantization backend for [Optimum](https://huggingface.co/docs/optimum/en/index). It has been designed with versatility and simplicity in mind: + +- All features are available in eager mode (works with non-traceable models) +- Supports quantization aware training +- Quantized models are compatible with `torch.compile` +- Quantized models are Device agnostic (e.g CUDA,XPU,MPS,CPU) + +In order to use the Quanto backend, you will first need to install `optimum-quanto>=0.2.6` and `accelerate` + +```shell +pip install optimum-quanto accelerate +``` + +Now you can quantize a model by passing the `QuantoConfig` object to the `from_pretrained()` method. Although the Quanto library does allow quantizing `nn.Conv2d` and `nn.LayerNorm` modules, currently, Diffusers only supports quantizing the weights in the `nn.Linear` layers of a model. The following snippet demonstrates how to apply `float8` quantization with Quanto. + +```python +import torch +from diffusers import FluxTransformer2DModel, QuantoConfig + +model_id = "black-forest-labs/FLUX.1-dev" +quantization_config = QuantoConfig(weights_dtype="float8") +transformer = FluxTransformer2DModel.from_pretrained( + model_id, + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, +) + +pipe = FluxPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch_dtype) +pipe.to("cuda") + +prompt = "A cat holding a sign that says hello world" +image = pipe( + prompt, num_inference_steps=50, guidance_scale=4.5, max_sequence_length=512 +).images[0] +image.save("output.png") +``` + +## Skipping Quantization on specific modules + +It is possible to skip applying quantization on certain modules using the `modules_to_not_convert` argument in the `QuantoConfig`. Please ensure that the modules passed in to this argument match the keys of the modules in the `state_dict` + +```python +import torch +from diffusers import FluxTransformer2DModel, QuantoConfig + +model_id = "black-forest-labs/FLUX.1-dev" +quantization_config = QuantoConfig(weights_dtype="float8", modules_to_not_convert=["proj_out"]) +transformer = FluxTransformer2DModel.from_pretrained( + model_id, + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, +) +``` + +## Using `from_single_file` with the Quanto Backend + +`QuantoConfig` is compatible with `~FromOriginalModelMixin.from_single_file`. + +```python +import torch +from diffusers import FluxTransformer2DModel, QuantoConfig + +ckpt_path = "https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/flux1-dev.safetensors" +quantization_config = QuantoConfig(weights_dtype="float8") +transformer = FluxTransformer2DModel.from_single_file(ckpt_path, quantization_config=quantization_config, torch_dtype=torch.bfloat16) +``` + +## Saving Quantized models + +Diffusers supports serializing Quanto models using the `~ModelMixin.save_pretrained` method. + +The serialization and loading requirements are different for models quantized directly with the Quanto library and models quantized +with Diffusers using Quanto as the backend. It is currently not possible to load models quantized directly with Quanto into Diffusers using `~ModelMixin.from_pretrained` + +```python +import torch +from diffusers import FluxTransformer2DModel, QuantoConfig + +model_id = "black-forest-labs/FLUX.1-dev" +quantization_config = QuantoConfig(weights_dtype="float8") +transformer = FluxTransformer2DModel.from_pretrained( + model_id, + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, +) +# save quantized model to reuse +transformer.save_pretrained("") + +# you can reload your quantized model with +model = FluxTransformer2DModel.from_pretrained("") +``` + +## Using `torch.compile` with Quanto + +Currently the Quanto backend supports `torch.compile` for the following quantization types: + +- `int8` weights + +```python +import torch +from diffusers import FluxPipeline, FluxTransformer2DModel, QuantoConfig + +model_id = "black-forest-labs/FLUX.1-dev" +quantization_config = QuantoConfig(weights_dtype="int8") +transformer = FluxTransformer2DModel.from_pretrained( + model_id, + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, +) +transformer = torch.compile(transformer, mode="max-autotune", fullgraph=True) + +pipe = FluxPipeline.from_pretrained( + model_id, transformer=transformer, torch_dtype=torch_dtype +) +pipe.to("cuda") +images = pipe("A cat holding a sign that says hello").images[0] +images.save("flux-quanto-compile.png") +``` + +## Supported Quantization Types + +### Weights + +- float8 +- int8 +- int4 +- int2 + + diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/torchao.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/torchao.md new file mode 100644 index 0000000000000000000000000000000000000000..18cc109e07859d744034ccfb2462a5c6f5d8e280 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quantization/torchao.md @@ -0,0 +1,189 @@ + + +# torchao + +[torchao](https://github.com/pytorch/ao) provides high-performance dtypes and optimizations based on quantization and sparsity for inference and training PyTorch models. It is supported for any model in any modality, as long as it supports loading with [Accelerate](https://hf.co/docs/accelerate/index) and contains `torch.nn.Linear` layers. + +Make sure Pytorch 2.5+ and torchao are installed with the command below. + +```bash +uv pip install -U torch torchao +``` + +Each quantization dtype is available as a separate instance of a [AOBaseConfig](https://docs.pytorch.org/ao/main/api_ref_quantization.html#inference-apis-for-quantize) class. This provides more flexible configuration options by exposing more available arguments. + +Pass the `AOBaseConfig` of a quantization dtype, like [Int4WeightOnlyConfig](https://docs.pytorch.org/ao/main/generated/torchao.quantization.Int4WeightOnlyConfig) to [`TorchAoConfig`] in [`~ModelMixin.from_pretrained`]. + +```py +import torch +from diffusers import DiffusionPipeline, PipelineQuantizationConfig, TorchAoConfig +from torchao.quantization import Int8WeightOnlyConfig + +pipeline_quant_config = PipelineQuantizationConfig( + quant_mapping={"transformer": TorchAoConfig(Int8WeightOnlyConfig(group_size=128)))} +) +pipeline = DiffusionPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + quantzation_config=pipeline_quant_config, + torch_dtype=torch.bfloat16, + device_map="cuda" +) +``` + +For simple use cases, you could also provide a string identifier in [`TorchAo`] as shown below. + +```py +import torch +from diffusers import DiffusionPipeline, PipelineQuantizationConfig, TorchAoConfig + +pipeline_quant_config = PipelineQuantizationConfig( + quant_mapping={"transformer": TorchAoConfig("int8wo")} +) +pipeline = DiffusionPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + quantzation_config=pipeline_quant_config, + torch_dtype=torch.bfloat16, + device_map="cuda" +) +``` + +## torch.compile + +torchao supports [torch.compile](../optimization/fp16#torchcompile) which can speed up inference with one line of code. + +```python +import torch +from diffusers import DiffusionPipeline, PipelineQuantizationConfig, TorchAoConfig +from torchao.quantization import Int4WeightOnlyConfig + +pipeline_quant_config = PipelineQuantizationConfig( + quant_mapping={"transformer": TorchAoConfig(Int4WeightOnlyConfig(group_size=128)))} +) +pipeline = DiffusionPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + quantzation_config=pipeline_quant_config, + torch_dtype=torch.bfloat16, + device_map="cuda" +) + +pipeline.transformer.compile(transformer, mode="max-autotune", fullgraph=True) +``` + +Refer to this [table](https://github.com/huggingface/diffusers/pull/10009#issue-2688781450) for inference speed and memory usage benchmarks with Flux and CogVideoX. More benchmarks on various hardware are also available in the torchao [repository](https://github.com/pytorch/ao/tree/main/torchao/quantization#benchmarks). + +> [!TIP] +> The FP8 post-training quantization schemes in torchao are effective for GPUs with compute capability of at least 8.9 (RTX-4090, Hopper, etc.). FP8 often provides the best speed, memory, and quality trade-off when generating images and videos. We recommend combining FP8 and torch.compile if your GPU is compatible. + +## autoquant + +torchao provides [autoquant](https://docs.pytorch.org/ao/stable/generated/torchao.quantization.autoquant.html#torchao.quantization.autoquant) an automatic quantization API. Autoquantization chooses the best quantization strategy by comparing the performance of each strategy on chosen input types and shapes. This is only supported in Diffusers for individual models at the moment. + +```py +import torch +from diffusers import DiffusionPipeline +from torchao.quantization import autoquant + +# Load the pipeline +pipeline = DiffusionPipeline.from_pretrained( + "black-forest-labs/FLUX.1-schnell", + torch_dtype=torch.bfloat16, + device_map="cuda" +) + +transformer = autoquant(pipeline.transformer) +``` + +## Supported quantization types + +torchao supports weight-only quantization and weight and dynamic-activation quantization for int8, float3-float8, and uint1-uint7. + +Weight-only quantization stores the model weights in a specific low-bit data type but performs computation with a higher-precision data type, like `bfloat16`. This lowers the memory requirements from model weights but retains the memory peaks for activation computation. + +Dynamic activation quantization stores the model weights in a low-bit dtype, while also quantizing the activations on-the-fly to save additional memory. This lowers the memory requirements from model weights, while also lowering the memory overhead from activation computations. However, this may come at a quality tradeoff at times, so it is recommended to test different models thoroughly. + +The quantization methods supported are as follows: + +| **Category** | **Full Function Names** | **Shorthands** | +|--------------|-------------------------|----------------| +| **Integer quantization** | `int4_weight_only`, `int8_dynamic_activation_int4_weight`, `int8_weight_only`, `int8_dynamic_activation_int8_weight` | `int4wo`, `int4dq`, `int8wo`, `int8dq` | +| **Floating point 8-bit quantization** | `float8_weight_only`, `float8_dynamic_activation_float8_weight`, `float8_static_activation_float8_weight` | `float8wo`, `float8wo_e5m2`, `float8wo_e4m3`, `float8dq`, `float8dq_e4m3`, `float8dq_e4m3_tensor`, `float8dq_e4m3_row` | +| **Floating point X-bit quantization** | `fpx_weight_only` | `fpX_eAwB` where `X` is the number of bits (1-7), `A` is exponent bits, and `B` is mantissa bits. Constraint: `X == A + B + 1` | +| **Unsigned Integer quantization** | `uintx_weight_only` | `uint1wo`, `uint2wo`, `uint3wo`, `uint4wo`, `uint5wo`, `uint6wo`, `uint7wo` | + +Some quantization methods are aliases (for example, `int8wo` is the commonly used shorthand for `int8_weight_only`). This allows using the quantization methods described in the torchao docs as-is, while also making it convenient to remember their shorthand notations. + +Refer to the [official torchao documentation](https://docs.pytorch.org/ao/stable/index.html) for a better understanding of the available quantization methods and the exhaustive list of configuration options available. + +## Serializing and Deserializing quantized models + +To serialize a quantized model in a given dtype, first load the model with the desired quantization dtype and then save it using the [`~ModelMixin.save_pretrained`] method. + +```python +import torch +from diffusers import AutoModel, TorchAoConfig + +quantization_config = TorchAoConfig("int8wo") +transformer = AutoModel.from_pretrained( + "black-forest-labs/Flux.1-Dev", + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, +) +transformer.save_pretrained("/path/to/flux_int8wo", safe_serialization=False) +``` + +To load a serialized quantized model, use the [`~ModelMixin.from_pretrained`] method. + +```python +import torch +from diffusers import FluxPipeline, AutoModel + +transformer = AutoModel.from_pretrained("/path/to/flux_int8wo", torch_dtype=torch.bfloat16, use_safetensors=False) +pipe = FluxPipeline.from_pretrained("black-forest-labs/Flux.1-Dev", transformer=transformer, torch_dtype=torch.bfloat16) +pipe.to("cuda") + +prompt = "A cat holding a sign that says hello world" +image = pipe(prompt, num_inference_steps=30, guidance_scale=7.0).images[0] +image.save("output.png") +``` + +If you are using `torch<=2.6.0`, some quantization methods, such as `uint4wo`, cannot be loaded directly and may result in an `UnpicklingError` when trying to load the models, but work as expected when saving them. In order to work around this, one can load the state dict manually into the model. Note, however, that this requires using `weights_only=False` in `torch.load`, so it should be run only if the weights were obtained from a trustable source. + +```python +import torch +from accelerate import init_empty_weights +from diffusers import FluxPipeline, AutoModel, TorchAoConfig + +# Serialize the model +transformer = AutoModel.from_pretrained( + "black-forest-labs/Flux.1-Dev", + subfolder="transformer", + quantization_config=TorchAoConfig("uint4wo"), + torch_dtype=torch.bfloat16, +) +transformer.save_pretrained("/path/to/flux_uint4wo", safe_serialization=False, max_shard_size="50GB") +# ... + +# Load the model +state_dict = torch.load("/path/to/flux_uint4wo/diffusion_pytorch_model.bin", weights_only=False, map_location="cpu") +with init_empty_weights(): + transformer = AutoModel.from_config("/path/to/flux_uint4wo/config.json") +transformer.load_state_dict(state_dict, strict=True, assign=True) +``` + +> [!TIP] +> The [`AutoModel`] API is supported for PyTorch >= 2.6 as shown in the examples below. + +## Resources + +- [TorchAO Quantization API](https://docs.pytorch.org/ao/stable/index.html) +- [Diffusers-TorchAO examples](https://github.com/sayakpaul/diffusers-torchao) diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quicktour.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quicktour.md new file mode 100644 index 0000000000000000000000000000000000000000..1ccc8eeadcc251c1c0f56a59e9a6cc23aadc0b03 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/quicktour.md @@ -0,0 +1,232 @@ + + +# Quickstart + +Diffusers is a library for developers and researchers that provides an easy inference API for generating images, videos and audio, as well as the building blocks for implementing new workflows. + +Diffusers provides many optimizations out-of-the-box that makes it possible to load and run large models on setups with limited memory or to accelerate inference. + +This Quickstart will give you an overview of Diffusers and get you up and generating quickly. + +> [!TIP] +> Before you begin, make sure you have a Hugging Face [account](https://huggingface.co/join) in order to use gated models like [Flux](https://huggingface.co/black-forest-labs/FLUX.1-dev). + +Follow the [Installation](./installation) guide to install Diffusers if it's not already installed. + +## DiffusionPipeline + +A diffusion model combines multiple components to generate outputs in any modality based on an input, such as a text description, image or both. + +For a standard text-to-image model: + +1. A text encoder turns a prompt into embeddings that guide the denoising process. Some models have more than one text encoder. +2. A scheduler contains the algorithmic specifics for gradually denoising initial random noise into clean outputs. Different schedulers affect generation speed and quality. +3. A UNet or diffusion transformer (DiT) is the workhorse of a diffusion model. + + At each step, it performs the denoising predictions, such as how much noise to remove or the general direction in which to steer the noise to generate better quality outputs. + + The UNet or DiT repeats this loop for a set amount of steps to generate the final output. + +4. A variational autoencoder (VAE) encodes and decodes pixels to a spatially compressed latent-space. *Latents* are compressed representations of an image and are more efficient to work with. The UNet or DiT operates on latents, and the clean latents at the end are decoded back into images. + +The [`DiffusionPipeline`] packages all these components into a single class for inference. There are several arguments in [`~DiffusionPipeline.__call__`] you can change, such as `num_inference_steps`, that affect the diffusion process. Try different values and arguments to see how they change generation quality or speed. + +Load a model with [`~DiffusionPipeline.from_pretrained`] and describe what you'd like to generate. The example below uses the default argument values. + + + + +Use `.images[0]` to access the generated image output. + +```py +import torch +from diffusers import DiffusionPipeline + +pipeline = DiffusionPipeline.from_pretrained( + "Qwen/Qwen-Image", torch_dtype=torch.bfloat16, device_map="cuda" +) + +prompt = """ +cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California +highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain +""" +pipeline(prompt).images[0] +``` + + + + +Use `.frames[0]` to access the generated video output and [`~utils.export_to_video`] to save the video. + +```py +import torch +from diffusers import AutoencoderKLWan, DiffusionPipeline +from diffusers.quantizers import PipelineQuantizationConfig +from diffusers.utils import export_to_video + +vae = AutoencoderKLWan.from_pretrained( + "Wan-AI/Wan2.2-T2V-A14B-Diffusers", + subfolder="vae", + torch_dtype=torch.float32 +) +pipeline = DiffusionPipeline.from_pretrained( + "Wan-AI/Wan2.2-T2V-A14B-Diffusers", + vae=vae + torch_dtype=torch.bfloat16, + device_map="cuda" +) + +prompt = """ +Cinematic video of a sleek cat lounging on a colorful inflatable in a crystal-clear turquoise pool in Palm Springs, +sipping a salt-rimmed margarita through a straw. Golden-hour sunlight glows over mid-century modern homes and swaying palms. +Shot in rich Sony a7S III: with moody, glamorous color grading, subtle lens flares, and soft vintage film grain. +Ripples shimmer as a warm desert breeze stirs the water, blending luxury and playful charm in an epic, gorgeously composed frame. +""" +video = pipeline(prompt=prompt, num_frames=81, num_inference_steps=40).frames[0] +export_to_video(video, "output.mp4", fps=16) +``` + + + + +## LoRA + +Adapters insert a small number of trainable parameters to the original base model. Only the inserted parameters are fine-tuned while the rest of the model weights remain frozen. This makes it fast and cheap to fine-tune a model on a new style. Among adapters, [LoRA's](./tutorials/using_peft_for_inference) are the most popular. + +Add a LoRA to a pipeline with the [`~loaders.QwenImageLoraLoaderMixin.load_lora_weights`] method. Some LoRA's require a special word to trigger it, such as `Realism`, in the example below. Check a LoRA's model card to see if it requires a trigger word. + +```py +import torch +from diffusers import DiffusionPipeline + +pipeline = DiffusionPipeline.from_pretrained( + "Qwen/Qwen-Image", torch_dtype=torch.bfloat16, device_map="cuda" +) +pipeline.load_lora_weights( + "flymy-ai/qwen-image-realism-lora", +) + +prompt = """ +super Realism cinematic film still of a cat sipping a margarita in a pool in Palm Springs in the style of umempart, California +highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain +""" +pipeline(prompt).images[0] +``` + +Check out the [LoRA](./tutorials/using_peft_for_inference) docs or Adapters section to learn more. + +## Quantization + +[Quantization](./quantization/overview) stores data in fewer bits to reduce memory usage. It may also speed up inference because it takes less time to perform calculations with fewer bits. + +Diffusers provides several quantization backends and picking one depends on your use case. For example, [bitsandbytes](./quantization/bitsandbytes) and [torchao](./quantization/torchao) are both simple and easy to use for inference, but torchao supports more [quantization types](./quantization/torchao#supported-quantization-types) like fp8. + +Configure [`PipelineQuantizationConfig`] with the backend to use, the specific arguments (refer to the [API](./api/quantization) reference for available arguments) for that backend, and which components to quantize. The example below quantizes the model to 4-bits and only uses 14.93GB of memory. + +```py +import torch +from diffusers import DiffusionPipeline +from diffusers.quantizers import PipelineQuantizationConfig + +quant_config = PipelineQuantizationConfig( + quant_backend="bitsandbytes_4bit", + quant_kwargs={"load_in_4bit": True, "bnb_4bit_quant_type": "nf4", "bnb_4bit_compute_dtype": torch.bfloat16}, + components_to_quantize=["transformer", "text_encoder"], +) +pipeline = DiffusionPipeline.from_pretrained( + "Qwen/Qwen-Image", + torch_dtype=torch.bfloat16, + quantization_config=quant_config, + device_map="cuda" +) + +prompt = """ +cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California +highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain +""" +pipeline(prompt).images[0] +print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB") +``` + +Take a look at the [Quantization](./quantization/overview) section for more details. + +## Optimizations + +> [!TIP] +> Optimization is dependent on hardware specs such as memory. Use this [Space](https://huggingface.co/spaces/diffusers/optimized-diffusers-code) to generate code examples that include all of Diffusers' available memory and speed optimization techniques for any model you're using. + +Modern diffusion models are very large and have billions of parameters. The iterative denoising process is also computationally intensive and slow. Diffusers provides techniques for reducing memory usage and boosting inference speed. These techniques can be combined with quantization to optimize for both memory usage and inference speed. + +### Memory usage + +The text encoders and UNet or DiT can use up as much as ~30GB of memory, exceeding the amount available on many free-tier or consumer GPUs. + +Offloading stores weights that aren't currently used on the CPU and only moves them to the GPU when they're needed. There are a few offloading types and the example below uses [model offloading](./optimization/memory#model-offloading). This moves an entire model, like a text encoder or transformer, to the CPU when it isn't actively being used. + +Call [`~DiffusionPipeline.enable_model_cpu_offload`] to activate it. By combining quantization and offloading, the following example only requires ~12.54GB of memory. + +```py +import torch +from diffusers import DiffusionPipeline +from diffusers.quantizers import PipelineQuantizationConfig + +quant_config = PipelineQuantizationConfig( + quant_backend="bitsandbytes_4bit", + quant_kwargs={"load_in_4bit": True, "bnb_4bit_quant_type": "nf4", "bnb_4bit_compute_dtype": torch.bfloat16}, + components_to_quantize=["transformer", "text_encoder"], +) +pipeline = DiffusionPipeline.from_pretrained( + "Qwen/Qwen-Image", + torch_dtype=torch.bfloat16, + quantization_config=quant_config, + device_map="cuda" +) +pipeline.enable_model_cpu_offload() + +prompt = """ +cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California +highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain +""" +pipeline(prompt).images[0] +print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB") +``` + +Refer to the [Reduce memory usage](./optimization/memory) docs to learn more about other memory reducing techniques. + +### Inference speed + +The denoising loop performs a lot of computations and can be slow. Methods like [torch.compile](./optimization/fp16#torchcompile) increases inference speed by compiling the computations into an optimized kernel. Compilation is slow for the first generation but successive generations should be much faster. + +The example below uses [regional compilation](./optimization/fp16#regional-compilation) to only compile small regions of a model. It reduces cold-start latency while also providing a runtime speed up. + +Call [`~ModelMixin.compile_repeated_blocks`] on the model to activate it. + +```py +import torch +from diffusers import DiffusionPipeline + +pipeline = DiffusionPipeline.from_pretrained( + "Qwen/Qwen-Image", torch_dtype=torch.bfloat16, device_map="cuda" +) + +pipeline.transformer.compile_repeated_blocks( + fullgraph=True, +) +prompt = """ +cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California +highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain +""" +pipeline(prompt).images[0] +``` + +Check out the [Accelerate inference](./optimization/fp16) or [Caching](./optimization/cache) docs for more methods that speed up inference. \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/stable_diffusion.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/stable_diffusion.md new file mode 100644 index 0000000000000000000000000000000000000000..93e399d3db88c53069e0ac8d8ebae3c3d2adad6d --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/stable_diffusion.md @@ -0,0 +1,132 @@ + + +[[open-in-colab]] + +# Basic performance + +Diffusion is a random process that is computationally demanding. You may need to run the [`DiffusionPipeline`] several times before getting a desired output. That's why it's important to carefully balance generation speed and memory usage in order to iterate faster, + +This guide recommends some basic performance tips for using the [`DiffusionPipeline`]. Refer to the Inference Optimization section docs such as [Accelerate inference](./optimization/fp16) or [Reduce memory usage](./optimization/memory) for more detailed performance guides. + +## Memory usage + +Reducing the amount of memory used indirectly speeds up generation and can help a model fit on device. + +The [`~DiffusionPipeline.enable_model_cpu_offload`] method moves a model to the CPU when it is not in use to save GPU memory. + +```py +import torch +from diffusers import DiffusionPipeline + +pipeline = DiffusionPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", + torch_dtype=torch.bfloat16, + device_map="cuda" +) +pipeline.enable_model_cpu_offload() + +prompt = """ +cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California +highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain +""" +pipeline(prompt).images[0] +print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB") +``` + +## Inference speed + +Denoising is the most computationally demanding process during diffusion. Methods that optimizes this process accelerates inference speed. Try the following methods for a speed up. + +- Add `device_map="cuda"` to place the pipeline on a GPU. Placing a model on an accelerator, like a GPU, increases speed because it performs computations in parallel. +- Set `torch_dtype=torch.bfloat16` to execute the pipeline in half-precision. Reducing the data type precision increases speed because it takes less time to perform computations in a lower precision. + +```py +import torch +import time +from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler + +pipeline = DiffusionPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", + torch_dtype=torch.bfloat16, + device_map="cuda +) +``` + +- Use a faster scheduler, such as [`DPMSolverMultistepScheduler`], which only requires ~20-25 steps. +- Set `num_inference_steps` to a lower value. Reducing the number of inference steps reduces the overall number of computations. However, this can result in lower generation quality. + +```py +pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config) + +prompt = """ +cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California +highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain +""" + +start_time = time.perf_counter() +image = pipeline(prompt).images[0] +end_time = time.perf_counter() + +print(f"Image generation took {end_time - start_time:.3f} seconds") +``` + +## Generation quality + +Many modern diffusion models deliver high-quality images out-of-the-box. However, you can still improve generation quality by trying the following. + +- Try a more detailed and descriptive prompt. Include details such as the image medium, subject, style, and aesthetic. A negative prompt may also help by guiding a model away from undesirable features by using words like low quality or blurry. + + ```py + import torch + from diffusers import DiffusionPipeline + + pipeline = DiffusionPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", + torch_dtype=torch.bfloat16, + device_map="cuda" + ) + + prompt = """ + cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California + highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain + """ + negative_prompt = "low quality, blurry, ugly, poor details" + pipeline(prompt, negative_prompt=negative_prompt).images[0] + ``` + + For more details about creating better prompts, take a look at the [Prompt techniques](./using-diffusers/weighted_prompts) doc. + +- Try a different scheduler, like [`HeunDiscreteScheduler`] or [`LMSDiscreteScheduler`], that gives up generation speed for quality. + + ```py + import torch + from diffusers import DiffusionPipeline, HeunDiscreteScheduler + + pipeline = DiffusionPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", + torch_dtype=torch.bfloat16, + device_map="cuda" + ) + pipeline.scheduler = HeunDiscreteScheduler.from_config(pipeline.scheduler.config) + + prompt = """ + cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California + highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain + """ + negative_prompt = "low quality, blurry, ugly, poor details" + pipeline(prompt, negative_prompt=negative_prompt).images[0] + ``` + +## Next steps + +Diffusers offers more advanced and powerful optimizations such as [group-offloading](./optimization/memory#group-offloading) and [regional compilation](./optimization/fp16#regional-compilation). To learn more about how to maximize performance, take a look at the Inference Optimization section. \ No newline at end of file diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/training/adapt_a_model.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/training/adapt_a_model.md new file mode 100644 index 0000000000000000000000000000000000000000..9b7efd2abfd8127a739694aa0d924e45bf6f30d4 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/training/adapt_a_model.md @@ -0,0 +1,47 @@ +# Adapt a model to a new task + +Many diffusion systems share the same components, allowing you to adapt a pretrained model for one task to an entirely different task. + +This guide will show you how to adapt a pretrained text-to-image model for inpainting by initializing and modifying the architecture of a pretrained [`UNet2DConditionModel`]. + +## Configure UNet2DConditionModel parameters + +A [`UNet2DConditionModel`] by default accepts 4 channels in the [input sample](https://huggingface.co/docs/diffusers/v0.16.0/en/api/models#diffusers.UNet2DConditionModel.in_channels). For example, load a pretrained text-to-image model like [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) and take a look at the number of `in_channels`: + +```py +from diffusers import StableDiffusionPipeline + +pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True) +pipeline.unet.config["in_channels"] +4 +``` + +Inpainting requires 9 channels in the input sample. You can check this value in a pretrained inpainting model like [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting): + +```py +from diffusers import StableDiffusionPipeline + +pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting", use_safetensors=True) +pipeline.unet.config["in_channels"] +9 +``` + +To adapt your text-to-image model for inpainting, you'll need to change the number of `in_channels` from 4 to 9. + +Initialize a [`UNet2DConditionModel`] with the pretrained text-to-image model weights, and change `in_channels` to 9. Changing the number of `in_channels` means you need to set `ignore_mismatched_sizes=True` and `low_cpu_mem_usage=False` to avoid a size mismatch error because the shape is different now. + +```py +from diffusers import AutoModel + +model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5" +unet = AutoModel.from_pretrained( + model_id, + subfolder="unet", + in_channels=9, + low_cpu_mem_usage=False, + ignore_mismatched_sizes=True, + use_safetensors=True, +) +``` + +The pretrained weights of the other components from the text-to-image model are initialized from their checkpoints, but the input channel weights (`conv_in.weight`) of the `unet` are randomly initialized. It is important to finetune the model for inpainting because otherwise the model returns noise. diff --git a/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/training/cogvideox.md b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/training/cogvideox.md new file mode 100644 index 0000000000000000000000000000000000000000..d0700c4da7639edca1ae01648e81c4d8240147b5 --- /dev/null +++ b/tasks/tasksmith-1c5704b1f07f/tests/source/docs/source/en/training/cogvideox.md @@ -0,0 +1,291 @@ + +# CogVideoX + +CogVideoX is a text-to-video generation model focused on creating more coherent videos aligned with a prompt. It achieves this using several methods. + +- a 3D variational autoencoder that compresses videos spatially and temporally, improving compression rate and video accuracy. + +- an expert transformer block to help align text and video, and a 3D full attention module for capturing and creating spatially and temporally accurate videos. + +The actual test of the video instruction dimension found that CogVideoX has good effects on consistent theme, dynamic information, consistent background, object information, smooth motion, color, scene, appearance style, and temporal style but cannot achieve good results with human action, spatial relationship, and multiple objects. + +Finetuning with Diffusers can help make up for these poor results. + +## Data Preparation + +The training scripts accepts data in two formats. + +The first format is suited for small-scale training, and the second format uses a CSV format, which is more appropriate for streaming data for large-scale training. In the future, Diffusers will support the `