Text-to-Image
Diffusers
TensorBoard
stable-diffusion-xl
stable-diffusion-xl-diffusers
lora
template:sd-lora
Instructions to use chechiamah/dithering-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use chechiamah/dithering-samples with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("chechiamah/dithering-samples") prompt = "a black and white image of a man with a hat in the style of <s0><s1>" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
|
Download README.md from chechiamah/dithering-samples: direct link, hf CLI and curl.
- Browser
- Download file 4.29 kB
-
https://huggingface.co/chechiamah/dithering-samples/resolve/main/README.md
- Command line
-
hf download hf://chechiamah/dithering-samples/README.md
-
curl -L -o README.md https://huggingface.co/chechiamah/dithering-samples/resolve/main/README.md
4.29 kB
| tags: | |
| - stable-diffusion-xl | |
| - stable-diffusion-xl-diffusers | |
| - text-to-image | |
| - diffusers | |
| - lora | |
| - template:sd-lora | |
| widget: | |
| - text: a black and white image of a man with a hat in the style of <s0><s1> | |
| output: | |
| url: image-0.png | |
| - text: a black and white image of a man with a hat in the style of <s0><s1> | |
| output: | |
| url: image-1.png | |
| - text: a colorful spiral pattern is shown in this image in the style of <s0><s1> | |
| output: | |
| url: image-2.png | |
| - text: a painting of a tree with blue and green leaves in the style of <s0><s1> | |
| output: | |
| url: image-3.png | |
| - text: the great wave off kanagawa by person in the style of <s0><s1> | |
| output: | |
| url: image-4.png | |
| - text: a pixel art drawing of a man in a hat in the style of <s0><s1> | |
| output: | |
| url: image-5.png | |
| - text: a man with glasses and a black shirt in the style of <s0><s1> | |
| output: | |
| url: image-6.png | |
| - text: a desert landscape with a blue sky and sand dunes in the style of <s0><s1> | |
| output: | |
| url: image-7.png | |
| - text: halftone dot pattern background vector in the style of <s0><s1> | |
| output: | |
| url: image-8.png | |
| - text: a set of four black and white abstract patterns in the style of <s0><s1> | |
| output: | |
| url: image-9.png | |
| base_model: stabilityai/stable-diffusion-xl-base-1.0 | |
| instance_prompt: in the style of <s0><s1> | |
| license: openrail++ | |
| # SDXL LoRA DreamBooth - chechiamah/dithering-samples | |
| <Gallery /> | |
| ## Model description | |
| ### These are chechiamah/dithering-samples LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. | |
| ## Download model | |
| ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke | |
| - **LoRA**: download **[`dithering-samples.safetensors` here 💾](/chechiamah/dithering-samples/blob/main/dithering-samples.safetensors)**. | |
| - Place it on your `models/Lora` folder. | |
| - On AUTOMATIC1111, load the LoRA by adding `<lora:dithering-samples:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). | |
| - *Embeddings*: download **[`dithering-samples_emb.safetensors` here 💾](/chechiamah/dithering-samples/blob/main/dithering-samples_emb.safetensors)**. | |
| - Place it on it on your `embeddings` folder | |
| - Use it by adding `dithering-samples_emb` to your prompt. For example, `in the style of dithering-samples_emb` | |
| (you need both the LoRA and the embeddings as they were trained together for this LoRA) | |
| ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) | |
| ```py | |
| from diffusers import AutoPipelineForText2Image | |
| import torch | |
| from huggingface_hub import hf_hub_download | |
| from safetensors.torch import load_file | |
| pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') | |
| pipeline.load_lora_weights('chechiamah/dithering-samples', weight_name='pytorch_lora_weights.safetensors') | |
| embedding_path = hf_hub_download(repo_id='chechiamah/dithering-samples', filename='dithering-samples_emb.safetensors' repo_type="model") | |
| state_dict = load_file(embedding_path) | |
| pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) | |
| pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) | |
| image = pipeline('in the style of <s0><s1>').images[0] | |
| ``` | |
| For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) | |
| ## Trigger words | |
| To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: | |
| to trigger concept `TOK` → use `<s0><s1>` in your prompt | |
| ## Details | |
| All [Files & versions](/chechiamah/dithering-samples/tree/main). | |
| The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). | |
| LoRA for the text encoder was enabled. False. | |
| Pivotal tuning was enabled: True. | |
| Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. | |