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main/README.md

@@ -56,8 +56,8 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
 | Stable Diffusion Mixture Tiling Pipeline SDXL | A pipeline generates cohesive images by integrating multiple diffusion processes, each focused on a specific image region and considering boundary effects for smooth blending | [Stable Diffusion Mixture Tiling Pipeline SDXL](#stable-diffusion-mixture-tiling-pipeline-sdxl) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/elismasilva/mixture-of-diffusers-sdxl-tiling) | [Eliseu Silva](https://github.com/DEVAIEXP/) |
 | Stable Diffusion MoD ControlNet Tile SR Pipeline SDXL | This is an advanced pipeline that leverages ControlNet Tile and Mixture-of-Diffusers techniques, integrating tile diffusion directly into the latent space denoising process. Designed to overcome the limitations of conventional pixel-space tile processing, this pipeline delivers Super Resolution (SR) upscaling for higher-quality images, reduced processing time, and greater adaptability. | [Stable Diffusion MoD ControlNet Tile SR Pipeline SDXL](#stable-diffusion-mod-controlnet-tile-sr-pipeline-sdxl) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/elismasilva/mod-control-tile-upscaler-sdxl) | [Eliseu Silva](https://github.com/DEVAIEXP/) |
 | FABRIC - Stable Diffusion with feedback Pipeline | pipeline supports feedback from liked and disliked images | [Stable Diffusion Fabric Pipeline](#stable-diffusion-fabric-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_diffusion_fabric.ipynb)| [Shauray Singh](https://shauray8.github.io/about_shauray/) |
-| sketch inpaint - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion Pipeline](#stable-diffusion-masked-im2im) | - | [Anatoly Belikov](https://github.com/noskill) |
-| sketch inpaint xl - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion XL Pipeline](#stable-diffusion-xl-masked-im2im) | - | [Anatoly Belikov](https://github.com/noskill) |
+| sketch inpaint - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion Pipeline](#masked-im2im-stable-diffusion-pipeline) | - | [Anatoly Belikov](https://github.com/noskill) |
+| sketch inpaint xl - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion XL Pipeline](#masked-im2im-stable-diffusion-pipeline-xl) | - | [Anatoly Belikov](https://github.com/noskill) |
 | prompt-to-prompt | change parts of a prompt and retain image structure (see [paper page](https://prompt-to-prompt.github.io/)) | [Prompt2Prompt Pipeline](#prompt2prompt-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/prompt_2_prompt_pipeline.ipynb) | [Umer H. Adil](https://twitter.com/UmerHAdil) |
 |   Latent Consistency Pipeline                                                                                                    | Implementation of [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://huggingface.co/papers/2310.04378)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Latent Consistency Pipeline](#latent-consistency-pipeline)      | - |              [Simian Luo](https://github.com/luosiallen) |
 |   Latent Consistency Img2img Pipeline                                                                                                    | Img2img pipeline for Latent Consistency Models                                                                                                                                                                                                                                                                                                                                                                                                                                    | [Latent Consistency Img2Img Pipeline](#latent-consistency-img2img-pipeline)      | - |              [Logan Zoellner](https://github.com/nagolinc) |
@@ -3526,7 +3526,7 @@ from controlnet_aux.midas import MidasDetector
 from PIL import Image
 
 from diffusers import AutoencoderKL, ControlNetModel, MultiAdapter, T2IAdapter
-from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+from diffusers.models.controlnets.multicontrolnet import MultiControlNetModel
 from diffusers.utils import load_image
 from examples.community.pipeline_stable_diffusion_xl_controlnet_adapter import (
     StableDiffusionXLControlNetAdapterPipeline,
@@ -3591,7 +3591,7 @@ from controlnet_aux.midas import MidasDetector
 from PIL import Image
 
 from diffusers import AutoencoderKL, ControlNetModel, MultiAdapter, T2IAdapter
-from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+from diffusers.models.controlnets.multicontrolnet import MultiControlNetModel
 from diffusers.utils import load_image
 from examples.community.pipeline_stable_diffusion_xl_controlnet_adapter_inpaint import (
     StableDiffusionXLControlNetAdapterInpaintPipeline,

main/README_community_scripts.md

@@ -9,6 +9,7 @@ If a community script doesn't work as expected, please open an issue and ping th
 | Using IP-Adapter with Negative Noise                                                                                                  | Using negative noise with IP-adapter to better control the generation (see the [original post](https://github.com/huggingface/diffusers/discussions/7167) on the forum for more details)                                                                                                                                                                                                                                                    | [IP-Adapter Negative Noise](#ip-adapter-negative-noise)                                   |[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/ip_adapter_negative_noise.ipynb) | [Álvaro Somoza](https://github.com/asomoza)|
 | Asymmetric Tiling                                                                                                  |configure seamless image tiling independently for the X and Y axes                                                                                                                                                                                                      | [Asymmetric Tiling](#Asymmetric-Tiling )                                   |[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/asymetric_tiling.ipynb) | [alexisrolland](https://github.com/alexisrolland)|
 | Prompt Scheduling Callback                                                                                                  |Allows changing prompts during a generation                                                                                                                                                                                                      | [Prompt Scheduling-Callback](#Prompt-Scheduling-Callback )                                   |[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/prompt_scheduling_callback.ipynb) | [hlky](https://github.com/hlky)|
+| CachedSearch: cheaper best-of-N for video pipelines | Test-time search where every candidate is generated under caching and only the winning seed is regenerated at full compute, from [CachedSearch](https://huggingface.co/papers/2607.23159). About 60% of the cost of full best-of-N with most of its gain. | [CachedSearch](#cachedsearch-cheaper-best-of-n-for-video-pipelines) | - | [Shreshth Saini](https://github.com/shreshthsaini)|
 
 
 ## Example usages
@@ -437,3 +438,84 @@ image = pipeline(
     ],
 ).images[0]
 ```
+
+### CachedSearch: cheaper best-of-N for video pipelines
+
+Best-of-N search (generate N candidates, keep the one a verifier scores highest) is the simplest way to spend more compute on a video model, and it is expensive: N full rollouts for one delivered video. [CachedSearch](https://huggingface.co/papers/2607.23159) (Saini, Birkbeck, Wang, Adsumilli, Bovik) makes the exploration phase cheap: every candidate is generated with a training-free cache enabled, the verifier ranks the cached drafts, and only the winning seed is regenerated with caching off. Because sampling is seed-deterministic, the delivered video is exactly what full-compute search would have returned whenever both pick the same seed; caching changes which candidate gets picked, never the quality of what ships. On Wan2.1-1.3B the paper measures 94.7% of best-of-8's gain at 63% of its cost.
+
+The script below uses Diffusers' built-in `FirstBlockCacheConfig` for the drafts and works with any pipeline whose denoiser supports `enable_cache` (Wan, HunyuanVideo, LTX-Video, CogVideoX, and image pipelines such as Flux). The threshold is the one model-specific number: 0.10 is a reasonable start for the Wan family, and other families need their own value (lower keeps more fidelity; higher is faster). The verifier is any callable `(frames, prompt) -> float`; the paper uses ImageReward averaged over eight uniformly spaced frames. The full package with `tau` calibration is `pip install cachedsearch`, https://github.com/shreshthsaini/CachedSearch.
+
+```py
+import numpy as np
+import torch
+from diffusers import FirstBlockCacheConfig
+
+
+def cached_search(pipe, prompt, verifier, n=8, threshold=0.10, num_inference_steps=50, seeds=None, commit=True, **gen_kwargs):
+    """Best-of-n over seeds where every candidate is generated under caching and only the winner is regenerated at full compute.
+
+    pipe: any diffusers video (or image) pipeline whose denoiser supports `enable_cache`.
+    verifier: callable (frames, prompt) -> float, higher is better.
+    threshold: FirstBlockCache threshold used for the drafts (calibrate per model family).
+    commit: regenerate the winning seed with caching off (recommended). False returns the cached draft itself.
+    Returns (video, winning_seed, draft_scores).
+    """
+    seeds = list(seeds) if seeds is not None else list(range(n))
+    denoisers = [m for m in (getattr(pipe, "transformer", None), getattr(pipe, "transformer_2", None)) if m is not None]
+
+    def generate(seed):
+        generator = torch.Generator(device=pipe._execution_device).manual_seed(seed)
+        out = pipe(prompt=prompt, num_inference_steps=num_inference_steps, generator=generator, **gen_kwargs)
+        return out.frames[0] if hasattr(out, "frames") else out.images[0]
+
+    # Explore: every candidate under caching. The pipeline resets the cache state after each call.
+    for m in denoisers:
+        m.enable_cache(FirstBlockCacheConfig(threshold=threshold))
+    drafts, scores = [], []
+    for seed in seeds:
+        frames = generate(seed)
+        drafts.append(frames)
+        scores.append(float(verifier(frames, prompt)))
+    for m in denoisers:
+        m.disable_cache()
+
+    best = int(np.argmax(scores))
+    if not commit:
+        return drafts[best], seeds[best], scores
+    # Commit: full compute only for the winner. Same seed, caching off, so this is a genuine full-compute sample.
+    return generate(seeds[best]), seeds[best], scores
+
+
+# Example: Wan2.1-1.3B with an ImageReward verifier (pip install image-reward)
+import ImageReward as RM
+from PIL import Image
+from diffusers import AutoencoderKLWan, WanPipeline
+from diffusers.utils import export_to_video
+
+model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16).to("cuda")
+
+reward = RM.load("ImageReward-v1.0", device="cuda")
+
+
+def imagereward_verifier(frames, prompt, num_frames=8):
+    idx = np.linspace(0, len(frames) - 1, min(num_frames, len(frames))).astype(int)
+    return float(np.mean([reward.score(prompt, Image.fromarray(np.asarray(frames[i]).astype("uint8"))) for i in idx]))
+
+
+video, seed, scores = cached_search(
+    pipe,
+    "a red fox running through deep snow",
+    imagereward_verifier,
+    n=8,
+    threshold=0.10,
+    num_inference_steps=50,
+    height=480,
+    width=832,
+    num_frames=81,
+    guidance_scale=5.0,
+)
+print(f"winner seed {seed}, draft scores {[round(s, 3) for s in scores]}")
+export_to_video(video, "cachedsearch.mp4", fps=16)
+```

main/fresco_v2v.py

@@ -29,9 +29,9 @@ from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
 from diffusers.loaders import StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
 from diffusers.models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel
 from diffusers.models.attention_processor import AttnProcessor2_0
+from diffusers.models.controlnets.multicontrolnet import MultiControlNetModel
 from diffusers.models.lora import adjust_lora_scale_text_encoder
 from diffusers.models.unets.unet_2d_condition import UNet2DConditionOutput
-from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
 from diffusers.pipelines.controlnet.pipeline_controlnet_img2img import StableDiffusionControlNetImg2ImgPipeline
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker

main/pipeline_animatediff_controlnet.py

@@ -24,10 +24,10 @@ from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPV
 from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
 from diffusers.loaders import IPAdapterMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
 from diffusers.models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel, UNetMotionModel
+from diffusers.models.controlnets.multicontrolnet import MultiControlNetModel
 from diffusers.models.lora import adjust_lora_scale_text_encoder
 from diffusers.models.unets.unet_motion_model import MotionAdapter
 from diffusers.pipelines.animatediff.pipeline_output import AnimateDiffPipelineOutput
-from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from diffusers.schedulers import (
     DDIMScheduler,

main/pipeline_stable_diffusion_xl_controlnet_adapter.py

@@ -25,8 +25,8 @@ from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokeniz
 from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
 from diffusers.loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
 from diffusers.models import AutoencoderKL, ControlNetModel, MultiAdapter, T2IAdapter, UNet2DConditionModel
+from diffusers.models.controlnets.multicontrolnet import MultiControlNetModel
 from diffusers.models.lora import adjust_lora_scale_text_encoder
-from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
 from diffusers.schedulers import KarrasDiffusionSchedulers

main/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py

@@ -43,8 +43,8 @@ from diffusers.models import (
     T2IAdapter,
     UNet2DConditionModel,
 )
+from diffusers.models.controlnets.multicontrolnet import MultiControlNetModel
 from diffusers.models.lora import adjust_lora_scale_text_encoder
-from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
 from diffusers.pipelines.pipeline_utils import StableDiffusionMixin
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
 from diffusers.schedulers import KarrasDiffusionSchedulers

main/pipeline_stable_diffusion_xl_instandid_img2img.py

@@ -25,7 +25,7 @@ import torch.nn as nn
 from diffusers import StableDiffusionXLControlNetImg2ImgPipeline
 from diffusers.image_processor import PipelineImageInput
 from diffusers.models import ControlNetModel
-from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+from diffusers.models.controlnets.multicontrolnet import MultiControlNetModel
 from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
 from diffusers.utils import (
     deprecate,

main/pipeline_stable_diffusion_xl_instantid.py

@@ -25,7 +25,7 @@ import torch.nn as nn
 from diffusers import StableDiffusionXLControlNetPipeline
 from diffusers.image_processor import PipelineImageInput
 from diffusers.models import ControlNetModel
-from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+from diffusers.models.controlnets.multicontrolnet import MultiControlNetModel
 from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
 from diffusers.utils import (
     deprecate,

main/rerender_a_video.py

@@ -26,7 +26,7 @@ from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
 from diffusers.models.attention_processor import Attention, AttnProcessor
-from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+from diffusers.models.controlnets.multicontrolnet import MultiControlNetModel
 from diffusers.pipelines.controlnet.pipeline_controlnet_img2img import StableDiffusionControlNetImg2ImgPipeline
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import KarrasDiffusionSchedulers

main/stable_diffusion_controlnet_img2img.py

@@ -9,7 +9,7 @@ import torch
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from diffusers import AutoencoderKL, ControlNetModel, UNet2DConditionModel, logging
-from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+from diffusers.models.controlnets.multicontrolnet import MultiControlNetModel
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
 from diffusers.schedulers import KarrasDiffusionSchedulers

main/stable_diffusion_controlnet_inpaint.py

@@ -10,7 +10,7 @@ import torch.nn.functional as F
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from diffusers import AutoencoderKL, ControlNetModel, UNet2DConditionModel, logging
-from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+from diffusers.models.controlnets.multicontrolnet import MultiControlNetModel
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
 from diffusers.schedulers import KarrasDiffusionSchedulers

main/stable_diffusion_controlnet_reference.py

@@ -8,8 +8,8 @@ import torch
 from diffusers import StableDiffusionControlNetPipeline
 from diffusers.models import ControlNetModel
 from diffusers.models.attention import BasicTransformerBlock
+from diffusers.models.controlnets.multicontrolnet import MultiControlNetModel
 from diffusers.models.unets.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
-from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.utils import logging
 from diffusers.utils.torch_utils import is_compiled_module, randn_tensor

main/stable_diffusion_xl_controlnet_reference.py

@@ -12,8 +12,8 @@ from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
 from diffusers.image_processor import PipelineImageInput
 from diffusers.models import ControlNetModel
 from diffusers.models.attention import BasicTransformerBlock
+from diffusers.models.controlnets.multicontrolnet import MultiControlNetModel
 from diffusers.models.unets.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
-from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
 from diffusers.utils import PIL_INTERPOLATION, deprecate, logging, replace_example_docstring
 from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor