AnimateDiff Video to Video

docs/source/en/api/pipelines/animatediff.md

@@ -25,13 +25,16 @@ The abstract of the paper is the following:
 | Pipeline | Tasks | Demo
 |---|---|:---:|
 | [AnimateDiffPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/animatediff/pipeline_animatediff.py) | *Text-to-Video Generation with AnimateDiff* |
+| [AnimateDiffVideoToVideoPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py) | *Video-to-Video Generation with AnimateDiff* |
 
 ## Available checkpoints
 
 Motion Adapter checkpoints can be found under [guoyww](https://huggingface.co/guoyww/). These checkpoints are meant to work with any model based on Stable Diffusion 1.4/1.5.
 
 ## Usage example
 
+### AnimateDiffPipeline
+
 AnimateDiff works with a MotionAdapter checkpoint and a Stable Diffusion 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 Stable Diffusion UNet.
 
 The following example demonstrates how to use a *MotionAdapter* checkpoint with Diffusers for inference based on StableDiffusion-1.4/1.5.
@@ -98,6 +101,114 @@ AnimateDiff tends to work better with finetuned Stable Diffusion models. If you
 
 </Tip>
 
+### AnimateDiffVideoToVideoPipeline
+
+AnimateDiff can also be used to generate visually similar videos or enable style/character/background or other edits starting from an initial video, allowing you to seamlessly explore creative possibilities.
+
+```python
+import imageio
+import requests
+import torch
+from diffusers import AnimateDiffVideoToVideoPipeline, DDIMScheduler, MotionAdapter
+from diffusers.utils import export_to_gif
+from io import BytesIO
+from PIL import Image
+
+# Load the motion adapter
+adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
+# load SD 1.5 based finetuned model
+model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
+pipe = AnimateDiffVideoToVideoPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)
+scheduler = DDIMScheduler.from_pretrained(
+    model_id,
+    subfolder="scheduler",
+    clip_sample=False,
+    timestep_spacing="linspace",
+    beta_schedule="linear",
+    steps_offset=1,
+)
+pipe.scheduler = scheduler
+
+# enable memory savings
+pipe.enable_vae_slicing()
+pipe.enable_model_cpu_offload()
+
+# helper function to load videos
+def load_video(file_path: str):
+    images = []
+
+    if file_path.startswith(('http://', 'https://')):
+        # If the file_path is a URL
+        response = requests.get(file_path)
+        response.raise_for_status()
+        content = BytesIO(response.content)
+        vid = imageio.get_reader(content)
+    else:
+        # Assuming it's a local file path
+        vid = imageio.get_reader(file_path)
+
+    for frame in vid:
+        pil_image = Image.fromarray(frame)
+        images.append(pil_image)
+
+    return images
+
+video = load_video("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-input-1.gif")
+
+output = pipe(
+    video = video,
+    prompt="panda playing a guitar, on a boat, in the ocean, high quality",
+    negative_prompt="bad quality, worse quality",
+    guidance_scale=7.5,
+    num_inference_steps=25,
+    strength=0.5,
+    generator=torch.Generator("cpu").manual_seed(42),
+)
+frames = output.frames[0]
+export_to_gif(frames, "animation.gif")
+```
+
+Here are some sample outputs:
+
+<table>
+    <tr>
+      <th align=center>Source Video</th>
+      <th align=center>Output Video</th>
+    </tr>
+    <tr>
+        <td align=center>
+          raccoon playing a guitar
+          <br />
+          <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-input-1.gif"
+              alt="racoon playing a guitar"
+              style="width: 300px;" />
+        </td>
+        <td align=center>
+          panda playing a guitar
+          <br/>
+          <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-output-1.gif"
+              alt="panda playing a guitar"
+              style="width: 300px;" />
+        </td>
+    </tr>
+    <tr>
+        <td align=center>
+          closeup of margot robbie, fireworks in the background, high quality
+          <br />
+          <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-input-2.gif"
+              alt="closeup of margot robbie, fireworks in the background, high quality"
+              style="width: 300px;" />
+        </td>
+        <td align=center>
+          closeup of tony stark, robert downey jr, fireworks
+          <br/>
+          <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-output-2.gif"
+              alt="closeup of tony stark, robert downey jr, fireworks"
+              style="width: 300px;" />
+        </td>
+    </tr>
+</table>
+
 ## Using Motion LoRAs
 
 Motion LoRAs are a collection of LoRAs that work with the `guoyww/animatediff-motion-adapter-v1-5-2` checkpoint. These LoRAs are responsible for adding specific types of motion to the animations.

src/diffusers/__init__.py

@@ -207,6 +207,7 @@
             "AmusedInpaintPipeline",
             "AmusedPipeline",
             "AnimateDiffPipeline",
+            "AnimateDiffVideoToVideoPipeline",
             "AudioLDM2Pipeline",
             "AudioLDM2ProjectionModel",
             "AudioLDM2UNet2DConditionModel",
@@ -567,6 +568,7 @@
             AmusedInpaintPipeline,
             AmusedPipeline,
             AnimateDiffPipeline,
+            AnimateDiffVideoToVideoPipeline,
             AudioLDM2Pipeline,
             AudioLDM2ProjectionModel,
             AudioLDM2UNet2DConditionModel,

src/diffusers/pipelines/__init__.py

@@ -109,7 +109,10 @@
         ]
     )
     _import_structure["amused"] = ["AmusedImg2ImgPipeline", "AmusedInpaintPipeline", "AmusedPipeline"]
-    _import_structure["animatediff"] = ["AnimateDiffPipeline"]
+    _import_structure["animatediff"] = [
+        "AnimateDiffPipeline",
+        "AnimateDiffVideoToVideoPipeline",
+    ]
     _import_structure["audioldm"] = ["AudioLDMPipeline"]
     _import_structure["audioldm2"] = [
         "AudioLDM2Pipeline",
@@ -341,7 +344,7 @@
         from ..utils.dummy_torch_and_transformers_objects import *
     else:
         from .amused import AmusedImg2ImgPipeline, AmusedInpaintPipeline, AmusedPipeline
-        from .animatediff import AnimateDiffPipeline
+        from .animatediff import AnimateDiffPipeline, AnimateDiffVideoToVideoPipeline
         from .audioldm import AudioLDMPipeline
         from .audioldm2 import (
             AudioLDM2Pipeline,

src/diffusers/pipelines/animatediff/__init__.py

@@ -11,7 +11,7 @@
 
 
 _dummy_objects = {}
-_import_structure = {}
+_import_structure = {"pipeline_output": ["AnimateDiffPipelineOutput"]}
 
 try:
     if not (is_transformers_available() and is_torch_available()):
@@ -21,7 +21,8 @@
 
     _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
 else:
-    _import_structure["pipeline_animatediff"] = ["AnimateDiffPipeline", "AnimateDiffPipelineOutput"]
+    _import_structure["pipeline_animatediff"] = ["AnimateDiffPipeline"]
+    _import_structure["pipeline_animatediff_video2video"] = ["AnimateDiffVideoToVideoPipeline"]
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     try:
@@ -31,7 +32,9 @@
         from ...utils.dummy_torch_and_transformers_objects import *
 
     else:
-        from .pipeline_animatediff import AnimateDiffPipeline, AnimateDiffPipelineOutput
+        from .pipeline_animatediff import AnimateDiffPipeline
+        from .pipeline_animatediff_video2video import AnimateDiffVideoToVideoPipeline
+        from .pipeline_output import AnimateDiffPipelineOutput
 
 else:
     import sys

src/diffusers/pipelines/animatediff/pipeline_animatediff.py

@@ -14,10 +14,8 @@
 
 import inspect
 import math
-from dataclasses import dataclass
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
-import numpy as np
 import torch
 import torch.fft as fft
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
@@ -37,7 +35,6 @@
 )
 from ...utils import (
     USE_PEFT_BACKEND,
-    BaseOutput,
     deprecate,
     logging,
     replace_example_docstring,
@@ -46,6 +43,7 @@
 )
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline
+from .pipeline_output import AnimateDiffPipelineOutput
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -147,11 +145,6 @@ def _freq_mix_3d(x: torch.Tensor, noise: torch.Tensor, LPF: torch.Tensor) -> tor
     return x_mixed
 
 
-@dataclass
-class AnimateDiffPipelineOutput(BaseOutput):
-    frames: Union[torch.Tensor, np.ndarray]
-
-
 class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin):
     r"""
     Pipeline for text-to-video generation.