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116faa2
add TextToVideoZeroPipeline and CrossFrameAttnProcessor
19and99 c12c98d
Merge branch 'main' into add-text2video-zero-pipeline
19and99 554d8f7
add docs for text-to-video zero
19and99 827b27f
Merge branch 'add-text2video-zero-pipeline' of https://github.com/19a…
19and99 063f817
Merge branch 'main' into add-text2video-zero-pipeline
19and99 5636129
add teaser image for text-to-video zero docs
19and99 c68e0d0
Merge branch 'add-text2video-zero-pipeline' of https://github.com/19a…
19and99 76eba6c
Fix review changes. Add Documentation. Add test
19and99 7ba88b7
Merge branch 'main' into add-text2video-zero-pipeline
19and99 76164ea
clean up the codes in pipeline_text_to_video.py. Add descriptive comm…
19and99 0bc0ebe
Merge branch 'add-text2video-zero-pipeline' of https://github.com/19a…
19and99 f44ce33
make style && make quality
19and99 0cc4440
make fix-copies
19and99 f56b88c
make requested changes to docs. use huggingface server links for reso…
19and99 a3b7635
make style && make quality && make fix-copies
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make style && make quality
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<!--Copyright 2023 The HuggingFace Team. All rights reserved. | ||||||
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with | ||||||
the License. You may obtain a copy of the License at | ||||||
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http://www.apache.org/licenses/LICENSE-2.0 | ||||||
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on | ||||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | ||||||
specific language governing permissions and limitations under the License. | ||||||
--> | ||||||
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# Zero-Shot Text-to-Video Generation | ||||||
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## Overview | ||||||
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[Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://arxiv.org/abs/2303.13439) 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). | ||||||
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Our method Text2Video-Zero enables zero-shot video generation using either | ||||||
1. A textual prompt, or | ||||||
2. A prompt combined with guidance from poses or edges, or | ||||||
3. Video Instruct-Pix2Pix, i.e., instruction-guided video editing. | ||||||
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Results are temporally consistent and follow closely the guidance and textual prompts. | ||||||
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 | ||||||
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The abstract of the paper is the following: | ||||||
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*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.* | ||||||
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Resources: | ||||||
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* [Project Page](https://text2video-zero.github.io/) | ||||||
* [Paper](https://arxiv.org/abs/2303.13439) | ||||||
* [Original Code](https://github.com/Picsart-AI-Research/Text2Video-Zero) | ||||||
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## Available Pipelines: | ||||||
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| Pipeline | Tasks | Demo | ||||||
|---|---|:---:| | ||||||
| [TextToVideoZeroPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py) | *Zero-shot Text-to-Video Generation* | [🤗 Space](https://huggingface.co/spaces/PAIR/Text2Video-Zero) | ||||||
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## Usage example | ||||||
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### Text-To-Video | ||||||
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To generate a video from prompt, run the following python command | ||||||
```python | ||||||
import torch | ||||||
from diffusers import TextToVideoZeroPipeline | ||||||
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model_id = "runwayml/stable-diffusion-v1-5" | ||||||
pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") | ||||||
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prompt = "A panda is playing guitar on times square" | ||||||
result = pipe(prompt=prompt).images | ||||||
imageio.mimsave("video.mp4", result, fps=4) | ||||||
``` | ||||||
You can change these parameters in the pipeline call: | ||||||
* Motion field strength (see the [paper](https://arxiv.org/abs/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://arxiv.org/abs/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` | ||||||
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### Text-To-Video with Pose Control | ||||||
To generate a video from prompt with additional pose control | ||||||
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1. Download a demo video | ||||||
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```python | ||||||
from huggingface_hub import hf_hub_download | ||||||
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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) | ||||||
``` | ||||||
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2. Read video containing extracted pose images | ||||||
```python | ||||||
import imageio | ||||||
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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](./stable_diffusion/controlnet). | ||||||
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3. Run `StableDiffusionControlNetPipeline` with our custom attention processor | ||||||
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```python | ||||||
import torch | ||||||
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel | ||||||
from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor | ||||||
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model_id = "runwayml/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") | ||||||
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# Set the attention processor | ||||||
pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2)) | ||||||
pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2)) | ||||||
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# fix latents for all frames | ||||||
latents = torch.randn((1, 4, 64, 64), device="cuda", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1) | ||||||
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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) | ||||||
``` | ||||||
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### Text-To-Video with Edge Control | ||||||
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To generate a video from prompt with additional pose control, | ||||||
follow the steps described above for pose-guided generation using [Canny edge ControlNet model](https://huggingface.co/lllyasviel/sd-controlnet-canny). | ||||||
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### Video Instruct-Pix2Pix | ||||||
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To perform text-guided video editing (with [InstructPix2Pix](./stable_diffusion/pix2pix)): | ||||||
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1. Download a demo video | ||||||
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```python | ||||||
from huggingface_hub import hf_hub_download | ||||||
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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) | ||||||
``` | ||||||
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2. Read video from path | ||||||
```python | ||||||
import imageio | ||||||
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reader = imageio.get_reader(video_path, "ffmpeg") | ||||||
frame_count = 8 | ||||||
video = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)] | ||||||
``` | ||||||
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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 | ||||||
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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)) | ||||||
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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) | ||||||
``` | ||||||
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### Dreambooth specialization | ||||||
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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 | ||||||
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1. Download demo video from huggingface | ||||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more.
Suggested change
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```python | ||||||
from huggingface_hub import hf_hub_download | ||||||
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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) | ||||||
``` | ||||||
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2. Read video from path | ||||||
```python | ||||||
import imageio | ||||||
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reader = imageio.get_reader(video_path, "ffmpeg") | ||||||
frame_count = 8 | ||||||
video = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)] | ||||||
``` | ||||||
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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 | ||||||
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# 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") | ||||||
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# Set the attention processor | ||||||
pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2)) | ||||||
pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2)) | ||||||
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# fix latents for all frames | ||||||
latents = torch.randn((1, 4, 64, 64), device="cuda", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1) | ||||||
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prompt = "oil painting of a beautiful girl avatar style" | ||||||
result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images | ||||||
imageio.mimsave("video.mp4", result, fps=4) | ||||||
``` | ||||||
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You can filter out some available DreamBooth-trained models with [this link](https://huggingface.co/models?search=dreambooth). | ||||||
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## TextToVideoZeroPipeline | ||||||
[[autodoc]] TextToVideoZeroPipeline | ||||||
- all | ||||||
- __call__ |
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