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Add diffusers example (#20)
Browse files- Add diffusers example (e9a76799bc397335fbfa74b52d7ef473c636a4d2)
Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>
README.md
CHANGED
@@ -16,6 +16,61 @@ zeroscope_v2_XL uses 15.3gb of vram when rendering 30 frames at 1024x576
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2. Replace the respective files in the 'stable-diffusion-webui\models\ModelScope\t2v' directory.
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### Upscaling recommendations
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For upscaling, it's recommended to use the 1111 extension. It works best at 1024x576 with a denoise strength between 0.66 and 0.85. Remember to use the same prompt that was used to generate the original clip.
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### Known issues
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Rendering at lower resolutions or fewer than 24 frames could lead to suboptimal outputs. <br />
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2. Replace the respective files in the 'stable-diffusion-webui\models\ModelScope\t2v' directory.
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### Upscaling recommendations
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For upscaling, it's recommended to use the 1111 extension. It works best at 1024x576 with a denoise strength between 0.66 and 0.85. Remember to use the same prompt that was used to generate the original clip.
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### Usage in 🧨 Diffusers
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Let's first install the libraries required:
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```bash
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$ pip install git+https://github.com/huggingface/diffusers.git
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$ pip install transformers accelerate torch
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```
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Now, let's first generate a low resolution video using [cerspense/zeroscope_v2_576w](https://huggingface.co/cerspense/zeroscope_v2_576w).
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```py
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import torch
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from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
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from diffusers.utils import export_to_video
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pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
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pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
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pipe.enable_model_cpu_offload()
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pipe.enable_vae_slicing()
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prompt = "Darth Vader is surfing on waves"
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video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=36).frames
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video_path = export_to_video(video_frames)
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```
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Next, we can upscale it using [cerspense/zeroscope_v2_XL](https://huggingface.co/cerspense/zeroscope_v2_XL).
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```py
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pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_XL", torch_dtype=torch.float16)
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pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
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pipe.enable_model_cpu_offload()
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pipe.enable_vae_slicing()
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video = [Image.fromarray(frame).resize((1024, 576)) for frame in video_frames]
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video_frames = pipe(prompt, video=video, strength=0.6).frames
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video_path = export_to_video(video_frames, output_video_path="/home/patrick/videos/video_1024_darth_vader_36.mp4")
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```
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Here are some results:
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<table>
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<tr>
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Darth vader is surfing on waves.
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<br>
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/darth_vader_36_1024.gif"
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alt="Darth vader surfing in waves."
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style="width: 576;" />
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</center></td>
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</tr>
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</table>
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### Known issues
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Rendering at lower resolutions or fewer than 24 frames could lead to suboptimal outputs. <br />
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