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#338
by
Hamed1364
- opened
- README.md +1 -1
- app.py +14 -19
- requirements.txt +1 -2
README.md
CHANGED
@@ -5,7 +5,7 @@ colorFrom: red
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colorTo: gray
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sdk: gradio
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python_version: 3.10.12
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sdk_version:
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app_file: app.py
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pinned: false
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---
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colorTo: gray
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sdk: gradio
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python_version: 3.10.12
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sdk_version: 3.50.2
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app_file: app.py
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pinned: false
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---
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app.py
CHANGED
@@ -1,31 +1,19 @@
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import gradio as gr
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import
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import numpy as np
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import tempfile
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import imageio
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import torch
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from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
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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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def export_to_video(frames: np.ndarray, fps: int) -> str:
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frames = np.clip((frames * 255), 0, 255).astype(np.uint8)
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out_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
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writer = imageio.get_writer(out_file.name, format="FFMPEG", fps=fps)
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for frame in frames:
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writer.append_data(frame)
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writer.close()
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return out_file.name
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def infer(prompt):
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negative_prompt = "text, watermark, copyright, blurry, nsfw"
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video_frames = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames
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video_path = export_to_video(video_frames, 12)
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print(video_path)
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return video_path
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css = """
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#col-container {max-width: 510px; margin-left: auto; margin-right: auto;}
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@@ -138,6 +126,11 @@ with gr.Blocks(css=css) as demo:
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video_result = gr.Video(label="Video Output", elem_id="video-output")
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with gr.Row():
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gr.Markdown("""
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[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-lg.svg#center)](https://huggingface.co/spaces/fffiloni/zeroscope-cloning?duplicate=true)
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""")
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@@ -163,8 +156,10 @@ with gr.Blocks(css=css) as demo:
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submit_btn.click(fn=infer,
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inputs=[prompt_in],
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outputs=[video_result],
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api_name="zrscp")
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demo.queue(max_size=12).launch(show_api=
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import gradio as gr
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from share_btn import community_icon_html, loading_icon_html, share_js
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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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def infer(prompt):
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negative_prompt = "text, watermark, copyright, blurry, nsfw"
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video_frames = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames
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video_path = export_to_video(video_frames)
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print(video_path)
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return video_path, gr.Group.update(visible=True)
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css = """
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#col-container {max-width: 510px; margin-left: auto; margin-right: auto;}
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video_result = gr.Video(label="Video Output", elem_id="video-output")
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with gr.Row():
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with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
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community_icon = gr.HTML(community_icon_html)
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loading_icon = gr.HTML(loading_icon_html)
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share_button = gr.Button("Share with Community", elem_id="share-btn")
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gr.Markdown("""
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[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-lg.svg#center)](https://huggingface.co/spaces/fffiloni/zeroscope-cloning?duplicate=true)
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""")
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submit_btn.click(fn=infer,
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inputs=[prompt_in],
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outputs=[video_result, share_group],
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api_name="zrscp")
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share_button.click(None, [], [], _js=share_js)
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demo.queue(max_size=12).launch(show_api=True)
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requirements.txt
CHANGED
@@ -3,5 +3,4 @@ transformers
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accelerate
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torch
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opencv-python
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numpy
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accelerate
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torch
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opencv-python
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+
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