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Update app.py
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app.py
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@@ -6,10 +6,6 @@ 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.to("cuda")
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pipe_xl = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_XL", torch_dtype=torch.float16, revision="refs/pr/17")
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pipe_xl.vae.enable_slicing()
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pipe_xl.scheduler = DPMSolverMultistepScheduler.from_config(pipe_xl.scheduler.config)
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@@ -17,14 +13,9 @@ pipe_xl.enable_model_cpu_offload()
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pipe_xl.to("cuda")
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def infer(prompt):
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#prompt = "Darth Vader is surfing on waves"
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#pipe.to("cuda")
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video_frames = pipe(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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video = [Image.fromarray(frame).resize((1024, 576)) for frame in
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#del pipe
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#pipe_xl.to("cuda")
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video_frames = pipe_xl(prompt, video=video, strength=0.6).frames
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@@ -111,6 +102,7 @@ with gr.Blocks(css=css) as demo:
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"""
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)
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prompt_in = gr.Textbox(label="Prompt", placeholder="Darth Vader is surfing on waves", elem_id="prompt-in")
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#inference_steps = gr.Slider(label="Inference Steps", minimum=10, maximum=100, step=1, value=40, interactive=False)
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submit_btn = gr.Button("Submit")
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@@ -122,7 +114,7 @@ with gr.Blocks(css=css) as demo:
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share_button = gr.Button("Share to community", elem_id="share-btn")
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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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share_button.click(None, [], [], _js=share_js)
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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_xl = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_XL", torch_dtype=torch.float16, revision="refs/pr/17")
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pipe_xl.vae.enable_slicing()
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pipe_xl.scheduler = DPMSolverMultistepScheduler.from_config(pipe_xl.scheduler.config)
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pipe_xl.to("cuda")
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def infer(prompt, video_in):
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video = [Image.fromarray(frame).resize((1024, 576)) for frame in video_in]
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#del pipe
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#pipe_xl.to("cuda")
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video_frames = pipe_xl(prompt, video=video, strength=0.6).frames
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"""
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)
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video_in = gr.Video(type="filepath", source="upload")
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prompt_in = gr.Textbox(label="Prompt", placeholder="Darth Vader is surfing on waves", elem_id="prompt-in")
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#inference_steps = gr.Slider(label="Inference Steps", minimum=10, maximum=100, step=1, value=40, interactive=False)
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submit_btn = gr.Button("Submit")
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share_button = gr.Button("Share to community", elem_id="share-btn")
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submit_btn.click(fn=infer,
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inputs=[prompt_in, video_in],
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outputs=[video_result, share_group])
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share_button.click(None, [], [], _js=share_js)
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