Spaces:
Running
on
Zero
Running
on
Zero
fn=generate_video,
Browse files
app.py
CHANGED
@@ -45,8 +45,31 @@ def initialize_model(model_path):
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print('Model initialized: ' + model_path)
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return hunyuan_video_sampler
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@spaces.GPU(duration=120)
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def generate_video(
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model,
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prompt,
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resolution,
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@@ -57,6 +80,7 @@ def generate_video(
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flow_shift,
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embedded_guidance_scale
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):
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if torch.cuda.device_count() == 0:
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gr.Warning('Set this space to GPU config to make it work.')
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return None
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@@ -65,6 +89,7 @@ def generate_video(
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width, height = resolution.split("x")
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width, height = int(width), int(height)
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negative_prompt = "" # not applicable in the inference
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outputs = model.predict(
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prompt=prompt,
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@@ -81,6 +106,7 @@ def generate_video(
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embedded_guidance_scale=embedded_guidance_scale
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)
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samples = outputs['samples']
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sample = samples[0].unsqueeze(0)
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@@ -92,6 +118,7 @@ def generate_video(
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save_videos_grid(sample, video_path, fps=24)
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logger.info(f'Sample saved to: {video_path}')
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return video_path
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def create_demo(model_path):
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@@ -158,7 +185,7 @@ If you can't use _Hunyuan Video_, you can use _[CogVideoX](https://huggingface.c
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""")
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generate_btn.click(
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fn=
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inputs=[
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prompt,
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resolution,
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print('Model initialized: ' + model_path)
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return hunyuan_video_sampler
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def generate_video(
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prompt,
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resolution,
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video_length,
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seed,
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num_inference_steps,
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guidance_scale,
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flow_shift,
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embedded_guidance_scale
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):
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print('generate_video (prompt: ' + prompt + ')')
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return generate_video_gpu(
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model,
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prompt,
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resolution,
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video_length,
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seed,
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num_inference_steps,
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guidance_scale,
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flow_shift,
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embedded_guidance_scale
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)
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@spaces.GPU(duration=120)
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def generate_video_gpu(
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model,
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prompt,
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resolution,
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flow_shift,
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embedded_guidance_scale
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):
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print('generate_video_gpu (prompt: ' + prompt + ')')
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if torch.cuda.device_count() == 0:
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gr.Warning('Set this space to GPU config to make it work.')
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return None
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width, height = resolution.split("x")
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width, height = int(width), int(height)
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negative_prompt = "" # not applicable in the inference
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print('Predicting video...')
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outputs = model.predict(
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prompt=prompt,
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embedded_guidance_scale=embedded_guidance_scale
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)
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print('Video predicted')
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samples = outputs['samples']
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sample = samples[0].unsqueeze(0)
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save_videos_grid(sample, video_path, fps=24)
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logger.info(f'Sample saved to: {video_path}')
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print('Return the video')
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return video_path
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def create_demo(model_path):
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""")
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generate_btn.click(
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fn=generate_video,
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inputs=[
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prompt,
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resolution,
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