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Kvikontent
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Parent(s):
5b99609
Update app.py
Browse files
app.py
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import gradio as gr
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import gradio as gr
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import moviepy.editor as mp
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from diffusers import DiffusionPipeline
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# Load diffusion pipelines
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image_pipeline = DiffusionPipeline.from_pretrained("ByteDance/SDXL-Lightning")
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video_pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt-1-1")
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@spaces.GPU(duration=120)
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def generate_images(prompt, num_images):
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"""Generates images using the image pipeline."""
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images = []
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for _ in range(num_images):
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generated_image = image_pipeline.pipe(prompt=prompt).images[0]
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images.append(generated_image)
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return images
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@spaces.GPU(duration=120)
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def generate_videos(images):
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"""Generates videos from a list of images using the video pipeline."""
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videos = []
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for image in images:
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generated_video = video_pipeline.pipe(images=image).images[0]
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videos.append(generated_video)
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return videos
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def combine_videos(video_clips):
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"""Combines video clips into a single video using moviepy."""
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final_clip = mp.concatenate_videoclips(video_clips)
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return final_clip
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def generate(prompt, num_images):
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"""Generates and combines images and videos."""
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images = generate_images(prompt, num_images)
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video_clips = generate_videos(images)
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combined_video = combine_videos(video_clips)
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return combined_video
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# Gradio interface with improved formatting and video output
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interface = gr.Interface(
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fn=generate,
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inputs="text",
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outputs="video",
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interpretation="markdown",
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title="AI Video Generation",
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description="Enter a prompt to generate a video using diffusion models.",
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css="""
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.output-video {
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width: 100%; /* Adjust width as needed */
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height: 400px; /* Adjust height as desired */
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}
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""",
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)
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# Launch the interface
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interface.launch()
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