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test gradio
Browse files- app.py +54 -0
- requirements.txt +5 -0
app.py
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import gradio as gr
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import torch
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from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter
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from diffusers.utils import export_to_gif
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from PIL import Image
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import io
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# Load the motion adapter
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adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
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model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
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pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)
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scheduler = DDIMScheduler.from_pretrained(
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model_id,
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subfolder="scheduler",
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clip_sample=False,
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timestep_spacing="linspace",
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beta_schedule="linear",
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steps_offset=1,
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)
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pipe.scheduler = scheduler
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pipe.enable_vae_slicing()
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pipe.enable_model_cpu_offload()
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# Define the animation function
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def generate_animation(prompt, negative_prompt, num_frames, guidance_scale, num_inference_steps):
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output = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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num_frames=num_frames,
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guidance_scale=guidance_scale,
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num_inference_steps=num_inference_steps,
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generator=torch.Generator("cpu").manual_seed(42),
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)
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frames = output.frames[0]
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gif_path = "animation.gif"
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export_to_gif(frames, gif_path)
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return gif_path
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# Gradio Interface
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iface = gr.Interface(
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fn=generate_animation,
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inputs=[
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gr.Textbox(value="masterpiece, best quality, highly detailed...", label="Prompt"),
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gr.Textbox(value="bad quality, worse quality", label="Negative Prompt"),
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gr.Slider(1, 24, value=16, label="Number of Frames"),
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gr.Slider(1.0, 10.0, value=7.5, step=0.1, label="Guidance Scale"),
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gr.Slider(1, 50, value=25, label="Inference Steps"),
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],
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outputs=gr.Image(label="Generated Animation"),
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title="Animated Stable Diffusion",
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description="Generate animations based on your prompt using Stable Diffusion.",
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)
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iface.launch()
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requirements.txt
ADDED
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@@ -0,0 +1,5 @@
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torch
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diffusers
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transformers
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accelerate
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gradio
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