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Update app.py
#1
by
ar08
- opened
app.py
CHANGED
@@ -3,56 +3,112 @@ import torch
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from diffusers import StableDiffusionImg2ImgPipeline
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from PIL import Image
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import numpy as np
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model_id = "nitrosocke/Ghibli-Diffusion"
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# Load the
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
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model_id,
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torch_dtype=torch.float16 if device == "cuda" else torch.float32,
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)
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pipe.to(device)
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pipe.enable_attention_slicing()
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intermediate_images = []
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def callback(step: int, timestep: int, latents):
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with torch.no_grad():
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with torch.inference_mode():
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prompt=prompt,
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image=
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num_inference_steps=steps,
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callback=callback,
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callback_steps=1
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)
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gr.
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from diffusers import StableDiffusionImg2ImgPipeline
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from PIL import Image
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import numpy as np
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from typing import Generator, List
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# Set up device and model
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model_id = "nitrosocke/Ghibli-Diffusion"
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# Load the pipeline
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
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model_id,
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torch_dtype=torch.float16 if device == "cuda" else torch.float32,
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)
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pipe = pipe.to(device)
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pipe.enable_attention_slicing()
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def generate_ghibli_style(
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input_image: Image.Image,
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steps: int = 25,
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strength: float = 0.6,
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guidance_scale: float = 7.0,
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progress: gr.Progress = gr.Progress()
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) -> Generator[List[Image.Image], None, None]:
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"""
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Generate Ghibli-style images in real-time with intermediate steps
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"""
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prompt = "ghibli style, high quality, detailed portrait"
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negative_prompt = "low quality, blurry, bad anatomy"
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intermediate_images = []
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def callback(step: int, timestep: int, latents: torch.Tensor):
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with torch.no_grad():
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# Decode the latents to image
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image = pipe.decode_latents(latents)
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image = pipe.numpy_to_pil(image)[0]
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intermediate_images.append(image)
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# Update progress and yield the current images
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progress(step / steps, desc="Generating...")
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yield intermediate_images
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# Run the pipeline
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with torch.inference_mode():
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# Create a generator that will yield the images
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generator = pipe(
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prompt=prompt,
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image=input_image,
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negative_prompt=negative_prompt,
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strength=strength,
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guidance_scale=guidance_scale,
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num_inference_steps=steps,
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callback=callback,
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callback_steps=1 # Call after every step
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)
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# Yield the final result
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final_image = generator.images[0]
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intermediate_images.append(final_image)
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yield intermediate_images
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# Custom CSS for better appearance
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css = """
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.gallery {
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min-height: 500px;
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}
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.gallery img {
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max-height: 400px;
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object-fit: contain;
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}
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"""
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# Gradio interface
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with gr.Blocks(css=css) as demo:
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gr.Markdown("# ✨ Studio Ghibli Portrait Generator ✨")
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gr.Markdown("Upload a photo and watch it transform into a Ghibli-style portrait in real-time!")
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with gr.Row():
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with gr.Column():
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input_image = gr.Image(label="Upload Photo", type="pil")
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steps_slider = gr.Slider(10, 50, value=25, step=1, label="Inference Steps")
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strength_slider = gr.Slider(0.1, 0.9, value=0.6, step=0.05, label="Transformation Strength")
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generate_btn = gr.Button("Generate", variant="primary")
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with gr.Column():
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gallery = gr.Gallery(
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label="Generation Progress",
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show_label=True,
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elem_id="gallery",
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preview=True
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)
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# Example images
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gr.Examples(
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examples=[
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["examples/portrait1.jpg", 25, 0.6],
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["examples/portrait2.jpg", 30, 0.5],
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],
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inputs=[input_image, steps_slider, strength_slider],
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label="Try these examples!"
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)
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generate_btn.click(
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fn=generate_ghibli_style,
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inputs=[input_image, steps_slider, strength_slider],
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outputs=gallery
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)
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# Launch the app
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if __name__ == "__main__":
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demo.queue(concurrency_count=1).launch(share=True)
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