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app.py
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import gradio as gr
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import torch
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from diffusers import AutoPipelineForText2Image, DDIMScheduler
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from transformers import CLIPVisionModelWithProjection
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from diffusers.utils import load_image
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from PIL import Image
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import os
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import json
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import gc
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import traceback
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STYLE_MAP = {
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"pixar": [
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"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img0.png",
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"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img1.png",
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"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img2.png",
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"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img3.png",
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"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img4.png"
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]
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}
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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device = "cuda" if torch.cuda.is_available() else "cpu"
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print(f"π Device: {device}, torch_dtype: {torch_dtype}")
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image_encoder = CLIPVisionModelWithProjection.from_pretrained(
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"h94/IP-Adapter",
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subfolder="models/image_encoder",
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torch_dtype=torch_dtype,
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)
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pipeline = AutoPipelineForText2Image.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0",
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torch_dtype=torch_dtype,
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image_encoder=image_encoder,
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variant="fp16" if torch.cuda.is_available() else None
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).to(device)
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pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
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pipeline.load_ip_adapter(
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"h94/IP-Adapter",
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subfolder="sdxl_models",
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weight_name=[
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"ip-adapter-plus_sdxl_vit-h.safetensors",
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"ip-adapter-plus-face_sdxl_vit-h.safetensors"
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]
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)
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pipeline.set_ip_adapter_scale([0.7, 0.3])
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pipeline.enable_model_cpu_offload()
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pipeline.enable_vae_tiling()
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def generate_single_scene(data):
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print("π₯ Full input received:")
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print(json.dumps(data, indent=2))
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try:
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character_image_url = data["character_image_url"]
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style = data["style"]
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scene_prompt = data["scene"]
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print("π Loading reference and style images...")
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face_image = load_image(character_image_url)
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style_images = [load_image(url) for url in STYLE_MAP.get(style, [])]
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torch.cuda.empty_cache()
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gc.collect()
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print("π¨ Starting generation...")
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result = pipeline(
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prompt=scene_prompt,
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ip_adapter_image=[style_images, face_image],
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negative_prompt="blurry, bad anatomy, low quality",
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width=512,
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height=768,
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guidance_scale=5.0,
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num_inference_steps=15,
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generator=torch.Generator(device).manual_seed(42)
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)
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image = result.images[0] if hasattr(result, "images") else result
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print(f"πΌοΈ Image generated. Type: {type(image)}")
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if isinstance(image, Image.Image):
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print("β
Valid image object returned.")
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return image
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else:
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print("β Invalid image object. Returning fallback.")
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return Image.open("/mnt/data/error_image.png")
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except Exception as e:
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print(f"β Exception occurred: {e}")
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traceback.print_exc()
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return Image.open("/mnt/data/error_image.png")
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def generate_from_json(json_input_text):
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try:
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data = json.loads(json_input_text)
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return generate_single_scene(data)
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except Exception as e:
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print(f"β JSON parsing error: {e}")
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traceback.print_exc()
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return Image.open("/mnt/data/error_image.png")
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iface = gr.Interface(
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fn=generate_from_json,
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inputs=gr.Textbox(label="Input JSON", lines=10),
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outputs=gr.Image(label="Generated Scene or Error"),
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title="Debug Storybook Scene Generator",
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description="Displays logs and returns fallback image on error."
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)
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iface.queue().launch(share=True)
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