Delete app.py

app.py

@@ -1,120 +0,0 @@
-
-import gradio as gr
-import torch
-from diffusers import AutoPipelineForText2Image, DDIMScheduler
-from transformers import CLIPVisionModelWithProjection
-from diffusers.utils import load_image
-from PIL import Image
-import os
-import json
-import gc
-import traceback
-
-STYLE_MAP = {
-    "pixar": [
-        "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img0.png",
-        "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img1.png",
-        "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img2.png",
-        "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img3.png",
-        "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img4.png"
-    ]
-}
-
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-print(f"🚀 Device: {device}, torch_dtype: {torch_dtype}")
-
-image_encoder = CLIPVisionModelWithProjection.from_pretrained(
-    "h94/IP-Adapter",
-    subfolder="models/image_encoder",
-    torch_dtype=torch_dtype,
-)
-
-pipeline = AutoPipelineForText2Image.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0",
-    torch_dtype=torch_dtype,
-    image_encoder=image_encoder,
-    variant="fp16" if torch.cuda.is_available() else None
-).to(device)
-
-pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
-pipeline.load_ip_adapter(
-    "h94/IP-Adapter",
-    subfolder="sdxl_models",
-    weight_name=[
-        "ip-adapter-plus_sdxl_vit-h.safetensors",
-        "ip-adapter-plus-face_sdxl_vit-h.safetensors"
-    ]
-)
-pipeline.set_ip_adapter_scale([0.7, 0.3])
-pipeline.enable_model_cpu_offload()
-
-def generate_storybook(data):
-    print("📥 Input JSON received:")
-    print(json.dumps(data, indent=2))
-
-    character_image_url = data["character_image_url"]
-    style = data["style"]
-    scenes = data["scenes"]
-
-    face_image = load_image(character_image_url)
-    style_images = [load_image(url) for url in STYLE_MAP.get(style, [])]
-
-    images = []
-
-    for i, prompt in enumerate(scenes):
-        print(f"🎬 Generating scene {i+1}: {prompt}")
-        try:
-            torch.cuda.empty_cache()
-            gc.collect()
-
-            result = pipeline(
-                prompt=prompt,
-                ip_adapter_image=[style_images, face_image],
-                negative_prompt="blurry, bad anatomy, low quality",
-                width=512,
-                height=768,
-                guidance_scale=7.5,
-                num_inference_steps=20,
-                generator=torch.Generator(device).manual_seed(i + 42)
-            )
-
-            print(f"⚙️ Pipeline result type: {type(result)}")
-            image = result.images[0]
-            print(f"🖼️ Image type: {type(image)}")
-
-            if isinstance(image, Image.Image):
-                images.append(image)
-                print(f"✅ Scene {i+1} generated successfully.")
-            else:
-                print(f"⚠️ Invalid image type for scene {i+1}")
-
-        except Exception as e:
-            print(f"❌ Exception during scene {i+1}: {e}")
-            traceback.print_exc()
-
-        # TEMPORARY: Stop after first image for testing
-        break
-
-    print(f"📦 Returning {len(images)} image(s)")
-    return images
-
-def generate_storybook_from_textbox(json_input_text):
-    try:
-        data = json.loads(json_input_text)
-        return generate_storybook(data)
-    except Exception as e:
-        print(f"❌ JSON parse or generation error: {e}")
-        traceback.print_exc()
-        return [f"Error: {str(e)}"]
-
-iface = gr.Interface(
-    fn=generate_storybook_from_textbox,
-    inputs=gr.Textbox(label="Input JSON", lines=20, placeholder="{...}"),
-    outputs=gr.Gallery(label="Generated Story Scenes", show_label=True, columns=1),
-    title="AI Storybook Generator (Enhanced Debug)",
-    description="Paste JSON to generate images with logging and debugging."
-)
-
-iface.launch()