Delete app.py

app.py

@@ -1,111 +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()
-pipeline.enable_vae_tiling()
-
-def generate_single_scene(data):
-    print("📥 Single scene input received:")
-    print(json.dumps(data, indent=2))
-
-    try:
-        character_image_url = data["character_image_url"]
-        style = data["style"]
-        scene_prompt = data["scene"]
-
-        face_image = load_image(character_image_url)
-        style_images = [load_image(url) for url in STYLE_MAP.get(style, [])]
-
-        torch.cuda.empty_cache()
-        gc.collect()
-
-        result = pipeline(
-            prompt=scene_prompt,
-            ip_adapter_image=[style_images, face_image],
-            negative_prompt="blurry, bad anatomy, low quality",
-            width=512,
-            height=768,
-            guidance_scale=5.0,
-            num_inference_steps=15,
-            generator=torch.Generator(device).manual_seed(42)
-        )
-
-        image = result.images[0] if hasattr(result, "images") else result
-        print(f"🖼️ Image type: {type(image)}")
-
-        if isinstance(image, Image.Image):
-            print("✅ Returning valid image object")
-            return image
-        else:
-            print("❌ Invalid image type. Not returning.")
-            return None
-
-    except Exception as e:
-        print(f"❌ Error: {e}")
-        traceback.print_exc()
-        return None
-
-def generate_from_json(json_input_text):
-    try:
-        data = json.loads(json_input_text)
-        return generate_single_scene(data)
-    except Exception as e:
-        print(f"❌ JSON parse or generation error: {e}")
-        traceback.print_exc()
-        return None
-
-iface = gr.Interface(
-    fn=generate_from_json,
-    inputs=gr.Textbox(label="Input JSON", lines=10, placeholder='{"character_image_url": "...", "style": "pixar", "scene": "..."}'),
-    outputs=gr.Image(label="Generated Scene"),
-    title="Single-Scene Storybook Generator",
-    description="Send one scene at a time to generate consistent character-based images."
-)
-
-iface.queue().launch(share=True)