Upload app.py

app.py

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+
+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)