Create app.py

app.py

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+# Stable Diffusion Hugging Face App (Gradio UI + Style Selection + Custom Loss Placeholder)
+
+import gradio as gr
+import torch
+from diffusers import StableDiffusionPipeline, DDIMScheduler
+from transformers import CLIPTextModel, CLIPTokenizer
+
+# Load pre-trained models
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = StableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+).to(device)
+pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+
+# Example styles from textual inversion (simulated via prompts)
+STYLE_MAP = {
+    "Van Gogh": "in the style of Van Gogh",
+    "Cyberpunk": "cyberpunk futuristic cityscape",
+    "Pixel Art": "8-bit pixel art style",
+    "Studio Ghibli": "studio ghibli anime style",
+    "Surrealism": "in surrealistic dreamscape style"
+}
+
+# Custom loss placeholder (not applied at inference, for academic purposes)
+def custom_loss_placeholder(image_tensor):
+    # Example: "yellow_loss" = penalize lack of yellow pixels
+    yellow = torch.tensor([1.0, 1.0, 0.0]).to(image_tensor.device)
+    image_mean = image_tensor.mean(dim=[1, 2])  # Average over H and W
+    yellow_loss = torch.nn.functional.mse_loss(image_mean, yellow)
+    return yellow_loss
+
+# Generate image based on prompt and style
+def generate(prompt, style, seed):
+    torch.manual_seed(seed)
+    full_prompt = f"{prompt}, {STYLE_MAP.get(style, '')}"
+    result = pipe(full_prompt, guidance_scale=7.5).images[0]
+    return result
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("""# Stable Diffusion Style Generator\nGenerate styled images using Stable Diffusion + Textual Inversion Styles.""")
+
+    with gr.Row():
+        prompt = gr.Textbox(label="Enter Prompt", placeholder="A cat riding a bicycle through space")
+        style = gr.Dropdown(choices=list(STYLE_MAP.keys()), label="Choose Style", value="Van Gogh")
+        seed = gr.Slider(minimum=0, maximum=9999, step=1, value=42, label="Random Seed")
+
+    btn = gr.Button("Generate Image")
+    output = gr.Image(label="Stylized Output")
+
+    btn.click(fn=generate, inputs=[prompt, style, seed], outputs=output)
+
+# Launch app
+demo.launch()