Deploy Gradio app with multiple files

app.py

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+import gradio as gr
+import torch
+import spaces
+from diffusers import DiffusionPipeline
+from PIL import Image
+from typing import List, Optional, Any
+
+# --- Model Configuration ---
+MODEL_V1 = "CompVis/stable-diffusion-v1-4"
+MODEL_V2 = "Manojb/stable-diffusion-2-1-base"
+DEVICE = "cuda"
+
+# Use bfloat16 for optimized performance on modern GPUs (H200/A100/H100)
+DTYPE = torch.bfloat16
+
+# Default prompts for generation when user input is empty
+DEFAULT_PROMPT_V1 = "A stunning photorealistic image of a golden retriever wearing a crown, in a grand hall, cinematic lighting, masterpiece, 4k"
+DEFAULT_PROMPT_V2 = "A detailed matte painting of an ancient ruined city overgrown with vines, dramatic sunset, fantasy art, 8k, cinematic"
+
+print("Loading Models...")
+pipe_v1 = DiffusionPipeline.from_pretrained(
+    MODEL_V1,
+    torch_dtype=DTYPE,
+    safety_checker=None,
+    requires_safety_checker=False,
+    # Use from_single_file=True if loading .ckpt or .safetensors files directly
+).to(DEVICE)
+
+pipe_v2 = DiffusionPipeline.from_pretrained(
+    MODEL_V2,
+    torch_dtype=DTYPE,
+    safety_checker=None,
+    requires_safety_checker=False,
+).to(DEVICE)
+print("Models Loaded.")
+
+
+@spaces.GPU(duration=1500)
+def compile_optimized_models():
+    """
+    Performs Ahead-of-Time (AoT) compilation for improved ZeroGPU performance.
+    """
+    # --- Compilation for SD 1.4 (pipe_v1) ---
+    print(f"Compiling UNet for {MODEL_V1} (SD 1.4)...")
+    try:
+        with spaces.aoti_capture(pipe_v1.unet) as call:
+            # Run a quick example call (512x512, low steps) to capture inputs
+            pipe_v1(
+                prompt="compilation test",
+                num_inference_steps=2,
+                height=512, width=512
+            )
+        exported_v1 = torch.export.export(pipe_v1.unet, args=call.args, kwargs=call.kwargs)
+        compiled_v1 = spaces.aoti_compile(exported_v1)
+        spaces.aoti_apply(compiled_v1, pipe_v1.unet)
+        print(f"Compilation for {MODEL_V1} complete.")
+    except Exception as e:
+        print(f"Warning: AoT compilation failed for SD 1.4. Running unoptimized. Error: {e}")
+
+    # --- Compilation for SD 2.1 Base (pipe_v2) ---
+    print(f"Compiling UNet for {MODEL_V2} (SD 2.1 Base)...")
+    try:
+        with spaces.aoti_capture(pipe_v2.unet) as call:
+            # Run a quick example call (512x512, low steps) to capture inputs
+            pipe_v2(
+                prompt="compilation test",
+                num_inference_steps=2,
+                height=512, width=512
+            )
+        exported_v2 = torch.export.export(pipe_v2.unet, args=call.args, kwargs=call.kwargs)
+        compiled_v2 = spaces.aoti_compile(exported_v2)
+        spaces.aoti_apply(compiled_v2, pipe_v2.unet)
+        print(f"Compilation for {MODEL_V2} complete.")
+    except Exception as e:
+        print(f"Warning: AoT compilation failed for SD 2.1 Base. Running unoptimized. Error: {e}")
+
+# Run compilation once at startup
+compile_optimized_models()
+
+
+@spaces.GPU
+def generate(
+    model_choice: str,
+    prompt: str,
+    guidance_scale: float,
+    num_inference_steps: int
+) -> List[Image.Image]:
+    """Generates images using the selected Stable Diffusion model."""
+
+    if model_choice == MODEL_V1:
+        pipe = pipe_v1
+        if not prompt:
+            prompt = DEFAULT_PROMPT_V1
+    elif model_choice == MODEL_V2:
+        pipe = pipe_v2
+        if not prompt:
+            prompt = DEFAULT_PROMPT_V2
+    else:
+        raise gr.Error("Invalid model selection.")
+
+    # We must use the resolution used during AoT compilation (512x512)
+    # for best performance.
+    result = pipe(
+        prompt=prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        num_images_per_prompt=4,  # Generate 4 images as implied by gallery output
+        height=512,
+        width=512
+    ).images
+
+    return result
+
+
+def display_uploads(files: Optional[List[Any]]) -> List[str]:
+    """Converts uploaded FileData objects to displayable paths."""
+    if files:
+        # FileData objects have a .path attribute pointing to the temporary file location
+        return [f.path for f in files]
+    return []
+
+
+# --- Gradio Interface ---
+with gr.Blocks(title="Stable Diffusion Models Demo") as demo:
+    gr.HTML(
+        """
+        <div style='text-align: center; max-width: 800px; margin: 0 auto;'>
+            <h1>Stable Diffusion v1.4 vs 2.1 Base</h1>
+            <p>Select a model and enter a prompt to generate up to 4 images. Empty prompts use a powerful default prompt.</p>
+            <p><a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank">Built with anycoder</a></p>
+        </div>
+        """
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            model_choice = gr.Radio(
+                choices=[MODEL_V1, MODEL_V2],
+                value=MODEL_V2,
+                label="Model Selection",
+                info="Select the base Stable Diffusion version to use."
+            )
+            prompt = gr.Textbox(
+                label="Prompt",
+                placeholder="Enter your prompt here (or leave empty for default demo prompt)"
+            )
+
+            with gr.Accordion("Generation Parameters", open=True):
+                guidance_scale = gr.Slider(
+                    minimum=1.0, maximum=15.0, value=7.5, step=0.5, label="Guidance Scale",
+                    info="Higher values push the generation closer to the prompt."
+                )
+                num_inference_steps = gr.Slider(
+                    minimum=10, maximum=100, value=50, step=5, label="Inference Steps",
+                    info="More steps lead to higher quality, but slower generation."
+                )
+
+            run_btn = gr.Button("Generate 4 Images", variant="primary")
+
+            # Handling image uploads (for auxiliary display/reference)
+            uploaded_files = gr.File(
+                label="Upload Reference Images (Max 4)",
+                file_count="multiple",
+                file_types=['image'],
+                max_files=4,
+                interactive=True
+            )
+            upload_display = gr.Gallery(
+                label="Uploaded Images for Reference",
+                columns=4,
+                object_fit="contain",
+                height=150,
+                allow_preview=False
+            )
+            uploaded_files.change(display_uploads, uploaded_files, upload_display)
+
+        with gr.Column(scale=3):
+            output_gallery = gr.Gallery(
+                label="Generated Images (512x512)",
+                columns=2,
+                object_fit="contain",
+                height=512,
+                preview=True
+            )
+
+    run_btn.click(
+        fn=generate,
+        inputs=[
+            model_choice,
+            prompt,
+            guidance_scale,
+            num_inference_steps
+        ],
+        outputs=output_gallery
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+torch
+diffusers
+numpy
+accelerate
+safetensors
+pillow
+git+https://github.com/huggingface/spaces@main
+xformers
+scipy
+ftfy
+opencv-python
+tensorboard
+clean-fid
+huggingface-hub