Deploy Gradio app with multiple files

app.py

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+import os
+import random
+from typing import Optional
+
+import gradio as gr
+import spaces
+import torch
+from diffusers import DiffusionPipeline
+
+MODEL_ID = "Comfy-Org/stable_diffusion_2.1_unclip_repackaged"
+DTYPE = torch.float16
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+if DEVICE != "cuda":
+    raise EnvironmentError("This Space requires a GPU runtime to run Stable Diffusion 2.1 UNCLIP.")
+
+pipe = DiffusionPipeline.from_pretrained(
+    MODEL_ID,
+    torch_dtype=DTYPE,
+    safety_checker=None,
+    use_safetensors=True,
+)
+if hasattr(pipe, "enable_xformers_memory_efficient_attention"):
+    pipe.enable_xformers_memory_efficient_attention()
+pipe.to(DEVICE)
+pipe.set_progress_bar_config(disable=True)
+
+
+@spaces.GPU(duration=1500)
+def compile_transformer():
+    """
+    Ahead-of-time compile the transformer for faster inference.
+    """
+    with spaces.aoti_capture(pipe.transformer) as call:
+        pipe(
+            prompt="high quality photo of a futuristic city skyline at sunset",
+            negative_prompt="low quality, blurry",
+            num_inference_steps=4,
+            guidance_scale=5.0,
+            width=512,
+            height=512,
+        )
+    exported = torch.export.export(
+        pipe.transformer,
+        args=call.args,
+        kwargs=call.kwargs,
+    )
+    return spaces.aoti_compile(exported)
+
+
+compiled_transformer = compile_transformer()
+spaces.aoti_apply(compiled_transformer, pipe.transformer)
+
+
+@spaces.GPU(duration=60)
+def generate_image(
+    prompt: str,
+    negative_prompt: str,
+    guidance_scale: float,
+    num_inference_steps: int,
+    width: int,
+    height: int,
+    seed: int,
+) -> torch.Tensor:
+    """
+    Run Stable Diffusion 2.1 UNCLIP to create an image.
+
+    Args:
+        prompt (str): Text prompt describing the desired image.
+        negative_prompt (str): Undesired attributes to avoid.
+        guidance_scale (float): CFG guidance strength.
+        num_inference_steps (int): Number of denoising steps.
+        width (int): Output image width.
+        height (int): Output image height.
+        seed (int): Random seed for reproducibility.
+
+    Returns:
+        torch.Tensor: Generated image.
+    """
+    cleaned_negative = negative_prompt.strip() or None
+    generator = torch.Generator(device=DEVICE)
+    generator.manual_seed(seed)
+    result = pipe(
+        prompt=prompt,
+        negative_prompt=cleaned_negative,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        width=width,
+        height=height,
+        generator=generator,
+    )
+    return result.images[0]
+
+
+with gr.Blocks(title="Stable Diffusion 2.1 UNCLIP Tester") as demo:
+    gr.Markdown(
+        """
+        # Stable Diffusion 2.1 UNCLIP (Comfy-Org)
+        [Built with anycoder](https://huggingface.co/spaces/akhaliq/anycoder)
+
+        Experiment with prompts using the repackaged SD 2.1 UNCLIP model.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="Prompt",
+                value="A hyper-detailed matte painting of a floating city above the clouds, cinematic lighting",
+                lines=3,
+                placeholder="Describe what you want to generate...",
+            )
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+                value="low quality, blurry, distorted, watermark",
+                lines=3,
+                placeholder="Describe what to avoid...",
+            )
+            with gr.Row():
+                guidance_scale = gr.Slider(1.0, 15.0, value=7.5, step=0.1, label="Guidance Scale")
+                steps = gr.Slider(10, 60, value=30, step=1, label="Inference Steps")
+            with gr.Row():
+                width = gr.Slider(512, 1024, value=768, step=64, label="Width")
+                height = gr.Slider(512, 1024, value=768, step=64, label="Height")
+            seed = gr.Slider(0, 2_147_483_647, value=42, step=1, label="Seed")
+            random_seed_btn = gr.Button("Randomize Seed", variant="secondary")
+            generate_btn = gr.Button("Generate", variant="primary")
+
+        with gr.Column():
+            output_image = gr.Image(label="Generated Image", show_download_button=True)
+
+    random_seed_btn.click(
+        fn=lambda: random.randint(0, 2_147_483_647),
+        inputs=None,
+        outputs=seed,
+    )
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[prompt, negative_prompt, guidance_scale, steps, width, height, seed],
+        outputs=output_image,
+    )
+
+demo.queue()
+demo.launch()

requirements.txt

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+gradio
+torch
+torchvision
+torchaudio
+accelerate
+safetensors
+Pillow
+git+https://github.com/huggingface/diffusers
+xformers