Add Stable Diffusion v1.5 Text→Image Gradio demo

app.py

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+import torch
+from diffusers import StableDiffusionPipeline
+import gradio as gr
+
+# Pick the fastest device available
+device = (
+    "mps" if torch.backends.mps.is_available()
+    else "cuda" if torch.cuda.is_available()
+    else "cpu"
+)
+
+# Load the model (you can remove safety_checker=None for public deploys)
+model_id = "runwayml/stable-diffusion-v1-5"
+pipe = StableDiffusionPipeline.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    safety_checker=None
+).to(device)
+
+def generate(prompt: str, steps: int, guidance: float, seed: float):
+    """
+    Generate one or more images from a text prompt.
+    """
+    # If seed > 0, use it; else let Diffusers pick a random seed.
+    generator = (
+        torch.Generator(device=device).manual_seed(int(seed))
+        if seed and seed > 0
+        else None
+    )
+    output = pipe(
+        prompt,
+        num_inference_steps=steps,
+        guidance_scale=guidance,
+        generator=generator
+    )
+    # returns a list of PIL images
+    return output.images
+
+# Build the Gradio UI
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown("# Stable Diffusion Text→Image Generation Demo")
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", placeholder="e.g. ‘A serene forest at dawn’")
+            steps  = gr.Slider(1, 100, value=50, step=1, label="Inference Steps")
+            guidance = gr.Slider(1, 15, value=7.5, step=0.1, label="Guidance Scale")
+            seed   = gr.Number(value=0, label="Random Seed (0 = random)")
+            btn    = gr.Button("Generate")
+        with gr.Column():
+            gallery = gr.Gallery(label="Generated Images", columns=2, height="auto")
+
+    # wire up the button
+    btn.click(
+        fn=generate,
+        inputs=[prompt, steps, guidance, seed],
+        outputs=gallery,
+    )
+
+if __name__ == "__main__":
+    demo.launch()

create_space.py

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+from huggingface_hub import HfApi
+
+api = HfApi()
+
+# Replace with your actual namespace (e.g. "stephenebert") and desired Space name
+repo_id = "stephenebert/sd-text2image"
+
+# Create a public Gradio Space
+api.create_repo(
+    repo_id=repo_id,
+    repo_type="space",
+    space_sdk="gradio",
+    private=False,
+)
+print(f"Created Space: https://huggingface.co/spaces/{repo_id}")

pyaudioop.py

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+__all__ = []
+# Stub for the removed audioop stdlib module on Python 3.13+
+# This lets pydub/gradio import pyaudioop without error,
+# even though we never actually use any audio functions here.

requirements.txt

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+torch>=2.2.0
+diffusers>=0.18.0
+transformers>=4.31.0
+accelerate>=0.20.3
+safetensors
+gradio>=3.50.2

space.yaml

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+# space.yaml
+sdk: gradio
+duplicate: false
+title: "Stable Diffusion v1.5 — Text → Image Demo"
+tags:
+  - stable-diffusion
+  - gradio
+  - diffusers

text2image_demo.py

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+import torch
+from diffusers import StableDiffusionPipeline
+import gradio as gr
+
+# Pick the fastest device available
+device = (
+    "mps" if torch.backends.mps.is_available()
+    else "cuda" if torch.cuda.is_available()
+    else "cpu"
+)
+
+# Load the model (you can remove safety_checker=None for public deploys)
+model_id = "runwayml/stable-diffusion-v1-5"
+pipe = StableDiffusionPipeline.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    safety_checker=None
+).to(device)
+
+def generate(prompt: str, steps: int, guidance: float, seed: float):
+    """
+    Generate one or more images from a text prompt.
+    """
+    # If seed > 0, use it; else let Diffusers pick a random seed.
+    generator = (
+        torch.Generator(device=device).manual_seed(int(seed))
+        if seed and seed > 0
+        else None
+    )
+    output = pipe(
+        prompt,
+        num_inference_steps=steps,
+        guidance_scale=guidance,
+        generator=generator
+    )
+    # returns a list of PIL images
+    return output.images
+
+# Build the Gradio UI
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown("# Stable Diffusion Text→Image Generation Demo")
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", placeholder="e.g. ‘A serene forest at dawn’")
+            steps  = gr.Slider(1, 100, value=50, step=1, label="Inference Steps")
+            guidance = gr.Slider(1, 15, value=7.5, step=0.1, label="Guidance Scale")
+            seed   = gr.Number(value=0, label="Random Seed (0 = random)")
+            btn    = gr.Button("Generate")
+        with gr.Column():
+            gallery = gr.Gallery(label="Generated Images", columns=2, height="auto")
+
+    # wire up the button
+    btn.click(
+        fn=generate,
+        inputs=[prompt, steps, guidance, seed],
+        outputs=gallery,
+    )
+
+if __name__ == "__main__":
+    demo.launch()