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| title: CompVis Stable Diffusion V1 4 | |
| emoji: 🏃 | |
| colorFrom: pink | |
| colorTo: purple | |
| sdk: gradio | |
| pinned: false | |
| license: bigscience-openrail-m | |
| sdk_version: 5.12.0 | |
| pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 # GPU support | |
| pip install diffusers transformers flask pillow accelerate | |
| from diffusers import StableDiffusionPipeline | |
| import torch | |
| # Authenticate Hugging Face | |
| from huggingface_hub import login | |
| login(token="your_hugging_face_token") | |
| # Load Stable Diffusion v1-4 | |
| model_id = "CompVis/stable-diffusion-v1-4" | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) | |
| pipe = pipe.to("cuda") # Use GPU for faster performance | |
| prompt = "A luxurious futuristic bathroom with marble walls and golden accents, panoramic views of a tropical jungle, ultra-realistic, 32k resolution" | |
| num_steps = 50 # Number of diffusion steps | |
| guidance_scale = 7.5 # Higher = more faithful to the prompt | |
| # Generate an image | |
| image = pipe(prompt, num_inference_steps=num_steps, guidance_scale=guidance_scale).images[0] | |
| # Save the image | |
| image.save("generated_image.png") | |
| from flask import Flask, request, jsonify, send_file | |
| from diffusers import StableDiffusionPipeline | |
| import torch | |
| app = Flask(__name__) | |
| # Load Stable Diffusion v1-4 | |
| model_id = "CompVis/stable-diffusion-v1-4" | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) | |
| pipe = pipe.to("cuda") | |
| @app.route("/generate", methods=["POST"]) | |
| def generate_image(): | |
| data = request.json | |
| prompt = data.get("prompt", "A beautiful fantasy landscape") | |
| num_steps = data.get("steps", 50) | |
| guidance_scale = data.get("guidance_scale", 7.5) | |
| # Generate image | |
| image = pipe(prompt, num_inference_steps=num_steps, guidance_scale=guidance_scale).images[0] | |
| output_path = "output.png" | |
| image.save(output_path) | |
| return send_file(output_path, mimetype="image/png") | |
| if __name__ == "__main__": | |
| app.run(host="0.0.0.0", port=5000) | |
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Stable Diffusion Generator</title> | |
| </head> | |
| <body> | |
| <h1>Stable Diffusion v1-4 Image Generator</h1> | |
| <form id="image-form"> | |
| <label for="prompt">Prompt:</label><br> | |
| <input type="text" id="prompt" name="prompt" required><br><br> | |
| <label for="steps">Inference Steps:</label><br> | |
| <input type="number" id="steps" name="steps" value="50"><br><br> | |
| <label for="guidance_scale">Guidance Scale:</label><br> | |
| <input type="number" id="guidance_scale" name="guidance_scale" value="7.5"><br><br> | |
| <button type="submit">Generate Image</button> | |
| </form> | |
| <h2>Generated Image:</h2> | |
| <img id="generated-image" alt="Generated Image" style="max-width: 100%;"> | |
| <script> | |
| document.getElementById("image-form").addEventListener("submit", async (event) => { | |
| event.preventDefault(); | |
| const prompt = document.getElementById("prompt").value; | |
| const steps = document.getElementById("steps").value; | |
| const guidanceScale = document.getElementById("guidance_scale").value; | |
| const response = await fetch("http://localhost:5000/generate", { | |
| method: "POST", | |
| headers: { | |
| "Content-Type": "application/json", | |
| }, | |
| body: JSON.stringify({ prompt, steps, guidance_scale: guidanceScale }), | |
| }); | |
| if (response.ok) { | |
| const blob = await response.blob(); | |
| const url = URL.createObjectURL(blob); | |
| document.getElementById("generated-image").src = url; | |
| } else { | |
| console.error("Error generating image"); | |
| } | |
| }); | |
| </script> | |
| </body> | |
| </html> |