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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> |