---
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)
Stable Diffusion Generator
Stable Diffusion v1-4 Image Generator
Generated Image:
![Generated Image]()