Update README.md
Browse filesOptimization for Stable Diffusion v1-4
FP16 Precision: Reduces memory usage and speeds up inference.
Batch Processing: Generate multiple images in a single request to maximize GPU utilization.
Custom LoRA Training: Train on specific datasets to customize the model for unique themes (e.g., luxury, fantasy).
This approach lets you use Stable Diffusion v1-4 for photorealistic, high-quality image generation, deployable in a user-friendly interface. Let me know if you need help fine-tuning or scaling this setup!
README.md
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sdk_version: 5.12.0
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---
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sdk_version: 5.12.0
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---
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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 # GPU support
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pip install diffusers transformers flask pillow accelerate
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from diffusers import StableDiffusionPipeline
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import torch
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# Authenticate Hugging Face
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from huggingface_hub import login
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login(token="your_hugging_face_token")
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# Load Stable Diffusion v1-4
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model_id = "CompVis/stable-diffusion-v1-4"
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pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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pipe = pipe.to("cuda") # Use GPU for faster performance
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prompt = "A luxurious futuristic bathroom with marble walls and golden accents, panoramic views of a tropical jungle, ultra-realistic, 32k resolution"
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num_steps = 50 # Number of diffusion steps
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guidance_scale = 7.5 # Higher = more faithful to the prompt
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# Generate an image
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image = pipe(prompt, num_inference_steps=num_steps, guidance_scale=guidance_scale).images[0]
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# Save the image
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image.save("generated_image.png")
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from flask import Flask, request, jsonify, send_file
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from diffusers import StableDiffusionPipeline
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import torch
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app = Flask(__name__)
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# Load Stable Diffusion v1-4
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model_id = "CompVis/stable-diffusion-v1-4"
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pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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pipe = pipe.to("cuda")
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@app.route("/generate", methods=["POST"])
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def generate_image():
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data = request.json
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prompt = data.get("prompt", "A beautiful fantasy landscape")
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num_steps = data.get("steps", 50)
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guidance_scale = data.get("guidance_scale", 7.5)
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# Generate image
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image = pipe(prompt, num_inference_steps=num_steps, guidance_scale=guidance_scale).images[0]
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output_path = "output.png"
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image.save(output_path)
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return send_file(output_path, mimetype="image/png")
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if __name__ == "__main__":
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app.run(host="0.0.0.0", port=5000)
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Stable Diffusion Generator</title>
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</head>
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<body>
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<h1>Stable Diffusion v1-4 Image Generator</h1>
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<form id="image-form">
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<label for="prompt">Prompt:</label><br>
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<input type="text" id="prompt" name="prompt" required><br><br>
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<label for="steps">Inference Steps:</label><br>
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<input type="number" id="steps" name="steps" value="50"><br><br>
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<label for="guidance_scale">Guidance Scale:</label><br>
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<input type="number" id="guidance_scale" name="guidance_scale" value="7.5"><br><br>
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<button type="submit">Generate Image</button>
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</form>
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<h2>Generated Image:</h2>
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<img id="generated-image" alt="Generated Image" style="max-width: 100%;">
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<script>
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document.getElementById("image-form").addEventListener("submit", async (event) => {
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event.preventDefault();
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const prompt = document.getElementById("prompt").value;
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const steps = document.getElementById("steps").value;
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const guidanceScale = document.getElementById("guidance_scale").value;
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const response = await fetch("http://localhost:5000/generate", {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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},
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body: JSON.stringify({ prompt, steps, guidance_scale: guidanceScale }),
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});
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if (response.ok) {
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const blob = await response.blob();
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const url = URL.createObjectURL(blob);
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document.getElementById("generated-image").src = url;
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} else {
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console.error("Error generating image");
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}
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});
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</script>
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</body>
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</html>
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