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from flask import Flask, request, jsonify
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import base64
from io import BytesIO
from huggingface_hub import login
# Authenticate with Hugging Face Hub (ensure you replace 'your_token_here')
import os
login(os.environ["HF_TOKEN"])
# Initialize Flask app
app = Flask(__name__)
# Load Hugging Face pipeline components
model_id = "fyp1/sketchToImage"
controlnet = ControlNetModel.from_pretrained(f"{model_id}/controlnet", torch_dtype=torch.float16)
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
scheduler = EulerAncestralDiscreteScheduler.from_pretrained(f"{model_id}/scheduler")
# Initialize Stable Diffusion XL ControlNet Pipeline
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
scheduler=scheduler,
safety_checker=None,
torch_dtype=torch.float16,
).to("cuda" if torch.cuda.is_available() else "cpu")
@app.route("/generate", methods=["POST"])
def generate_image():
data = request.json
# Extract prompt, sketch image (Base64), and optional parameters
prompt = data.get("prompt", "A default prompt")
negative_prompt = data.get("negative_prompt", "low quality, blurry, bad details")
sketch_base64 = data.get("sketch", None)
if not sketch_base64:
return jsonify({"error": "Sketch image is required."}), 400
try:
# Decode and preprocess the sketch image
sketch_bytes = base64.b64decode(sketch_base64)
sketch_image = Image.open(BytesIO(sketch_bytes)).convert("L") # Convert to grayscale
sketch_image = sketch_image.resize((1024, 1024))
# Generate the image using the pipeline
with torch.no_grad():
images = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=sketch_image,
controlnet_conditioning_scale=1.0,
width=1024,
height=1024,
num_inference_steps=30,
).images
# Convert output image to Base64
buffered = BytesIO()
images[0].save(buffered, format="PNG")
image_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
return jsonify({"image": image_base64})
except Exception as e:
return jsonify({"error": str(e)}), 500
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860)
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