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from fastapi import FastAPI
from pydantic import BaseModel
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import base64
from io import BytesIO
# Initialize FastAPI app
app = FastAPI()
# Load Hugging Face pipeline components
model_id = "fyp1/sketchToImage"
controlnet = ControlNetModel.from_pretrained(f"{model_id}",subfolder="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}",subfolder="scheduler", torch_dtype=torch.float16)
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")
class GenerateRequest(BaseModel):
prompt: str
negative_prompt: str
sketch: str # Base64 encoded image
@app.post("/generate")
async def generate_image(data: GenerateRequest):
try:
# Decode and preprocess the sketch image
sketch_bytes = base64.b64decode(data.sketch)
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=data.prompt,
negative_prompt=data.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 {"image": image_base64}
except Exception as e:
return {"error": str(e)}
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