routers/training.py

from fastapi import APIRouter, Form, BackgroundTasks
from config import settings
import os
import json
from routers.donut_evaluate import run_evaluate_donut
from routers.donut_training import run_training_donut
import utils
import torch
import requests
from PIL import Image
from io import BytesIO

from diffusers import StableDiffusionImg2ImgPipeline

model_id_or_path = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
pipe = pipe.to("cuda")


router = APIRouter()

@router.get("/hi")
async def hifunction():
    
    url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
    response = requests.get(url)
    init_image = Image.open(BytesIO(response.content)).convert("RGB")
    init_image = init_image.resize((768, 512))
    prompt = "A fantasy landscape, trending on artstation"
    images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images
    print(images)
    print(images[0])

    buffered = BytesIO()
    images[0].save(buffered, format="JPEG")
    img_str = base64.b64encode(buffered.getvalue())

    # images[0].save("fantasy_landscape.png")

    return {
        "image": img_str
    }