APOGEA/apiExample

1from fastapi import FastAPI, UploadFile, Form2from fastapi.responses import StreamingResponse3#import torch4from PIL import Image5#from diffusers import StableDiffusionDepth2ImgPipeline6import numpy as np7from io import BytesIO8 9app = FastAPI()10 11"""12pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(13   "stabilityai/stable-diffusion-2-depth",14   torch_dtype=torch.float16,15).to("cuda")16"""17 18def pad_image(input_image):19    pad_w, pad_h = np.max(((2, 2), np.ceil(20        np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size21    im_padded = Image.fromarray(22        np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))23    w, h = im_padded.size24    if w == h:25        return im_padded26    elif w > h:27        new_image = Image.new(im_padded.mode, (w, w), (0, 0, 0))28        new_image.paste(im_padded, (0, (w - h) // 2))29        return new_image30    else:31        new_image = Image.new(im_padded.mode, (h, h), (0, 0, 0))32        new_image.paste(im_padded, ((h - w) // 2, 0))33        return new_image34 35def predict(input_image, prompt, steps, scale, seed, strength, depth_image=None):36    depth = None37    if depth_image is not None:38        depth_image = pad_image(depth_image)39        depth_image = depth_image.resize((512, 512))40        depth = np.array(depth_image.convert("L"))41        depth = depth.astype(np.float32) / 255.042        depth = depth[None, None]43        depth = torch.from_numpy(depth)44    init_image = input_image.convert("RGB")45    image = pad_image(init_image)  # resize to integer multiple of 3246    image = image.resize((512, 512))47    result = pipe(prompt=prompt, image=image, strength=strength)48 49    return result['images']50 51def grayscale(image,52              prompt,53              steps,54              scale,55              seed,56              strength):57    image = image.convert('L') #convert to grayscale58 59    return image60 61@app.post("/convert_ifc_img/")62async def convert_ifc_img(file: UploadFile, 63                          prompt: str = Form(default=""), 64                          steps: int = Form(default=50), 65                          scale: float = Form(default=9), 66                          seed: int = Form(default=178106186), 67                          strength: float = Form(default=0.9)68                        ):69    70    image = Image.open(file.file)                        71    image_result = grayscale(image, prompt, steps, scale, seed, strength)72    buffer = BytesIO()73    image_result.save(buffer, format="PNG")74    buffer.seek(0)75    return StreamingResponse(buffer, media_type="image/png")