from diffusers import DiffusionPipeline as Pipe import torch class Converter: import io from PIL import Image img = Image.open(fh, mode='r') roi_img = img.crop(box) img_byte_arr = io.BytesIO() roi_img.save(img_byte_arr, format='PNG') img_byte_arr = img_byte_arr.getvalue() class Generador: def img_to_bytes(image) -> bytes: import io _imgByteArr = io.BytesIO() image.save(_imgByteArr, format=image.format) return _imgByteArr.getvalue() def using_runway_sd_15(prompt:str)->bytes|None: try: _generador = Pipe.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) _generador.to("cuda") _imagen = _generador(prompt).images[0] _response = Generador.img_to_bytes(img=_imagen) except Exception as e: _response = None finally: return _response def using_stability_sd_21(prompt:str)->bytes|None: try: _generador = Pipe.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16) _generador.to("cuda") _imagen = _generador(prompt).images[0] _response = Generador.img_to_bytes(img=_imagen) except Exception as e: _response = None finally: return _response def using_realistic_v14(prompt:str)->bytes|None: try: _generador = Pipe.from_pretrained("SG161222/Realistic_Vision_V1.4", torch_dtype=torch.float16) _generador.to("cuda") _imagen = _generador(prompt).images[0] _response = Generador.img_to_bytes(img=_imagen) except Exception as e: _response = None finally: return _response def using_prompthero_openjourney(prompt:str)->bytes|None: try: _generador = Pipe.from_pretrained("prompthero/openjourney", torch_dtype=torch.float16) _generador.to("cuda") _imagen = _generador(prompt).images[0] _response = Generador.img_to_bytes(img=_imagen) except Exception as e: _response = None finally: return _response