| #!/usr/bin/env python3 | |
| import torch | |
| import os | |
| from huggingface_hub import HfApi | |
| from pathlib import Path | |
| from diffusers.utils import load_image | |
| from PIL import Image | |
| import numpy as np | |
| from diffusers import ( | |
| ControlNetModel, | |
| StableDiffusionControlNetPipeline, | |
| UniPCMultistepScheduler, | |
| ) | |
| import sys | |
| checkpoint = sys.argv[1] | |
| image = load_image("https://huggingface.co/lllyasviel/sd-controlnet-seg/resolve/main/images/house.png").convert('RGB') | |
| prompt = "make it on fire" | |
| controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16) | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 | |
| ) | |
| pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) | |
| pipe.enable_model_cpu_offload() | |
| generator = torch.manual_seed(0) | |
| out_image = pipe(prompt, num_inference_steps=30, generator=generator, image=image).images[0] | |
| path = os.path.join(Path.home(), "images", "aa.png") | |
| out_image.save(path) | |
| api = HfApi() | |
| api.upload_file( | |
| path_or_fileobj=path, | |
| path_in_repo=path.split("/")[-1], | |
| repo_id="patrickvonplaten/images", | |
| repo_type="dataset", | |
| ) | |
| print("https://huggingface.co/datasets/patrickvonplaten/images/blob/main/aa.png") | |