| | |
| | import torch |
| | import numpy as np |
| | import os |
| | from huggingface_hub import HfApi |
| | from pathlib import Path |
| | import cv2 |
| | from PIL import Image |
| | from diffusers.utils import load_image |
| |
|
| | from diffusers import ( |
| | ControlNetModel, |
| | StableDiffusionControlNetPipeline, |
| | UniPCMultistepScheduler, |
| | ) |
| |
|
| | image = load_image( |
| | "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png" |
| | ) |
| |
|
| | image = np.array(image) |
| |
|
| | low_threshold = 100 |
| | high_threshold = 200 |
| |
|
| | image = cv2.Canny(image, low_threshold, high_threshold) |
| | image = image[:, :, None] |
| | image = np.concatenate([image, image, image], axis=2) |
| | canny_image = Image.fromarray(image) |
| |
|
| | controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", 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("futuristic-looking woman", num_inference_steps=20, generator=generator, image=canny_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") |
| |
|
