| |
| 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 transformers import pipeline |
|
|
| from diffusers import ( |
| ControlNetModel, |
| StableDiffusionControlNetPipeline, |
| UniPCMultistepScheduler, |
| ) |
| import sys |
|
|
| checkpoint = sys.argv[1] |
|
|
| image = load_image("https://huggingface.co/lllyasviel/sd-controlnet-depth/resolve/main/images/stormtrooper.png") |
|
|
| prompt = "Stormtrooper's lecture in beautiful lecture hall" |
|
|
|
|
| depth_estimator = pipeline('depth-estimation') |
| image = depth_estimator(image)['depth'] |
| image = np.array(image) |
| image = image[:, :, None] |
| image = np.concatenate([image, image, image], axis=2) |
| image = Image.fromarray(image) |
|
|
| 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=40, 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") |
|
|
