|
|
|
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") |
|
|
