File size: 1,617 Bytes
e58dd86 b88c59f e58dd86 c51632b e58dd86 b88c59f e58dd86 b88c59f e58dd86 c5d43bc e58dd86 c51632b e58dd86 c5d43bc e58dd86 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
#!/usr/bin/env python3
import torch
import os
from huggingface_hub import HfApi
from pathlib import Path
from diffusers.utils import load_image
import cv2
from PIL import Image
import numpy as np
from diffusers import (
ControlNetModel,
EulerDiscreteScheduler,
StableDiffusionControlNetPipeline,
UniPCMultistepScheduler,
)
import sys
checkpoint = sys.argv[1]
image = load_image(
"https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.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(checkpoint, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=[controlnet, controlnet], torch_dtype=torch.float16
)
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
generator = torch.manual_seed(33)
out_image = pipe("a blue paradise bird in the jungle", control_guidance_start=[0.2, 0.2], num_inference_steps=20, generator=generator, image=[canny_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")
|