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import torch |
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import os |
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from huggingface_hub import HfApi |
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from pathlib import Path |
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from diffusers.utils import load_image |
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import cv2 |
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from PIL import Image |
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import numpy as np |
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from diffusers import ( |
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ControlNetModel, |
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EulerDiscreteScheduler, |
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StableDiffusionControlNetPipeline, |
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UniPCMultistepScheduler, |
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) |
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import sys |
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checkpoint = sys.argv[1] |
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image = load_image( |
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"https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png" |
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) |
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image = np.array(image) |
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low_threshold = 100 |
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high_threshold = 200 |
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image = cv2.Canny(image, low_threshold, high_threshold) |
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image = image[:, :, None] |
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image = np.concatenate([image, image, image], axis=2) |
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canny_image = Image.fromarray(image) |
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controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16) |
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pipe = StableDiffusionControlNetPipeline.from_pretrained( |
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"runwayml/stable-diffusion-v1-5", controlnet=[controlnet, controlnet], torch_dtype=torch.float16 |
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) |
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pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config) |
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pipe.enable_model_cpu_offload() |
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generator = torch.manual_seed(33) |
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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] |
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path = os.path.join(Path.home(), "images", "aa.png") |
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out_image.save(path) |
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api = HfApi() |
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api.upload_file( |
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path_or_fileobj=path, |
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path_in_repo=path.split("/")[-1], |
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repo_id="patrickvonplaten/images", |
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repo_type="dataset", |
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) |
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print("https://huggingface.co/datasets/patrickvonplaten/images/blob/main/aa.png") |
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