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from huggingface_hub import HfApi |
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from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, StableDiffusionInpaintPipeline |
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from pathlib import Path |
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import os |
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import sys |
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import requests |
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from PIL import Image |
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from io import BytesIO |
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org_name = "lykon-models" |
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org_name = "lykon-absolute-realism" |
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file_path = sys.argv[1] |
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file_name = file_path.split("/")[-1].split(".safetensors")[0].replace("_", "-").replace(".", "-").lower() |
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if file_name.endswith("-"): |
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file_name = file_name[:-1] |
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def download_image(url): |
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response = requests.get(url) |
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return Image.open(BytesIO(response.content)).convert("RGB") |
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kwargs = {} |
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if "xl" in file_name or "alpha2" in file_name: |
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pipe = StableDiffusionXLPipeline.from_single_file(file_path) |
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elif "inpaint" in file_name: |
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pipe = StableDiffusionInpaintPipeline.from_single_file(file_path) |
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img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" |
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mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" |
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init_image = download_image(img_url).resize((512, 512)) |
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mask_image = download_image(mask_url).resize((512, 512)) |
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kwargs = {"image": init_image, "mask_image": mask_image} |
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else: |
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pipe = StableDiffusionPipeline.from_single_file(file_path) |
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print("Test...") |
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prompt = "A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k" |
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pipe.to("cuda") |
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images = pipe(prompt=prompt, num_inference_steps=25, **kwargs).images |
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pipe.to("cpu") |
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api = HfApi() |
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for i, image in enumerate(images): |
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image_file_name = f"bb_1_{i}" |
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path = os.path.join(Path.home(), "images", f"{image_file_name}.png") |
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image.save(path) |
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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(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/{image_file_name}.png") |
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print("Upload...") |
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model_id = os.path.join(org_name, file_name) |
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pipe.push_to_hub(model_id, private=True) |
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