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from diffusers import StableDiffusionPipeline, KDPM2DiscreteScheduler, StableDiffusionImg2ImgPipeline, HeunDiscreteScheduler, KDPM2AncestralDiscreteScheduler |
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import time |
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import os |
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from huggingface_hub import HfApi |
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import torch |
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import sys |
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from pathlib import Path |
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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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path = sys.argv[1] |
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api = HfApi() |
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start_time = time.time() |
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained(path, torch_dtype=torch.float16, safety_checker=None) |
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pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config) |
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pipe = pipe.to("cuda") |
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prompt = "ghibli style, a fantasy landscape with castles" |
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url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" |
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response = requests.get(url) |
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image = Image.open(BytesIO(response.content)).convert("RGB") |
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image.thumbnail((768, 768)) |
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generator = torch.Generator(device="cpu").manual_seed(0) |
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images = pipe(prompt=prompt, image=image, generator=generator, strength=0.75, num_inference_steps=30).images |
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print("Time", time.time() - start_time) |
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for i, image in enumerate(images): |
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path = os.path.join(Path.home(), "images", f"aa_{i}.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/aa_{i}.png") |
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