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from diffusers import StableDiffusionPipeline, DDIMScheduler |
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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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begin = ["a picture of <rickmann>", "a photo of <rickmann>", "The <rickmann>", "an image of <rickmann>"] |
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mid = ["", " on a bike", " with sunglasses", " at the beach", " in front of a mountain", " in the water", " on a boat", " at a fashion show", " as a superstar model", " while it snows", " in a forest", " with a nice landscape"] |
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end = ["", " , disco light style", ", minecraft style", " , picasso style", " as a lego person", ""] |
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api = HfApi() |
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start_time = time.time() |
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path = "patrickvonplaten/papa_out_5" |
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pipe = StableDiffusionPipeline.from_pretrained(path, safety_checker=None, torch_dtype=torch.float16) |
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pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) |
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pipe = pipe.to("cuda") |
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counter = 0 |
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for b in begin: |
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for m in mid: |
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for e in end: |
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prompt = b + mid + e + ", highly realistic, super resolution, high quality photography, beautiful" |
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images = pipe(prompt=prompt, num_images_per_prompt=2, eta=1.0, negative_prompt="ugly, bad quality, deformed", num_inference_steps=50).images |
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for i, image in enumerate(images): |
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path = os.path.join(Path.home(), "papa", f"{counter}.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/papa", |
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repo_type="dataset", |
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) |
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print(f"https://huggingface.co/datasets/patrickvonplaten/papa/blob/main/{counter}.png") |
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counter += 1 |
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