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import torch |
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from diffusers import AutoPipelineForText2Image |
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from huggingface_hub import HfApi |
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from pathlib import Path |
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import os |
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from PIL import Image |
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import numpy as np |
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api = HfApi() |
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pipe = AutoPipelineForText2Image.from_pretrained("warp-diffusion/WuerstchenGeneratorPipeline", torch_dtype=torch.float16).to("cuda") |
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prompt = [ |
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"An old destroyed car standing on a cliff in norway, cinematic photography", |
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"Western movie, closeup cinematic photography", |
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"Pink nike shoe commercial, closeup cinematic photography", |
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"Croatia, closeup cinematic photography", |
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"South Tyrol mountains at sunset, closeup cinematic photography", |
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] |
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images = pipe(prompt, guidance_scale=8.0, width=1024, height=1024).images |
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for i, image in enumerate(images): |
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file_name = f"bb_1_{i}" |
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path = os.path.join(Path.home(), "images", f"{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/{file_name}.png") |
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