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--- |
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language: |
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- en |
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thumbnail: TBD |
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tags: |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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- text-to-image |
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datasets: |
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- ChristophSchuhmann/improved_aesthetics_6plus |
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--- |
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# Mini Stable Diffusion (miniSD) |
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MiniSD is a latent text-to-image diffusion model that has been conditionned on 256x256 images through finetuning. |
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## Examples |
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WIP |
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## Usage |
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``` |
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!pip install diffusers==0.3.0 |
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!pip install transformers scipy ftfy |
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``` |
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``` |
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import torch |
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from diffusers import StableDiffusionPipeline |
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from torch import autocast |
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# TODO: change model_id to "lambdalabs/miniSD" |
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pipe = StableDiffusionPipeline.from_pretrained("eolecvk/model-test", torch_dtype=torch.float16) |
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pipe = pipe.to("cuda") |
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prompt = "Yoda" |
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scale = 10 |
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n_samples = 4 |
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# Sometimes the nsfw checker is confused, you can disable it at your own risk here |
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disable_safety = False |
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if disable_safety: |
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def null_safety(images, **kwargs): |
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return images, False |
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pipe.safety_checker = null_safety |
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with autocast("cuda"): |
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images = pipe(n_samples*[prompt], guidance_scale=scale).images |
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for idx, im in enumerate(images): |
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im.save(f"{idx:06}.png") |
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``` |
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