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Create README.md
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            ---
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            language: 
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              - en
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            thumbnail: "https://s3.amazonaws.com/moonup/production/uploads/1663756797814-62bd5f951e22ec84279820e8.png"
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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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            - lambdalabs/pokemon-blip-captions
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            ---
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            __Stable Diffusion fine tuned on Pokémon by [Lambda Labs](https://lambdalabs.com/).__
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            Put in a text prompt and generate your own Pokémon character, no "prompt engineering" required!
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            If you want to find out how to train your own Stable Diffusion variants, see this [example](https://github.com/LambdaLabsML/examples/tree/main/stable-diffusion-finetuning) from Lambda Labs.
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            > Girl with a pearl earring, Cute Obama creature, Donald Trump, Boris Johnson, Totoro, Hello Kitty
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            ## Usage
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            ```bash
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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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            ```python
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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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            pipe = StableDiffusionPipeline.from_pretrained("lambdalabs/sd-pokemon-diffusers", 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 by the Pokémon images, you can disable
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            # 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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            ## Model description
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            Trained on [BLIP captioned Pokémon images](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) using 2xA6000 GPUs on [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud) for around 15,000 step (about 6 hours, at a cost of about $10).
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            ## Links
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            - [Lambda Diffusers](https://github.com/LambdaLabsML/lambda-diffusers)
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            - [Captioned Pokémon dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions)
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            - [Model weights in Diffusers format](https://huggingface.co/lambdalabs/sd-pokemon-diffusers)
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            - [Original model weights](https://huggingface.co/justinpinkney/pokemon-stable-diffusion)
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            - [Training code](https://github.com/justinpinkney/stable-diffusion)
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            Trained by [Justin Pinkney](justinpinkney.com) ([@Buntworthy](https://twitter.com/Buntworthy)) at [Lambda Labs](https://lambdalabs.com/).
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