Instructions to use lambda/sd-pokemon-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lambda/sd-pokemon-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lambda/sd-pokemon-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
| language: | |
| - en | |
| thumbnail: "https://s3.amazonaws.com/moonup/production/uploads/1663756797814-62bd5f951e22ec84279820e8.png" | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| datasets: | |
| - lambdalabs/pokemon-blip-captions | |
| __Stable Diffusion fine tuned on Pokémon by [Lambda Labs](https://lambdalabs.com/).__ | |
| Put in a text prompt and generate your own Pokémon character, no "prompt engineering" required! | |
| 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. | |
|  | |
| > Girl with a pearl earring, Cute Obama creature, Donald Trump, Boris Johnson, Totoro, Hello Kitty | |
| ## Usage | |
| ```bash | |
| !pip install diffusers==0.3.0 | |
| !pip install transformers scipy ftfy | |
| ``` | |
| ```python | |
| import torch | |
| from diffusers import StableDiffusionPipeline | |
| from torch import autocast | |
| pipe = StableDiffusionPipeline.from_pretrained("lambdalabs/sd-pokemon-diffusers", torch_dtype=torch.float16) | |
| pipe = pipe.to("cuda") | |
| prompt = "Yoda" | |
| scale = 10 | |
| n_samples = 4 | |
| # Sometimes the nsfw checker is confused by the Pokémon images, you can disable | |
| # it at your own risk here | |
| disable_safety = False | |
| if disable_safety: | |
| def null_safety(images, **kwargs): | |
| return images, False | |
| pipe.safety_checker = null_safety | |
| with autocast("cuda"): | |
| images = pipe(n_samples*[prompt], guidance_scale=scale).images | |
| for idx, im in enumerate(images): | |
| im.save(f"{idx:06}.png") | |
| ``` | |
| ## Model description | |
| 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). | |
| ## Links | |
| - [Lambda Diffusers](https://github.com/LambdaLabsML/lambda-diffusers) | |
| - [Captioned Pokémon dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) | |
| - [Model weights in Diffusers format](https://huggingface.co/lambdalabs/sd-pokemon-diffusers) | |
| - [Original model weights](https://huggingface.co/justinpinkney/pokemon-stable-diffusion) | |
| - [Training code](https://github.com/justinpinkney/stable-diffusion) | |
| Trained by [Justin Pinkney](justinpinkney.com) ([@Buntworthy](https://twitter.com/Buntworthy)) at [Lambda Labs](https://lambdalabs.com/). |