Instructions to use AACEE/pokemon-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AACEE/pokemon-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("AACEE/pokemon-lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from AACEE/pokemon-lora: direct link, hf CLI and curl.
- Browser
- Download file 539 Bytes
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https://huggingface.co/AACEE/pokemon-lora/resolve/main/README.md
- Command line
-
hf download hf://AACEE/pokemon-lora/README.md
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curl -L -o README.md https://huggingface.co/AACEE/pokemon-lora/resolve/main/README.md
539 Bytes
metadata
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
LoRA text2image fine-tuning - AACEE/pokemon-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.



