Instructions to use bryant0918/pokemon-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bryant0918/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("bryant0918/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
- Draw Things
- DiffusionBee
LoRA text2image fine-tuning - bryant0918/pokemon-lora
You can access my notebook to train the model here
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the diffusers/pokemon-gpt4-captions dataset. You can find some example images in the following.
- Downloads last month
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Model tree for bryant0918/pokemon-lora
Base model
runwayml/stable-diffusion-v1-5


