Instructions to use tenith/pokemon-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tenith/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("stable-diffusion-v1-5/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("tenith/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
metadata
tags:
- stable-diffusion
- lora
- text-to-image
- diffusers
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
LoRA fine-tuned on custom dataset
Details
- Base model:
stable-diffusion-v1-5/stable-diffusion-v1-5 - Resolution: 512px
- Method: LoRA (rank 16)
- Training steps: 1500
- Learning rate: 0.0001
Usage
import torch
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
).to("cuda")
pipe.load_lora_weights("tenith/pokemon-lora")
image = pipe("your prompt here", num_inference_steps=30, guidance_scale=7.5).images[0]
image.save("output.png")