from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(stable-diffusion-3.5-large, torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('dmega/Yaiza', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('Yaiza').images[0]

Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke

LoRA: download diffusers_lora_weights.safetensors here 💾.
- Rename it and place it on your models/Lora folder.
- On AUTOMATIC1111, load the LoRA by adding <lora:your_new_name:1> to your prompt. On ComfyUI just load it as a regular LoRA.

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers

License

Please adhere to the licensing terms as described here.

Training details

Trained on Replicate using: lucataco/stable-diffusion-3.5-large-lora-trainer

Intended uses & limitations

How to use

# TODO: add an example code snippet for running this diffusion pipeline

Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

Training details

[TODO: describe the data used to train the model]