ariG23498 / sukumar-ray-characters-sd3.5-lora

SD3.5-Large DreamBooth LoRA - ariG23498/sukumar-ray-characters-sd3.5-lora

Model description

These are ariG23498/sukumar-ray-characters-sd3.5-lora DreamBooth LoRA weights for stabilityai/stable-diffusion-3.5-large.

The weights were trained using DreamBooth with the SD3 diffusers trainer .

Was LoRA for the text encoder enabled? False.

Trigger words

You should use SK style figure of a student holding a large, round object, wearing a plaid jacket, facing an angry teacher who is pointing at the student with a stick, plain environment with minimal details, harsh contrast lighting, scene evokes tension and discipline. to trigger the image generation.

Download model

Download the *.safetensors LoRA in the Files & versions tab.

Use it with the 🧨 diffusers library

from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(stabilityai/stable-diffusion-3.5-large, torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('ariG23498/sukumar-ray-characters-sd3.5-lora', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('SK style figure of a student holding a large, round object, wearing a plaid jacket, facing an angry teacher who is pointing at the student with a stick, plain environment with minimal details, harsh contrast lighting, scene evokes tension and discipline.').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 .

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]