sd_3_5m_dreambooth_lora_ft2

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SD3 DreamBooth LoRA - bhomik7/sd_3_5m_dreambooth_lora_ft2

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Model description

These are bhomik7/sd_3_5m_dreambooth_lora_ft2 DreamBooth LoRA weights for stabilityai/stable-diffusion-3.5-medium.

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

Was LoRA for the text encoder enabled? True.

Trigger words

You should use A beautiful <TOK> UI for music app 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-medium, torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('bhomik7/sd_3_5m_dreambooth_lora_ft2', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('Create a modern user interface design for a mobile application that focuses on fitness tracking. The layout should include a vibrant dashboard displaying key metrics such as steps taken, calories burned, and workout summaries. Incorporate interactive elements like buttons for starting workouts, viewing progress, and accessing nutrition information. Use a color palette that conveys energy and motivation, with clear typography and intuitive navigation.').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]