test-can-4-2

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SD3 DreamBooth LoRA - rdeinla/test-can-4-2

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Model description

These are rdeinla/test-can-4-2 DreamBooth LoRA weights for stabilityai/stable-diffusion-3-medium-diffusers.

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

Was LoRA for the text encoder enabled? False.

Trigger words

You should use a photo of a canola plant in the early bolting stage. It is about 50 days old. It has a long stem and a few yellow buds at the top of the plant. 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-medium-diffusers, torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('rdeinla/test-can-4-2', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('A photo of a canola plant in the early bolting stages. The stem of the plant is long and tapers towards the top. The leaves are concentrated at the lower half of the plant, with a few yellow buds clustered at the top. It is about 50 days old. It is growing in dark, nutrient-rich soil. It is contained within a smooth, bright blue cylindrical cup on a bright blue background').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]