babyface_v7 / README.md
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metadata
base_model: stabilityai/stable-diffusion-3.5-large
library_name: diffusers
license: other
instance_prompt: >-
  photo of a korean hcwf baby wearing a plain cream beanie, wrapped in a plain
  cream swaddle, eyes closed, soft lighting, shallow depth of field, 50mm prime
  lens, pastel background
widget: []
tags:
  - text-to-image
  - diffusers-training
  - diffusers
  - lora
  - template:sd-lora
  - sd3.5-large
  - sd3.5
  - sd3.5-diffusers

SD3.5-Large DreamBooth LoRA - cwhuh/babyface_v7

Model description

These are cwhuh/babyface_v7 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 photo of a korean hcwf baby wearing a plain cream beanie, wrapped in a plain cream swaddle, eyes closed, soft lighting, shallow depth of field, 50mm prime lens, pastel background 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('cwhuh/babyface_v7', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('photo of a korean hcwf baby wearing a plain cream beanie, wrapped in a plain cream swaddle, eyes closed, soft lighting, shallow depth of field, 50mm prime lens, pastel background').images[0]

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

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]