metadata

tags:
  - stable-diffusion-xl
  - stable-diffusion-xl-diffusers
  - text-to-image
  - diffusers
  - lora
  - template:sd-lora
widget:
  - text: a <s0><s1> woman with pink hair at a party
    output:
      url: image_0.png
  - text: a <s0><s1> woman with pink hair at a party
    output:
      url: image_1.png
  - text: a <s0><s1> woman with pink hair at a party
    output:
      url: image_2.png
  - text: a <s0><s1> woman with pink hair at a party
    output:
      url: image_3.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of a <s0><s1> woman
license: openrail++

SDXL LoRA DreamBooth - linoyts/linoy_lora_pivotal

Prompt
a <s0><s1> woman with pink hair at a party

Model description

These are linoyts/linoy_lora_pivotal LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.

Download model

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

LoRA: download linoy_lora_pivotal.safetensors here 💾.
- Place it on your models/Lora folder.
- On AUTOMATIC1111, load the LoRA by adding <lora:linoy_lora_pivotal:1> to your prompt. On ComfyUI just load it as a regular LoRA.
Embeddings: download linoy_lora_pivotal_emb.safetensors here 💾.
- Place it on it on your embeddings folder
- Use it by adding linoy_lora_pivotal_emb to your prompt. For example, photo of a linoy_lora_pivotal_emb woman (you need both the LoRA and the embeddings as they were trained together for this LoRA)

Use it with the 🧨 diffusers library

from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
        
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('linoyts/linoy_lora_pivotal', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='linoyts/linoy_lora_pivotal', filename='linoy_lora_pivotal_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
        
image = pipeline('a <s0><s1> woman with pink hair at a party').images[0]

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

Trigger words

To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:

to trigger concept TOK → use <s0><s1> in your prompt

Details

All Files & versions.

The weights were trained using 🧨 diffusers Advanced Dreambooth Training Script.

LoRA for the text encoder was enabled. False.

Pivotal tuning was enabled: True.

Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.