SDXL LoRA DreamBooth - cst7/cat_sdxl_lora_350_rank_8_object_ti_1

Prompt

a <s0><s1> floating in the sea

Prompt

a <s0><s1> floating in the sea

Prompt

a <s0><s1> floating in the sea

Prompt

a <s0><s1> floating in the sea

Model description

These are cst7/cat_sdxl_lora_350_rank_8_object_ti_1 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 output/rinons_cat_toy/object/cat_sdxl_lora_350_rank_8_object_ti_1.safetensors here 💾.
  • Place it on your models/Lora folder.
  • On AUTOMATIC1111, load the LoRA by adding <lora:output/rinons_cat_toy/object/cat_sdxl_lora_350_rank_8_object_ti_1:1> to your prompt. On ComfyUI just load it as a regular LoRA.
• Embeddings: download output/rinons_cat_toy/object/cat_sdxl_lora_350_rank_8_object_ti_1_emb.safetensors here 💾.
  • Place it on it on your embeddings folder
  • Use it by adding output/rinons_cat_toy/object/cat_sdxl_lora_350_rank_8_object_ti_1_emb to your prompt. For example, a photo of output/rinons_cat_toy/object/cat_sdxl_lora_350_rank_8_object_ti_1_emb (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('cst7/cat_sdxl_lora_350_rank_8_object_ti_1', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='cst7/cat_sdxl_lora_350_rank_8_object_ti_1', filename='output/rinons_cat_toy/object/cat_sdxl_lora_350_rank_8_object_ti_1_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> floating in the sea').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.