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SDXL_LoRA_faucet

Text-to-Image
Diffusers
stable-diffusion-xl
stable-diffusion-xl-diffusers
lora
template:sd-lora
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SDXL_LoRA_faucet / README.md
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metadata
tags:
  - stable-diffusion-xl
  - stable-diffusion-xl-diffusers
  - text-to-image
  - diffusers
  - lora
  - template:sd-lora
widget:
  - text: photo of a <s0><s1> kitchen faucet
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of a <s0><s1> kitchen faucet
license: openrail++

SDXL LoRA DreamBooth - DavideTHU/SDXL_LoRA_faucet

Model description

These are DavideTHU/SDXL_LoRA_faucet 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 SDXL_LoRA_faucet.safetensors here 💾.
    • Place it on your models/Lora folder.
    • On AUTOMATIC1111, load the LoRA by adding <lora:SDXL_LoRA_faucet:1> to your prompt. On ComfyUI just load it as a regular LoRA.
  • Embeddings: download SDXL_LoRA_faucet_emb.safetensors here 💾.
    • Place it on it on your embeddings folder
    • Use it by adding SDXL_LoRA_faucet_emb to your prompt. For example, photo of a SDXL_LoRA_faucet_emb kitchen faucet (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('DavideTHU/SDXL_LoRA_faucet', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='DavideTHU/SDXL_LoRA_faucet', filename='SDXL_LoRA_faucet_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('photo of a <s0><s1> kitchen faucet').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.