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SDXL LoRA DreamBooth - saddad/criley

Prompt
A photo of <s0><s1> a man holding up a passport and an eagle
Prompt
A photo of <s0><s1> a man with a beard and a black shirt
Prompt
A photo of <s0><s1> a man taking a selfie in front of a colorful building
Prompt
A photo of <s0><s1> a man in a blue scrub suit and hat
Prompt
A photo of <s0><s1> a man holding up a piece of wood with a note on it
Prompt
A photo of <s0><s1> a man with a beard and a black shirt

Model description

These are saddad/criley LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.

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

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('saddad/criley', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='saddad/criley', filename='criley_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 photo of <s0><s1>').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.

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