SDXL LoRA DreamBooth - multimodalart/apolinario-face-final

Prompt

A photo of <s0><s1> man wearing headphones and a blue shirt

Prompt

A photo of <s0><s1> man with glasses and a beard smiles

Prompt

A photo of <s0><s1> bald man with glasses and a colorful shirt

Prompt

A photo of <s0><s1> man with glasses and a hat wearing an orange cap

Prompt

A photo of <s0><s1> man wearing glasses and a yellow hat taking a selfie

Prompt

A photo of <s0><s1> man wearing a yellow hat and glasses

Model description

These are multimodalart/apolinario-face-final 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 apolinario-face-final.safetensors here 💾.
  • Place it on your models/Lora folder.
  • On AUTOMATIC1111, load the LoRA by adding <lora:apolinario-face-final:1> to your prompt. On ComfyUI just load it as a regular LoRA.
• Embeddings: download apolinario-face-final_emb.safetensors here 💾.
  • Place it on it on your embeddings folder
  • Use it by adding apolinario-face-final_emb to your prompt. For example, A photo of apolinario-face-final_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('multimodalart/apolinario-face-final', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/apolinario-face-final', filename='apolinario-face-final_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.