SDXL LoRA DreamBooth - Maartenvdvvv/selena-mar

Prompt

A <s0><s1> character a sexy black woman with big tits posing in a white lingerie

Prompt

A <s0><s1> character a woman with curly hair and a white top

Prompt

A <s0><s1> character a beautiful naked woman posing nude

Prompt

A <s0><s1> character a woman in a black bikini sitting on the beach

Prompt

A <s0><s1> character a woman in a bikini standing on the beach

Prompt

A <s0><s1> character a woman with big ass and big tits posing in bed

Prompt

A <s0><s1> character a woman in a white bra top and jeans standing in an elevator

Prompt

A <s0><s1> character a woman in a bikini laying in the water

Prompt

A <s0><s1> character a woman in a bikini sitting on a boat

Prompt

A <s0><s1> character a woman with long curly hair sitting in the back seat of a car

Model description

These are Maartenvdvvv/selena-mar 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 selena-mar.safetensors here 💾.
  • Place it on your models/Lora folder.
  • On AUTOMATIC1111, load the LoRA by adding <lora:selena-mar:1> to your prompt. On ComfyUI just load it as a regular LoRA.
• Embeddings: download selena-mar_emb.safetensors here 💾.
  • Place it on it on your embeddings folder
  • Use it by adding selena-mar_emb to your prompt. For example, A selena-mar_emb character (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('Maartenvdvvv/selena-mar', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='Maartenvdvvv/selena-mar', filename='selena-mar_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> character').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.