README.md

metadata

tags:
  - stable-diffusion-xl
  - stable-diffusion-xl-diffusers
  - text-to-image
  - diffusers
  - lora
  - template:sd-lora
widget:
  - text: A photo of <s0><s1> a man wearing headphones and a blue shirt
    output:
      url: image-0.png
  - text: A photo of <s0><s1> a bald man wearing glasses and a white t - shirt
    output:
      url: image-1.png
  - text: A photo of <s0><s1> a man with glasses and a beard smiles
    output:
      url: image-2.png
  - text: A photo of <s0><s1> a bald man with glasses and a colorful shirt
    output:
      url: image-3.png
  - text: A photo of <s0><s1> a man with glasses and a hat wearing an orange cap
    output:
      url: image-4.png
  - text: A photo of <s0><s1> a man wearing glasses and a yellow hat taking a selfie
    output:
      url: image-5.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: A photo of <s0><s1>
license: openrail++
inference:
  parameters:
    num_inference_steps: 30
    scheduler: EulerAncestralDiscreteScheduler

SDXL LoRA DreamBooth - multimodalart/poli-100-multiplier-face

Prompt

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

Prompt

A photo of <s0><s1> a bald man wearing glasses and a white t - shirt

Prompt

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

Prompt

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

Prompt

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

Prompt

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

Model description

These are multimodalart/poli-100-multiplier-face LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.

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

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/poli-100-multiplier-face', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/poli-100-multiplier-face', filename="embeddings.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

Download model

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

• Download the LoRA *.safetensors here. Rename it and place it on your Lora folder.
• Download the text embeddings *.safetensors here. Rename it and place it on it on your embeddings folder.

All Files & versions.

Details

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.