SDXL LoRA DreamBooth - cookey39/teratera

Model description

Generate Examples

https://www.pixiv.net/artworks/119150548

https://www.pixiv.net/artworks/119243202

https://www.pixiv.net/artworks/119243522

These are cookey39/teratera LoRA adaption weights for cookey39/aam_xl.

Download model

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

• LoRA: download teratera.safetensors here 💾.
  • Place it on your models/Lora folder.
  • On AUTOMATIC1111, load the LoRA by adding <lora:teratera:1> to your prompt. On ComfyUI just load it as a regular LoRA.
• Embeddings: download teratera_emb.safetensors here 💾.
  • Place it on it on your embeddings folder
  • Use it by adding teratera_emb to your prompt. For example, In the style of Terada, (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('cookey39/teratera', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='cookey39/teratera', filename='teratera_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
# load embeddings of text_encoder 1 (CLIP ViT-L/14)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
# load embeddings of text_encoder 2 (CLIP ViT-G/14)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
        
instance_token = "<s0><s1>"
prompt = f"a {instance_token}masterpiece, best quality, full-length phoor portrait，Vibrant, solo, 1girl, smile, long hair, hair between eyes, multicolored eyes, hooded jacket, open jacket, shirt, long sleeves, ribbon, best quality, perfect anatomy, highres, absurdres{instance_token}  "
negative_prompt = "nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, bad feet"
image = pipeline(prompt=prompt, negative_prompt = negative_prompt, num_inference_steps=100, cross_attention_kwargs={"scale": 1.0},width = 960, height=1280).images[0]
image.save("./save.png")

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: None.