metadata

tags:
  - stable-diffusion-xl
  - stable-diffusion-xl-diffusers
  - text-to-image
  - diffusers
  - lora
  - template:sd-lora
widget:
  - text: <s0><s1> ad of a llama wearing headphones
    output:
      url: <s0><s1>ad of a llama wearing headphones.jpeg
  - text: <s0><s1> ad for banana flavored toothpaste
    output:
      url: <s0><s1> ad for banana flavored toothpaste.jpeg
  - text: <s0><s1> ad of the Mona Lisa eating ramen
    output:
      url: <s0><s1> ad of the Mona Lisa eating ramen.jpeg
  - text: <s0><s1> ad for speakers
    output:
      url: <s0><s1> ad for speakers.jpeg
  - text: <s0><s1> ad of Santa clause eating cereal
    output:
      url: <s0><s1> ad of Santa clause eating cereal.jpeg
  - text: <s0><s1> ad of the an astronaut on a horse
    output:
      url: <s0><s1> ad of the an astronaut on a horse.jpeg
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: an ad in the style of <s0><s1>
license: openrail++

SDXL LoRA DreamBooth - LinoyTsaban/2000_ads

Prompt
<s0><s1> ad of a llama wearing headphones

Prompt
<s0><s1> ad for banana flavored toothpaste

Prompt
<s0><s1> ad of the Mona Lisa eating ramen

Prompt
<s0><s1> ad of Santa clause eating cereal

Prompt
<s0><s1> ad of the an astronaut on a horse

Model description

These are LinoyTsaban/2000_ads 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 2000_ads.safetensors here 💾.
- Place it on your models/Lora folder.
- On AUTOMATIC1111, load the LoRA by adding <lora:2000_ads:1> to your prompt. On ComfyUI just load it as a regular LoRA.
Embeddings: download 2000_ads_emb.safetensors here 💾.
- Place it on it on your embeddings folder
- Use it by adding 2000_ads_emb to your prompt. For example, an ad in the style of 2000_ads_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('LinoyTsaban/2000_ads', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='LinoyTsaban/2000_ads', filename='2000_ads_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('<s0><s1> ad of a llama wearing headphones').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.