SDXL LoRA DreamBooth - dataautogpt3/nightmarerunning

Prompt

a man in a black suit is running through a hallway in the style of <s0><s1>

Prompt

a skeleton is running through a hallway in the style of <s0><s1>

Prompt

a man in a dark room with a light coming from the ceiling in the style of <s0><s1>

Prompt

a creepy skeleton running through an empty hallway in the style of <s0><s1>

Prompt

a zombie is walking through a hallway in the style of <s0><s1>

Prompt

a man in a dark room with a zombie running in the style of <s0><s1>

Model description

These are dataautogpt3/nightmarerunning 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 nightmarerunning.safetensors here 💾.
  • Place it on your models/Lora folder.
  • On AUTOMATIC1111, load the LoRA by adding <lora:nightmarerunning:1> to your prompt. On ComfyUI just load it as a regular LoRA.
• Embeddings: download nightmarerunning_emb.safetensors here 💾.
  • Place it on it on your embeddings folder
  • Use it by adding nightmarerunning_emb to your prompt. For example, in the style of nightmarerunning_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('dataautogpt3/nightmarerunning', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='dataautogpt3/nightmarerunning', filename='nightmarerunning_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('in the style 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.

LoRA for the text encoder was enabled. False.

Pivotal tuning was enabled: True.

Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.