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SDXL LoRA DreamBooth - Bloof/unsettling-image

Prompt
a painting of a monster with sharp teeth in the style of <s0><s1>
Prompt
a ghostly image of a unsettling creature walking down a wooden walkway in the style of <s0><s1>
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a red hand is hanging from the ceiling of a stairwell in the style of <s0><s1>
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a creepy face with big eyes and a black background in the style of <s0><s1>
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creepy black and white image of face in the style of <s0><s1>
Prompt
loab in the style of <s0><s1>
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flesh monster with a face in the style of <s0><s1>
Prompt
the alien is shown in a black and white photo in the style of <s0><s1>
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a dog, hand is reaching out in the background in the style of <s0><s1>
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uncanny white featureless face in the style of <s0><s1>
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two people standing in front of a tree with glowing eyes in the style of <s0><s1>
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a skull with a red face and teeth in the style of <s0><s1>
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a creepy looking man in a dark room in the style of <s0><s1>
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the man in the mask is shown in the dark in the style of <s0><s1>
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a creepy man in a black hat is standing in the dark in the style of <s0><s1>
Prompt
a black and white photo of an uncanny creature with eyes in the style of <s0><s1>
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loab in the style of <s0><s1>
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a creepy face with long hair and a long face in the style of <s0><s1>
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a painting of people in a dark room in the style of <s0><s1>
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a creepy face with eyes and a smile in the style of <s0><s1>
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a red and black mask with a large head in the style of <s0><s1>

Model description

These are Bloof/unsettling-image LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.

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

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('Bloof/unsettling-image', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='Bloof/unsettling-image', filename='unsettling-image_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.

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.

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Examples
This model can be loaded on Inference API (serverless).

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