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habulaj/
1712202390407x231756570084896930

Name: 1712202390407x231756570084896930
Availability: InStock

diffusers

stable-diffusion-xl

stable-diffusion-xl-diffusers

text-to-image

lora

jamine23/getone

stabilityai/stable-diffusion-xl-base-1.0

adapter

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LoRA DreamBooth - squaadinc/1712202390407x231756570084896930

These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0 trained on @fffiloni's SD-XL trainer. The weights were trained on the concept prompt:

A photo of TOK

Use this keyword to trigger your custom model in your prompts. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

Usage

Make sure to upgrade diffusers to >= 0.19.0:

pip install diffusers --upgrade

In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark:

pip install invisible_watermark transformers accelerate safetensors

To just use the base model, you can run:

python

import torch
from diffusers import DiffusionPipeline, AutoencoderKL
device = "cuda" if torch.cuda.is_available() else "cpu"
vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16)
pipe = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    vae=vae, torch_dtype=torch.float16, variant="fp16",
    use_safetensors=True
)
pipe.to(device)
# This is where you load your trained weights
specific_safetensors = "pytorch_lora_weights.safetensors"
lora_scale = 0.9
pipe.load_lora_weights(
    'squaadinc/1712202390407x231756570084896930', 
    weight_name = specific_safetensors,
    # use_auth_token = True 
)
prompt = "A majestic A photo of TOK jumping from a big stone at night"
image = pipe(
    prompt=prompt, 
    num_inference_steps=50,
    cross_attention_kwargs={"scale": lora_scale}
).images[0]

habulaj/1712202390407x231756570084896930

LoRA DreamBooth - squaadinc/1712202390407x231756570084896930

Usage

habulaj/
1712202390407x231756570084896930