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multimodalart/
poliphoto-repeats-4-steps-500-rank64-no-prior

Name: poliphoto-repeats-4-steps-500-rank64-no-prior
Availability: InStock

diffusers

tensorboard

stable-diffusion-xl

stable-diffusion-xl-diffusers

text-to-image

lora

sd-lora

stabilityai/stable-diffusion-xl-base-1.0

adapter

openrail++

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SDXL LoRA DreamBooth - multimodalart/poliphoto-repeats-4-steps-500-rank64-no-prior

Model description

These are multimodalart/poliphoto-repeats-4-steps-500-rank64-no-prior LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.

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

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('multimodalart/poliphoto-repeats-4-steps-500-rank64-no-prior', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/poliphoto-repeats-4-steps-500-rank64-no-prior', filename="embeddings.safetensors", repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
        
image = pipeline('A photo of <s0><s1>').images[0]

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers

Download model

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

Download the LoRA *.safetensors here. Rename it and place it on your Lora folder.
Download the text embeddings *.safetensors here. Rename it and place it on it on your embeddings folder.

All Files & versions.

Details

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.