SDXL LoRA DreamBooth - lfischbe/m3d3quip

Prompt
A product photo of <s0><s1> a metal clamp with a hook attached to it

Prompt
A product photo of <s0><s1> a metal frame with a camera attached to it

Prompt
A product photo of <s0><s1> a pair of metal bars with two handles

Prompt
A product photo of <s0><s1> a green cover is on a bed in a hospital

Prompt
A product photo of <s0><s1> two different images of a camera tripod and a camera

Prompt
A product photo of <s0><s1> a mannequin with a camera attached to it

Prompt
A product photo of <s0><s1> a mannequin with a microphone attached to it

Prompt
A product photo of <s0><s1> a metal pipe clamp with a handle

Prompt
A product photo of <s0><s1> a white metal tripod with two arms

Prompt
A product photo of <s0><s1> a metal pipe with a hose attached to it

Prompt
A product photo of <s0><s1> a woman sitting on a chair with a medical device

Model description

These are lfischbe/m3d3quip 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 m3d3quip.safetensors here 💾.
- Place it on your models/Lora folder.
- On AUTOMATIC1111, load the LoRA by adding <lora:m3d3quip:1> to your prompt. On ComfyUI just load it as a regular LoRA.
Embeddings: download m3d3quip_emb.safetensors here 💾.
- Place it on it on your embeddings folder
- Use it by adding m3d3quip_emb to your prompt. For example, A product photo of m3d3quip_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('lfischbe/m3d3quip', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='lfischbe/m3d3quip', filename='m3d3quip_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('A product photo 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.

lfischbe
/

m3d3quip