Downloads · 30 days
516
13% of all-time downloads
MaxNoichl/engraving-sdxl-lora-001
engraving-sdxl-lora-001 is a text-to-image model from MaxNoichl. Use it when you need an image from a text prompt. It is set up for diffusers. The card lists the license as openrail++.
Downloads · 30 days
516
13% of all-time downloads
All-time downloads
4K
Public
Repo size
23.7 GB
Likes
3
Public
Click a slice to open those files.
.bin21.3 GB · 90%
From the Hugging Face model README
engraving-sdxl-lora-001.safetensors here 💾.
models/Lora folder.<lora:engraving-sdxl-lora-001:1> to your prompt. On ComfyUI just load it as a regular LoRA.engraving-sdxl-lora-001_emb.safetensors here 💾.
embeddings folderengraving-sdxl-lora-001_emb to your prompt. For example, engraving in the style of engraving-sdxl-lora-001_emb, showing a skull and a technical instrument
(you need both the LoRA and the embeddings as they were trained together for this LoRA)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('m7n/engraving-sdxl-lora-001', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='m7n/engraving-sdxl-lora-001', filename='engraving-sdxl-lora-001_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('engraving in the style of <s0><s1>, showing a skull and a technical instrument').images[0]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
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
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.