ZipQDoRA
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pixel-art-sdxl-dora-v0-9.safetensors
here 💾.models/Lora
folder.<lora:pixel-art-sdxl-dora-v0-9:1>
to your prompt. On ComfyUI just load it as a regular LoRA.pixel-art-sdxl-dora-v0-9_emb.safetensors
here 💾.embeddings
folderpixel-art-sdxl-dora-v0-9_emb
to your prompt. For example, in the style of pixel-art-sdxl-dora-v0-9_emb a Pixel Art style
(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('thliang01/pixel-art-sdxl-dora-v0-9', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='thliang01/pixel-art-sdxl-dora-v0-9', filename='pixel-art-sdxl-dora-v0-9_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> a Pixel Art style').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 SZN
→ 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.
Base model
stabilityai/stable-diffusion-xl-base-1.0