SDXL LoRA DreamBooth - GazTrab/hui
![](https://huggingface.co/GazTrab/hui/resolve/main/image_0.png)
- Prompt
- a photo of <s0><s1>, Asian guy, outdoor setting, soft light, full body, focused subject, blurry background, film colors
![](https://huggingface.co/GazTrab/hui/resolve/main/image_1.png)
- Prompt
- a photo of <s0><s1>, Asian guy, outdoor setting, soft light, full body, focused subject, blurry background, film colors
![](https://huggingface.co/GazTrab/hui/resolve/main/image_2.png)
- Prompt
- a photo of <s0><s1>, Asian guy, outdoor setting, soft light, full body, focused subject, blurry background, film colors
![](https://huggingface.co/GazTrab/hui/resolve/main/image_3.png)
- Prompt
- a photo of <s0><s1>, Asian guy, outdoor setting, soft light, full body, focused subject, blurry background, film colors
Model description
These are GazTrab/hui 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
hui.safetensors
here 💾.- Place it on your
models/Lora
folder. - On AUTOMATIC1111, load the LoRA by adding
<lora:hui:1>
to your prompt. On ComfyUI just load it as a regular LoRA.
- Place it on your
- Embeddings: download
hui_emb.safetensors
here 💾.- Place it on it on your
embeddings
folder - Use it by adding
hui_emb
to your prompt. For example,a photo of hui_emb
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
- Place it on it on your
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('GazTrab/hui', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='GazTrab/hui', filename='hui_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 photo of <s0><s1>, Asian guy, outdoor setting, soft light, full body, focused subject, blurry background, film colors').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.
- Downloads last month
- 9
Model tree for GazTrab/hui
Base model
stabilityai/stable-diffusion-xl-base-1.0
Adapter
this model