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Sponsored by
APIMart
multimodalart / apolinario-face-final
Model's Last Updated: January 03 2024
text-to-image
Introduction of apolinario-face-final
Model Details of apolinario-face-final
SDXL LoRA DreamBooth - multimodalart/apolinario-face-final
- Prompt
- A photo of <s0><s1> man wearing headphones and a blue shirt
- Prompt
- A photo of <s0><s1> man with glasses and a beard smiles
- Prompt
- A photo of <s0><s1> bald man with glasses and a colorful shirt
- Prompt
- A photo of <s0><s1> man with glasses and a hat wearing an orange cap
- Prompt
- A photo of <s0><s1> man wearing glasses and a yellow hat taking a selfie
- Prompt
- A photo of <s0><s1> man wearing a yellow hat and glasses
Model description
These are multimodalart/apolinario-face-final 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
apolinario-face-final.safetensorshere 💾 .-
Place it on your
models/Lorafolder. -
On AUTOMATIC1111, load the LoRA by adding
<lora:apolinario-face-final:1>to your prompt. On ComfyUI just load it as a regular LoRA .
-
Place it on your
-
Embeddings
: download
apolinario-face-final_emb.safetensorshere 💾 .-
Place it on it on your
embeddingsfolder -
Use it by adding
apolinario-face-final_embto your prompt. For example,A photo of apolinario-face-final_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('multimodalart/apolinario-face-final', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/apolinario-face-final', filename='apolinario-face-final_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>').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.
Runs of multimodalart apolinario-face-final on huggingface.co
28
Total runs
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24-hour runs
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3-day runs
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30-day runs
More Information About apolinario-face-final huggingface.co Model
Url of apolinario-face-final
apolinario-face-final huggingface.co Url
Provider of apolinario-face-final huggingface.co
Other API from multimodalart
Updated:June 05 2026
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