Instructions to use ajaynegi0079/serene-sdxl-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ajaynegi0079/serene-sdxl-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ajaynegi0079/serene-sdxl-lora") prompt = "ohwx_serene, a portrait photograph of a young woman in soft studio light, magazine cover quality" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Serene — SDXL LoRA
A custom SDXL LoRA that renders a consistent brand-ambassador likeness named Serene. Trained on a small curated dataset for use as a recurring face across social-media campaigns.
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Trigger token
ohwx_serene
Include the trigger anywhere in your prompt. Best results when the trigger appears at the start of the prompt and the prompt clearly describes a single woman as the subject of the frame.
Quick example
ohwx_serene, a portrait photograph of a young woman, soft natural light,
neutral background, looking at the camera, magazine-cover quality,
professional studio lighting on her face.
Use with 🤗 Diffusers
from diffusers import StableDiffusionXLPipeline
import torch
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
).to("cuda")
pipe.load_lora_weights(
"ajaynegi0079/serene-sdxl-lora",
weight_name="pytorch_lora_weights.safetensors",
)
image = pipe(
"ohwx_serene, portrait, soft studio light, magazine cover quality",
num_inference_steps=30,
guidance_scale=7.0,
).images[0]
image.save("serene.png")
Use on Replicate
The repo also ships a Replicate-compatible bundle (serene_lora_v2.tar) for use with cog-style SDXL+LoRA predictors such as fofr/sdxl-multi-controlnet-lora:
{
"prompt": "ohwx_serene, ...",
"lora_weights": "https://huggingface.co/ajaynegi0079/serene-sdxl-lora/resolve/main/serene_lora_v2.tar",
"lora_scale": 0.95,
"num_inference_steps": 30,
"scheduler": "K_EULER"
}
Recommended settings
| Setting | Value |
|---|---|
lora_scale |
0.85 – 0.95 (0.95 dominates product context, 0.85 lets the scene breathe) |
| Inference steps | 30 |
| Guidance scale | 7.0 |
| Negative prompt | blurry, deformed, extra fingers, bad anatomy, cartoon, plastic skin, watermark, low quality, distorted face |
Training
| Detail | Value |
|---|---|
| Base model | stabilityai/stable-diffusion-xl-base-1.0 |
| Method | LoRA (rank 8) via 🤗 PEFT |
| Resolution | 1024 × 1024 |
| Steps | 1000 |
| Final loss | ~0.017 |
| Hardware | NVIDIA T4 (Google Colab) |
Files in this repo
pytorch_lora_weights.safetensors— raw diffusers/PEFT LoRA, for directpipe.load_lora_weights()useserene_lora_v2.tar— Replicate cog-sdxl bundle (lora.safetensorswith cog-style key names + emptyembeddings.ptistub)preview/serene-1.png…preview/serene-6.png— sample images from the training dataset
License
CC BY-NC 4.0 — non-commercial use only. For commercial / brand-ambassador licensing, contact the repo owner.
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Model tree for ajaynegi0079/serene-sdxl-lora
Base model
stabilityai/stable-diffusion-xl-base-1.0




