Instructions to use SkyAsl/Portrait-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SkyAsl/Portrait-generator 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("SkyAsl/Portrait-generator") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
metadata
license: apache-2.0
datasets:
- Rapidata/Face_Generation_Benchmark
language:
- en
base_model:
- stabilityai/stable-diffusion-xl-base-1.0
pipeline_tag: text-to-image
library_name: diffusers
tags:
- text-to-image
- lora
- diffusers
Model description
Portrait-generator
Portrait-generator is a LoRA fine-tuned adapter for the stable-diffusion-xl-base-1.0 model, trained on the Rapidata/Face_Generation_Benchmark dataset. It specializes in generating face images.
Usage
- Install dependencies (if not installed):
pip install diffusers transformers accelerate safetensor torch
- Load stable-diffusion-xl-base-1.0 and LoRA adapter:
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
).to("cuda")
pipe.load_lora_weights("SkyAsl/Portrait-generator")
pipe.fuse_lora()
image = pipe("a high-quality portrait of a woman").images[0]
image.save("test.png")
Metrics
- Average CLIPScore: 16.6980