Instructions to use eramth/asian-flux-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use eramth/asian-flux-4bit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("eramth/asian-flux-4bit", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
A Flux model for asian portrait with 4bit transformer and T5 encoder.
Usage
pip install bitsandbytes
from diffusers import FluxPipeline
import torch
pipeline = FluxPipeline.from_pretrained("eramth/asian-flux-4bit",torch_dtype=torch.float16).to("cuda")
# This allows you to generate higher resolution images without much extra VRAM usage.
pipeline.vae.enable_tiling()
image = pipeline(prompt="a cute cat",num_inference_steps=25,guidance_scale=3.5).images[0]
image
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Model tree for eramth/asian-flux-4bit
Base model
black-forest-labs/FLUX.1-dev