AfriVision-Base

AfriVision-Base is a standalone text-to-image model fine-tuned from black-forest-labs/FLUX.2-klein-base-4B on AfriVision-30K, a curated dataset of African and Nigerian cultural imagery.

Developed by Axiveri AI Research.

Model Details

Usage

from diffusers import Flux2KleinPipeline
import torch

pipe = Flux2KleinPipeline.from_pretrained(
    "Axiveri/AfriVision-Base",
    torch_dtype=torch.bfloat16,
).to("cuda")

image = pipe(
    "AFRVS a Yoruba bride in traditional iro and buba at a Lagos wedding",
    num_inference_steps=28,
    guidance_scale=3.5,
).images[0]

image.save("afrivision_output.png")

Trigger Word

Include AFRVS at the start of your prompt to activate the African cultural generation style. Every training caption was prefixed with AFRVS, so the model reliably associates that token with the learned style; omitting it produces closer-to-base-model behavior.

Benchmark

10 prompts spanning Yoruba, Hausa, Igbo, Pidgin, and general Nigerian contexts, each rendered three ways: base model (no LoRA), AfriVision-Base without the trigger, and AfriVision-Base with AFRVS. Same seed and sampler settings across all three for a direct comparison.

Full manifest (prompts, seeds, sampler settings, image paths): benchmark/benchmark_manifest.json

Citation

@misc{afrivision2026,
  author = {Emmanuel Ariyo},
  title = {AfriVision-Base: Nigerian Cultural Image Generation},
  year = {2026},
  publisher = {Axiveri AI Research},
  url = {https://huggingface.co/Axiveri/AfriVision-Base}
}