README.md

metadata

license: other
library_name: diffusers
pipeline_tag: text-to-image
tags:
  - text-to-image
  - diffusion
  - flow-matching
  - linear-attention
  - boomer

Linarix-v2 Text-to-Image Diffusion Model

Usage

Requires diffusers >= 0.38.0 — earlier versions have a trust_remote_code RCE (advisory). For production, pin a commit hash with revision= so the remote code cannot change under you.

Install

pip install -U "diffusers>=0.38.0" transformers accelerate safetensors torchvision scipy
pip install "flash-linear-attention @ git+https://github.com/fla-org/flash-linear-attention.git@3c4c54ae7397d37130d7101edd0f4eb596af896d"

FLA is required, not optional:Image attention runs on PyTorch SDPA by design — flash-attn is not used and installing it changes nothing. The STORK scheduler is bundled with this repo no separate install.

Generate

import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    "Akrao9/Linarix-v2",
    custom_pipeline="pipeline_boomer",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
).to("cuda")

image = pipe("a lighthouse on a rocky cliff above crashing waves at golden hour")[0]
image.save("output.png")

Every sampler setting defaults to the value this model was tuned and showcased at (STORK4, 20 steps, cfg_scale=4.0, flow_shift=5.0), so passing nothing reproduces the grid above. Override only what you want to change:

image = pipe(
    "a quiet cobblestone street in an old European town, evening",
    seed=42,          # None (default) draws a fresh seed each call
    cfg_scale=3.0,    # lower = softer, less saturated; raise for prompt adherence
    steps=32,         # more steps buy little past the default
)[0]

The transformer weights come from this repo. The DC-AE VAE and Qwen/Qwen3.5-4B text encoder are fetched from their upstream repos on first use — run hf auth login first if this repo is gated for you.

Batched inference

Pass a list of prompts to generate a batch in one call:

images = pipe([
    "a lighthouse above crashing waves",
    "a red fox in fresh snow",
    "a steam locomotive on a stone bridge",
])
images[0].save("a.png")

Two things matter for throughput. VAE slicing is on by default, decoding one image at a time so batched-decode peak memory stays flat; toggle with pipe.disable_vae_slicing(), or add pipe.enable_vae_tiling() for large images on low VRAM (both work before the VAE is lazily loaded). And keep components resident on the GPU when benchmarking — the default offload_text_encoder=True moves the text encoder to CPU after each call, which is the right trade for VRAM but adds per-call transfer overhead that dominates small batches.

Samples

All 1024px, EMA weights, STORK4 / 20 steps, derivative_order=1, substeps=14, cfg_scale=4.0, cfg_rescale=0.5, cfg_interval=[0.1, 0.9], and flow_shift=5.0. Prompts (left→right, top→bottom):

1. a snow-covered mountain village at blue hour, warm windows glowing
2. a desert canyon at sunset, layered red rock walls
3. a wooden pier stretching into a misty lake at dawn
4. a stone castle on a green hilltop under drifting clouds
5. an elderly fisherman mending nets on a harbour wall
6. a ginger cat asleep on a sunlit windowsill
7. a vintage motorcycle parked on a rain-slicked city street at night
8. a field of sunflowers under a bright summer sky
9. a narrow canal in Venice with weathered facades, late afternoon
10. a steaming bowl of ramen on a dark wooden table
11. a great horned owl perched on a bare branch at dusk
12. a glass greenhouse full of ferns, soft diffused light