README.md

metadata

license: other
tags:
  - text-to-image
  - diffusion
  - linear-attention
  - gated-deltanet
  - pytorch
  - safetensors
language:
  - en
pipeline_tag: text-to-image
library_name: diffusers

Linarix-v1 — Text-to-Image

Linarix-v1 is a ~712M parameter text-to-image diffusion prototype model that generates 1024×1024px images from text prompts.

Sample Outputs

Generated at 1024×1024px, STORK-2, 32 steps, CFG 4.0. Prompts (left→right, top→bottom):

1. a misty pine forest at dawn, sunbeams through the trees
2. a calm lake reflecting autumn mountains, golden hour
3. a lavender field under a dramatic stormy sky
4. a lighthouse on a rocky cliff above crashing waves
5. a close-up portrait of a young woman, natural window light
6. an old craftsman working at a wooden workbench, warm lamplight
7. a red fox standing in fresh snow, looking at the camera
8. a pair of swans on a still pond at sunrise
9. a steam locomotive crossing a stone bridge
10. a quiet cobblestone street in an old European town, evening
11. a rustic bowl of strawberries on a linen cloth

---

Performance

Footprint is dominated by the text encoder; the denoiser itself is small (linear-attention backbone, near-flat with batch size). Approximate resident VRAM:

Component	VRAM
DiT weights (EMA, bf16)	~1.4 GB
Qwen3.5-2B text encoder	~4.6 GB
DC-AE VAE	~0.6 GB
All loaded (resident)	~7 GB

For best speed, install the fused linear-attention kernels (see below) — without them the GDN mixer falls back to a slower torch reference path (same outputs, lower throughput). Pre-encoding prompts and keeping the text encoder off the GPU drops resident VRAM to roughly the DiT + VAE (~2 GB).

---

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 can't change under you.

Install

pip install -U "diffusers>=0.38.0" transformers accelerate safetensors torchvision scipy
pip install git+https://github.com/fla-org/flash-linear-attention.git
pip install causal-conv1d   # optional, enables the fused GDN fast path

Generate

import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    "Akrao9/Linarix-v1",
    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")

Optional generation parameters:

image = pipe(
    "a quiet cobblestone street in an old European town, evening",
    steps=32,        # STORK-2 denoising steps
    cfg_scale=4.0,   # classifier-free guidance (4.0 recommended)
    cfg_rescale=0.5, # reduces over-saturation / dark crush at higher CFG
    seed=42,
)[0]

The transformer weights download from this repo. The DC-AE VAE and Qwen3.5-2B text encoder are fetched from their upstream HuggingFace repos on first use. Run hf auth login before first use.

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"], cfg_scale=4.0)
images[0].save("a.png"); images[1].save("b.png")

For throughput, two things matter:

• Install the fused kernels — without flash-linear-attention (and optionally flash-attn) the GDN mixer and the full-attention layers run slow, memory-heavy PyTorch fallbacks, so both speed and peak VRAM scale far worse than they should.
• VAE slicing is on by default (decodes one image at a time) so batched-decode memory stays flat. Toggle the standard diffusers methods if you need to: pipe.enable_vae_slicing() / pipe.disable_vae_slicing(), and pipe.enable_vae_tiling() for very large images on low VRAM. These apply even before the VAE is lazily loaded.
• Keep components resident (.to("cuda")) for benchmarking; CPU offload adds per-call module-shuffle overhead that dominates small batches.

Prompting tips

• Use composed, descriptive prompts (scene + subject). Bare one-word prompts can produce duplicated subjects; adding composition resolves it.
• CFG 4.0 is the sweet spot. Higher (5+) over-saturates and hardens fine detail; lower softens prompt adherence.
• For exact object counts (e.g. "a pair"), do a quick 4–6 seed sweep and pick the one that lands the count — count is set early by the seed, not by guidance.
• For faces, prefer plain descriptors; words like "freckles" can render as skin blemishes. Three-quarter / profile poses are cleaner than direct frontal close-ups.

---

Capabilities and limitations

Strong:

• Landscapes, natural environments, atmospheric and scenic scenes
• Rigid man-made structures — lighthouses, locomotives, stone bridges, architecture render coherently
• Portraits with composed prompts — clean faces and skin
• Still life and subject-in-scene composition

Works, with care:

• Frontal close-up faces — good, but eyes/iris are the remaining frontier; keep CFG ≤ 4 and avoid blemish-trigger words
• Exact object counts — "a pair" may render three; resolve with a seed sweep
• Dense, multi-attribute prompts — spatially-localized attributes render well; occasional minor blemishes on small repeated elements

Less reliable:

• Very fine detail (iris, small faces), legible text in images
• Precise counting of repeated subjects

Other notes:

• Not safety filtered — outputs may reflect biases in the training data
• Maximum tested resolution: 1024×1024px

---

Acknowledgements

• GatedDeltaNet-2 — linear-attention backbone, via flash-linear-attention
• STORK-2 — inference sampling (Tan et al., 2025)
• DC-AE — latent autoencoder (mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers)
• Qwen3.5-2B — text encoder (Qwen/Qwen3.5-2B)
• Flow-matching with logit-normal timestep sampling and flow shift 3.0 (SANA-style)