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WaveCut/Image21-Turbo

sourceHugging Faceotherupdated 3h agoView on Hugging Face

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Model Card

Image21 Turbo

Qwen-Image-2.1 with the Viggle turbo v0.2.1 distillation LoRA merged into the transformer and the Texture-Fix VAE in place of the stock decoder. Text-to-image and editing with reference images in 6 steps without guidance, loaded by the stock QwenImage21Pipeline. Built with Qwen.

A 4-bit build with a quantized text encoder is published as WaveCut/Image21-Turbo-OrbitQuant-W4A4.

[image]

Component	Contents	Size
`transformer/`	Qwen-Image-2.1 DiT, Viggle turbo v0.2.1 (rank 256) merged in fp32 and stored in fp16	14.2 GB
`text_encoder/`	Qwen3-VL-8B, unchanged	17.5 GB
`vae/`	Texture-Fix VAE, fp32	1.35 GB
`scheduler/`	Viggle turbo config: dynamic shift, `shift_terminal` null

Run

bash

pip install -U torch "transformers>=5.17,<6" accelerate safetensors pillow
pip install "git+https://github.com/huggingface/diffusers.git@80c7ed262aeffbeb43ef13ae04baeb9b84515a69"

python

import torch
from diffusers import QwenImage21Pipeline

pipe = QwenImage21Pipeline.from_pretrained("WaveCut/Image21-Turbo", dtype=torch.float16)
pipe.enable_model_cpu_offload()

SIGMAS = [1.0, 0.9375, 0.875, 0.75, 0.5, 0.25]
image = pipe(
    prompt="A studio portrait of an old fisherman mending a net, warm rim light, 85mm",
    width=1024, height=1024,
    num_inference_steps=6, sigmas=SIGMAS,
    generator=torch.Generator("cuda").manual_seed(0),
).images[0]

edited = pipe(
    prompt="Replace the background of <image1> with a sunset beach; keep the man unchanged.",
    image=[image], output_resolution=1024,
    num_inference_steps=6, sigmas=SIGMAS,
    generator=torch.Generator("cuda").manual_seed(0),
).images[0]

Load the pipeline in fp16. The merge keeps 99.7 % of the LoRA update in fp16; loading the same weights in bf16 rounds away about a third of it (64 % kept). The text encoder and the VAE work in fp16 as well: text embeddings match bf16 at cosine 0.998–0.9997, VAE decodes match fp32 at 58–64 dB PSNR.

All weights together are 33 GB, so a 32 GB card needs enable_model_cpu_offload() (≈22 s per 1024² image on an RTX 5090, most of it host transfers). Encoding a batch of prompts first and then running the transformer and the VAE takes 2.69 s per 1024² image hot (6 steps, peak 22.5 GB) and 17.9 s at 2048². The OrbitQuant build keeps every component resident in 13.6 GB and needs 2.25 s.

Keep the VAE untiled in fp16. Tiled decoding in fp16 produces non-finite pixels; for 2048² text-to-image cast it first: pipe.vae.to(torch.bfloat16); pipe.vae.enable_tiling() (54 dB PSNR against fp32).

Sampling recipes

Tested at 1024² on 8 prompts (photo, poster, anime, product, night scene, two Russian-text prompts) with fixed seeds, plus 4 edits. Times are transformer + VAE on an RTX 5090.

[image]

Columns: default, 8 steps, 4 steps, static shift 3, CFG 2.

Recipe	Settings	Result	Time
Default	6 steps, `sigmas=[1, 0.9375, 0.875, 0.75, 0.5, 0.25]`, shipped scheduler, no CFG	Sharpest detail and textures; Latin and Cyrillic text mostly right	2.9 s
Text, posters, natural skin	the same 6 sigmas with a static shift of 3: `pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(pipe.scheduler.config, use_dynamic_shifting=False, shift=3.0)`	Cleanest lettering in the set (long Russian paragraph almost error-free), smoother skin, calmer micro-texture	2.9 s
Dense text	8 steps, `sigmas=[1, 0.9375, 0.875, 0.75, 0.625, 0.5, 0.25, 0.125]`	Same composition as the default, small print slightly cleaner	3.8 s
Guidance	6 steps, `true_cfg_scale=2` with a negative prompt	Higher contrast and saturation, bolder type	5.8 s
Avoid	4 steps `[1, 0.75, 0.5, 0.25]`	Ghosted double contours, garbled text	2.0 s
Avoid	static shift 5	Soft, smeared detail

Editing: reference images are <image1>, <image2>, … in the prompt, in the order passed; the canvas follows the last reference unless width/height are given; references are encoded at output_resolution² area.

Merge

	Value
LoRA	Viggle turbo v0.2.1, rank 256, alpha 256, 227 projections (attention, image MLP, modulation, timestep embedder)
Factors	F32 from `peft_v0.2.1/`
Update size	median 0.11 % of the weight norm, max 1.6 %
Kept after rounding	fp16 99.7 % (rounding noise 18.7 % of the update); bf16 would keep 64 %
Output vs runtime LoRA	0.4–2.8 % latent difference at the same precision and seed

Everything outside the 227 projections is the upstream bf16 value stored in fp16.

Files

transformer/, text_encoder/, processor/, vae/, scheduler/, model_index.json — the diffusers pipeline; media/ — example images; LICENSE, NOTICE — license and attribution.

License

Derivative of Qwen-Image-2.1 under the Qwen RESEARCH LICENSE AGREEMENT (LICENSE): non-commercial research and evaluation only. NOTICE lists the modified files and the upstream notices of Viggle and madebyollin.

Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) 2026 Hangzhou Tongyi Laboratory Technology Co., Ltd. All Rights Reserved.