Viggle Shrinks Qwen-Image-2.1 Generation From 40 Steps to Five
Viggle's v0.2 LoRA compresses Qwen-Image-2.1 from 40 diffusion steps to 5, keeping sample diversity at 93% of the teacher.
- Viggle released v0.2 of its turbo LoRA for Qwen-Image-2.1, cutting sampling from 40 to 5 steps.
- Rank-256 adapter, 1.3 GB bf16, loaded at runtime with no CFG and no negative prompt.
- Diversity hits 93% of the base model, composition drift measured at zero on 96 held-out prompts.
- Handles both text-to-image and instruction editing with up to 3 reference images.
- Distilled via DMD2 with SenseFlow-style intra-segment guidance and prompt-enhanced teacher targets.
- Preview status; struggles with multi-reference edits, face swaps, and small rendered text. Qwen Research license, non-commercial.
Viggle cuts Qwen-Image-2.1 sampling to five passes
Viggle has released v0.2 of its turbo LoRA for Qwen-Image-2.1, reducing the base model’s 40-step sampling process to five transformer passes. Viggle reports 93% of the base model’s sample diversity and no average composition drift on its internal benchmark.
A LoRA stores compact weight updates that modify selected model layers at runtime. Cutting 40 transformer evaluations to five can substantially reduce generation time and compute, although text encoding, reference-image processing, and VAE decoding prevent a full eightfold reduction in end-to-end latency. Viggle has not published hardware-specific latency or memory benchmarks.
Five passes, one adapter
The release consists of one 1.3 GB adapter file, Qwen-Image-2.1-viggle-turbo-v0.2-5step-lora-r256.safetensors, loaded over the base transformer through the adapter repository.
| LoRA rank and alpha | 256 and 256 |
|---|---|
| Precision | BF16 |
| File size | 1.3 GB |
| Inference steps | 5 |
| Sigma schedule | [1.0, 0.875, 0.75, 0.5, 0.25] |
| Classifier-free guidance | Disabled with true_cfg_scale=1.0 |
| Supported tasks | Text-to-image generation and instruction-guided editing |
| Reference images | One to three during editing |
| License | Qwen Research License |
The repository lists the Qwen Research License, which permits noncommercial use. Commercial deployment requires a separate license from the Qwen team.
A 96-prompt benchmark
Viggle compared v0.2 with the v0.1 rank-64 LoRA and v0.1 full fine-tune on a held-out set of 96 user prompts. The reference was the 40-step base model using Qwen’s official prompt enhancement.
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