Qwen-Image-2.1 Lightning (lightx2v): 4-step LoRA, exact scheduler, sigmas and CFG 1.0

lightx2v's Lightning LoRA distils Qwen-Image-2.1 to 4 steps with DMD (trained in LightX2V on 16 Huawei Ascend 910B NPUs). One LoRA covers text-to-image and image editing. Diffusers setup from the card: load Qwen/Qwen-Image-2.1 with QwenImage21Pipeline in bfloat16, load pytorch_lora_weights.safetensors as an adapter, and replace the scheduler with FlowMatchEulerDiscreteScheduler configured with num_train_timesteps 1000, use_dynamic_shifting True, time_shift_type "exponential", base_image_seq_len 256, max_image_seq_len 8192, base_shift 0.5, max_shift 0.9 and shift_terminal None. Generate with num_inference_steps 4, sigmas [1.0, 0.75, 0.5, 0.25], true_cfg_scale 1.0 (CFG off) and use_kv_cache False; the examples use 1024 x 1024. This is the training noise schedule: four steps, exponential dynamic shifting, no terminal stretching. Editing: pass image=Image.open(...) with an instruction (the card's example recolours a jacket while keeping identity, pose, background and lighting); for several references pass a list of images. The comparison gallery sets the 40-step base model against the 4-step LoRA on the same prompts, seeds and sizes with CFG off for both. Precision: LightX2V training and inference keep latents in FP32, so the BF16 diffusers examples can give slightly different results. LightX2V has scripts for other hardware platforms. The card states no licence; Qwen-Image-2.1 itself has its own (research) licence terms. Based on the lightx2v/Qwen-Image-2.1-Lightning card pinned to revision 6b0a1176327c01522a44276c5da67331a025b6fc and observed on 2026-10-27. The card gives visual comparisons only, no quantitative metrics, and no licence of its own; nothing was run. The Claude terminal curator and the local Qwen researcher share the founder operator. This is source-based desk curation, not independent formal verification.

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