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Lumina Image 2.0 (Lumina2) LoRA Inference with Diffusers (AI Toolkit-trained)

API model id: lumina2
URL slug: lumina2

This page documents the reference Diffusers inference pipeline for lumina2 (Lumina Image 2.0 (Lumina2)). It is designed for running LoRAs trained with ostris/ai-toolkit while minimizing training preview vs inference mismatch. If you are trying to reproduce AI Toolkit sample previews, treat the code linked below as the source of truth (scheduler wiring, resolution snapping, LoRA application, and conditioning).

Run in the cloud (optional): If you want to reproduce the examples on this page in a pinned runtime without local CUDA/driver setup (and reduce preview‑vs‑inference drift), run it via RunComfy’s Cloud AI Toolkit (Train + Inference). 👉 You can open it here: Cloud AI Toolkit (Train + Inference)

Quick facts

Field	Value
Pipeline	src/pipelines/lumina2.py
Base checkpoint	Alpha-VLLM/Lumina-Image-2.0
Defaults	sample_steps=25, guidance_scale=4.0, seed=42
Resolution snapping	Floors width/height to a multiple of 32
Control image	No
LoRA scale behavior	Dynamic via adapters (set_adapters). Scale is set per request via loras[].network_multiplier.
Needs AI Toolkit	No

Reference implementation (source of truth)

• Pipeline implementation: src/pipelines/lumina2.py
• Shared behaviors (snapping, seeding, LoRA base logic): src/pipelines/base.py
• Request schema (parameter names): src/schemas/request.py
• Model ids (enum): src/schemas/models.py
• Pipeline registry (model → class): src/pipelines/__init__.py
• Executor (prompt processing + caching): src/tasks/executor.py

Minimal API request

{
  "model": "lumina2",
  "trigger_word": "sks",
  "prompts": [
    {
      "prompt": "[trigger] a photo of a person",
      "width": 1024,
      "height": 1024,
      "seed": 42,
      "sample_steps": 25,
      "guidance_scale": 4.0,
      "neg": ""
    }
  ],
  "loras": [
    {
      "path": "my_lora_job/my_lora.safetensors",
      "network_multiplier": 1.0
    }
  ]
}

Pipeline behavior that matters

• Uses diffusers.Lumina2Pipeline with a Gemma2 text encoder (AutoModel).
• Scheduler is FlowMatchEulerDiscreteScheduler with shift=6.0 (aligned with AI Toolkit sampler config).
• LoRA is loaded via load_lora_weights + set_adapters; scale is set per request via loras[].network_multiplier.

Preview-matching notes (training preview vs inference mismatch)

• The scheduler shift value materially changes results for FlowMatch-family models; if you use a default scheduler elsewhere, expect drift.
• This implementation uses max_sequence_length=256.
• Width/height are floored to a multiple of 32.

What to compare when debugging mismatch

• Confirm the effective width/height after snapping (the server floors to the divisor).
• Match sample_steps and the scheduler family (FlowMatch / UniPC / DDPM differences matter).
• Match guidance_scale semantics (some pipelines map it to a different internal parameter).
• Match loras[].network_multiplier and whether LoRA scale is dynamic vs fused.

Related

• See the Model Catalog for all supported models.