← Docs Home · Model Catalog · HTTP API · Troubleshooting

Wan 2.1 I2V 14B (720P) LoRA Inference with Diffusers (AI Toolkit-trained)

API model id: wan21_i2v_14b
URL slug: wan21-i2v-14b

This page documents the reference Diffusers inference pipeline for wan21_i2v_14b (Wan 2.1 I2V 14B (720P)). 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/wan21.py`
Base checkpoint	`Wan-AI/Wan2.1-I2V-14B-720P-Diffusers`
Defaults	`sample_steps=25`, `guidance_scale=4.0`, `seed=42`
Resolution snapping	Floors width/height to a multiple of 16
Control image	Required (`ctrl_img`)
Video	Yes (`num_frames=41`, `fps=16` by default)
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/wan21.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
Control-image loading (base64/URL): src/libs/image_utils.py
Executor (prompt processing + caching): src/tasks/executor.py

Minimal API request

{
  "model": "wan21_i2v_14b",
  "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": "",
      "num_frames": 41,
      "fps": 16,
      "ctrl_img": "<base64_or_url>"
    }
  ],
  "loras": [
    {
      "path": "my_lora_job/my_lora.safetensors",
      "network_multiplier": 1.0
    }
  ]
}

Control image

This model requires a control image. In the API request, set ctrl_img to either:

a base64-encoded image, or
an http(s) URL that the server can fetch.

Pipeline behavior that matters

Uses diffusers.WanImageToVideoPipeline (image-to-video).
The server resizes the control image to (width, height) using LANCZOS before inference.

Preview-matching notes (training preview vs inference mismatch)

I2V conditioning is extremely sensitive to control-image resize and aspect ratio. Match the exact (width, height) and resample method.
Width/height are floored to a multiple of 16.

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.
If a control image is involved, match the resize method and the exact image(s) used.
If video: match num_frames and fps (and any frame-count constraints).