Question 1

Is this text-to-video or image-to-video?

Accepted Answer

Image-to-video. You provide a source image as base64 in image_b64 plus a text prompt describing the motion. The model animates the image; TurboDiffusion also publishes separate T2V checkpoints, but this chute serves the I2V-A14B-720P model.

Question 2

Why only 4 sampling steps?

Accepted Answer

The checkpoint is distilled with rCM (score-regularized continuous-time consistency), which trains the model to produce in 4 steps what the base Wan2.2 needs a full sampling schedule for. Combined with SLA sparse attention, the TurboDiffusion paper reports 100-200x end-to-end acceleration.

Question 3

What resolution and aspect ratio can I generate?

Accepted Answer

The checkpoint is the 720P variant of Wan2.2 I2V-A14B (the base model supports 480P and 720P). The chute's example call uses resolution "480p" with aspect_ratio "16:9" and adaptive_resolution true, which adapts output dimensions to the source image.

Question 4

How do I call it? Is there an OpenAI-compatible endpoint?

Accepted Answer

No, this is not an LLM gateway model. POST a flat JSON body to https://vonkaiser-turbowani2v.chutes.ai/generate with Authorization: Bearer $CHUTES_API_KEY. The response body is the MP4 file itself, so save the raw bytes (curl --output, response.content in Python).

Question 5

Can I use the output commercially?

Accepted Answer

The TurboDiffusion checkpoint and the base Wan2.2-I2V-A14B are both Apache-2.0 licensed, which permits commercial use. Check your own content and input-image rights as usual.

Question 6

How do I get consistent results across runs?

Accepted Answer

Pass a fixed seed integer in the request body (the endpoint example uses seed 42). Same seed, prompt, image, and settings reproduce the same clip.

Question 7

Does the video include audio?

Accepted Answer

No. Output is a silent MP4. If you need synchronized audio, use a joint audio-video model such as LTX 2.3 on Chutes instead.

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