Wan2.2 A14B - Text to Video

Try Wan2.2 A14B - Text to Video in the Workbench

Run this model interactively, tune parameters, and compare outputs.

Model ID: wan-ai-wan2-2-t2v-a14b-diffusers Wan-AI/Wan2.2-T2V-A14B-Diffusers is a video generation model (diffusion-based) with a Mixture-of-Experts architecture, designed for high-quality text-to-video and image-to-video synthesis at 720P resolution and 24fps. It excels in generating high-definition videos with efficient resource usage, enabling professional-level output while maintaining practical hardware requirements (e.g., consumer-grade GPUs or single 80GB VRAM GPU). Some other noteworthy features of Wan-AI/Wan2.2-T2V-A14B-Diffusers include support for both academic research and industrial applications, and the ability to handle both text-to-video and image-to-video tasks within a unified framework.

Metric	Value
Parameter Count	27 billion (14 billion active per step)
Mixture of Experts	Yes
Active Parameter Count	14 billion
Context Length	Unknown
Multilingual	Unknown
Quantized*	No

*Quantization is specific to the inference provider and the model may be offered with different quantization levels by other providers.

Example request

Use the Workbench as a request builder: configure parameters for this model in the UI, then open the API tab to copy the exact cURL or Python call.

Sync
Async
Async with SSE

This blocks until the video is ready (typically 5-15 minutes). Prefer Async or Async with SSE for anything beyond quick experimentation.See the video generation reference for more details.

Minimal
All parameters

curl -X POST https://hub.oxen.ai/api/ai/videos/generate \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OXEN_API_KEY" \
  -d '{
  "model": "wan-ai-wan2-2-t2v-a14b-diffusers",
  "prompt": "A beautiful landscape painting of a serene lake with mountains in the background and an ox in the foreground."
}'

curl -X POST https://hub.oxen.ai/api/ai/videos/generate \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OXEN_API_KEY" \
  -d '{
  "model": "wan-ai-wan2-2-t2v-a14b-diffusers",
  "prompt": "A beautiful landscape painting of a serene lake with mountains in the background and an ox in the foreground.",
  "height": 480,
  "width": 832,
  "negative_prompt": " ",
  "num_inference_steps": 4,
  "num_frames": 41,
  "guidance_scale": 5.0,
  "run_fast": true
}'

See the async queue reference for more details.

Minimal
All parameters

# Enqueue, capture the generation id.
GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OXEN_API_KEY" \
  -d '{
  "model": "wan-ai-wan2-2-t2v-a14b-diffusers",
  "prompt": "A beautiful landscape painting of a serene lake with mountains in the background and an ox in the foreground."
}' | jq -r '.generations[0].generation_id')

# Poll the single generation until it 404s (terminal state).
while curl -s -o /dev/null -w "%{http_code}" \
    -H "Authorization: Bearer $OXEN_API_KEY" \
    "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | grep -q "^200$"; do
  sleep 5
done
echo "Done. See the 'Async with SSE' tab to receive the result URL."

# Enqueue, capture the generation id.
GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OXEN_API_KEY" \
  -d '{
  "model": "wan-ai-wan2-2-t2v-a14b-diffusers",
  "prompt": "A beautiful landscape painting of a serene lake with mountains in the background and an ox in the foreground.",
  "height": 480,
  "width": 832,
  "negative_prompt": " ",
  "num_inference_steps": 4,
  "num_frames": 41,
  "guidance_scale": 5.0,
  "run_fast": true
}' | jq -r '.generations[0].generation_id')

# Poll the single generation until it 404s (terminal state).
while curl -s -o /dev/null -w "%{http_code}" \
    -H "Authorization: Bearer $OXEN_API_KEY" \
    "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | grep -q "^200$"; do
  sleep 5
done
echo "Done. See the 'Async with SSE' tab to receive the result URL."

See the async queue reference for more details.

Minimal
All parameters

# Enqueue, capture the generation id.
GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OXEN_API_KEY" \
  -d '{
  "model": "wan-ai-wan2-2-t2v-a14b-diffusers",
  "prompt": "A beautiful landscape painting of a serene lake with mountains in the background and an ox in the foreground."
}' | jq -r '.generations[0].generation_id')

# Stream the SSE channel, grab the data line that follows a
# media_generation_completed event for our id, and pretty-print it.
curl -sN -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/events \
  | awk -v id="$GEN_ID" '
    /^event: media_generation_completed$/ { expect=1; next }
    /^data: / && expect {
      payload = substr($0, 7)
      if (index(payload, "\"generation_id\":\"" id "\"")) { print payload; exit }
      expect = 0
    }
  ' | jq .

# Enqueue, capture the generation id.
GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OXEN_API_KEY" \
  -d '{
  "model": "wan-ai-wan2-2-t2v-a14b-diffusers",
  "prompt": "A beautiful landscape painting of a serene lake with mountains in the background and an ox in the foreground.",
  "height": 480,
  "width": 832,
  "negative_prompt": " ",
  "num_inference_steps": 4,
  "num_frames": 41,
  "guidance_scale": 5.0,
  "run_fast": true
}' | jq -r '.generations[0].generation_id')

# Stream the SSE channel, grab the data line that follows a
# media_generation_completed event for our id, and pretty-print it.
curl -sN -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/events \
  | awk -v id="$GEN_ID" '
    /^event: media_generation_completed$/ { expect=1; next }
    /^data: / && expect {
      payload = substr($0, 7)
      if (index(payload, "\"generation_id\":\"" id "\"")) { print payload; exit }
      expect = 0
    }
  ' | jq .

Fetch model details

The models endpoint returns the full model object, including its json_request_schema.

curl -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/ai/models/wan-ai-wan2-2-t2v-a14b-diffusers

Request parameters

Required parameters

Field	Type	Default	Description
`prompt`	`string`	`"A beautiful landscape painting of a serene lake with mountains in the background and an ox in the foreground."`	Prompt for generated image

Optional parameters

Field	Type	Default	Description
`height`	`integer`	`480`	Height of the video Range: 1 – 720.
`width`	`integer`	`832`	Width of the video Range: 1 – 832.
`negative_prompt`	`string`	`" "`	Negative prompt for generated image
`num_inference_steps`	`integer`	`4`	Number of diffusion steps to take Range: 1 – 100.
`num_frames`	`integer`	`41`	Number of frames of video to generate (`num_frames - 1` should be divisible by 4) Range: 1 – 120.
`guidance_scale`	`number`	`5.0`	Guidance for generated video. Lower values can give more realistic videos. Range: 0 – 10.
`seed`	`integer`	—	Random seed. Set for reproducible generation
`run_fast`	`boolean`	`true`	Run the model with a lightning LoRA

Inference API

Try Wan2.2 A14B - Text to Video in the Workbench

​Example request

​Fetch model details

​Request parameters

​Required parameters

​Optional parameters

Example request

Fetch model details

Request parameters

Required parameters

Optional parameters