Documentation Index Fetch the complete documentation index at: https://docs.oxen.ai/llms.txt
Use this file to discover all available pages before exploring further.
Try Topaz Iris - Face Detail Video Upscaler in the Workbench Run this model interactively, tune parameters, and compare outputs.
Model ID: topazlabs-iris-mq-videoTopaz Iris MQ is a Proteus-family model for enhancing face detail in medium-quality video sources.
Use this model when restoring faces in compressed web video, video calls, or other medium-quality footage where facial fidelity matters.
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. curl -X POST https://hub.oxen.ai/api/ai/videos/generate \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OXEN_API_KEY " \ -d '{ "model": "topazlabs-iris-mq-video", "input_video": "https://hub.oxen.ai/api/repos/ox/Oxen-AI-Assets/file/main/images/winter_summer_ox.mp4" }'
curl -X POST https://hub.oxen.ai/api/ai/videos/generate \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OXEN_API_KEY " \ -d '{ "model": "topazlabs-iris-mq-video", "input_video": "https://hub.oxen.ai/api/repos/ox/Oxen-AI-Assets/file/main/images/winter_summer_ox.mp4", "resolution": "1080p", "blur": 0, "compression": 0, "details": 0, "grain": 0.05, "halo": 0, "noise": 0, "target_fps": 24 }'
See the async queue reference for more details. # 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": "topazlabs-iris-mq-video", "input_video": "https://hub.oxen.ai/api/repos/ox/Oxen-AI-Assets/file/main/images/winter_summer_ox.mp4" }' | jq -r '.generations[0].generation_id' ) # Poll until the generation reaches a terminal status. while true ; do STATUS = $( curl -s -H "Authorization: Bearer $OXEN_API_KEY " \ "https://hub.oxen.ai/api/ai/queue/ $GEN_ID " | jq -r '.status' ) echo "Status: $STATUS " case $STATUS in succeeded | failed | cancelled ) break ;; esac sleep 5 done # Print the result. curl -s -H "Authorization: Bearer $OXEN_API_KEY " \ "https://hub.oxen.ai/api/ai/queue/ $GEN_ID " | 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": "topazlabs-iris-mq-video", "input_video": "https://hub.oxen.ai/api/repos/ox/Oxen-AI-Assets/file/main/images/winter_summer_ox.mp4", "resolution": "1080p", "blur": 0, "compression": 0, "details": 0, "grain": 0.05, "halo": 0, "noise": 0, "target_fps": 24 }' | jq -r '.generations[0].generation_id' ) # Poll until the generation reaches a terminal status. while true ; do STATUS = $( curl -s -H "Authorization: Bearer $OXEN_API_KEY " \ "https://hub.oxen.ai/api/ai/queue/ $GEN_ID " | jq -r '.status' ) echo "Status: $STATUS " case $STATUS in succeeded | failed | cancelled ) break ;; esac sleep 5 done # Print the result. curl -s -H "Authorization: Bearer $OXEN_API_KEY " \ "https://hub.oxen.ai/api/ai/queue/ $GEN_ID " | jq .
See the async queue reference for more details. # 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": "topazlabs-iris-mq-video", "input_video": "https://hub.oxen.ai/api/repos/ox/Oxen-AI-Assets/file/main/images/winter_summer_ox.mp4" }' | 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": "topazlabs-iris-mq-video", "input_video": "https://hub.oxen.ai/api/repos/ox/Oxen-AI-Assets/file/main/images/winter_summer_ox.mp4", "resolution": "1080p", "blur": 0, "compression": 0, "details": 0, "grain": 0.05, "halo": 0, "noise": 0, "target_fps": 24 }' | 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/topazlabs-iris-mq-video
Request parameters Required parameters Field Type Default Description input_videostring— Video file to upscale (URL or file path). Format: uri.
Optional parameters Field Type Default Description resolutionstring"1080p"Target output resolution. One of: 720p, 1080p, 2k, 4k. blurnumber0Anti-blur / sharpening. Higher values produce a sharper result. Range: -1 – 1. compressionnumber0Reduces compression artifacts. Higher values remove more artifacts. Range: -1 – 1. detailsnumber0Detail enhancement. Higher values boost fine detail. Range: -1 – 1. grainnumber0.05Film grain intensity. Range: 0 – 0.1. grain_typestring— Film grain style. One of: silver_rich, gaussian, grey. halonumber0Halo and ringing reduction. Higher values suppress halos more strongly. Range: -1 – 1. noisenumber0Noise handling. Negative reduces noise, positive adds grain-like noise. Range: -1 – 1. target_fpsnumber24Target frame rate (fps). One of: 24, 23.976, 25, 29.97, 30, 50, 59.94, 60.
