Topaz Rhea - Fine Detail Video Upscaler

Try Topaz Rhea - Fine Detail Video Upscaler in the Workbench

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

Model ID: topazlabs-rhea-video Topaz Rhea is a next-generation general-purpose upscaling and enhancement model from the Proteus family, tuned for modern camera footage and mixed sources. Use Rhea as an updated alternative to Proteus when you want stronger detail reconstruction with the same fine-grained enhancement controls.

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": "topazlabs-rhea-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-rhea-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.

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": "topazlabs-rhea-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 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": "topazlabs-rhea-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 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": "topazlabs-rhea-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-rhea-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-rhea-video

Request parameters

Required parameters

Field	Type	Default	Description
`input_video`	`string`	—	Video file to upscale (URL or file path). Format: uri.

Optional parameters

Field	Type	Default	Description
`resolution`	`string`	`"1080p"`	Target output resolution. One of: 720p, 1080p, 2k, 4k.
`blur`	`number`	`0`	Anti-blur / sharpening. Higher values produce a sharper result. Range: -1 – 1.
`compression`	`number`	`0`	Reduces compression artifacts. Higher values remove more artifacts. Range: -1 – 1.
`details`	`number`	`0`	Detail enhancement. Higher values boost fine detail. Range: -1 – 1.
`grain`	`number`	`0.05`	Film grain intensity. Range: 0 – 0.1.
`grain_type`	`string`	—	Film grain style. One of: silver_rich, gaussian, grey.
`halo`	`number`	`0`	Halo and ringing reduction. Higher values suppress halos more strongly. Range: -1 – 1.
`noise`	`number`	`0`	Noise handling. Negative reduces noise, positive adds grain-like noise. Range: -1 – 1.
`target_fps`	`integer`	`24`	Target frame rate (fps), up to 60. Range: 1 – 60.

Inference API

Try Topaz Rhea - Fine Detail Video Upscaler 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