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Recall Parameters API - LoRAs, ControlNets, and IP Adapters with Images#

Overview#

The Recall Parameters API supports recalling LoRAs, ControlNets (including T2I Adapters and Control LoRAs), and IP Adapters along with their associated weights and settings. Control Layers and IP Adapters can now include image references from the INVOKEAI_ROOT/outputs/images directory for fully functional control and image prompt functionality.

Key Features#

✅ LoRAs: Fully functional - adds to UI, queries model configs, applies weights ✅ Control Layers: Full support with optional images from outputs/images ✅ IP Adapters: Full support with optional reference images from outputs/images ✅ Model Name Resolution: Automatic lookup from human-readable names to internal keys ✅ Image Validation: Backend validates that image files exist before sending

Endpoints#

POST /api/v1/recall/{queue_id}#

Updates recallable parameters for the frontend, including LoRAs, control adapters, and IP adapters with optional images.

Path Parameters: - queue_id (string): The queue ID to associate parameters with (typically "default")

Request Body:

All fields are optional. Include only the parameters you want to update.

{
  // Standard parameters
  positive_prompt?: string;
  negative_prompt?: string;
  model?: string;           // Model name or key
  steps?: number;
  cfg_scale?: number;
  width?: number;
  height?: number;
  seed?: number;
  // ... other standard parameters

  // LoRAs
  loras?: Array<{
    model_name: string;     // LoRA model name
    weight?: number;        // Default: 0.75, Range: -10 to 10
    is_enabled?: boolean;   // Default: true
  }>;

  // Control Layers (ControlNet, T2I Adapter, Control LoRA)
  control_layers?: Array<{
    model_name: string;            // Control adapter model name
    image_name?: string;           // Optional image filename from outputs/images
    weight?: number;               // Default: 1.0, Range: -1 to 2
    begin_step_percent?: number;   // Default: 0.0, Range: 0 to 1
    end_step_percent?: number;     // Default: 1.0, Range: 0 to 1
    control_mode?: "balanced" | "more_prompt" | "more_control";  // ControlNet only
  }>;

  // IP Adapters
  ip_adapters?: Array<{
    model_name: string;            // IP Adapter model name
    image_name?: string;           // Optional reference image filename from outputs/images
    weight?: number;               // Default: 1.0, Range: -1 to 2
    begin_step_percent?: number;   // Default: 0.0, Range: 0 to 1
    end_step_percent?: number;     // Default: 1.0, Range: 0 to 1
    method?: "full" | "style" | "composition";  // Default: "full"
    influence?: "Lowest" | "Low" | "Medium" | "High" | "Highest";  // Flux Redux only; default: "highest"
  }>;
}

Model Name Resolution#

The backend automatically resolves model names to their internal keys:

  1. Main Models: Resolved from the name to the model key
  2. LoRAs: Searched in the LoRA model database
  3. Control Adapters: Tried in order - ControlNet → T2I Adapter → Control LoRA
  4. IP Adapters: Searched in the IP Adapter model database

Models that cannot be resolved are skipped with a warning in the logs.

Image File Handling#

Image Path Resolution#

When you specify an image_name, the backend: 1. Constructs the full path: {INVOKEAI_ROOT}/outputs/images/{image_name} 2. Validates that the file exists 3. Includes the image reference in the event sent to the frontend 4. Logs whether the image was found or not

Image Naming#

Images should be referenced by their filename as it appears in the outputs/images directory: - ✅ Correct: "image_name": "example.png" - ✅ Correct: "image_name": "my_control_image_20240110.jpg" - ❌ Incorrect: "image_name": "outputs/images/example.png" (use relative filename only) - ❌ Incorrect: "image_name": "/full/path/to/example.png" (use relative filename only)

Frontend Behavior#

LoRAs#

  • Fully Supported: LoRAs are immediately added to the LoRA list in the UI
  • Existing LoRAs are cleared before adding new ones
  • Each LoRA's model config is fetched and applied with the specified weight
  • LoRAs appear in the LoRA selector panel

Control Layers with Images#

  • Fully Supported: Control layers now support images from outputs/images
  • Configuration includes model, weights, step percentages, and image reference
  • Image availability is logged in frontend console
  • Images can be used to create actual control layers through the UI

IP Adapters with Images#

  • Fully Supported: IP Adapters now support reference images from outputs/images
  • Configuration includes model, weights, step percentages, method, and image reference
  • Image availability is logged in frontend console
  • Images can be used to create actual reference image layers through the UI

Examples#

1. Add LoRAs Only#

curl -X POST http://localhost:9090/api/v1/recall/default \
  -H "Content-Type: application/json" \
  -d '{
    "loras": [
      {
        "model_name": "add-detail-xl",
        "weight": 0.8,
        "is_enabled": true
      },
      {
        "model_name": "sd_xl_offset_example-lora_1.0",
        "weight": 0.5,
        "is_enabled": true
      }
    ]
  }'

2. Configure Control Layers with Image#

Replace my_control_image.png with an actual image filename from your outputs/images directory.

curl -X POST http://localhost:9090/api/v1/recall/default \
  -H "Content-Type: application/json" \
  -d '{
    "control_layers": [
      {
        "model_name": "controlnet-canny-sdxl-1.0",
        "image_name": "my_control_image.png",
        "weight": 0.75,
        "begin_step_percent": 0.0,
        "end_step_percent": 0.8,
        "control_mode": "balanced"
      }
    ]
  }'

3. Configure IP Adapters with Reference Image#

Replace reference_face.png with an actual image filename from your outputs/images directory.

curl -X POST http://localhost:9090/api/v1/recall/default \
  -H "Content-Type: application/json" \
  -d '{
    "ip_adapters": [
      {
        "model_name": "ip-adapter-plus-face_sd15",
        "image_name": "reference_face.png",
        "weight": 0.7,
        "begin_step_percent": 0.0,
        "end_step_percent": 1.0,
        "method": "composition"
      }
    ]
  }'

4. Complete Configuration with All Features#

curl -X POST http://localhost:9090/api/v1/recall/default \
  -H "Content-Type: application/json" \
  -d '{
    "positive_prompt": "masterpiece, detailed photo with specific style",
    "negative_prompt": "blurry, low quality",
    "model": "FLUX Schnell",
    "steps": 25,
    "cfg_scale": 8.0,
    "width": 1024,
    "height": 768,
    "seed": 42,
    "loras": [
      {
        "model_name": "add-detail-xl",
        "weight": 0.6,
        "is_enabled": true
      }
    ],
    "control_layers": [
      {
        "model_name": "controlnet-depth-sdxl-1.0",
        "image_name": "depth_map.png",
        "weight": 1.0,
        "begin_step_percent": 0.0,
        "end_step_percent": 0.7
      }
    ],
    "ip_adapters": [
      {
        "model_name": "ip-adapter-plus-face_sd15",
        "image_name": "style_reference.png",
        "weight": 0.5,
        "begin_step_percent": 0.0,
        "end_step_percent": 1.0,
        "method": "style"
      }
    ]
  }'

Response Format#

{
  "status": "success",
  "queue_id": "default",
  "updated_count": 15,
  "parameters": {
    "positive_prompt": "...",
    "steps": 25,
    "loras": [
      {
        "model_key": "abc123...",
        "weight": 0.6,
        "is_enabled": true
      }
    ],
    "control_layers": [
      {
        "model_key": "controlnet-xyz...",
        "weight": 1.0,
        "image": {
          "image_name": "depth_map.png"
        }
      }
    ],
    "ip_adapters": [
      {
        "model_key": "ip-adapter-xyz...",
        "weight": 0.5,
        "image": {
          "image_name": "style_reference.png"
        }
      }
    ]
  }
}

WebSocket Events#

When parameters are updated, a recall_parameters_updated event is emitted via WebSocket to the queue room. The frontend automatically:

  1. Applies standard parameters (prompts, steps, dimensions, etc.)
  2. Loads and adds LoRAs to the LoRA list
  3. Logs control layer and IP adapter configurations with image information
  4. Makes image references available for manual canvas/reference image creation

Logging#

Backend Logs#

Backend logs show: - Model name → key resolution (success/failure) - Image file validation (found/not found) - Parameter storage confirmation - Event emission status

Example log messages: 

INFO: Resolved ControlNet model name 'controlnet-canny-sdxl-1.0' to key 'controlnet-xyz...'
INFO: Found image file: depth_map.png
INFO: Updated 12 recall parameters for queue default
INFO: Resolved 1 LoRA(s)
INFO: Resolved 1 control layer(s)
INFO: Resolved 1 IP adapter(s)

Frontend Logs#

Frontend logs (check browser console): - Set localStorage.ROARR_FILTER = 'debug' to see all debug messages - Look for messages from the events namespace - LoRA loading, model resolution, and parameter application are logged

Example log messages: 

INFO: Applied 5 recall parameters to store
INFO: Received 1 control layer(s) with image support
INFO: Control layer 1: controlnet-xyz... (weight: 0.75, image: depth_map.png)
DEBUG: Control layer 1 image available at: outputs/images/depth_map.png
INFO: Received 1 IP adapter(s) with image support
INFO: IP adapter 1: ip-adapter-xyz... (weight: 0.7, image: style_reference.png)
DEBUG: IP adapter 1 image available at: outputs/images/style_reference.png

Limitations#

  1. Canvas Integration: Control layers and IP adapters with images are currently logged but not automatically added to canvas layers
  2. Users can view the configuration and manually create canvas layers with the provided images

  3. Future enhancement: Auto-create canvas layers with stored images

  4. Model Availability: Models must be installed in InvokeAI before they can be recalled

  5. Image Availability: Images must exist in the outputs/images directory

  6. Missing images are logged as warnings but don't fail the request

  7. Other parameters are still applied even if images are missing

  8. Image URLs: Only local filenames from outputs/images are supported

  9. Remote image URLs are not currently supported

Testing#

Use the provided test script:

./test_recall_loras_controlnets.sh

This will test: - LoRA addition with multiple models - Control layer configuration with image references - IP adapter configuration with image references - Combined parameter updates with all features

Note: Update the image names in the test script to match actual images in your outputs/images directory.

Troubleshooting#

Images Not Found#

If you see "Image file not found" in the logs: 1. Verify the image filename matches exactly (case-sensitive) 2. Ensure the image is in {INVOKEAI_ROOT}/outputs/images/ 3. Check that the filename doesn't include the outputs/images/ prefix

Models Not Found#

If you see "Could not find model" messages: 1. Verify the model name matches exactly (case-sensitive) 2. Ensure the model is installed in InvokeAI 3. Check the model name using the models browser in the UI

Event Not Received#

If the frontend doesn't receive the event: 1. Check browser console for connection errors 2. Verify the queue_id matches the frontend's queue (usually "default") 3. Check backend logs for event emission errors

Future Enhancements#

Potential improvements: 1. Auto-create canvas layers with provided control layer images 2. Auto-create reference image layers with provided IP adapter images 3. Support for image URLs 4. Batch operations for multiple queue IDs 5. Image upload capability (accept base64 or file upload)