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add support for midjourney provider settings

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This commit is contained in:
Sirshendu Ganguly
2025-11-12 15:49:34 -05:00
parent a89d170893
commit 8a1ff27e43
7 changed files with 185 additions and 21 deletions
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__init__.py
+2
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@@ -49,6 +49,7 @@ from .modules.providerSettings.minimaxProviderSettings import RunwareMiniMaxProv
from .modules.providerSettings.klingProviderSettings import RunwareKlingProviderSettings
from .modules.providerSettings.runwayProviderSettings import RunwareRunwayProviderSettings
from .modules.providerSettings.lumaProviderSettings import RunwareLumaProviderSettings
from .modules.providerSettings.midjourneyProviderSettings import RunwareMidjourneyProviderSettings
from .modules.vectorize import vectorize
from .modules.videoBgRemoval import videoBgRemoval
from .modules.videoUpscaler import videoUpscaler
@@ -119,6 +120,7 @@ NODE_CLASS_MAPPINGS = {
"Runware Luma Provider Settings": RunwareLumaProviderSettings,
"Runware KlingAI Provider Settings": RunwareKlingProviderSettings,
"Runware Runway Provider Settings": RunwareRunwayProviderSettings,
"Runware Midjourney Provider Settings": RunwareMidjourneyProviderSettings,
"Runware Vectorize": vectorize,
"Runware Video Background Removal": videoBgRemoval,
"Runware Video Upscaler": videoUpscaler,
clientlibs/main.js
+3 -1
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@@ -1,6 +1,6 @@
import { app } from "../../scripts/app.js";
import { api } from "../../scripts/api.js";
import { promptEnhanceHandler, syncDimensionsNodeHandler, searchNodeHandler, APIKeyHandler, captionNodeHandler, mediaUUIDHandler, videoTranscriptionHandler, handleCustomErrors, videoInferenceDimensionsHandler, videoModelSearchFilterHandler, audioModelSearchFilterHandler, useParameterToggleHandler, imageInferenceToggleHandler, upscalerToggleHandler, audioInferenceToggleHandler, acceleratorOptionsToggleHandler, bytedanceProviderSettingsToggleHandler, openaiProviderSettingsToggleHandler, klingProviderSettingsToggleHandler, lumaProviderSettingsToggleHandler } from "./utils.js";
import { promptEnhanceHandler, syncDimensionsNodeHandler, searchNodeHandler, APIKeyHandler, captionNodeHandler, mediaUUIDHandler, videoTranscriptionHandler, handleCustomErrors, videoInferenceDimensionsHandler, videoModelSearchFilterHandler, audioModelSearchFilterHandler, useParameterToggleHandler, imageInferenceToggleHandler, upscalerToggleHandler, videoUpscalerToggleHandler, audioInferenceToggleHandler, acceleratorOptionsToggleHandler, bytedanceProviderSettingsToggleHandler, openaiProviderSettingsToggleHandler, klingProviderSettingsToggleHandler, lumaProviderSettingsToggleHandler } from "./utils.js";
import { RUNWARE_NODE_TYPES, RUNWARE_NODE_PROPS, SEARCH_TERMS } from "./types.js";
const nodeInitList = [];
@@ -51,6 +51,8 @@ app.registerExtension({
videoInferenceDimensionsHandler(node);
} else if(nodeClass === RUNWARE_NODE_TYPES.UPSCALER) {
upscalerToggleHandler(node);
} else if(nodeClass === RUNWARE_NODE_TYPES.VIDEOUPSCALER) {
videoUpscalerToggleHandler(node);
} else if(nodeClass === RUNWARE_NODE_TYPES.AUDIOINFERENCE) {
audioInferenceToggleHandler(node);
} else if(nodeClass === RUNWARE_NODE_TYPES.ACCELERATOROPTIONS) {
clientlibs/types.js
+5
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@@ -98,6 +98,7 @@ const RUNWARE_NODE_TYPES = {
KLINGPROVIDERSETTINGS: "Runware KlingAI Provider Settings",
RUNWAYPROVIDERSETTINGS: "Runware Runway Provider Settings",
LUMAPROVIDERSETTINGS: "Runware Luma Provider Settings",
MIDJOURNEYPROVIDERSETTINGS: "Runware Midjourney Provider Settings",
VECTORIZE: "Runware Vectorize",
VIDEOBGREMOVAL: "Runware Video Background Removal",
VIDEOUPSCALER: "Runware Video Upscaler",
@@ -314,6 +315,10 @@ const RUNWARE_NODE_PROPS = {
bgColor: DEFAULT_BGCOLOR,
colorModeOnly: true,
},
[RUNWARE_NODE_TYPES.MIDJOURNEYPROVIDERSETTINGS]: {
bgColor: DEFAULT_BGCOLOR,
colorModeOnly: true,
},
[RUNWARE_NODE_TYPES.VECTORIZE]: {
bgColor: DEFAULT_BGCOLOR,
},
clientlibs/utils.js
+23
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@@ -657,6 +657,28 @@ function toggleWidgetEnabled(widget, enabled, node) {
}
}
function videoUpscalerToggleHandler(videoUpscalerNode) {
if (!videoUpscalerNode?.widgets) return;
const useUpscaleFactorWidget = videoUpscalerNode.widgets.find(w => w && w.name === "useUpscaleFactor");
const upscaleFactorWidget = videoUpscalerNode.widgets.find(w => w && w.name === "upscaleFactor");
if (!useUpscaleFactorWidget || !upscaleFactorWidget) return;
function applyToggleState() {
const enabled = useUpscaleFactorWidget.value === true;
toggleWidgetEnabled(upscaleFactorWidget, enabled, videoUpscalerNode);
videoUpscalerNode.setDirtyCanvas(true);
}
// Initialize state once widgets are rendered
setTimeout(applyToggleState, 100);
appendWidgetCB(useUpscaleFactorWidget, () => {
setTimeout(applyToggleState, 50);
});
}
function useParameterToggleHandler(node) {
// Prevent double registration
if (node._useParameterToggleHandlerRegistered) return;
@@ -1947,6 +1969,7 @@ export {
useParameterToggleHandler,
imageInferenceToggleHandler,
upscalerToggleHandler,
videoUpscalerToggleHandler,
audioInferenceToggleHandler,
acceleratorOptionsToggleHandler,
bytedanceProviderSettingsToggleHandler,
modules/providerSettings/midjourneyProviderSettings.py
+120
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@@ -0,0 +1,120 @@
"""
Runware Midjourney Provider Settings Node
Provides Midjourney-specific settings for image generation workflows.
"""
from typing import Any, Dict, Optional, Tuple
class RunwareMidjourneyProviderSettings:
"""Runware Midjourney Provider Settings Node"""
QUALITY_STEP = 0.25
QUALITY_MIN = 0.25
QUALITY_MAX = 2.0
DEFAULT_QUALITY = 1.0
DEFAULT_STYLIZE = 0
STYLIZE_MIN = 0
STYLIZE_MAX = 1000
DEFAULT_CHAOS = 0
CHAOS_MIN = 0
CHAOS_MAX = 100
DEFAULT_WEIRD = 0
WEIRD_MIN = 0
WEIRD_MAX = 3000
NIJI_OPTIONS = [
"", # Allow provider default when omitted
"close",
"0",
"5",
"6",
]
@classmethod
def INPUT_TYPES(cls):
return {
"required": {},
"optional": {
"quality": ("FLOAT", {
"tooltip": "Midjourney quality multiplier (provider key: providerSettings.midjourney.quality).",
"default": cls.DEFAULT_QUALITY,
"min": cls.QUALITY_MIN,
"max": cls.QUALITY_MAX,
"step": cls.QUALITY_STEP,
}),
"stylize": ("INT", {
"tooltip": "Controls how strongly Midjourney's artistic style is applied (provider key: providerSettings.midjourney.stylize).",
"default": cls.DEFAULT_STYLIZE,
"min": cls.STYLIZE_MIN,
"max": cls.STYLIZE_MAX,
}),
"chaos": ("INT", {
"tooltip": "Controls variation and unpredictability (provider key: providerSettings.midjourney.chaos).",
"default": cls.DEFAULT_CHAOS,
"min": cls.CHAOS_MIN,
"max": cls.CHAOS_MAX,
}),
"weird": ("INT", {
"tooltip": "Adds surreal, experimental characteristics (provider key: providerSettings.midjourney.weird).",
"default": cls.DEFAULT_WEIRD,
"min": cls.WEIRD_MIN,
"max": cls.WEIRD_MAX,
}),
"niji": (cls.NIJI_OPTIONS, {
"tooltip": "Select the Midjourney anime-focused rendering engine (provider key: providerSettings.midjourney.niji).",
"default": "close",
}),
}
}
RETURN_TYPES = ("RUNWAREPROVIDERSETTINGS",)
RETURN_NAMES = ("Provider Settings",)
FUNCTION = "create_provider_settings"
CATEGORY = "Runware/Provider Settings"
DESCRIPTION = "Configure Midjourney-specific provider settings such as quality, stylize, chaos, weird, and niji options for image generation."
def create_provider_settings(self, **kwargs) -> Tuple[Optional[Dict[str, Any]],]:
"""Build Midjourney provider settings payload."""
quality = kwargs.get("quality", self.DEFAULT_QUALITY)
stylize = kwargs.get("stylize", self.DEFAULT_STYLIZE)
chaos = kwargs.get("chaos", self.DEFAULT_CHAOS)
weird = kwargs.get("weird", self.DEFAULT_WEIRD)
niji = kwargs.get("niji", "close")
settings: Dict[str, Any] = {}
if quality is not None:
settings["quality"] = float(quality)
if stylize is not None:
settings["stylize"] = int(stylize)
if chaos is not None:
settings["chaos"] = int(chaos)
if weird is not None:
settings["weird"] = int(weird)
if isinstance(niji, str) and niji.strip():
settings["niji"] = niji.strip()
# Clean None values so the inference request only includes populated options.
settings = {k: v for k, v in settings.items() if v is not None}
return (settings if settings else None,)
NODE_CLASS_MAPPINGS = {
"RunwareMidjourneyProviderSettings": RunwareMidjourneyProviderSettings,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"RunwareMidjourneyProviderSettings": "Runware Midjourney Provider Settings",
}
modules/utils/runwareUtils.py
+1 -1
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@@ -158,7 +158,7 @@ def getCustomEndpoint():
custom_endpoint = os.getenv("RUNWARE_CUSTOM_ENDPOINT")
if custom_endpoint and isinstance(custom_endpoint, str):
return custom_endpoint
return "https://api.runware.ai/v1"
return "https://api.runware.dev/v1"
SESSION_TIMEOUT = getTimeout()
RUNWARE_API_KEY = getAPIKey()
modules/videoUpscaler.py
+31 -19
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@@ -14,16 +14,22 @@ class videoUpscaler:
"Video": ("STRING", {
"tooltip": "MediaUUID from Runware Media Upload node for the video to upscale."
}),
"upscaleFactor": ([2, 4], {
"tooltip": "Integer scale factor for upscaling (2x or 4x).",
"default": 2,
}),
"Model": (list(cls.RUNWARE_VUPSCALER_MODELS.keys()), {
"tooltip": "Select the video upscaler model to use.",
"default": "Bria Video Upscaler",
}),
"useUpscaleFactor": ("BOOLEAN", {
"label_on": "Yes",
"label_off": "No",
"tooltip": "Enable to explicitly set the upscale factor for the request.",
"default": False,
}),
},
"optional": {
"upscaleFactor": ([2, 4], {
"tooltip": "Integer scale factor for upscaling (2x or 4x).",
"default": 2,
}),
"Output Format": (["mp4", "webm"], {
"default": "mp4",
"tooltip": "Choose the output video format.",
@@ -39,7 +45,8 @@ class videoUpscaler:
def upscale(self, **kwargs):
video_uuid = kwargs.get("Video")
upscaleFactor = kwargs.get("upscaleFactor", 2)
useUpscaleFactor = kwargs.get("useUpscaleFactor", False)
upscaleFactor = kwargs.get("upscaleFactor")
modelName = kwargs.get("Model", "Bria Video Upscaler")
outputFormat = kwargs.get("Output Format", "mp4")
@@ -51,20 +58,25 @@ class videoUpscaler:
modelAIR = self.RUNWARE_VUPSCALER_MODELS.get(modelName, "bria:50@1")
# Build the API request
genConfig = [
{
"taskType": "upscale",
"taskUUID": rwUtils.genRandUUID(),
"upscaleFactor": upscaleFactor,
"inputs": {
"video": video_uuid
},
"outputType": "URL",
"outputFormat": outputFormat.upper(),
"model": modelAIR,
"includeCost": True
}
]
task_payload = {
"taskType": "upscale",
"taskUUID": rwUtils.genRandUUID(),
"inputs": {
"video": video_uuid
},
"outputType": "URL",
"outputFormat": outputFormat.upper(),
"model": modelAIR,
"includeCost": True
}
if useUpscaleFactor:
if upscaleFactor in (2, 4):
task_payload["upscaleFactor"] = upscaleFactor
else:
raise ValueError("upscaleFactor must be 2 or 4 when useUpscaleFactor is enabled.")
genConfig = [task_payload]
try:
# Debug: Print the request being sent