- Add Ovi/Bria provider settings support for upscaler and background removal - Add new Safety Inputs node (modules/safetyInputs.py) for content moderation settings - Add new Video Advanced Feature Inputs node (modules/videoAdvancedFeatureInputs.py) for CFG scales, negative prompts, and SLG layer settings - Fix advanced feature inputs to be properly nested under 'advancedFeatures' key (matching safety inputs pattern) - Update bgremoval.py with provider settings support - Update upscaler.py with provider settings support - Update videoInference.py to handle safety and advanced feature inputs correctly - Update acceleratorOptions.py with improved caching options - Update briaImageProviderSettings.py with additional settings - Update videoInferenceInputs.py with new input support - Update videoModelSearch.py with model enhancements - Update all workflow JSON files to use new nodes - Add new Runware_Image_to_Video_Ovi.json workflow - Update TypeScript types in clientlibs/types.js - Register new nodes in __init__.py
180 lines
8.7 KiB
Python
180 lines
8.7 KiB
Python
from .utils import runwareUtils as rwUtils
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class acceleratorOptions:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {},
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"optional": {
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# FBCache parameters
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"fbcache": ("BOOLEAN", {
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"tooltip": "Enable Frame Buffer Cache for faster generation",
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"default": None,
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}),
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"useCacheDistance": ("BOOLEAN", {
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"tooltip": "Enable to include cacheDistance parameter in API request.",
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"default": False,
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}),
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"cacheDistance": ("FLOAT", {
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"tooltip": "Cache distance parameter for FBCache",
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"default": 0.0,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01,
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}),
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# TeaCache parameters
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"teaCache": ("BOOLEAN", {
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"tooltip": "Enable TeaCache for transformer-based models (Flux, SD3). Does not work with UNet models like SDXL or SD1.5. Particularly effective for iterative editing workflows.",
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"default": None,
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}),
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"useTeaCacheDistance": ("BOOLEAN", {
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"tooltip": "Enable to include teaCacheDistance parameter in API request.",
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"default": False,
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}),
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"teaCacheDistance": ("FLOAT", {
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"tooltip": "Controls TeaCache aggressiveness: 0.0 (conservative/quality) to 1.0 (aggressive/speed). Lower values maintain quality, higher values prioritize speed.",
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"default": 0.0,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01,
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}),
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# DeepCache parameters
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"deepCache": ("BOOLEAN", {
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"tooltip": "Enable DeepCache for UNet-based models (SDXL, SD1.5). Not applicable to transformer models. Provides performance improvements for high-throughput scenarios.",
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"default": None,
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}),
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"useDeepCacheOptions": ("BOOLEAN", {
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"tooltip": "Enable to include DeepCache options (deepCacheInterval and deepCacheBranchId) in API request.",
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"default": False,
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}),
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"deepCacheInterval": ("INT", {
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"tooltip": "Frequency of feature caching - steps between cache operations. Larger values = faster but may impact quality. Smaller values = better quality but slower.",
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"default": 1,
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"min": 1,
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"max": 20,
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}),
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"deepCacheBranchId": ("INT", {
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"tooltip": "Network branch for caching (0=shallowest, deeper=more conservative). Lower IDs = aggressive caching/faster, higher IDs = conservative caching/better quality.",
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"default": 0,
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"min": 0,
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"max": 10,
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}),
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# Cache step control parameters (step numbers)
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"useCacheSteps": ("BOOLEAN", {
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"tooltip": "Enable to include cache step control parameters (step numbers) in API request.",
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"default": False,
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}),
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"cacheStartStep": ("INT", {
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"tooltip": "Inference step number where caching begins (0 to total steps). Only included when useCacheSteps is enabled and value > 0.",
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"default": 0,
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"min": 0,
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"max": 200,
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}),
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"cacheEndStep": ("INT", {
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"tooltip": "Inference step number where caching stops (must be > cacheStartStep). Only included when useCacheSteps is enabled.",
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"default": 1,
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"min": 1,
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"max": 200,
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}),
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# Cache step control parameters (percentages)
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"useCachePercentageSteps": ("BOOLEAN", {
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"tooltip": "Enable to include cache step control parameters (percentages) in API request.",
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"default": False,
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}),
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"cacheStartStepPercentage": ("INT", {
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"tooltip": "Percentage of total steps where caching begins (0-99). Only included when useCachePercentageSteps is enabled and value > 0.",
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"default": 0,
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"min": 0,
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"max": 99,
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}),
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"cacheEndStepPercentage": ("INT", {
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"tooltip": "Percentage of total steps where caching stops (must be > cacheStartStepPercentage). Only included when useCachePercentageSteps is enabled.",
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"default": 1,
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"min": 1,
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"max": 100,
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}),
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"useCacheMaxConsecutiveSteps": ("BOOLEAN", {
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"tooltip": "Enable to include cacheMaxConsecutiveSteps parameter in API request.",
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"default": False,
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}),
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"cacheMaxConsecutiveSteps": ("INT", {
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"tooltip": "Max consecutive steps using cached computations before forcing fresh computation. Prevents quality degradation from extended cache reuse.",
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"default": 1,
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"min": 1,
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"max": 5,
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}),
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},
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}
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DESCRIPTION = "Configure Runware accelerator options for faster image generation. Supports TeaCache (transformer models like Flux/SD3) and DeepCache (UNet models like SDXL/SD1.5) with fine-grained control over caching parameters and step ranges. Note: These caching methods work best with deterministic schedulers (Euler, DDIM) rather than stochastic schedulers."
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FUNCTION = "acceleratorOptions"
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RETURN_TYPES = ("RUNWAREACCELERATOR",)
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RETURN_NAMES = ("Runware Accelerator",)
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CATEGORY = "Runware"
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def acceleratorOptions(self, **kwargs):
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# Get all optional parameters
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fbcache = kwargs.get("fbcache")
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useCacheDistance = kwargs.get("useCacheDistance", False)
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cacheDistance = kwargs.get("cacheDistance", 0.0)
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teaCache = kwargs.get("teaCache")
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useTeaCacheDistance = kwargs.get("useTeaCacheDistance", False)
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teaCacheDistance = kwargs.get("teaCacheDistance", 0.0)
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deepCache = kwargs.get("deepCache")
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# Get toggle and value parameters
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useDeepCacheOptions = kwargs.get("useDeepCacheOptions", False)
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deepCacheInterval = kwargs.get("deepCacheInterval", 1)
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deepCacheBranchId = kwargs.get("deepCacheBranchId", 0)
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useCacheSteps = kwargs.get("useCacheSteps", False)
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cacheStartStep = kwargs.get("cacheStartStep", 0)
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cacheEndStep = kwargs.get("cacheEndStep", 1)
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useCachePercentageSteps = kwargs.get("useCachePercentageSteps", False)
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cacheStartStepPercentage = kwargs.get("cacheStartStepPercentage", 0)
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cacheEndStepPercentage = kwargs.get("cacheEndStepPercentage", 1)
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useCacheMaxConsecutiveSteps = kwargs.get("useCacheMaxConsecutiveSteps", False)
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cacheMaxConsecutiveSteps = kwargs.get("cacheMaxConsecutiveSteps", 1)
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# Build accelerator options dictionary
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acceleratorOptions = {}
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# FBCache options
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if fbcache is True:
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acceleratorOptions["fbcache"] = fbcache
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if useCacheDistance:
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acceleratorOptions["cacheDistance"] = cacheDistance
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# TeaCache options
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if teaCache is True:
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acceleratorOptions["teaCache"] = teaCache
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if useTeaCacheDistance:
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acceleratorOptions["teaCacheDistance"] = teaCacheDistance
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# DeepCache options
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if deepCache is True:
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acceleratorOptions["deepCache"] = deepCache
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if useDeepCacheOptions:
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acceleratorOptions["deepCacheInterval"] = deepCacheInterval
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acceleratorOptions["deepCacheBranchId"] = deepCacheBranchId
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# Cache step control options (step numbers)
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if useCacheSteps:
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if cacheStartStep > 0:
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acceleratorOptions["cacheStartStep"] = cacheStartStep
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acceleratorOptions["cacheEndStep"] = cacheEndStep
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# Cache step control options (percentages)
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if useCachePercentageSteps:
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if cacheStartStepPercentage > 0:
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acceleratorOptions["cacheStartStepPercentage"] = cacheStartStepPercentage
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acceleratorOptions["cacheEndStepPercentage"] = cacheEndStepPercentage
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if useCacheMaxConsecutiveSteps:
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acceleratorOptions["cacheMaxConsecutiveSteps"] = cacheMaxConsecutiveSteps
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return (acceleratorOptions,)
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