- Remove individual TeaCache and DeepCache modules - Create new unified 'Runware Accelerator Options' node - Support all accelerator parameters: fbcache, cacheDistance, teaCache, teaCacheDistance, deepCache, deepCacheInterval, deepCacheBranchId - Add cache step control: cacheStartStep, cacheStartStepPercentage, cacheEndStep, cacheEndStepPercentage, cacheMaxConsecutiveSteps - Update imageInference.py to support new accelerator system
144 lines
6.8 KiB
Python
144 lines
6.8 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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"cacheDistance": ("FLOAT", {
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"tooltip": "Cache distance parameter for FBCache",
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"default": None,
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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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"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": None,
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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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"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": None,
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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": None,
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"min": 0,
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"max": 10,
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}),
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# Cache step control parameters
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"cacheStartStep": ("INT", {
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"tooltip": "Inference step number where caching begins (0 to total steps). Alternative to cacheStartStepPercentage.",
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"default": None,
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"min": 0,
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"max": 200,
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}),
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"cacheStartStepPercentage": ("INT", {
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"tooltip": "Percentage of total steps where caching begins (0-99). Alternative to cacheStartStep.",
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"default": None,
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"min": 0,
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"max": 99,
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}),
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"cacheEndStep": ("INT", {
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"tooltip": "Inference step number where caching stops (must be > cacheStartStep). Alternative to cacheEndStepPercentage.",
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"default": None,
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"min": 1,
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"max": 200,
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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). Alternative to cacheEndStep.",
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"default": None,
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"min": 1,
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"max": 100,
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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": None,
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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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cacheDistance = kwargs.get("cacheDistance")
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teaCache = kwargs.get("teaCache")
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teaCacheDistance = kwargs.get("teaCacheDistance")
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deepCache = kwargs.get("deepCache")
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deepCacheInterval = kwargs.get("deepCacheInterval")
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deepCacheBranchId = kwargs.get("deepCacheBranchId")
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cacheStartStep = kwargs.get("cacheStartStep")
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cacheStartStepPercentage = kwargs.get("cacheStartStepPercentage")
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cacheEndStep = kwargs.get("cacheEndStep")
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cacheEndStepPercentage = kwargs.get("cacheEndStepPercentage")
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cacheMaxConsecutiveSteps = kwargs.get("cacheMaxConsecutiveSteps")
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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 not None:
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acceleratorOptions["fbcache"] = fbcache
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if cacheDistance is not None:
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acceleratorOptions["cacheDistance"] = cacheDistance
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# TeaCache options
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if teaCache is not None:
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acceleratorOptions["teaCache"] = teaCache
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if teaCacheDistance is not None:
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acceleratorOptions["teaCacheDistance"] = teaCacheDistance
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# DeepCache options
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if deepCache is not None:
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acceleratorOptions["deepCache"] = deepCache
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if deepCacheInterval is not None:
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acceleratorOptions["deepCacheInterval"] = deepCacheInterval
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if deepCacheBranchId is not None:
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acceleratorOptions["deepCacheBranchId"] = deepCacheBranchId
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# Cache step control options
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if cacheStartStep is not None:
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acceleratorOptions["cacheStartStep"] = cacheStartStep
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if cacheStartStepPercentage is not None:
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acceleratorOptions["cacheStartStepPercentage"] = cacheStartStepPercentage
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if cacheEndStep is not None:
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acceleratorOptions["cacheEndStep"] = cacheEndStep
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if cacheEndStepPercentage is not None:
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acceleratorOptions["cacheEndStepPercentage"] = cacheEndStepPercentage
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if cacheMaxConsecutiveSteps is not None:
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acceleratorOptions["cacheMaxConsecutiveSteps"] = cacheMaxConsecutiveSteps
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return (acceleratorOptions,)
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