nodes/ttulttul-ComfyUI-FlowMatching-Inverter
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Add tooltips for node arguments

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Ken Simpson
2025-10-04 15:12:25 -07:00
parent 68f1bcb6b8
commit 1139196b14
1 changed files with 34 additions and 34 deletions
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__init__.py
+34 -34
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@@ -282,17 +282,17 @@ class LatentHybridInverter:
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"latent_image": ("LATENT",),
"positive": ("CONDITIONING", ),
"steps": ("INT", {"default": 20, "min": 1, "max": 1000}),
"model": ("MODEL", {"tooltip": "UNet model used for both inversion and diffusion passes."}),
"latent_image": ("LATENT", {"tooltip": "Latent tensor to invert and re-noise."}),
"positive": ("CONDITIONING", {"tooltip": "Positive prompt conditioning that guides both passes."}),
"steps": ("INT", {"default": 20, "min": 1, "max": 1000, "tooltip": "Solver steps executed for inversion and forward diffusion."}),
"strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Strength for both inversion and forward diffusion. The target noise level."}),
"blend_factor": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "0.0 = 100% Forward Diffusion (Stable), 1.0 = 100% Inverter (Creative)"}),
"inverter_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Seed for the creative, perturbed inversion process."}),
"forward_diffusion_seed": ("INT", {"default": 1, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Seed for the stable, 'perfect' noise anchor."}),
"velocity_amplification": ("FLOAT", {"default": 1.0, "min": -0.9, "max": 2.0, "step": 0.05}),
"velocity_perturb_strength": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 5.0, "step": 0.01}),
"internal_precision": (["bfloat16", "float16", "force_float32"], {"default": "bfloat16"}),
"velocity_amplification": ("FLOAT", {"default": 1.0, "min": -0.9, "max": 2.0, "step": 0.05, "tooltip": "Deterministically scales the predicted velocity each step."}),
"velocity_perturb_strength": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 5.0, "step": 0.01, "tooltip": "Random velocity noise level for the inverter stage."}),
"internal_precision": (["bfloat16", "float16", "force_float32"], {"default": "bfloat16", "tooltip": "Precision used for inverter UNet autocast."}),
"normalize_output": (["enable", "disable"], {"default": "enable", "tooltip": "Normalizing the inverter output is highly recommended."}),
}
}
@@ -359,12 +359,12 @@ class QwenRectifiedFlowInverter:
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"latent_image": ("LATENT",),
"positive": ("CONDITIONING", ),
"model": ("MODEL", {"tooltip": "UNet to integrate during the rectified flow inversion."}),
"latent_image": ("LATENT", {"tooltip": "Latent to be inverted toward the clean state."}),
"positive": ("CONDITIONING", {"tooltip": "Positive prompt conditioning applied during inversion."}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Seed for the random velocity perturbation."}),
"steps": ("INT", {"default": 20, "min": 1, "max": 1000}),
"inversion_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"steps": ("INT", {"default": 20, "min": 1, "max": 1000, "tooltip": "Number of integration steps available for the inverter."}),
"inversion_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Fraction of the schedule to walk backward during inversion."}),
"velocity_amplification": ("FLOAT", {
"default": 0.0,
"min": -0.9,
@@ -379,7 +379,7 @@ class QwenRectifiedFlowInverter:
"step": 0.01,
"tooltip": "Adds seeded random noise to the velocity. >1.0 will heavily randomize the path."
}),
"internal_precision": (["bfloat16", "float16", "force_float32"], {"default": "bfloat16"}),
"internal_precision": (["bfloat16", "float16", "force_float32"], {"default": "bfloat16", "tooltip": "Autocast precision used while running the inverter UNet."}),
"normalize_output": (["enable", "disable"], {"default": "enable", "tooltip": "Crucial for high inversion strengths. Rescales the output latent to a standard distribution (mean=0, std=1) to prevent the sampler from collapsing."}),
}
}
@@ -471,9 +471,9 @@ class LatentGaussianBlur:
def INPUT_TYPES(cls):
return {
"required": {
"latent": ("LATENT",),
"sigma": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1}),
"blur_mode": (["Spatial Only", "Spatial and Channel"], {"default": "Spatial Only"}),
"latent": ("LATENT", {"tooltip": "Latent tensor to blur."}),
"sigma": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1, "tooltip": "Standard deviation for the Gaussian kernel."}),
"blur_mode": (["Spatial Only", "Spatial and Channel"], {"default": "Spatial Only", "tooltip": "Choose whether to blur across spatial dimensions only or include channels."}),
}
}
RETURN_TYPES = ("LATENT",)
@@ -543,7 +543,7 @@ class LatentFrequencySplit:
def INPUT_TYPES(cls):
return {
"required": {
"latent": ("LATENT",),
"latent": ("LATENT", {"tooltip": "Latent to decompose into low/high frequency bands."}),
"sigma": ("FLOAT", {
"default": 1.5,
"min": 0.0,
@@ -611,8 +611,8 @@ class LatentAddNoise:
def INPUT_TYPES(s):
return {
"required": {
"latent": ("LATENT",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"latent": ("LATENT", {"tooltip": "Latent to receive additional Gaussian noise."}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Seed for generating repeatable noise."}),
"strength": ("FLOAT", {
"default": 1.0,
"min": 0.0,
@@ -660,8 +660,8 @@ class LatentPerlinFractalNoise:
def INPUT_TYPES(cls):
return {
"required": {
"latent": ("LATENT",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"latent": ("LATENT", {"tooltip": "Latent that will be perturbed with fractal Perlin noise."}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Seed controlling the procedural noise pattern."}),
"frequency": ("FLOAT", {
"default": 2.0,
"min": 0.01,
@@ -765,8 +765,8 @@ class LatentSwirlNoise:
def INPUT_TYPES(cls):
return {
"required": {
"latent": ("LATENT",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"latent": ("LATENT", {"tooltip": "Latent to deform with vortex-style warps."}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Seed for vortex placement and direction randomness."}),
"vortices": ("INT", {
"default": 1,
"min": 1,
@@ -947,10 +947,10 @@ class LatentForwardDiffusion:
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"latent": ("LATENT",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"model": ("MODEL", {"tooltip": "Diffusion model that defines the forward noise schedule."}),
"latent": ("LATENT", {"tooltip": "Clean latent to push forward along the schedule."}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Seed for the forward diffusion noise."}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "Number of steps in the sampler's schedule."}),
"noise_strength": ("FLOAT", {
"default": 0.8,
"min": 0.0,
@@ -1003,8 +1003,8 @@ class ConditioningAddNoise:
def INPUT_TYPES(cls):
return {
"required": {
"conditioning": ("CONDITIONING",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"conditioning": ("CONDITIONING", {"tooltip": "Conditioning list to perturb with Gaussian noise."}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Seed that drives the conditioning noise."}),
"strength": ("FLOAT", {
"default": 0.1,
"min": 0.0,
@@ -1065,7 +1065,7 @@ class ConditioningGaussianBlur:
def INPUT_TYPES(cls):
return {
"required": {
"conditioning": ("CONDITIONING",),
"conditioning": ("CONDITIONING", {"tooltip": "Conditioning list whose token dimension will be blurred."}),
"sigma": ("FLOAT", {
"default": 0.75,
"min": 0.0,
@@ -1148,7 +1148,7 @@ class ConditioningFrequencySplit:
def INPUT_TYPES(cls):
return {
"required": {
"conditioning": ("CONDITIONING",),
"conditioning": ("CONDITIONING", {"tooltip": "Conditioning list to separate into low/high bands."}),
"sigma": ("FLOAT", {
"default": 0.75,
"min": 0.0,
@@ -1246,8 +1246,8 @@ class ConditioningFrequencyMerge:
def INPUT_TYPES(cls):
return {
"required": {
"low_pass": ("CONDITIONING",),
"high_pass": ("CONDITIONING",),
"low_pass": ("CONDITIONING", {"tooltip": "Low-frequency conditioning list produced by the split node."}),
"high_pass": ("CONDITIONING", {"tooltip": "High-frequency conditioning list to recombine."}),
"low_gain": ("FLOAT", {
"default": 1.0,
"min": -5.0,
@@ -1306,7 +1306,7 @@ class ConditioningScale:
def INPUT_TYPES(cls):
return {
"required": {
"conditioning": ("CONDITIONING",),
"conditioning": ("CONDITIONING", {"tooltip": "Conditioning list to scale."}),
"factor": ("FLOAT", {
"default": 1.0,
"min": 0.0,