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Added TT_LatentMultiTransformNode w/docs, minor bugfixes

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TenserTensor
2026-03-03 23:16:10 +03:00
parent fe8715ed1b
commit 5e65ba0df5
4 changed files with 129 additions and 26 deletions
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node_mappings.py
+2
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@@ -147,6 +147,7 @@ NODE_CLASS_MAPPINGS = {
"TT_LatentFactory": TT_LatentFactory,
# Latent V3 Nodes
"TT_LatentFactoryNode": TT_LatentFactoryNode,
"TT_LatentMultiTransformNode": TT_LatentMultiTransformNode,
# Loaders
"TT_SdxlModelsLoader": TT_SdxlModelsLoader,
"TT_SdxlModelsLoaderAdvanced": TT_SdxlModelsLoaderAdvanced,
@@ -228,6 +229,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"TT_LatentFactory": "TT Latent Factory (Deprecated)",
# Latent V3 Nodes
"TT_LatentFactoryNode": "TT Latent Factory",
"TT_LatentMultiTransformNode": "TT Latent Multi Transform",
# Loaders
"TT_SdxlModelsLoader": "TT SDXL Models Loader",
"TT_SdxlModelsLoaderAdvanced": "TT SDXL Models Loader (Advanced)",
nodes_latent.py
+89 -4
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@@ -5,16 +5,19 @@ from typing import Any, override
import torch
from comfy import sample
from comfy import sample, utils
from comfy_api.latest import IO, ComfyExtension
CATEGORY = "TenserTensor/Latent"
ASPECT_RATIOS = ["1:1", "4:3", "3:2", "16:9", "21:9"]
MEGAPIXELS = ["0.25 MP", "0.5 MP", "1 MP", "2 MP", "4 MP", "8 MP"]
ORIENTATIONS = ["landscape", "portrait"]
MODEL_TYPES = ["FLUX1.D", "FLUX2.D", "SDXL"]
CLIP_MULTIPLIERS = ["1x", "2x", "4x"]
MEGAPIXELS = ["0.25 MP", "0.5 MP", "1 MP", "2 MP", "4 MP", "8 MP"]
MODEL_TYPES = ["FLUX1.D", "FLUX2.D", "SDXL"]
ORIENTATIONS = ["landscape", "portrait"]
ROTATE_ANGLES = ["90°", "180°", "270°"]
SCALE_METHODS = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"]
SCALE_FACTORS = ["0.25x", "0.5x", "1x", "2x", "4x", "8x"]
class RandomNoise:
@@ -131,6 +134,86 @@ class TT_LatentFactoryNode(IO.ComfyNode):
return IO.NodeOutput(*args.values())
def set_latent_mask(latent, mask):
samples = latent
samples["noise_mask"] = mask.unsqueeze(0) if mask.ndim == 2 else mask
return samples
def scale_latent(**kwargs):
samples = kwargs.get("latent")["samples"]
orig_height, orig_width = samples.shape[2:]
scale_factor = float(kwargs.get("scale_factor").replace("x", ""))
final_width, final_height = round(orig_width * scale_factor), round(orig_height * scale_factor)
scaled = utils.common_upscale(
samples,
final_width,
final_height, kwargs.get("scale_method"),
"disabled"
)
return {"samples": scaled}
def rotate_latent(latent, turns):
samples = latent["samples"]
return {"samples": torch.rot90(samples, k=turns, dims=[3, 2])}
def flip_latent(latent, axis):
samples = latent["samples"]
return {"samples": torch.flip(samples, dims=[2 if axis == "x" else 3])}
class TT_LatentMultiTransformNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="TT_LatentMultiTransformNode",
display_name="TT Latent MultiTransform",
category=CATEGORY,
description="",
inputs=[
IO.Latent.Input("latent"),
IO.Mask.Input("mask", optional=True),
IO.Boolean.Input("scale_latent", default=True, label_on="Scale", label_off="Skip"),
IO.Combo.Input("scale_factor", options=SCALE_FACTORS, default="1x"),
IO.Combo.Input("scale_method", options=SCALE_METHODS, default="nearest-exact"),
IO.Boolean.Input("rotate_latent", default=True, label_on="Rotate", label_off="Skip"),
IO.Combo.Input("rotate_angle", options=ROTATE_ANGLES),
IO.Boolean.Input("flip_latent", default=True, label_on="Flip", label_off="Skip"),
IO.Combo.Input("flip_direction", options=["horizontal", "vertical"]),
],
outputs=[
IO.Latent.Output("LATENT"),
]
)
@classmethod
def execute(cls, **kwargs) -> IO.NodeOutput:
latent = kwargs.get("latent").copy()
mask = kwargs.get("mask")
if mask is not None:
latent = set_latent_mask(latent, mask)
if kwargs.get("scale_latent"):
latent = scale_latent(**kwargs)
if kwargs.get("rotate_latent"):
turns = int(kwargs.get("rotate_angle").replace("°", "")) // 90
latent = rotate_latent(latent, turns)
if kwargs.get("flip_latent"):
axis = "x" if kwargs.get("flip_direction") == "vertical" else "y"
latent = flip_latent(latent, axis)
return IO.NodeOutput(latent)
# ==============================================================================
# V3 entrypoint — registers context nodes with ComfyUI
# ==============================================================================
@@ -140,6 +223,7 @@ class LatentNodesExtension(ComfyExtension):
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
return [
TT_LatentFactoryNode,
TT_LatentMultiTransformNode,
]
@@ -153,4 +237,5 @@ async def comfy_entrypoint() -> LatentNodesExtension:
__all__ = [
"TT_LatentFactoryNode",
"TT_LatentMultiTransformNode",
]
nodes_sampling.py
+3 -22
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@@ -5,11 +5,9 @@ from typing import override
import latent_preview
from comfy import sample, utils, model_management
from comfy_api.latest import ComfyExtension, IO
from .nodes_latent import SCALE_FACTORS, SCALE_METHODS, scale_latent
CATEGORY = "TenserTensor/Sampling"
SCALE_METHODS = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"]
MEMORY_POLICIES = ["default", "balanced", "aggressive"]
SCALE_FACTORS = ["0.25x", "0.5x", "1x", "2x", "4x", "8x"]
def execute_guided_sampling(**kwargs):
@@ -79,21 +77,6 @@ class TT_GuidedKSamplerNode(IO.ComfyNode):
return IO.NodeOutput(samples)
def scale_latent(latent, **kwargs):
samples = latent.copy()["samples"]
orig_height, orig_width = samples.shape[2:]
scale_factor = float(kwargs.get("scale_factor").replace("x", ""))
final_width, final_height = round(orig_width * scale_factor), round(orig_height * scale_factor)
scaled = utils.common_upscale(
samples,
final_width,
final_height, kwargs.get("scale_method"),
"disabled"
)
return {"samples": scaled}
class TT_GuidedUpscaleKSamplerNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
@@ -108,7 +91,7 @@ class TT_GuidedUpscaleKSamplerNode(IO.ComfyNode):
IO.Sigmas.Input("sigmas"),
IO.Sampler.Input("sampler"),
IO.Noise.Input("random_noise"),
IO.Boolean.Input("scale_latent", default=True, label_on="Scale latent", label_off="Keep size"),
IO.Boolean.Input("scale_latent", default=False, label_on="Scale", label_off="Skip"),
IO.Combo.Input("scale_factor", options=SCALE_FACTORS, default="1x", advanced=True),
IO.Combo.Input("scale_method", options=SCALE_METHODS, default="nearest-exact", advanced=True),
],
@@ -122,12 +105,10 @@ class TT_GuidedUpscaleKSamplerNode(IO.ComfyNode):
samples = execute_guided_sampling(**kwargs)
if kwargs.get("scale_latent"):
samples = scale_latent(samples, **kwargs)
samples = scale_latent(**kwargs)
return IO.NodeOutput(samples)
class TT_LatentMultiTransformNode(IO.ComfyNode):
pass
# ==============================================================================
# V3 entrypoint — registers context nodes with ComfyUI
web/docs/TT_LatentMultiTransformNode.md
+35
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@@ -0,0 +1,35 @@
## TT Latent MultiTransform *TT_LatentMultiTransformNode*
*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
Applies multiple geometric transformations to a latent in a single node: scaling, rotation, and flipping. Each transformation can be enabled or
disabled independently.
### Inputs
| Parameter | Type | Required | Description |
|------------------|---------|----------|--------------------------------------------------------|
| `latent` | LATENT | Yes | Input latent to transform |
| `mask` | MASK | No | Optional noise mask to attach to the latent |
| `scale_latent` | BOOLEAN | Yes | Scale latent (Scale) or keep original size (Keep size) |
| `scale_factor` | COMBO | No | Scale multiplier (advanced) |
| `scale_method` | COMBO | No | Interpolation method for scaling (advanced) |
| `rotate_latent` | BOOLEAN | Yes | Rotate latent (Rotate) or keep orientation (Keep) |
| `rotate_angle` | COMBO | Yes | Rotation angle in degrees |
| `flip_latent` | BOOLEAN | Yes | Flip latent (Flip) or keep orientation (Keep) |
| `flip_direction` | COMBO | Yes | Flip axis: horizontal or vertical |
### Outputs
| Parameter | Type | Description |
|-----------|--------|--------------------|
| `LATENT` | LATENT | Transformed latent |
### Usage
Apply scale, rotation, and flip to a latent before or after sampling. Transformations are applied in order: scale → rotate → flip. Use the boolean
toggles to enable only the transformations you need.
---
[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)