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added TT_LatentMultiTransformOnPixelSpaceNode w/docs, minor bugfixes

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TenserTensor
2026-03-04 02:41:46 +03:00
parent f84df53f5b
commit 45b000223c
5 changed files with 206 additions and 89 deletions
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node_mappings.py
+2
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@@ -148,6 +148,7 @@ NODE_CLASS_MAPPINGS = {
# Latent V3 Nodes
"TT_LatentFactoryNode": TT_LatentFactoryNode,
"TT_LatentMultiTransformNode": TT_LatentMultiTransformNode,
"TT_LatentMultiTransformOnPixelSpaceNode": TT_LatentMultiTransformOnPixelSpaceNode,
# Loaders
"TT_SdxlModelsLoader": TT_SdxlModelsLoader,
"TT_SdxlModelsLoaderAdvanced": TT_SdxlModelsLoaderAdvanced,
@@ -230,6 +231,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
# Latent V3 Nodes
"TT_LatentFactoryNode": "TT Latent Factory",
"TT_LatentMultiTransformNode": "TT Latent Multi Transform",
"TT_LatentMultiTransformOnPixelSpaceNode": "TT Latent MultiTransform On Pixel Space",
# Loaders
"TT_SdxlModelsLoader": "TT SDXL Models Loader",
"TT_SdxlModelsLoaderAdvanced": "TT SDXL Models Loader (Advanced)",
nodes_image.py
+77 -88
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@@ -14,16 +14,15 @@ from spandrel import ModelLoader, ImageModelDescriptor
import folder_paths
from comfy import model_management, utils, samplers
from comfy_api.latest import ComfyExtension, IO, ui
from comfy_api.latest._io import UploadType
from node_helpers import conditioning_set_values
from node_helpers import pillow
from .nodes_latent import MEGAPIXELS
from comfy_api.latest import ComfyExtension, io, ui
from node_helpers import conditioning_set_values, pillow
from .nodes_vae import vae_encode
CATEGORY = "TenserTensor/Image"
RESIZE_METHODS = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"]
FILENAME_FORMATS = ["name-###", "date-name-###", "name-datetime"]
FORMAT_EXT = {"PNG": ".png", "JPEG": ".jpg", "WEBP": ".webp", }
MEGAPIXELS = ["0.25 MP", "0.5 MP", "1 MP", "2 MP", "4 MP", "8 MP"]
class SingleCondCFGGuider(samplers.CFGGuider):
@@ -88,6 +87,14 @@ def resize_image_to_megapixels(timage, resize_method, megapixels, dimension_step
return resized.squeeze(-1) if is_mask else resized
def rotate_image(pixels, turns):
return torch.rot90(pixels, k=turns, dims=[1, 2])
def flip_image(pixels, axis):
return torch.flip(pixels, dims=[2 if axis == "x" else 1])
def get_torch_device(device=None):
return (
model_management.get_torch_device()
@@ -156,40 +163,40 @@ def get_image_files():
])
class TT_ImageLoaderResizerNode(IO.ComfyNode):
class TT_ImageLoaderResizerNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_ImageLoaderResizerNode",
display_name="TT Image Loader / Resizer",
category=CATEGORY,
description="",
inputs=[
IO.Boolean.Input("resize_image", default=False, label_on="Scale", label_off="Keep"),
IO.Combo.Input("resize_method", options=RESIZE_METHODS, advanced=True),
IO.Int.Input("dimension_step", default=1, min=1, max=256, advanced=True),
IO.Combo.Input("megapixels", options=MEGAPIXELS, advanced=True),
IO.Combo.Input("image", options=get_image_files(), upload=UploadType.image)
io.Boolean.Input("resize_image", default=False, label_on="Scale", label_off="Skip"),
io.Combo.Input("resize_method", options=RESIZE_METHODS, advanced=True),
io.Int.Input("dimension_step", default=1, min=1, max=256, advanced=True),
io.Combo.Input("megapixels", options=MEGAPIXELS, advanced=True),
io.Combo.Input("image", options=get_image_files(), upload=io.UploadType.image)
],
outputs=[
IO.Image.Output(display_name="IMAGE"),
IO.Mask.Output(display_name="MASK"),
io.Image.Output(display_name="IMAGE"),
io.Mask.Output(display_name="MASK"),
],
)
@classmethod
def fingerprint_inputs(cls, **kwargs) -> dict[str, Any]:
path = folder_paths.get_annotated_filepath(kwargs.get("image"))
resize = kwargs.get("resize_image")
resize_image = kwargs.get("resize_image")
result = {
"image": path + str(os.path.getmtime(path)),
"resize_image": resize,
"resize_method": kwargs.get("resize_method"),
"dimension_step": kwargs.get("dimension_step"),
"resize_image": resize_image,
}
if resize:
if resize_image:
result["megapixels"] = kwargs.get("megapixels")
result["resize_method"] = kwargs.get("resize_method")
result["dimension_step"] = kwargs.get("dimension_step")
return result
@@ -202,7 +209,7 @@ class TT_ImageLoaderResizerNode(IO.ComfyNode):
return True
@classmethod
def execute(cls, **kwargs) -> IO.NodeOutput:
def execute(cls, **kwargs) -> io.NodeOutput:
timage, tmask = load_image(kwargs.get("image"))
if kwargs.get("resize_image"):
resize_method, megapixels, dimension_step = (
@@ -210,20 +217,10 @@ class TT_ImageLoaderResizerNode(IO.ComfyNode):
kwargs.get("megapixels"),
kwargs.get("dimension_step")
)
timage = resize_image_to_megapixels(
timage,
resize_method,
megapixels,
dimension_step
)
tmask = resize_image_to_megapixels(
tmask,
resize_method,
megapixels,
dimension_step
)
timage = resize_image_to_megapixels(timage, resize_method, megapixels, dimension_step)
tmask = resize_image_to_megapixels(tmask, resize_method, megapixels, dimension_step)
return IO.NodeOutput(timage, tmask, ui=ui.PreviewImage(timage, cls=cls))
return io.NodeOutput(timage, tmask, ui=ui.PreviewImage(timage, cls=cls))
class InvalidSavePathError(Exception):
@@ -283,36 +280,36 @@ def save_image(**kwargs) -> None:
tframe.save(os.path.join(save_path, filename), format=image_format, **params)
class TT_ImagePreviewSaveNode(IO.ComfyNode):
class TT_ImagePreviewSaveNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_ImagePreviewSaveNode",
display_name="TT Image Preview / Save",
category=CATEGORY,
description="",
is_output_node=True,
inputs=[
IO.Image.Input("image"),
IO.Boolean.Input("save_image", default=True, label_on="Save image", label_off="Only preview"),
IO.String.Input("filename_prefix", default="TT"),
IO.Combo.Input("filename_format", options=FILENAME_FORMATS, default="name-datetime", advanced=True),
IO.String.Input("subfolder", default="", advanced=True),
IO.Combo.Input("image_format", options=list(FORMAT_EXT.keys()), default="PNG", advanced=True),
IO.Int.Input("image_quality", default=75, min=1, max=100, advanced=True),
IO.Int.Input("compression_level", default=6, min=0, max=9, advanced=True),
io.Image.Input("image"),
io.Boolean.Input("save_image", default=True, label_on="Save image", label_off="Only preview"),
io.String.Input("filename_prefix", default="TT"),
io.Combo.Input("filename_format", options=FILENAME_FORMATS, default="name-datetime", advanced=True),
io.String.Input("subfolder", default="", advanced=True),
io.Combo.Input("image_format", options=list(FORMAT_EXT.keys()), default="PNG", advanced=True),
io.Int.Input("image_quality", default=75, min=1, max=100, advanced=True),
io.Int.Input("compression_level", default=6, min=0, max=9, advanced=True),
],
outputs=[]
)
@classmethod
def execute(cls, **kwargs) -> IO.NodeOutput:
def execute(cls, **kwargs) -> io.NodeOutput:
timage = kwargs.get("image")
if kwargs.get("save_image"):
save_image(**kwargs)
return IO.NodeOutput(ui=ui.PreviewImage(timage, cls=cls))
return io.NodeOutput(ui=ui.PreviewImage(timage, cls=cls))
DEVICES = ["default", "cpu"]
@@ -391,35 +388,35 @@ def upscale(timage, **kwargs):
return torch.clamp(scaled.movedim(1, -1), min=0.0, max=1.0)
class TT_ImagePreviewUpscaleSaveNode(IO.ComfyNode):
class TT_ImagePreviewUpscaleSaveNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_ImagePreviewUpscaleSaveNode",
display_name="TT Image Preview / Upscale / Save",
category=CATEGORY,
description="",
is_output_node=True,
inputs=[
IO.Image.Input("image"),
IO.Boolean.Input("save_image", default=True, label_on="Save image", label_off="Only preview"),
IO.Boolean.Input("upscale_image", default=True, label_on="Upscale image", label_off="Keep size"),
IO.String.Input("filename_prefix", default="TT"),
IO.Combo.Input("filename_format", options=FILENAME_FORMATS, default="name-datetime", advanced=True),
IO.String.Input("subfolder", default="", advanced=True),
IO.Combo.Input("image_format", options=list(FORMAT_EXT.keys()), default="PNG", advanced=True),
IO.Int.Input("image_quality", default=75, min=1, max=100, advanced=True),
IO.Int.Input("compression_level", default=6, min=0, max=9, advanced=True),
IO.Combo.Input("upscaler_device", options=DEVICES, advanced=True),
IO.Combo.Input("upscale_model", options=get_upscale_models(), advanced=True),
IO.Int.Input("upscale_tile", default=512, min=128, max=4096, step=64, advanced=True),
IO.Int.Input("upscale_overlap", default=64, min=8, max=256, step=8, advanced=True),
io.Image.Input("image"),
io.Boolean.Input("save_image", default=True, label_on="Save image", label_off="Only preview"),
io.Boolean.Input("upscale_image", default=True, label_on="Upscale image", label_off="Keep size"),
io.String.Input("filename_prefix", default="TT"),
io.Combo.Input("filename_format", options=FILENAME_FORMATS, default="name-datetime", advanced=True),
io.String.Input("subfolder", default="", advanced=True),
io.Combo.Input("image_format", options=list(FORMAT_EXT.keys()), default="PNG", advanced=True),
io.Int.Input("image_quality", default=75, min=1, max=100, advanced=True),
io.Int.Input("compression_level", default=6, min=0, max=9, advanced=True),
io.Combo.Input("upscaler_device", options=DEVICES, advanced=True),
io.Combo.Input("upscale_model", options=get_upscale_models(), advanced=True),
io.Int.Input("upscale_tile", default=512, min=128, max=4096, step=64, advanced=True),
io.Int.Input("upscale_overlap", default=64, min=8, max=256, step=8, advanced=True),
],
outputs=[]
)
@classmethod
def execute(cls, **kwargs) -> IO.NodeOutput:
def execute(cls, **kwargs) -> io.NodeOutput:
timage = kwargs.get("image")
if kwargs.get("upscale_image"):
@@ -429,36 +426,33 @@ class TT_ImagePreviewUpscaleSaveNode(IO.ComfyNode):
kwargs["image"] = timage
save_image(**kwargs)
return IO.NodeOutput(ui=ui.PreviewImage(timage, cls=cls))
return io.NodeOutput(ui=ui.PreviewImage(timage, cls=cls))
TILE_SIZE, OVERLAP = 512, 64
class TT_GuiderImageReferenceNode(IO.ComfyNode):
class TT_GuiderImageReferenceNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_GuiderImageReferenceNode",
display_name="TT Guider Image Reference",
category=CATEGORY,
description="",
inputs=[
IO.Vae.Input("vae"),
IO.Guider.Input("guider"),
IO.Combo.Input("megapixels", options=MEGAPIXELS),
IO.Combo.Input("resize_method", options=RESIZE_METHODS, advanced=True),
IO.Int.Input("dimension_step", default=1, min=1, max=256, advanced=True),
IO.Combo.Input("image", options=get_image_files(), upload=UploadType.image)
io.Vae.Input("vae"),
io.Guider.Input("guider"),
io.Combo.Input("megapixels", options=MEGAPIXELS),
io.Combo.Input("resize_method", options=RESIZE_METHODS, advanced=True),
io.Int.Input("dimension_step", default=1, min=1, max=256, advanced=True),
io.Combo.Input("image", options=get_image_files(), upload=io.UploadType.image)
],
outputs=[
IO.Vae.Output("VAE"),
IO.Guider.Output("GUIDER"),
io.Vae.Output("VAE"),
io.Guider.Output("GUIDER"),
]
)
@classmethod
def execute(cls, **kwargs) -> IO.NodeOutput:
def execute(cls, **kwargs) -> io.NodeOutput:
timage, _ = load_image(kwargs.get("image"))
vae, guider, resize_method, megapixels, dimension_step = (
kwargs.get("vae"),
@@ -469,12 +463,7 @@ class TT_GuiderImageReferenceNode(IO.ComfyNode):
)
resized = resize_image_to_megapixels(timage, resize_method, megapixels, dimension_step)
samples = vae.encode_tiled(
resized,
tile_x=TILE_SIZE,
tile_y=TILE_SIZE,
overlap=OVERLAP
)
samples = vae_encode(resized, vae)
conditioning = guider.get_conds()
@@ -484,7 +473,7 @@ class TT_GuiderImageReferenceNode(IO.ComfyNode):
else:
raise ValueError("ERROR: Guider has no conditioning")
return IO.NodeOutput(vae, guider, ui=ui.PreviewImage(timage, cls=cls))
return io.NodeOutput(vae, guider, ui=ui.PreviewImage(timage, cls=cls))
# ==============================================================================
@@ -493,7 +482,7 @@ class TT_GuiderImageReferenceNode(IO.ComfyNode):
class ImageNodesExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
TT_ImageLoaderResizerNode,
TT_ImagePreviewSaveNode,
nodes_latent.py
+62 -1
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@@ -7,12 +7,13 @@ import torch
from comfy import sample, utils
from comfy_api.latest import IO, ComfyExtension
from .nodes_image import MEGAPIXELS, resize_image, rotate_image, flip_image
from .nodes_vae import vae_decode, vae_encode
CATEGORY = "TenserTensor/Latent"
ASPECT_RATIOS = ["1:1", "4:3", "3:2", "16:9", "21:9"]
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°"]
@@ -214,6 +215,64 @@ class TT_LatentMultiTransformNode(IO.ComfyNode):
return IO.NodeOutput(latent)
class TT_LatentMultiTransformOnPixelSpaceNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="TT_LatentMultiTransformOnPixelSpaceNode",
display_name="TT Latent MultiTransform On Pixel Space",
category=CATEGORY,
description="",
inputs=[
IO.Vae.Input("vae"),
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()
vae = kwargs.get("vae")
pixels = vae_decode(latent, vae)
orig_height, orig_width = pixels.shape[1:3]
if kwargs.get("scale_latent"):
scale_factor = float(kwargs.get("scale_factor").replace("x", ""))
pixels = resize_image(
pixels,
int(orig_width * scale_factor),
int(orig_height * scale_factor),
kwargs.get("scale_method"),
)
if kwargs.get("rotate_latent"):
turns = int(kwargs.get("rotate_angle").replace("°", "")) // 90
pixels = rotate_image(pixels, turns)
if kwargs.get("flip_latent"):
axis = "x" if kwargs.get("flip_direction") == "vertical" else "y"
pixels = flip_image(pixels, axis)
latent = vae_encode(pixels, vae)
mask = kwargs.get("mask")
if mask is not None:
latent = set_latent_mask(latent, mask)
return IO.NodeOutput(latent)
# ==============================================================================
# V3 entrypoint — registers context nodes with ComfyUI
# ==============================================================================
@@ -224,6 +283,7 @@ class LatentNodesExtension(ComfyExtension):
return [
TT_LatentFactoryNode,
TT_LatentMultiTransformNode,
TT_LatentMultiTransformOnPixelSpaceNode,
]
@@ -238,4 +298,5 @@ async def comfy_entrypoint() -> LatentNodesExtension:
__all__ = [
"TT_LatentFactoryNode",
"TT_LatentMultiTransformNode",
"TT_LatentMultiTransformOnPixelSpaceNode",
]
nodes_vae.py
+29
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@@ -2,8 +2,37 @@
from typing import override
import torch
from comfy_api.latest import IO, ComfyExtension
TILE_SIZE, OVERLAP = 512, 64
def vae_encode(image, vae, tile_width=TILE_SIZE, tile_height=TILE_SIZE, overlap=OVERLAP):
samples = vae.encode_tiled(
image,
tile_x=tile_width,
tile_y=tile_height,
overlap=overlap
)
return {"samples": samples}
def vae_decode(latent, vae, tile_width=TILE_SIZE, tile_height=TILE_SIZE, overlap=OVERLAP):
samples = latent["samples"]
compression = vae.spacial_compression_decode()
images = vae.decode_tiled(
samples,
tile_x=tile_width // compression,
tile_y=tile_height // compression,
overlap=overlap // compression
)
return images
# ==============================================================================
# V3 entrypoint — registers context nodes with ComfyUI
web/docs/TT_LatentMultiTransformOnPixelSpaceNode.md
+36
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@@ -0,0 +1,36 @@
## TT Latent MultiTransform On Pixel Space *TT_LatentMultiTransformOnPixelSpaceNode*
*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 by first decoding it to pixel space, transforming, and re-encoding. Produces better quality
results than direct latent manipulation.
### Inputs
| Parameter | Type | Required | Description |
|------------------|---------|----------|----------------------------------------------------|
| `vae` | VAE | Yes | VAE model for decoding and re-encoding the latent |
| `latent` | LATENT | Yes | Input latent to transform |
| `mask` | MASK | No | Optional noise mask to attach to the output latent |
| `scale_latent` | BOOLEAN | Yes | Scale image (Scale) or skip (Skip) |
| `scale_factor` | COMBO | Yes | Scale multiplier |
| `scale_method` | COMBO | Yes | Interpolation method for scaling |
| `rotate_latent` | BOOLEAN | Yes | Rotate image (Rotate) or skip (Skip) |
| `rotate_angle` | COMBO | Yes | Rotation angle in degrees, clockwise |
| `flip_latent` | BOOLEAN | Yes | Flip image (Flip) or skip (Skip) |
| `flip_direction` | COMBO | Yes | Flip axis: horizontal or vertical |
### Outputs
| Parameter | Type | Description |
|-----------|--------|-----------------------------------|
| `LATENT` | LATENT | Transformed and re-encoded latent |
### Usage
Decode the latent to pixel space, apply scale, rotation, and flip transformations, then re-encode back to latent space. Transformations are applied in
order: scale → rotate → flip. Requires a VAE — use the same VAE that was used to encode the original latent.
---
[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)