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Migrated 4 nodes in "TenserTensor/Sampling" category to API V3 w/docs

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This commit is contained in:
TenserTensor
2026-03-12 19:23:29 +03:00
parent e91c78224b
commit 07d53d8ba3
13 changed files with 632 additions and 44 deletions
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node_mappings.py
+10 -4
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@@ -183,7 +183,7 @@ NODE_CLASS_MAPPINGS = {
"TT_GgufModelsLoaderNode": TT_Flux2GgufModelsLoaderNode,
"TT_GgufModelsLoaderAdvancedNode": TT_Flux2GgufModelsLoaderAdvancedNode,
"TT_Sd35GgufModelsLoaderNode": TT_Sd35GgufModelsLoaderNode,
# "TT_Sd35GgufModelsLoaderAdvancedNode": TT_Sd35GgufModelsLoaderAdvancedNode,
"TT_Sd35GgufModelsLoaderAdvancedNode": TT_Sd35GgufModelsLoaderAdvancedNode,
# Postproduction
"TT_ApplyLut": TT_ApplyLut,
"TT_AddFilmGrain": TT_AddFilmGrain,
@@ -198,9 +198,12 @@ NODE_CLASS_MAPPINGS = {
"TT_KSamplerTwoStage": TT_KSamplerTwoStage,
"TT_KSamplerGuided": TT_KSamplerGuided,
# Samplers V3 Nodes
"TT_KSamplerNode": TT_KSamplerNode,
"TT_KSamplerAdvancedNode": TT_KSamplerAdvancedNode,
"TT_KSamplerContextNode": TT_KSamplerContextNode,
"TT_KSamplerTwoStageNode": TT_KSamplerTwoStageNode,
"TT_GuidedKSamplerNode": TT_GuidedKSamplerNode,
"TT_GuidedUpscaleKSamplerNode": TT_GuidedUpscaleKSamplerNode,
"TT_GuidedKSamplerWithPreviewNode": TT_GuidedKSamplerWithPreviewNode,
# Text Encoder
"TT_ClipTextEncodeFlux": TT_ClipTextEncodeFlux,
"TT_ClipTextEncodeFluxContext": TT_ClipTextEncodeFluxContext,
@@ -296,7 +299,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"TT_GgufModelsLoaderNode": "TT GGUF Models Loader",
"TT_GgufModelsLoaderAdvancedNode": "TT GGUF Models Loader (Advanced)",
"TT_Sd35GgufModelsLoaderNode": "TT SD3.5 GGUF Models Loader",
# "TT_Sd35ModelsLoaderAdvancedNode": "TT SD3.5 GGUF Models Loader (Advanced)",
"TT_Sd35ModelsLoaderAdvancedNode": "TT SD3.5 GGUF Models Loader (Advanced)",
# Postproduction
"TT_ApplyLut": "TT Apply LUT",
"TT_AddFilmGrain": "TT Add Film Grain",
@@ -311,9 +314,12 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"TT_KSamplerTwoStage": "TT KSampler (Two Stages)",
"TT_KSamplerGuided": "TT KSampler (Guided)",
# Samplers V3 Nodes
"TT_KSamplerNode": "TT KSampler",
"TT_KSamplerAdvancedNode": "TT KSampler (Advanced)",
"TT_KSamplerContextNode": "TT KSampler (Context)",
"TT_KSamplerTwoStageNode": "TT KSampler (Two Stage)",
"TT_GuidedKSamplerNode": "TT Guided KSampler",
"TT_GuidedUpscaleKSamplerNode": "TT Guided Upscale KSampler",
"TT_GuidedKSamplerWithPreviewNode": "TT Guided KSampler (With Preview)",
# Text Encoder
"TT_ClipTextEncodeFlux": "TT CLIP Text Encode FLUX",
"TT_ClipTextEncodeFluxContext": "TT CLIP Text Encode FLUX (Context)",
nodes/sampling/ksampler.py
+1 -1
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@@ -25,7 +25,7 @@ class TT_KSampler():
RETURN_TYPES = ("LATENT",)
RETURN_NAMES = ("SAMPLES",)
FUNCTION = "execute_sampling"
CATEGORY = "TenserTensor/Sampling"
CATEGORY = "TenserTensor/Deprecated/Sampling"
def execute_sampling(self, **kwargs):
return (sample_latents(**kwargs),)
nodes/sampling/ksampler_advanced.py
+1 -1
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@@ -30,7 +30,7 @@ class TT_KSamplerAdvanced():
RETURN_TYPES = ("LATENT",)
RETURN_NAMES = ("SAMPLES",)
FUNCTION = "execute_sampling"
CATEGORY = "TenserTensor/Sampling"
CATEGORY = "TenserTensor/Deprecated/Sampling"
def execute_sampling(self, **kwargs):
return (sample_latents(**kwargs),)
nodes/sampling/ksampler_context.py
+1 -1
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@@ -15,7 +15,7 @@ class TT_KSamplerContext():
RETURN_TYPES = ("TT_CONTEXT", "LATENT",)
RETURN_NAMES = ("CONTEXT", "SAMPLES",)
FUNCTION = "execute_sampling"
CATEGORY = "TenserTensor/Sampling"
CATEGORY = "TenserTensor/Deprecated/Sampling"
def execute_sampling(self, context):
model = context.get("model", None)
nodes/sampling/ksampler_two_stage.py
+1 -1
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@@ -30,7 +30,7 @@ class TT_KSamplerTwoStage():
RETURN_TYPES = ("LATENT",)
RETURN_NAMES = ("SAMPLES",)
FUNCTION = "execute_sampling"
CATEGORY = "TenserTensor/Sampling"
CATEGORY = "TenserTensor/Deprecated/Sampling"
def execute_sampling(self, **kwargs):
draft_steps = kwargs["draft_steps"]
nodes_loaders.py
+2 -2
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@@ -786,7 +786,7 @@ class LoaderNodesExtension(ComfyExtension):
TT_Flux2GgufModelsLoaderNode,
TT_Flux2GgufModelsLoaderAdvancedNode,
TT_Sd35GgufModelsLoaderNode,
# TT_Sd35GgufModelsLoaderAdvancedNode,
TT_Sd35GgufModelsLoaderAdvancedNode,
]
@@ -806,5 +806,5 @@ __all__ = [
"TT_Flux2GgufModelsLoaderNode",
"TT_Flux2GgufModelsLoaderAdvancedNode",
"TT_Sd35GgufModelsLoaderNode",
# "TT_Sd35GgufModelsLoaderAdvancedNode",
"TT_Sd35GgufModelsLoaderAdvancedNode",
]
nodes_postproduction.py
+130 -2
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@@ -2,7 +2,135 @@
from typing import override
from comfy_api.latest import ComfyExtension, IO
from comfy_api.latest import ComfyExtension, io
CATEGORY = "TenserTensor/Postproduction"
class TT_AddFilmGrainNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_AddFilmGrainNode",
display_name="TT Add Film Grain",
category=CATEGORY,
description="",
inputs=[
],
outputs=[
]
)
@classmethod
def execute(cls, **kwargs) -> io.NodeOutput:
raise NotImplementedError
class TT_ApplyLutNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_ApplyLutNode",
display_name="TT Apply LUT",
category=CATEGORY,
description="",
inputs=[
],
outputs=[
]
)
@classmethod
def execute(cls, **kwargs) -> io.NodeOutput:
raise NotImplementedError
class TT_ImageEnhancerNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_ImageEnhancerNode",
display_name="TT Image Enhancer",
category=CATEGORY,
description="",
inputs=[
],
outputs=[
]
)
@classmethod
def execute(cls, **kwargs) -> io.NodeOutput:
raise NotImplementedError
class TT_QuickImageUpscalerNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_QuickImageUpscalerNode",
display_name="TT Quick Image Upscaler",
category=CATEGORY,
description="",
inputs=[
],
outputs=[
]
)
@classmethod
def execute(cls, **kwargs) -> io.NodeOutput:
raise NotImplementedError
class TT_PostproductionNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_PostproductionNode",
display_name="TT Postproduction",
category=CATEGORY,
description="",
inputs=[
],
outputs=[
]
)
@classmethod
def execute(cls, **kwargs) -> io.NodeOutput:
raise NotImplementedError
class TT_PostproductionAdvancedNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_PostproductionAdvancedNode",
display_name="TT Postproduction (Advanced)",
category=CATEGORY,
description="",
inputs=[
],
outputs=[
]
)
@classmethod
def execute(cls, **kwargs) -> io.NodeOutput:
raise NotImplementedError
# ==============================================================================
@@ -11,7 +139,7 @@ from comfy_api.latest import ComfyExtension, IO
class PostproductionNodesExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
# TT_Node,
]
nodes_sampling.py
+254 -31
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@@ -2,15 +2,258 @@
from typing import override
import torch
import latent_preview
from comfy import sample, model_management
from comfy import sample, model_management, samplers
from comfy_api.latest import ComfyExtension, io
from .nodes_context import Context
from .nodes_latent import SCALE_FACTORS, SCALE_METHODS, scale_latent
from .nodes_text_encoder import SingleCondCFGGuider
CATEGORY = "TenserTensor/Sampling"
def do_sample(**kwargs):
model = kwargs.get("model", None)
if model is None:
raise ValueError("Model is required for sampling")
latent = kwargs.get("latent", None)
if latent is None:
raise ValueError("Latent image is required for sampling")
latent_dict = latent.copy()
samples, add_noise, seed, steps, device = (
latent_dict["samples"],
kwargs.get("add_noise", True),
kwargs.get("seed", 0),
kwargs.get("steps", 0),
kwargs.get("device", "cpu")
)
latent_tensor = sample.fix_empty_latent_channels(model, samples, latent.get("downscale_ratio_spacial", None))
if add_noise:
batch_idx = latent.get("batch_index", None)
noise = sample.prepare_noise(samples, seed, batch_idx)
else:
noise = torch.zeros(samples.size(), dtype=samples.dtype, layout=samples.layout, device="cpu")
interim_buffer = {}
if kwargs.get("preview_latent"):
latent_preview.set_preview_method("latent2rgb")
callback = latent_preview.prepare_callback(model, steps - 1, interim_buffer)
else:
callback = None
args = {
"model": model,
"noise": noise,
"steps": steps,
"cfg": kwargs.get("cfg"),
"sampler_name": kwargs.get("sampler_name"),
"scheduler": kwargs.get("scheduler"),
"positive": kwargs.get("positive"),
"negative": kwargs.get("negative"),
"latent_image": latent_tensor,
"denoise": kwargs.get("denoise", 1.0),
"disable_noise": not add_noise,
"start_step": kwargs.get("start_step"),
"last_step": kwargs.get("last_step"),
"force_full_denoise": kwargs.get("full_denoise", True),
"noise_mask": latent.get("noise_mask"),
"callback": callback,
"seed": seed,
}
samples = sample.sample(**args)
sampled_latent = latent_dict.copy()
sampled_latent.pop("downscale_ratio_spacial", None)
sampled_latent["samples"] = samples
return sampled_latent
class TT_KSamplerNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_KSamplerNode",
display_name="TT KSampler",
category=CATEGORY,
description="",
inputs=[
io.Model.Input("model"),
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.Latent.Input("latent"),
io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
io.Int.Input("steps", default=30, min=1, max=10_000),
io.Float.Input("cfg", default=3.0, min=0.0, max=100.0, step=0.1),
io.Combo.Input("sampler_name", options=samplers.KSampler.SAMPLERS),
io.Combo.Input("scheduler", options=samplers.KSampler.SCHEDULERS),
io.Boolean.Input("preview_latent", default=False, label_on="Show Preview", label_off="Only Sample"),
],
outputs=[
io.Latent.Output("LATENT"),
]
)
@classmethod
def execute(cls, **kwargs) -> io.NodeOutput:
latent = do_sample(**kwargs)
return io.NodeOutput(latent)
class TT_KSamplerAdvancedNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_KSamplerAdvancedNode",
display_name="TT KSampler (Advanced)",
category=CATEGORY,
description="",
inputs=[
io.Model.Input("model"),
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.Latent.Input("latent"),
io.Boolean.Input("add_noise", default=False, label_on="Random Noise", label_off="Zero Noise"),
io.Boolean.Input("full_denoise", default=False, label_on="Complete", label_off="Partial"),
io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
io.Int.Input("steps", default=30, min=1, max=10_000),
io.Float.Input("cfg", default=3.0, min=0.0, max=100.0, step=0.1),
io.Int.Input("start_step", default=0, min=0, max=10_000),
io.Int.Input("last_step", default=10_000, min=0, max=10_000),
io.Combo.Input("sampler_name", options=samplers.KSampler.SAMPLERS),
io.Combo.Input("scheduler", options=samplers.KSampler.SCHEDULERS),
io.Boolean.Input("preview_latent", default=False, label_on="Show Preview", label_off="Only Sample"),
],
outputs=[
io.Latent.Output("LATENT"),
]
)
@classmethod
def execute(cls, **kwargs) -> io.NodeOutput:
latent = do_sample(**kwargs)
return io.NodeOutput(latent)
class TT_KSamplerContextNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_KSamplerContextNode",
display_name="TT KSampler (Context)",
category=CATEGORY,
description="",
inputs=[
Context.Input("context"),
io.Boolean.Input("preview_latent", default=False, label_on="Show Preview", label_off="Only Sample"),
],
outputs=[
Context.Output("CONTEXT"),
io.Latent.Output("LATENT"),
]
)
@classmethod
def execute(cls, context) -> io.NodeOutput:
model = context.get("model")
if model is None:
raise ValueError("Model is required for sampling")
latent = context.get("latent")
if latent is None:
raise ValueError("Latent image is required for sampling")
config = context.get("workflow_config")
if config is None:
raise ValueError("Workflow Config is required for sampling")
args = {
"model": model,
"positive": context.get_attr("positive"),
"negative": context.get_attr("negative"),
"latent": latent,
"seed": config.get_attr("seed"),
"steps": config.get_attr("steps"),
"cfg": config.get_attr("cfg"),
"sampler_name": config.get_attr("sampler_name"),
"scheduler": config.get_attr("scheduler"),
}
latent = do_sample(**args)
context.set_attr("latent", latent)
return io.NodeOutput(context, latent)
class TT_KSamplerTwoStageNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_KSamplerTwoStageNode",
display_name="TT KSampler (Two Stage)",
category=CATEGORY,
description="",
inputs=[
io.Model.Input("model"),
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.Latent.Input("latent"),
io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
io.Float.Input("cfg", default=3.0, min=0.0, max=100.0, step=0.1),
io.Int.Input("draft_steps", default=25, min=1, max=10_000),
io.Int.Input("refiner_steps", default=25, min=1, max=10_000),
io.Combo.Input("draft_sampler_name", options=samplers.KSampler.SAMPLERS),
io.Combo.Input("draft_scheduler", options=samplers.KSampler.SCHEDULERS),
io.Combo.Input("refiner_sampler_name", options=samplers.KSampler.SAMPLERS),
io.Combo.Input("refiner_scheduler", options=samplers.KSampler.SCHEDULERS),
io.Float.Input("draft_denoise", default=1.0, min=0.0, max=1.0, step=0.01),
io.Float.Input("refiner_denoise", default=1.0, min=0.0, max=1.0, step=0.01),
io.Boolean.Input("preview_latent", default=False, label_on="Show Preview", label_off="Only Sample"),
],
outputs=[
io.Latent.Output("LATENT"),
]
)
@classmethod
def execute(cls, **kwargs) -> io.NodeOutput:
draft_steps = kwargs["draft_steps"]
refiner_steps = kwargs["refiner_steps"]
args = kwargs.copy()
args.update({
"steps": draft_steps,
"start_step": 0,
"add_noise": True,
"sampler_name": kwargs["draft_sampler_name"],
"scheduler": kwargs["draft_scheduler"],
"full_denoise": False,
"denoise": kwargs["draft_denoise"],
})
latent = do_sample(**args)
args.update({
"latent": latent,
"steps": draft_steps + refiner_steps,
"start_step": draft_steps,
"add_noise": True,
"sampler_name": kwargs["refiner_sampler_name"],
"scheduler": kwargs["refiner_scheduler"],
"full_denoise": True,
"denoise": kwargs["refiner_denoise"],
})
latent = do_sample(**args)
return io.NodeOutput(latent)
def execute_guided_sampling(**kwargs):
guider, latent, sigmas, random_noise, sampler, preview_latent = (
kwargs.get("guider"),
@@ -73,6 +316,7 @@ class TT_GuidedKSamplerNode(io.ComfyNode):
io.Sigmas.Input("sigmas"),
io.Sampler.Input("sampler"),
io.Noise.Input("random_noise"),
io.Boolean.Input("preview_latent", default=False, label_on="Show Preview", label_off="Only Sample"),
],
outputs=[
io.Latent.Output("SAMPLES")
@@ -103,6 +347,7 @@ class TT_GuidedUpscaleKSamplerNode(io.ComfyNode):
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="bicubic", advanced=True),
io.Boolean.Input("preview_latent", default=False, label_on="Show Preview", label_off="Only Sample"),
],
outputs=[
io.Latent.Output("SAMPLES")
@@ -119,34 +364,6 @@ class TT_GuidedUpscaleKSamplerNode(io.ComfyNode):
return io.NodeOutput(samples)
class TT_GuidedKSamplerWithPreviewNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_GuidedKSamplerWithPreviewNode",
display_name="TT Guided KSampler (With Preview)",
category=CATEGORY,
description="",
inputs=[
io.Latent.Input("latent"),
io.Guider.Input("guider"),
io.Sigmas.Input("sigmas"),
io.Sampler.Input("sampler"),
io.Noise.Input("random_noise"),
io.Boolean.Input("preview_latent", default=True, label_on="Show Peview", label_off="Only Sample"),
],
outputs=[
io.Latent.Output("SAMPLES")
]
)
@classmethod
def execute(cls, **kwargs) -> io.NodeOutput:
samples = execute_guided_sampling(**kwargs)
return io.NodeOutput(samples)
# ==============================================================================
# V3 entrypoint — registers context nodes with ComfyUI
# ==============================================================================
@@ -155,9 +372,12 @@ class SamplingNodesExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
TT_KSamplerNode,
TT_KSamplerAdvancedNode,
TT_KSamplerContextNode,
TT_KSamplerTwoStageNode,
TT_GuidedKSamplerNode,
TT_GuidedUpscaleKSamplerNode,
TT_GuidedKSamplerWithPreviewNode,
]
@@ -170,7 +390,10 @@ async def comfy_entrypoint() -> SamplingNodesExtension:
# ==============================================================================
__all__ = [
"TT_KSamplerNode",
"TT_KSamplerAdvancedNode",
"TT_KSamplerContextNode",
"TT_KSamplerTwoStageNode",
"TT_GuidedKSamplerNode",
"TT_GuidedUpscaleKSamplerNode",
"TT_GuidedKSamplerWithPreviewNode",
]
nodes_vae.py
+87 -1
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@@ -4,7 +4,9 @@ from typing import override
import torch
from comfy_api.latest import IO, ComfyExtension
from comfy_api.latest import io, ComfyExtension
CATEGORY="TenserTensor/VAE"
TILE_SIZE, OVERLAP = 512, 64
@@ -34,6 +36,90 @@ def vae_decode(latent, vae, tile_width=TILE_SIZE, tile_height=TILE_SIZE, overlap
return images
class TT_KSamplerNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_KSamplerNode",
display_name="TT_KSampler",
category=CATEGORY,
description="",
inputs=[
],
outputs=[
]
)
@classmethod
def execute(cls, **kwargs) -> io.NodeOutput:
raise NotImplementedError
class TT_KSamplerAdvancedNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_KSamplerAdvancedNode",
display_name="TT_KSampler (Advanced)",
category=CATEGORY,
description="",
inputs=[
],
outputs=[
]
)
@classmethod
def execute(cls, **kwargs) -> io.NodeOutput:
raise NotImplementedError
class TT_KSamplerContextNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_KSamplerContextNode",
display_name="TT_KSampler (Context)",
category=CATEGORY,
description="",
inputs=[
],
outputs=[
]
)
@classmethod
def execute(cls, **kwargs) -> io.NodeOutput:
raise NotImplementedError
class TT_KSamplerTwoStageNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="TT_KSamplerTwoStageNode",
display_name="TT_KSampler (Two Stage)",
category=CATEGORY,
description="",
inputs=[
],
outputs=[
]
)
@classmethod
def execute(cls, **kwargs) -> io.NodeOutput:
raise NotImplementedError
# ==============================================================================
# V3 entrypoint — registers context nodes with ComfyUI
# ==============================================================================
web/docs/TT_KSamplerAdvancedNode.md
+40
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@@ -0,0 +1,40 @@
## TT KSampler (Advanced) *TT_KSamplerAdvancedNode*
*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
Extended KSampler with fine-grained control over noise, denoising range, and step boundaries. Useful for multi-stage workflows and img2img.
### Inputs
| Parameter | Type | Required | Description |
|------------------|--------------|----------|---------------------------------------------------------|
| `model` | MODEL | Yes | Diffusion model |
| `positive` | CONDITIONING | Yes | Positive conditioning |
| `negative` | CONDITIONING | Yes | Negative conditioning |
| `latent` | LATENT | Yes | Input latent tensor |
| `add_noise` | BOOLEAN | Yes | Random Noise — use random noise; Zero Noise — use zeros |
| `full_denoise` | BOOLEAN | Yes | Complete — force full denoise; Partial — stop early |
| `denoise` | FLOAT | Yes | Denoising strength, default 1.0 |
| `seed` | INT | Yes | Generation seed |
| `steps` | INT | Yes | Total number of sampling steps, default 30 |
| `cfg` | FLOAT | Yes | Guidance scale, default 3.0 |
| `start_step` | INT | No | Step to start sampling from, default 0 |
| `last_step` | INT | No | Step to stop sampling at, default 10000 |
| `sampler_name` | COMBO | Yes | Sampler algorithm |
| `scheduler` | COMBO | Yes | Noise schedule |
| `preview_latent` | BOOLEAN | No | Show step-by-step latent preview during sampling |
### Outputs
| Parameter | Type | Description |
|-----------|--------|-----------------------|
| `LATENT` | LATENT | Sampled latent tensor |
### Usage
Use `start_step` and `last_step` to run only a portion of the schedule — useful for chaining two samplers in a draft/refine workflow. Set `add_noise`
to Zero Noise when passing a pre-noised latent from a previous stage.
---
[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)
web/docs/TT_KSamplerContextNode.md
+29
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@@ -0,0 +1,29 @@
## TT KSampler (Context) *TT_KSamplerContextNode*
*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
Context-based KSampler that reads all sampling parameters from a connected context object. Designed for use in context-driven pipelines where model,
latent, and workflow config are passed as a single object.
### Inputs
| Parameter | Type | Required | Description |
|------------------|---------|----------|----------------------------------------------------------|
| `context` | CONTEXT | Yes | Pipeline context with model, latent, and workflow config |
| `preview_latent` | BOOLEAN | No | Show step-by-step latent preview during sampling |
### Outputs
| Parameter | Type | Description |
|-----------|---------|-------------------------------------|
| `CONTEXT` | CONTEXT | Updated context with sampled latent |
| `LATENT` | LATENT | Sampled latent tensor |
### Usage
Connect a context output from a workflow settings or text encoder node. The node reads `model`, `latent`, `seed`, `steps`, `cfg`, `sampler_name`, and
`scheduler` from the context automatically. Sampled latent is written back into the context for downstream nodes.
---
[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)
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## TT KSampler *TT_KSamplerNode*
*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
Standard KSampler node with built-in latent preview support. Accepts model, conditioning, and latent as separate inputs.
### Inputs
| Parameter | Type | Required | Description |
|------------------|--------------|----------|--------------------------------------------------|
| `model` | MODEL | Yes | Diffusion model |
| `positive` | CONDITIONING | Yes | Positive conditioning |
| `negative` | CONDITIONING | Yes | Negative conditioning |
| `latent` | LATENT | Yes | Input latent tensor |
| `seed` | INT | Yes | Generation seed |
| `steps` | INT | Yes | Number of sampling steps, default 30 |
| `cfg` | FLOAT | Yes | Guidance scale, default 3.0 |
| `sampler_name` | COMBO | Yes | Sampler algorithm |
| `scheduler` | COMBO | Yes | Noise schedule |
| `preview_latent` | BOOLEAN | No | Show step-by-step latent preview during sampling |
### Outputs
| Parameter | Type | Description |
|-----------|--------|-----------------------|
| `LATENT` | LATENT | Sampled latent tensor |
### Usage
Connect `MODEL`, `CONDITIONING`, and `LATENT` outputs from loader and encoder nodes. Enable `preview_latent` to see intermediate results during
sampling.
---
[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)
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## TT KSampler (Two Stage) *TT_KSamplerTwoStageNode*
*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
Runs sampling in two sequential stages — draft and refine — in a single node. Draft stage generates a rough result, refine stage continues from where
draft stopped with different sampler settings.
### Inputs
| Parameter | Type | Required | Description |
|------------------------|--------------|----------|--------------------------------------------------|
| `model` | MODEL | Yes | Diffusion model |
| `positive` | CONDITIONING | Yes | Positive conditioning |
| `negative` | CONDITIONING | Yes | Negative conditioning |
| `latent` | LATENT | Yes | Input latent tensor |
| `seed` | INT | Yes | Generation seed |
| `cfg` | FLOAT | Yes | Guidance scale, default 3.0 |
| `draft_steps` | INT | Yes | Number of draft stage steps, default 25 |
| `refiner_steps` | INT | Yes | Number of refine stage steps, default 25 |
| `draft_sampler_name` | COMBO | Yes | Sampler algorithm for draft stage |
| `draft_scheduler` | COMBO | Yes | Noise schedule for draft stage |
| `refiner_sampler_name` | COMBO | Yes | Sampler algorithm for refine stage |
| `refiner_scheduler` | COMBO | Yes | Noise schedule for refine stage |
| `draft_denoise` | FLOAT | No | Denoising strength for draft stage, default 1.0 |
| `refiner_denoise` | FLOAT | No | Denoising strength for refine stage, default 1.0 |
| `preview_latent` | BOOLEAN | No | Show step-by-step latent preview during sampling |
### Outputs
| Parameter | Type | Description |
|-----------|--------|-----------------------|
| `LATENT` | LATENT | Sampled latent tensor |
### Usage
Use different samplers and schedulers for each stage — for example, `dpmpp_2m` with `karras` for draft and `euler` with `normal` for refine. Total
steps equal `draft_steps + refiner_steps`.
---
[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)