Merge pull request #2 from tenser-tensor/feature/guided-sampler

v1.4.7: Add FLUX2 support, guided sampler, and reference image injection

- Add TT KSampler (Guided) with external guider/sigmas/sampler
- Add TT Guider Image Reference for reference-based generation
- Add TT FLUX2 GGUF loaders, CLIP encoders, workflow settings
- Add TT Image Preview / Upscale / Save all-in-one node
- Add TT Context Set Guider
- Add BasicGuider and CommonTypes to shared lib
- Fix missing f-strings in error prints
- Fix workflow settings module filenames (workfow → workflow)

CHANGELOG.md

@@ -0,0 +1,20 @@
+## v1.4.7 (2026-02-25)
+
+### New Nodes
+- **TT KSampler (Guided)** — guided diffusion sampling with external guider, sigmas, and sampler
+- **TT Guider Image Reference** — encodes reference image into latent space and injects it into Guider conditioning
+- **TT Image Preview / Upscale / Save** — all-in-one preview, upscale, and save node
+- **TT FLUX2 GGUF Models Loader** / **(Advanced)** — loaders for FLUX2 GGUF-quantized models
+- **TT CLIP Text Encode FLUX2** / **(Context)** — text encoders for FLUX2 models
+- **TT FLUX2 Workflow Settings** / **(Advanced)** — workflow configuration nodes for FLUX2
+- **TT Context Set Guider** — injects guider into TT_CONTEXT object
+
+### Improvements
+- Added `BasicGuider` and `CommonTypes` to shared lib (`lib/common.py`)
+- Fixed missing f-strings in all error print statements across node imports
+- Fixed workflow settings module filenames (typo: `workfow` → `workflow`)
+
+### Docs
+- Added docs for all new nodes
+- Updated NODES.md with descriptions for all new nodes
+- Updated Latent Factory description

NODES.md

@@ -10,6 +10,20 @@ Node for configuring Flux workflow generation parameters.
 
 Advanced node for configuring Flux workflow generation parameters with separate CLIP-L and T5XXL prompts.
 
+#### TT Flux2 Workflow Settings
+
+Use this node as the single source of truth for FLUX2 workflow parameters. The `WORKFLOW_CONFIG` output is
+designed for context-aware nodes that consume a TT_WORKFLOW_CONFIG directly. Individual outputs are available
+for connecting to standard ComfyUI nodes. The sigma schedule (`SCHEDULER`) is computed automatically from
+the seed and resolution via `get_schedule`.
+
+#### TT Flux2 Workflow Settings (Advanced)
+
+Advanced version of TT Flux2 Workflow Settings with `prompt` and `lora_triggers` fields included directly in the
+node. Both are stored in the `WORKFLOW_CONFIG` output alongside all other sampling parameters, making this node
+sufficient as the sole configuration source for full context-driven FLUX2 pipelines. Individual outputs remain
+available for connecting to standard ComfyUI nodes.
+
 #### TT SDXL Workflow Settings
 
 Node for configuring SDXL workflow generation parameters.
@@ -22,9 +36,10 @@ Advanced SDXL workflow configuration with separate CLIP-L/G prompts and aestheti
 
 #### TT Latent Factory
 
-Generate empty latent images with precise dimension control based on aspect ratio and megapixel count.
-
-Automatically calculates correct dimensions for FLUX and SDXL models, plus separate CLIP conditioning sizes.
+Select aspect ratio, resolution, and orientation to automatically compute pixel dimensions suited for the chosen
+model. FLUX1.D uses 16-channel latents, FLUX2.D uses 128-channel, SDXL uses 4-channel. `clip_multiplier` scales the TARGET
+dimensions relative to the image dimensions — useful for multi-resolution CLIP conditioning. Connect `RANDOM_NOISE`
+directly to a sampler node for reproducible noise tied to `noise_seed`.
 
 ### Loaders
 
@@ -36,6 +51,14 @@ All-in-one model loader for SDXL workflows with checkpoint merging support.
 
 Extended all-in-one model loader for SDXL with checkpoint merging and LoRA support.
 
+#### TT GGUF Models Loader
+
+All-in-one loader for GGUF-quantized models. Place your `.gguf` diffusion model files in the `diffusion_models` or `unet` directory, CLIP files in `text_encoders` or `clip` directory. VAE can be any standard ComfyUI-compatible VAE file.
+
+#### TT GGUF Models Loader (Advanced)
+
+Extended version of GGUF Models Loader. Supports up to 4 LoRAs loaded simultaneously. Enable `apply_sampling` to apply flux sampling shift patch — useful for improving quality at non-standard resolutions. Advanced parameters are hidden by default and can be revealed via the node's show/hide controls.
+
 #### TT FLUX Models Loader
 
 All-in-one model loader for Flux workflows.
@@ -62,6 +85,19 @@ SDXL text encoder with separate CLIP-L and CLIP-G prompt inputs and conditioning
 
 Context-based SDXL text encoder that extracts prompts and parameters from workflow config.
 
+#### TT Clip Text Encode Flux2
+
+Encodes text prompts specifically for FLUX2 models. Enter your main prompt in `prompt` and any LoRA-specific trigger
+words separately in `lora_triggers` — the node combines them internally. Adjust `guidance` to control how strongly
+the model follows the prompt (3.5 is a good starting point for FLUX2).
+
+#### TT Clip Text Encode Flux2 (Context)
+
+Context-aware version of the FLUX2 text encoder. Instead of individual inputs, it reads `model`, `clip`, and
+`workflow_config` (containing `prompt`, `lora_triggers`, and `guidance`) from the passed `context`. The resulting
+guider is both returned as an output and stored back into the context under the `guider` key. Raises an error if
+any of the required fields are missing from the context.
+
 ### Samplers
 
 #### TT KSampler
@@ -90,6 +126,12 @@ Automated two-stage sampling workflow. First performs a draft pass with partial
 a second pass. Each stage can use different samplers, schedulers, and step counts. Useful for quick previews followed by
 quality refinement, or combining different sampling strategies.
 
+#### TT KSampler (Guided)
+
+Runs guided diffusion sampling on the input latent. Connect a guider from a text encoder node, sigmas from a
+scheduler, and a sampler from a workflow settings node. `random_noise` controls stochasticity — use a seeded
+noise node for reproducible results.
+
 ### VAE
 
 #### TT VAE Decode (Context)
@@ -164,6 +206,10 @@ saturation, sharpness, posterize, and solarize adjustments. All effects are opti
 
 Dual-purpose node that can preview images in the UI and/or save them to disk with extensive formatting options.
 
+#### TT Image Preview / Upscale / Save
+
+All-in-one node that can preview, upscale, and save images in a single step. Upscaling is applied before saving — the upscaled result is both displayed in the UI and written to disk. When `upscale_image` is disabled, the original image is used. When `save_image` is disabled, the image is only previewed in the UI without writing to disk.
+
 ### Context
 
 #### TT Base Context
@@ -199,6 +245,11 @@ Stores pixel-space image in context for use by downstream nodes. Overwrites exis
 
 Stores latent representation in context for use by downstream nodes. Overwrites existing latent if present.
 
+#### TT Context Set Guider
+
+Sets the `guider` field inside a TT_CONTEXT object. Use this node when you have a guider produced outside the
+context pipeline and need to inject it back in for use by subsequent context-aware nodes.
+
 #### TT Context Passthrough
 
 Use this node to pass context through your workflow while optionally modifying specific parameters without changing the

__init__.py

@@ -6,9 +6,12 @@ WEB_DIRECTORY = "./web"
 
 __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'WEB_DIRECTORY']
 
+nodes_count = len(NODE_CLASS_MAPPINGS)
+
+print()
 SEP = f"{CYAN}{'=' * 60}{RESET}"
 print(SEP)
-print(f"{YELLOW}TenserTensor ComfyUI Nodes Pack (v1.3.1): {BRIGHT_GREEN}38 Nodes Loaded{RESET}")
+print(f"{YELLOW}TenserTensor ComfyUI Nodes Pack (v1.4.7): {BRIGHT_GREEN}{nodes_count} Nodes Loaded{RESET}")
 print(SEP)
 print(f"{BRIGHT_CYAN}[V] Repository: https://github.com/tenser-tensor/ComfyUI-TenserTensor{RESET}")
 print(f"{BRIGHT_CYAN}[V] ComfyUI Registry: https://registry.comfy.org/publishers/tenser-tensor{RESET}")

lib/common.py

@@ -0,0 +1,13 @@
+import comfy.samplers as S
+
+
+class BasicGuider(S.CFGGuider):
+    def set_conds(self, conditioning):
+        self.inner_set_conds({"positive": conditioning})
+
+    def get_conds(self, key="positive"):
+        return [[c.get("cross_attn", None), c] for c in self.original_conds[key]]
+
+
+class CommonTypes():
+    MEGAPIXELS = ["0.25 MP", "0.5 MP", "1 MP", "2 MP", "4 MP", "8 MP"]

node_mappings.py

@@ -17,31 +17,34 @@ try:
     from .nodes.context.context_passthrough import TT_ContextPassthrough
     from .nodes.context.context_set_image import TT_ContextSetImage
     from .nodes.context.context_set_latent import TT_ContextSetLatent
+    from .nodes.context.context_set_guider import TT_ContextSetGuider
     from .nodes.context.even_larger_context import TT_EvenLargerContext
     from .nodes.context.large_context_flux import TT_LargeContextFlux
     from .nodes.context.large_context_sdxl import TT_LargeContextSdxl
 except ImportError:
-    print("{YELLOW}TenserTensor: {RED}ERROR: Context nodes unavailable{RESET}")
+    print(f"{YELLOW}TenserTensor: {RED}ERROR: Context nodes unavailable{RESET}")
 
 try:
     from .nodes.image.image_preview_save import TT_ImagePreviewSave
+    from .nodes.image.image_preview_upscale_save import TT_ImagePreviewUpscaleSave
+    from .nodes.image.guider_image_reference import TT_GuiderImageReference
 except ImportError:
-    print("{YELLOW}TenserTensor: {RED}ERROR: Image nodes unavailable{RESET}")
+    print(f"{YELLOW}TenserTensor: {RED}ERROR: Image nodes unavailable{RESET}")
 
 try:
     from .nodes.latent.latent_factory import TT_LatentFactory
 except ImportError:
-    print("{YELLOW}TenserTensor: {RED}ERROR: Latent nodes unavailable{RESET}")
+    print(f"{YELLOW}TenserTensor: {RED}ERROR: Latent nodes unavailable{RESET}")
 
 try:
     from .nodes.loaders.flux_models_loader import TT_FluxModelsLoader
     from .nodes.loaders.flux_models_loader_advanced import TT_FluxModelsLoaderAdvanced
-    from .nodes.loaders.gguf_models_loader import TT_GgufModelsLoader
-    from .nodes.loaders.gguf_models_loader_advanced import TT_GgufModelsLoaderAdvanced
+    from .nodes.loaders.gguf_models_loader import TT_Flux2GgufModelsLoader
+    from .nodes.loaders.gguf_models_loader_advanced import TT_Flux2GgufModelsLoaderAdvanced
     from .nodes.loaders.sdxl_models_loader import TT_SdxlModelsLoader
     from .nodes.loaders.sdxl_models_loader_advanced import TT_SdxlModelsLoaderAdvanced
 except ImportError:
-    print("{YELLOW}TenserTensor: {RED}ERROR: Loader nodes unavailable{RESET}")
+    print(f"{YELLOW}TenserTensor: {RED}ERROR: Loader nodes unavailable{RESET}")
 
 try:
     from .nodes.postproduction.add_film_grain import TT_AddFilmGrain
@@ -51,23 +54,26 @@ try:
     from .nodes.postproduction.postproduction_advanced import TT_PostproductionAdvanced
     from .nodes.postproduction.quick_image_upscaler import TT_QuickImageUpscaler
 except ImportError:
-    print("{YELLOW}TenserTensor: {RED}ERROR: Postproduction nodes unavailable{RESET}")
+    print(f"{YELLOW}TenserTensor: {RED}ERROR: Postproduction nodes unavailable{RESET}")
 
 try:
     from .nodes.sampling.ksampler import TT_KSampler
     from .nodes.sampling.ksampler_advanced import TT_KSamplerAdvanced
     from .nodes.sampling.ksampler_context import TT_KSamplerContext
     from .nodes.sampling.ksampler_two_stage import TT_KSamplerTwoStage
+    from .nodes.sampling.ksampler_guided import TT_KSamplerGuided
 except ImportError:
-    print("{YELLOW}TenserTensor: {RED}ERROR: Sampler nodes unavailable{RESET}")
+    print("f{YELLOW}TenserTensor: {RED}ERROR: Sampler nodes unavailable{RESET}")
 
 try:
     from .nodes.text_encoders.clip_text_encode_flux import TT_ClipTextEncodeFlux
     from .nodes.text_encoders.clip_text_encode_flux_context import TT_ClipTextEncodeFluxContext
     from .nodes.text_encoders.clip_text_encode_sdxl import TT_ClipTextEncodeSdxl
     from .nodes.text_encoders.clip_text_encode_sdxl_context import TT_ClipTextEncodeSdxlContext
+    from .nodes.text_encoders.clip_text_encode_flux2 import TT_ClipTextEncodeFlux2
+    from .nodes.text_encoders.clip_text_encode_flux2_context import TT_ClipTextEncodeFlux2Context
 except ImportError:
-    print("{YELLOW}TenserTensor: {RED}ERROR: Text Encoder nodes unavailable{RESET}")
+    print(f"{YELLOW}TenserTensor: {RED}ERROR: Text Encoder nodes unavailable{RESET}")
 
 try:
     from .nodes.vae.vae_decode_context import TT_VaeDecodeContext
@@ -75,15 +81,17 @@ try:
     from .nodes.vae.vae_encode_context import TT_VaeEncodeContext
     from .nodes.vae.vae_encode_tiled import TT_VaeEncodeTiled
 except ImportError:
-    print("{YELLOW}TenserTensor: {RED}ERROR: VAE nodes unavailable{RESET}")
+    print(f"{YELLOW}TenserTensor: {RED}ERROR: VAE nodes unavailable{RESET}")
 
 try:
-    from .nodes.workflow.flux_workfow_settings import TT_FluxWorkflowSettings
-    from .nodes.workflow.flux_workfow_settings_advanced import TT_FluxWorkflowSettingsAdvanced
-    from .nodes.workflow.sdxl_workfow_settings import TT_SdxlWorkflowSettings
-    from .nodes.workflow.sdxl_workfow_settings_advanced import TT_SdxlWorkflowSettingsAdvanced
+    from .nodes.workflow.flux_workflow_settings import TT_FluxWorkflowSettings
+    from .nodes.workflow.flux_workflow_settings_advanced import TT_FluxWorkflowSettingsAdvanced
+    from .nodes.workflow.sdxl_workflow_settings import TT_SdxlWorkflowSettings
+    from .nodes.workflow.sdxl_workflow_settings_advanced import TT_SdxlWorkflowSettingsAdvanced
+    from .nodes.workflow.flux2_workflow_settings import TT_Flux2WorkflowSettings
+    from .nodes.workflow.flux2_workflow_settings_advanced import TT_Flux2WorkflowSettingsAdvanced
 except ImportError:
-    print("{YELLOW}TenserTensor: {RED}ERROR: Workflow nodes unavailable{RESET}")
+    print(f"{YELLOW}TenserTensor: {RED}ERROR: Workflow nodes unavailable{RESET}")
 
 NODE_CLASS_MAPPINGS = {
     # Context
@@ -94,17 +102,20 @@ NODE_CLASS_MAPPINGS = {
     "TT_EvenLargerContext": TT_EvenLargerContext,
     "TT_ContextSetImage": TT_ContextSetImage,
     "TT_ContextSetLatent": TT_ContextSetLatent,
+    "TT_ContextSetGuider": TT_ContextSetGuider,
     # Context Passthrough
     "TT_ContextPassthrough": TT_ContextPassthrough,
     # Image
     "TT_ImagePreviewSave": TT_ImagePreviewSave,
+    "TT_ImagePreviewUpscaleSave": TT_ImagePreviewUpscaleSave,
+    "TT_GuiderImageReference": TT_GuiderImageReference,
     # Latent
     "TT_LatentFactory": TT_LatentFactory,
     # Loaders
     "TT_SdxlModelsLoader": TT_SdxlModelsLoader,
     "TT_SdxlModelsLoaderAdvanced": TT_SdxlModelsLoaderAdvanced,
-    "TT_GgufModelsLoader": TT_GgufModelsLoader,
-    "TT_GgufModelsLoaderAdvanced": TT_GgufModelsLoaderAdvanced,
+    "TT_Flux2GgufModelsLoader": TT_Flux2GgufModelsLoader,
+    "TT_Flux2GgufModelsLoaderAdvanced": TT_Flux2GgufModelsLoaderAdvanced,
     "TT_FluxModelsLoader": TT_FluxModelsLoader,
     "TT_FluxModelsLoaderAdvanced": TT_FluxModelsLoaderAdvanced,
     # Postproduction
@@ -119,11 +130,14 @@ NODE_CLASS_MAPPINGS = {
     "TT_KSamplerAdvanced": TT_KSamplerAdvanced,
     "TT_KSamplerContext": TT_KSamplerContext,
     "TT_KSamplerTwoStage": TT_KSamplerTwoStage,
+    "TT_KSamplerGuided": TT_KSamplerGuided,
     # Text Encoder
     "TT_ClipTextEncodeFlux": TT_ClipTextEncodeFlux,
     "TT_ClipTextEncodeFluxContext": TT_ClipTextEncodeFluxContext,
     "TT_ClipTextEncodeSdxl": TT_ClipTextEncodeSdxl,
     "TT_ClipTextEncodeSdxlContext": TT_ClipTextEncodeSdxlContext,
+    "TT_ClipTextEncodeFlux2": TT_ClipTextEncodeFlux2,
+    "TT_ClipTextEncodeFlux2Context": TT_ClipTextEncodeFlux2Context,
     # VAE
     "TT_VaeDecodeContext": TT_VaeDecodeContext,
     "TT_VaeDecodeTiled": TT_VaeDecodeTiled,
@@ -134,6 +148,8 @@ NODE_CLASS_MAPPINGS = {
     "TT_FluxWorkflowSettingsAdvanced": TT_FluxWorkflowSettingsAdvanced,
     "TT_SdxlWorkflowSettings": TT_SdxlWorkflowSettings,
     "TT_SdxlWorkflowSettingsAdvanced": TT_SdxlWorkflowSettingsAdvanced,
+    "TT_Flux2WorkflowSettings": TT_Flux2WorkflowSettings,
+    "TT_Flux2WorkflowSettingsAdvanced": TT_Flux2WorkflowSettingsAdvanced,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -145,17 +161,20 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "TT_EvenLargerContext": "TT Even Larger Context",
     "TT_ContextSetImage": "TT Context Set Image",
     "TT_ContextSetLatent": "TT Context Set Latent",
+    "TT_ContextSetGuider": "TT Context Set Guider",
     # Context Passthrough
     "TT_ContextPassthrough": "TT Context Passthrough",
     # Image
     "TT_ImagePreviewSave": "TT Image Preview / Save",
+    "TT_ImagePreviewUpscaleSave": "TT Image Preview / Upscale / Save",
+    "TT_GuiderImageReference": "TT Guider Image Reference",
     # Latent
     "TT_LatentFactory": "TT Latent Factory",
     # Loaders
     "TT_SdxlModelsLoader": "TT SDXL Models Loader",
     "TT_SdxlModelsLoaderAdvanced": "TT SDXL Models Loader (Advanced)",
-    "TT_GgufModelsLoader": "TT GGUF Models Loader",
-    "TT_GgufModelsLoaderAdvanced": "TT GGUF Models Loader (Advanced)",
+    "TT_Flux2GgufModelsLoader": "TT FLUX2 GGUF Models Loader",
+    "TT_Flux2GgufModelsLoaderAdvanced": "TT FLUX2 GGUF Models Loader (Advanced)",
     "TT_FluxModelsLoader": "TT FLUX Models Loader",
     "TT_FluxModelsLoaderAdvanced": "TT FLUX Models Loader (Advanced)",
     # Postproduction
@@ -170,11 +189,14 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "TT_KSamplerAdvanced": "TT KSampler (Advanced)",
     "TT_KSamplerContext": "TT KSampler (Context)",
     "TT_KSamplerTwoStage": "TT KSampler (Two Stages)",
+    "TT_KSamplerGuided": "TT KSampler (Guided)",
     # Text Encoder
     "TT_ClipTextEncodeFlux": "TT CLIP Text Encode FLUX",
     "TT_ClipTextEncodeFluxContext": "TT CLIP Text Encode FLUX (Context)",
     "TT_ClipTextEncodeSdxl": "TT CLIP Text Encode SDXL",
     "TT_ClipTextEncodeSdxlContext": "TT CLIP Text Encode SDXL (Context)",
+    "TT_ClipTextEncodeFlux2": "TT CLIP Text Encode FLUX2",
+    "TT_ClipTextEncodeFlux2Context": "TT CLIP Text Encode FLUX2 (Context)",
     # VAE
     "TT_VaeDecodeContext": "TT VAE Decode (Context)",
     "TT_VaeDecodeTiled": "TT VAE Decode (Tiled)",
@@ -185,4 +207,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "TT_FluxWorkflowSettingsAdvanced": "TT FLUX Workflow Settings (Advanced)",
     "TT_SdxlWorkflowSettings": "TT SDXL Workflow Settings",
     "TT_SdxlWorkflowSettingsAdvanced": "TT SDXL Workflow Settings (Advanced)",
+    "TT_Flux2WorkflowSettings": "TT FLUX2 Workflow Settings",
+    "TT_Flux2WorkflowSettingsAdvanced": "TT FLUX2 Workflow Settings (Advanced)",
 }

nodes/context/context_helpers.py

@@ -1,6 +1,6 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
 
-import comfy.samplers
+import comfy.samplers as S
 
 CONTEXT_FIELDS = [
     # input_name, return_name, field_type
@@ -13,14 +13,18 @@ CONTEXT_FIELDS = [
     ("negative", "NEGATIVE", "CONDITIONING"),
     ("image", "IMAGE", "IMAGE"),
     ("mask", "MASK", "MASK"),
+    ("guider", "GUIDER", "GUIDER"),
     ("control_net", "CONTROL_NET", "CONTROL_NET"),
     ("workflow_config", "WORKFLOW_CONFIG", "TT_WORKFLOW_CONFIG",),
     ("seed", "SEED", "INT"),
     ("steps", "STEPS", "INT"),
     ("cfg", "CFG", "FLOAT"),
-    ("sampler_name", "SAMPLER_NAME", comfy.samplers.KSampler.SAMPLERS),
-    ("scheduler", "SCHEDULER", comfy.samplers.KSampler.SCHEDULERS),
+    ("sampler_name", "SAMPLER_NAME", S.KSampler.SAMPLERS),
+    ("sampler", "SAMPLER", "SAMPLER"),
+    ("scheduler", "SCHEDULER", S.KSampler.SCHEDULERS),
     ("guidance", "GUIDANCE", "FLOAT"),
+    ("prompt", "PROMPT", "STRING"),
+    ("lora_triggers", "LORA_TRIGGERS", "STRING"),
     ("clip_l_positive", "CLIP_L_POSITIVE", "STRING"),
     ("clip_g_positive", "CLIP_L_POSITIVE", "STRING"),
     ("t5xxl_positive", "T5XXL_POSITIVE", "STRING"),
@@ -93,6 +97,28 @@ CONTEXTS = {
             "height",
         )
     },
+    "big_flux2": {
+        "optional": (
+            "context",
+            "workflow_config",
+            "model",
+            "clip",
+            "vae",
+            "latent",
+            "image",
+            "seed",
+            "steps",
+            "cfg",
+            "sampler_name",
+            "sampler",
+            "scheduler",
+            "guidance",
+            "prompt",
+            "lora_triggers",
+            "width",
+            "height",
+        )
+    },
     "big_sdxl": {
         "optional": (
             "context",
@@ -130,7 +156,7 @@ CONTEXTS = {
             "context",
         )
     },
-    "set_image":{
+    "set_image": {
         "required": (
             "context",
             "image",
@@ -139,7 +165,7 @@ CONTEXTS = {
             "context",
         )
     },
-    "set_latent":{
+    "set_latent": {
         "required": (
             "context",
             "latent",
@@ -148,6 +174,15 @@ CONTEXTS = {
             "context",
         )
     },
+    "set_guider": {
+        "required": (
+            "context",
+            "guider",
+        ),
+        "return": (
+            "context",
+        )
+    },
 }
 
 FORCE_INPUT_TYPES = {
@@ -162,6 +197,7 @@ WORKFOW_SETTINGS_KEYS = [
     "steps",
     "cfg",
     "sampler_name",
+    "sampler",
     "scheduler",
     "pos_a_score",
     "clip_l_positive",

nodes/context/context_set_guider.py

@@ -0,0 +1,18 @@
+# (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+
+from .context_helpers import create_context_schema
+
+
+class TT_ContextSetGuider:
+    INPUT_TYPES_DICT, RETURN_TYPES, RETURN_NAMES = create_context_schema("set_guider")
+    FUNCTION = "process_context"
+    CATEGORY = "TenserTensor/Context"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return cls.INPUT_TYPES_DICT
+
+    def process_context(self, context, guider):
+        context["guider"] = guider
+
+        return (context,)

nodes/image/guider_image_reference.py

@@ -0,0 +1,58 @@
+# (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+
+import os
+
+import folder_paths as FP
+from node_helpers import conditioning_set_values
+
+from ..image.image_helpers import load_image, resize_image_to_megapixels
+from ...lib.common import CommonTypes
+
+UPSCALE_METHODS = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
+TILE_SIZE, OVERLAP = 512, 64
+
+
+class TT_GuiderImageReference():
+    @classmethod
+    def INPUT_TYPES(cls):
+        input_dir = FP.get_input_directory()
+        files = FP.filter_files_content_types(
+            [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))], ["image"]
+        )
+
+        return {
+            "required": {
+                "vae": ("VAE",),
+                "guider": ("GUIDER",),
+                "megapixels": (CommonTypes.MEGAPIXELS,),
+                "upscale_method": (UPSCALE_METHODS, {"default": "bicubic", "advanced": True}),
+                "dimension_step": ("INT", {"default": 1, "min": 1, "max": 10000, "advanced": True}),
+                "image": (sorted(files), {"image_upload": True}),
+            },
+        }
+
+    RETURN_TYPES = ("VAE", "GUIDER",)
+    RETURN_NAMES = ("VAE", "GUIDER",)
+    FUNCTION = "update_latent"
+    CATEGORY = "TenserTensor/Image"
+
+    def update_latent(self, vae, guider, megapixels, upscale_method, dimension_step, image):
+        timage, _ = load_image(image)
+        scaled = resize_image_to_megapixels(timage, upscale_method, megapixels, dimension_step)
+
+        samples = vae.encode_tiled(
+            scaled,
+            tile_x=TILE_SIZE,
+            tile_y=TILE_SIZE,
+            overlap=OVERLAP
+        )
+
+        conditioning = guider.get_conds()
+
+        if conditioning is not None:
+            conditioning = conditioning_set_values(conditioning, {"reference_latents": [samples]}, append=True)
+            guider.set_conds(conditioning)
+        else:
+            raise ValueError("ERROR: Guider has no conditioning")
+
+        return (vae, guider,)

nodes/image/image_helpers.py

@@ -1,14 +1,20 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
 
 import fnmatch
+import math
 import os
 import random
 import string as S
 from datetime import datetime
 
 import folder_paths as FP
+import numpy
 import numpy as NP
-from PIL import Image
+import torch
+from PIL import Image, ImageOps, ImageSequence
+from comfy import model_management as MM
+from comfy import utils as U
+from node_helpers import pillow
 
 FORMAT_EXT = {
     "PNG": ".png",
@@ -16,9 +22,11 @@ FORMAT_EXT = {
     "WEBP": ".webp",
 }
 
+
 class InvalidSavePathError(Exception):
     pass
 
+
 def _build_filename(save_path, filename_mask):
     count = sum(1 for f in os.scandir(save_path) if fnmatch.fnmatch(f.name, filename_mask)) + 1
 
@@ -112,3 +120,78 @@ def preview(image, filename_prefix):
         filename_prefix=prefix,
         output_path=temp_dir,
     )
+
+
+def load_image(image_filename, device=None):
+    tdevice = MM.get_torch_device() if device == None else torch.device("cpu")
+
+    image_path = FP.get_annotated_filepath(image_filename)
+    pil_image = pillow(Image.open, image_path)
+
+    images, masks = [], []
+    width, height = None, None
+
+    for frame in ImageSequence.Iterator(pil_image):
+        pil_frame = pillow(ImageOps.exif_transpose, frame)
+
+        if pil_frame.mode == "I":
+            pil_frame = pil_frame.point(lambda i: i * (1 / 255))
+
+        if len(images) == 0:
+            width, height = pil_frame.size
+
+        if pil_frame.size != (width, height,):
+            continue
+
+        alpha = numpy.zeros((height, width,), dtype=numpy.float32)
+        if 'A' in pil_frame.getbands():
+            alpha = numpy.array(pil_frame.getchannel('A'), dtype=numpy.float32) / 255.0
+        elif pil_frame.mode == 'P' and 'transparency' in pil_frame.info:
+            alpha = numpy.array(pil_frame.convert('RGBA').getchannel('A'), dtype=numpy.float32) / 255.0
+
+        tmask = torch.from_numpy(1.0 - alpha).to(tdevice)
+
+        rgb_frame = pil_frame.convert("RGB")
+        np_image = numpy.array(rgb_frame, dtype=numpy.float32) / 255.0
+        timage = torch.from_numpy(np_image)[None].to(tdevice)
+
+        images.append(timage)
+        masks.append(tmask)
+
+        if pil_image.format == "MPO":
+            break
+
+    if len(images) == 0:
+        raise ValueError(f"ERROR: No frames found in {image_path}")
+
+    oimage = torch.cat(images, dim=0) if len(images) > 1 else images[0]
+    omask = torch.cat(masks, dim=0) if len(masks) > 1 else masks[0]
+
+    return oimage, omask
+
+
+def _align_to_step(size, scale_factor, dimension_step):
+    return round(size * scale_factor / dimension_step) * dimension_step
+
+
+# crop = "center" / "disabled"
+def resize_image(timage, to_width, to_height, upscale_method, dimension_step=1, crop="disabled"):
+    samples = timage.movedim(-1, 1)
+    orig_height, orig_width = samples.shape[2:]
+    scale_factor = max(to_width / orig_width, to_height / orig_height)
+    to_width = _align_to_step(orig_width, scale_factor, dimension_step)
+    to_height = _align_to_step(orig_height, scale_factor, dimension_step)
+
+    return U.common_upscale(samples, int(to_width), int(to_height), upscale_method, crop).movedim(1, -1)
+
+
+def resize_image_to_megapixels(timage, upscale_method, megapixels, dimension_step=1):
+    mp_value = float(megapixels.split()[0])
+    total_pixels = int(mp_value * 1_000_000)
+    samples = timage.movedim(-1, 1)
+    orig_height, orig_width = samples.shape[2:]
+    scale_factor = math.sqrt(total_pixels / (orig_width * orig_height))
+    to_width = _align_to_step(orig_width, scale_factor, dimension_step)
+    to_height = _align_to_step(orig_height, scale_factor, dimension_step)
+
+    return U.common_upscale(samples, int(to_width), int(to_height), upscale_method, "disabled").movedim(1, -1)

nodes/image/image_preview_save.py

@@ -14,11 +14,11 @@ class TT_ImagePreviewSave():
                 "image": ("IMAGE",),
                 "save_image": ("BOOLEAN", {"default": True, "label_on": "Enabled", "label_off": "Disabled"}),
                 "filename_prefix": ("STRING", {"default": "tenser-tensor"}),
-                "filename_format": (FILENAME_FORMATS, {"advanced": True, "default": "name-###"}),
+                "filename_format": (FILENAME_FORMATS, {"default": "name-###", "advanced": True}),
                 "subfolder": ("STRING", {"default": "", "advanced": True}),
-                "image_format": (IMAGE_FORMATS, {"advanced": True, "default": "PNG"}),
-                "image_quality": ("INT", {"default": 100, "min": 0, "max": 100}),
-                "compression_level": ("INT", {"default": 9, "min": 0, "max": 9}),
+                "image_format": (IMAGE_FORMATS, {"default": "PNG", "advanced": True}),
+                "image_quality": ("INT", {"default": 100, "min": 0, "max": 100, "advanced": True}),
+                "compression_level": ("INT", {"default": 9, "min": 0, "max": 9, "advanced": True}),
             }
         }
 

nodes/image/image_preview_upscale_save.py

@@ -0,0 +1,71 @@
+# (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+import folder_paths as FP
+
+from .image_helpers import store, preview
+from ..postproduction.postproduction_helpers import upscale_image as upscale
+
+
+class TT_ImagePreviewUpscaleSave():
+    @classmethod
+    def INPUT_TYPES(cls):
+        FILENAME_FORMATS = ["name-###", "date-name-###", "name-datetime"]
+        IMAGE_FORMATS = ["PNG", "JPEG", "WEBP"]
+        DEVICES = ["default", "cpu"]
+        UPSCALE_MODELS = FP.get_filename_list("upscale_models")
+
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "save_image": ("BOOLEAN", {"default": True, "label_on": "Enabled", "label_off": "Disabled"}),
+                "filename_prefix": ("STRING", {"default": "tenser-tensor"}),
+                "filename_format": (FILENAME_FORMATS, {"advanced": True, "default": "name-###"}),
+                "subfolder": ("STRING", {"default": "", "advanced": True}),
+                "image_format": (IMAGE_FORMATS, {"advanced": True, "default": "PNG"}),
+                "image_quality": ("INT", {"default": 100, "min": 0, "max": 100, "advanced": True}),
+                "compression_level": ("INT", {"default": 9, "min": 0, "max": 9, "advanced": True}),
+                "upscale_image": ("BOOLEAN", {"default": True, "label_on": "Enabled", "label_off": "Disabled"}),
+                "upscaler_device": (DEVICES, {"advanced": True}),
+                "upscale_model_name": (UPSCALE_MODELS, {"advanced": True}),
+                "tile": ("INT", {"default": 512, "min": 256, "max": 4096, "step": 64, "advanced": True}),
+                "overlap": ("INT", {"default": 64, "min": 0, "max": 256, "step": 8, "advanced": True}),
+            }
+        }
+
+    RETURN_TYPES = ()
+    RETURN_NAMES = ()
+    OUTPUT_NODE = True
+    FUNCTION = "preview_upscale_save_image"
+    CATEGORY = "TenserTensor/Image"
+
+    def preview_upscale_save_image(self, **kwargs):
+        image = kwargs.get("image")
+        timage = image.clone()
+
+        upscale_image = kwargs.get("upscale_image")
+        if upscale_image:
+            timage = upscale(
+                timage=timage,
+                device=kwargs.get("upscaler_device"),
+                upscale_model_name=kwargs.get("upscale_model_name"),
+                tile=kwargs.get("tile"),
+                overlap=kwargs.get("overlap")
+            )
+
+        save_image = kwargs.get("save_image")
+        if save_image:
+            timage = store(
+                image=timage,
+                filename_prefix=kwargs.get("filename_prefix"),
+                subfolder=kwargs.get("subfolder"),
+                filename_format=kwargs.get("filename_format"),
+                image_format=kwargs.get("image_format"),
+                image_quality=kwargs.get("image_quality"),
+                compress_level=kwargs.get("compression_level"),
+            )
+        else:
+            timage = preview(
+                image=timage,
+                filename_prefix=kwargs.get("filename_prefix"),
+            )
+
+        return {"ui": {"images": timage}}

nodes/latent/latent_factory.py

@@ -1,40 +1,12 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
 
-import torch
+from .latent_helpers import create_empty_latent
+from ...lib.common import CommonTypes
 
-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"
-]
+ASPECT_RATIOS = ["1:1", "4:3", "3:2", "16:9", "21:9"]
+ORIENTATIONS = ["landscape", "portrait"]
+MODEL_TYPES = ["FLUX1.D", "FLUX2.D", "SDXL"]
+CLIP_MULTIPLIERS = ["1x", "2x", "4x"]
 
 
 class TT_LatentFactory:
@@ -42,75 +14,21 @@ class TT_LatentFactory:
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff},),
+                "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff},),
                 "aspect_ratio": (ASPECT_RATIOS,),
-                "megapixels": (MEGAPIXELS,),
+                "megapixels": (CommonTypes.MEGAPIXELS,),
                 "orientation": (ORIENTATIONS,),
                 "model_type": (MODEL_TYPES,),
-                "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
-                "clip_multiplier": (CLIP_MULTIPLIERS,)
+                "batch_size": ("INT", {"default": 1, "min": 1, "max": 64, "advanced": True},),
+                "clip_multiplier": (CLIP_MULTIPLIERS, {"advanced": True},)
             }
         }
 
-    RETURN_TYPES = ("LATENT", "INT", "INT", "INT", "INT", "INT")
-    RETURN_NAMES = ("LATENT", "SEED", "WIDTH", "HEIGHT", "TARGET_WIDTH", "TARGET_HEIGHT")
+    RETURN_TYPES = ("LATENT", "NOISE", "INT", "INT", CommonTypes.MEGAPIXELS, "INT", "INT", "INT", "INT")
+    RETURN_NAMES = ("LATENT", "RANDOM_NOISE", "SEED", "NOISE_SEED", "MEGAPIXELS", "WIDTH", "HEIGHT", "TARGET_WIDTH", "TARGET_HEIGHT")
     FUNCTION = "create_latent"
     CATEGORY = "TenserTensor/Latent"
 
-    def calculate_dimensions(self, total_pixels, ratio_w, ratio_h):
-        import math
-
-        height = math.sqrt(total_pixels * ratio_h / ratio_w)
-        width = height * ratio_w / ratio_h
-
-        width = int(width)
-        height = int(height)
-
-        width = round(width / 64) * 64
-        height = round(height / 64) * 64
-
-        return width, height
-
-    def create_latent(self, seed, aspect_ratio, megapixels, orientation, model_type, batch_size, clip_multiplier):
-        mp_value = float(megapixels.split()[0])
-        total_pixels = int(mp_value * 1_000_000)
-
-        ratio_parts = aspect_ratio.split(':')
-        ratio_w = int(ratio_parts[0])
-        ratio_h = int(ratio_parts[1])
-
-        if orientation == "portrait":
-            ratio_w, ratio_h = ratio_h, ratio_w
-
-        width, height = self.calculate_dimensions(total_pixels, ratio_w, ratio_h)
-
-        multiplier = int(clip_multiplier.replace('x', ''))
-        clip_width = width * multiplier
-        clip_height = height * multiplier
-
-        match model_type:
-            case "FLUX1.D":
-                scale_factor = 8
-                channels = 16
-            case "FLUX2.D":
-                scale_factor = 16
-                channels = 128
-            case "SDXL":
-                scale_factor = 8
-                channels = 4
-
-        latent_width = width // scale_factor
-        latent_height = height // scale_factor
-
-        generator = torch.Generator().manual_seed(seed)
-        latent = torch.randn(
-            batch_size,
-            channels,
-            latent_height,
-            latent_width,
-            generator=generator
-        )
-
-        latent_dict = {"samples": latent}
-
-        return (latent_dict, seed, width, height, clip_width, clip_height)
+    def create_latent(self, **kwargs):
+        return create_empty_latent(**kwargs)

nodes/latent/latent_helpers.py

@@ -0,0 +1,90 @@
+# (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+
+import comfy.sample as S
+import torch
+import math
+
+
+class RandomNoise:
+    def __init__(self, seed):
+        self.seed = seed
+
+    def generate_noise(self, input_latent):
+        latent_image = input_latent["samples"]
+        batch_idx = input_latent["batch_index"] if "batch_index" in input_latent else None
+        return S.prepare_noise(latent_image, self.seed, batch_idx)
+
+
+LATENT_DIMENSION_STEP = 64
+
+
+def _calculate_dimensions(total_pixels, ratio_w, ratio_h):
+    height = math.sqrt(total_pixels * ratio_h / ratio_w)
+    width = height * ratio_w / ratio_h
+    width = int(width)
+    height = int(height)
+    width = round(width / LATENT_DIMENSION_STEP) * LATENT_DIMENSION_STEP
+    height = round(height / LATENT_DIMENSION_STEP) * LATENT_DIMENSION_STEP
+
+    return width, height
+
+
+def create_empty_latent(seed, noise_seed, aspect_ratio, megapixels, orientation, model_type, batch_size, clip_multiplier):
+    mp_value = float(megapixels.split()[0])
+    total_pixels = int(mp_value * 1_000_000)
+
+    ratio_parts = aspect_ratio.split(':')
+    ratio_w = int(ratio_parts[0])
+    ratio_h = int(ratio_parts[1])
+
+    if orientation == "portrait":
+        ratio_w, ratio_h = ratio_h, ratio_w
+
+    width, height = _calculate_dimensions(total_pixels, ratio_w, ratio_h)
+
+    multiplier = int(clip_multiplier.replace('x', ''))
+    clip_width = width * multiplier
+    clip_height = height * multiplier
+
+    match model_type:
+        case "FLUX1.D":
+            scale_factor = 8
+            channels = 16
+        case "FLUX2.D":
+            scale_factor = 16
+            channels = 128
+        case "SDXL":
+            scale_factor = 8
+            channels = 4
+
+    latent_width = width // scale_factor
+    latent_height = height // scale_factor
+
+    generator = torch.Generator().manual_seed(seed)
+    latent = torch.randn(
+        batch_size,
+        channels,
+        latent_height,
+        latent_width,
+        generator=generator
+    )
+
+    latent_dict = {"samples": latent}
+    noise = RandomNoise(noise_seed)
+
+    return (latent_dict, noise, seed, noise_seed, megapixels, width, height, clip_width, clip_height,)
+
+
+def vae_encode(latent, image, vae):
+    tile_width, tile_height, overlap = 512, 512, 64
+
+    samples = vae.encode_tiled(
+        image,
+        tile_x=tile_width,
+        tile_y=tile_height,
+        overlap=overlap
+    )
+
+    latent["samples"] = samples
+
+    return (latent,)

nodes/loaders/flux_models_loader_advanced.py

@@ -7,6 +7,12 @@ from .loader_helpers import load_unet, patch_flux_sampling, load_flux_clip, appl
 
 
 class TT_FluxModelsLoaderAdvanced():
+    def __init__(self):
+        self.loaded_lora_1 = None
+        self.loaded_lora_2 = None
+        self.loaded_lora_3 = None
+        self.loaded_lora_4 = None
+
     @classmethod
     def INPUT_TYPES(cls):
         UNET_DTYPES = ["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"]

nodes/loaders/gguf_loader_helpers.py

@@ -156,3 +156,15 @@ def load_clip(clip_name, type="flux2"):
     clip_type = getattr(SD.CLIPType, type.upper(), SD.CLIPType.FLUX2)
 
     return _load_patcher(clip_type, _load_data([clip_path]))
+
+
+def apply_bypass_lora_for_models(loaded_lora, model, clip, lora_name, strength):
+    if loaded_lora == None:
+        lora_path = FP.get_full_path_or_raise("loras", lora_name)
+        lora = U.load_torch_file(lora_path, safe_load=True)
+    else:
+        lora = loaded_lora
+
+    patched_model, patched_clip = SD.load_bypass_lora_for_models(model, clip, lora, strength, strength)
+
+    return (patched_model, patched_clip, lora,)

nodes/loaders/gguf_models_loader.py

@@ -10,7 +10,7 @@ update_folder_names_and_paths("unet_gguf", ["diffusion_models", "unet"])
 update_folder_names_and_paths("clip_gguf", ["text_encoders", "clip"])
 
 
-class TT_GgufModelsLoader():
+class TT_Flux2GgufModelsLoader():
     @classmethod
     def INPUT_TYPES(cls):
         UNET_NAMES = [x for x in FP.get_filename_list("unet_gguf")]
@@ -28,7 +28,7 @@ class TT_GgufModelsLoader():
     RETURN_TYPES = ("MODEL", "CLIP", "VAE",)
     RETURN_NAMES = ("MODEL", "CLIP", "VAE",)
     FUNCTION = "load_models"
-    CATEGORY = "TenserTensor/Loaders/GGUF"
+    CATEGORY = "TenserTensor/Loaders/FLUX2 GGUF"
 
     VAE_DEVICE = "cpu"
     VAE_DTYPE = "bfloat16"

nodes/loaders/gguf_models_loader_advanced.py

@@ -3,14 +3,20 @@
 import folder_paths as FP
 
 from nodes import VAELoader as VL, MAX_RESOLUTION
-from .gguf_loader_helpers import update_folder_names_and_paths, load_unet, load_clip
-from .loader_helpers import load_vae, patch_flux_sampling, apply_lora
+from .gguf_loader_helpers import update_folder_names_and_paths, load_unet, load_clip, apply_bypass_lora_for_models
+from .loader_helpers import load_vae, patch_flux_sampling
 
 update_folder_names_and_paths("unet_gguf", ["diffusion_models", "unet"])
 update_folder_names_and_paths("clip_gguf", ["text_encoders", "clip"])
 
 
-class TT_GgufModelsLoaderAdvanced():
+class TT_Flux2GgufModelsLoaderAdvanced():
+    def __init__(self):
+        self.loaded_lora_1 = None
+        self.loaded_lora_2 = None
+        self.loaded_lora_3 = None
+        self.loaded_lora_4 = None
+
     @classmethod
     def INPUT_TYPES(cls):
         UNET_NAMES = [x for x in FP.get_filename_list("unet_gguf")]
@@ -24,8 +30,8 @@ class TT_GgufModelsLoaderAdvanced():
         return {
             "required": {
                 "unet_name": (UNET_NAMES,),
-                "dequant_dtype": (DTYPES, {"default": "default", "advanced": True},),
-                "patch_dtype": (DTYPES, {"default": "default", "advanced": True},),
+                "dequant_dtype": (DTYPES, {"default": "bfloat16", "advanced": True},),
+                "patch_dtype": (DTYPES, {"default": "bfloat16", "advanced": True},),
                 "apply_sampling": ("BOOLEAN", {"default": True, "label_on": "Enabled", "label_off": "Disabled"},),
                 "base_sampling_shift": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step": 0.01, "advanced": True},),
                 "max_sampling_shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step": 0.01, "advanced": True},),
@@ -49,7 +55,7 @@ class TT_GgufModelsLoaderAdvanced():
     RETURN_TYPES = ("MODEL", "CLIP", "VAE",)
     RETURN_NAMES = ("MODEL", "CLIP", "VAE",)
     FUNCTION = "load_models"
-    CATEGORY = "TenserTensor/Loaders/GGUF"
+    CATEGORY = "TenserTensor/Loaders/FLUX2 GGUF"
 
     def load_models(self, **kwargs):
         model = load_unet(
@@ -82,7 +88,7 @@ class TT_GgufModelsLoaderAdvanced():
         for name, strength, attr in loras:
             if name != "None" and strength != 0:
                 cached = getattr(self, attr)
-                model, clip, lora = apply_lora(cached, model, clip, name, strength)
+                model, clip, lora = apply_bypass_lora_for_models(cached, model, clip, name, strength)
                 setattr(self, attr, lora)
 
         vae = load_vae(kwargs.get("vae_name"), kwargs.get("vae_device"), kwargs.get("vae_dtype"))

nodes/postproduction/postproduction_helpers.py

@@ -196,6 +196,7 @@ def solarize(image, thresholds=0.5, additions=None):
 
     return kornia.enhance.solarize(timage, thresholds, additions).movedim(1, -1)
 
+
 def enhance_image(
         timage,
         brightness_factor,

nodes/postproduction/quick_image_upscaler.py

@@ -26,7 +26,6 @@ class TT_QuickImageUpscaler():
     FUNCTION = "upscale_image"
     CATEGORY = "TenserTensor/Postproduction"
 
-
     def upscale_image(self, image, device, upscale_model_name, tile, overlap):
         timage = image.clone()
 

nodes/sampling/ksampler.py

@@ -23,7 +23,7 @@ class TT_KSampler():
         }
 
     RETURN_TYPES = ("LATENT",)
-    RETURN_NAMES = ("LATENT",)
+    RETURN_NAMES = ("SAMPLES",)
     FUNCTION = "execute_sampling"
     CATEGORY = "TenserTensor/Sampling"
 

nodes/sampling/ksampler_advanced.py

@@ -28,7 +28,7 @@ class TT_KSamplerAdvanced():
         }
 
     RETURN_TYPES = ("LATENT",)
-    RETURN_NAMES = ("LATENT",)
+    RETURN_NAMES = ("SAMPLES",)
     FUNCTION = "execute_sampling"
     CATEGORY = "TenserTensor/Sampling"
 

nodes/sampling/ksampler_context.py

@@ -13,7 +13,7 @@ class TT_KSamplerContext():
         }
 
     RETURN_TYPES = ("TT_CONTEXT", "LATENT",)
-    RETURN_NAMES = ("CONTEXT", "LATENT",)
+    RETURN_NAMES = ("CONTEXT", "SAMPLES",)
     FUNCTION = "execute_sampling"
     CATEGORY = "TenserTensor/Sampling"
 

nodes/sampling/ksampler_guided.py

@@ -0,0 +1,25 @@
+# (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+
+from .ksampler_helpers import guided_sample_latents
+
+
+class TT_KSamplerGuided:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "latent": ("LATENT",),
+                "guider": ("GUIDER",),
+                "sigmas": ("SIGMAS",),
+                "sampler": ("SAMPLER",),
+                "random_noise": ("NOISE",),
+            },
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    RETURN_NAMES = ("SAMPLES",)
+    FUNCTION = "execute_sampling"
+    CATEGORY = "TenserTensor/Sampling"
+
+    def execute_sampling(self, latent, guider, sigmas, sampler, random_noise):
+        return (guided_sample_latents(latent, guider, sigmas, sampler, random_noise),)

nodes/sampling/ksampler_helpers.py

@@ -1,8 +1,9 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
 
 import comfy
-import latent_preview
+import latent_preview as LP
 import torch
+from comfy import sample as S, utils as U, model_management as MM, nested_tensor as NT
 
 
 def sample_latents(**kwargs):
@@ -26,7 +27,7 @@ def sample_latents(**kwargs):
     else:
         noise = torch.zeros(latent_img.size(), dtype=latent_img.dtype, layout=latent_img.layout, device="cpu")
 
-    callback = latent_preview.prepare_callback(model, steps)
+    callback = LP.prepare_callback(model, steps)
 
     args = {
         "model": model,
@@ -55,3 +56,39 @@ def sample_latents(**kwargs):
     retval["samples"] = samples
 
     return retval
+
+
+def guided_sample_latents(latent, guider, sigmas, sampler, random_noise):
+    model_patcher = guider.model_patcher
+    latent_dict = latent.copy()
+    latent_tensor = S.fix_empty_latent_channels(model_patcher, latent_dict["samples"], latent.get("downscale_ratio_spacial", None))
+    latent_dict["samples"] = latent_tensor
+
+    noise_mask = latent.get("noise_mask", None)
+
+    interim_buffer = {}
+    callback = LP.prepare_callback(model_patcher, sigmas.shape[-1] - 1, interim_buffer)
+    disable_pbar = not U.PROGRESS_BAR_ENABLED
+
+    samples = guider.sample(
+        random_noise.generate_noise(latent),
+        latent_tensor,
+        sampler,
+        sigmas,
+        denoise_mask=noise_mask,
+        callback=callback,
+        disable_pbar=disable_pbar,
+        seed=random_noise.seed
+    ).to(MM.intermediate_device())
+
+    retval = latent_dict.copy()
+    retval.pop("downscale_ratio_spacial", None)
+    retval["samples"] = samples
+
+#    if "x0" in interim_buffer:
+#        clean_latent = guider.model_patcher.model.process_latent_out(interim_buffer["x0"].cpu())
+#        if samples.is_nested:
+#            clean_latent = NT.NestedTensor(U.unpack_latents(clean_latent, [t.shape for t in samples.unbind()]))
+#        retval["samples"] = clean_latent
+
+    return retval

nodes/sampling/ksampler_two_stage.py

@@ -28,7 +28,7 @@ class TT_KSamplerTwoStage():
         }
 
     RETURN_TYPES = ("LATENT",)
-    RETURN_NAMES = ("LATENT",)
+    RETURN_NAMES = ("SAMPLES",)
     FUNCTION = "execute_sampling"
     CATEGORY = "TenserTensor/Sampling"
 

nodes/text_encoders/clip_text_encode_flux.py

@@ -9,22 +9,10 @@ class TT_ClipTextEncodeFlux():
         return {
             "required": {
                 "clip": ("CLIP",),
-                "clip_l_positive": (
-                    "STRING",
-                    {"multiline": True, "placeholder": "CLIP_L Positive", "dynamicPrompts": True}
-                ),
-                "t5xxl_positive": (
-                    "STRING",
-                    {"multiline": True, "placeholder": "T5XXL Positive", "dynamicPrompts": True}
-                ),
-                "clip_l_negative": (
-                    "STRING",
-                    {"multiline": True, "placeholder": "CLIP_L Negative", "dynamicPrompts": True}
-                ),
-                "t5xxl_negative": (
-                    "STRING",
-                    {"multiline": True, "placeholder": "CLIP_L Negative", "dynamicPrompts": True}
-                ),
+                "clip_l_positive": ("STRING", {"multiline": True, "placeholder": "CLIP_L Positive", "dynamicPrompts": True}),
+                "t5xxl_positive": ("STRING", {"multiline": True, "placeholder": "T5XXL Positive", "dynamicPrompts": True}),
+                "clip_l_negative": ("STRING", {"multiline": True, "placeholder": "CLIP_L Negative", "dynamicPrompts": True}),
+                "t5xxl_negative": ("STRING", {"multiline": True, "placeholder": "CLIP_L Negative", "dynamicPrompts": True}),
                 "guidance": ("FLOAT", {"default": 3.5, "min": 1.0, "max": 10.0, "step": 0.1})
             }
         }
@@ -34,5 +22,5 @@ class TT_ClipTextEncodeFlux():
     FUNCTION = "execute"
     CATEGORY = "TenserTensor/Text Encoders/FLUX"
 
-    def execute(self, clip, clip_l_positive, t5xxl_positive, clip_l_negative, t5xxl_negative, guidance):
-        return encode_prompts_flux(clip, clip_l_positive, t5xxl_positive, clip_l_negative, t5xxl_negative, guidance)
+    def execute(self, **kwargs):
+        return encode_prompts_flux(**kwargs)

nodes/text_encoders/clip_text_encode_flux2.py

@@ -0,0 +1,25 @@
+# (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+
+from .encoder_helpers import encode_prompts_flux2
+
+
+class TT_ClipTextEncodeFlux2():
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "clip": ("CLIP",),
+                "prompt": ("STRING", {"multiline": True, "placeholder": "Prompt", "dynamicPrompts": True}),
+                "lora_triggers": ("STRING", {"multiline": True, "placeholder": "LoRA Triggers", "dynamicPrompts": True}),
+                "guidance": ("FLOAT", {"default": 3.5, "min": 1.0, "max": 10.0, "step": 0.1})
+            }
+        }
+
+    RETURN_TYPES = ("GUIDER",)
+    RETURN_NAMES = ("GUIDER",)
+    FUNCTION = "execute"
+    CATEGORY = "TenserTensor/Text Encoders/FLUX2"
+
+    def execute(self, model, clip, prompt, lora_triggers, guidance):
+        return encode_prompts_flux2(model, clip, prompt, lora_triggers, guidance)

nodes/text_encoders/clip_text_encode_flux2_context.py

@@ -0,0 +1,41 @@
+# (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+
+from .encoder_helpers import encode_prompts_flux2
+
+
+class TT_ClipTextEncodeFlux2Context():
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "context": ("TT_CONTEXT",)
+            }
+        }
+
+    RETURN_TYPES = ("TT_CONTEXT", "GUIDER",)
+    RETURN_NAMES = ("CONTEXT", "GUIDER",)
+    FUNCTION = "execute"
+    CATEGORY = "TenserTensor/Text Encoders/FLUX2"
+
+    def execute(self, context):
+        model = context.get("model", None)
+        clip = context.get("clip", None)
+        config = context.get("workflow_config", None)
+
+        if model is None:
+            raise ValueError("Model is required for text encoder")
+        if clip is None:
+            raise ValueError("CLIP is required for text encoder")
+        if config is None:
+            raise ValueError("Workflow Config is required for text encoder")
+
+        guider = encode_prompts_flux2(
+            model,
+            clip,
+            config.get("prompt", None),
+            config.get("lora_triggers", None),
+            config.get("guidance", None),
+        )
+        context["guider"] = guider
+
+        return (context, guider,)

nodes/text_encoders/clip_text_encode_sdxl.py

@@ -1,6 +1,6 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
 
-import nodes
+from nodes import MAX_RESOLUTION
 from .encoder_helpers import encode_prompts_sdxl
 
 
@@ -10,28 +10,16 @@ class TT_ClipTextEncodeSdxl():
         return {
             "required": {
                 "clip": ("CLIP",),
-                "clip_l_positive": (
-                    "STRING",
-                    {"multiline": True, "placeholder": "CLIP_L Positive", "dynamicPrompts": True}
-                ),
-                "clip_g_positive": (
-                    "STRING",
-                    {"multiline": True, "placeholder": "CLIP_G Positive", "dynamicPrompts": True}
-                ),
-                "clip_l_negative": (
-                    "STRING",
-                    {"multiline": True, "placeholder": "CLIP_L Negative", "dynamicPrompts": True}
-                ),
-                "clip_g_negative": (
-                    "STRING",
-                    {"multiline": True, "placeholder": "CLIP_G Negative", "dynamicPrompts": True}
-                ),
+                "clip_l_positive": ("STRING", {"multiline": True, "placeholder": "CLIP_L Positive", "dynamicPrompts": True}),
+                "clip_g_positive": ("STRING", {"multiline": True, "placeholder": "CLIP_G Positive", "dynamicPrompts": True}),
+                "clip_l_negative": ("STRING", {"multiline": True, "placeholder": "CLIP_L Negative", "dynamicPrompts": True}),
+                "clip_g_negative": ("STRING", {"multiline": True, "placeholder": "CLIP_G Negative", "dynamicPrompts": True}),
                 "ascore_positive": ("FLOAT", {"default": 9.0, "min": 0.0, "max": 1000.0, "step": 0.1}),
                 "ascore_negative": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.1}),
-                "width": ("INT", {"default": 512, "min": 0.0, "max": nodes.MAX_RESOLUTION}),
-                "height": ("INT", {"default": 512, "min": 0.0, "max": nodes.MAX_RESOLUTION}),
-                "target_width": ("INT", {"default": 512, "min": 0.0, "max": nodes.MAX_RESOLUTION}),
-                "target_height": ("INT", {"default": 512, "min": 0.0, "max": nodes.MAX_RESOLUTION}),
+                "width": ("INT", {"default": 512, "min": 0.0, "max": MAX_RESOLUTION}),
+                "height": ("INT", {"default": 512, "min": 0.0, "max": MAX_RESOLUTION}),
+                "target_width": ("INT", {"default": 512, "min": 0.0, "max": MAX_RESOLUTION}),
+                "target_height": ("INT", {"default": 512, "min": 0.0, "max": MAX_RESOLUTION}),
             }
         }
 
@@ -40,7 +28,5 @@ class TT_ClipTextEncodeSdxl():
     FUNCTION = "execute"
     CATEGORY = "TenserTensor/Text Encoders/SDXL"
 
-    def execute(self, clip, clip_l_positive, clip_g_positive, clip_l_negative, clip_g_negative,
-                ascore_positive, ascore_negative, width, height, target_width, target_height):
-        return encode_prompts_sdxl(clip, clip_l_positive, clip_g_positive, clip_l_negative, clip_g_negative,
-                                   ascore_positive, ascore_negative, width, height, target_width, target_height)
+    def execute(self, **kwargs):
+        return encode_prompts_sdxl(**kwargs)

nodes/text_encoders/encoder_helpers.py

@@ -1,25 +1,20 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
 
-def encode_prompts_flux(clip, clip_l_positive, t5xxl_positive, clip_l_negative, t5xxl_negative, guidance):
-    positive_tokens = clip.tokenize(clip_l_positive)
-    positive_tokens["t5xxl"] = clip.tokenize(t5xxl_positive)["t5xxl"]
-    negative_tokens = clip.tokenize(clip_l_negative)
-    negative_tokens["t5xxl"] = clip.tokenize(t5xxl_negative)["t5xxl"]
+import comfy.samplers as S
 
-    return (
-        clip.encode_from_tokens_scheduled(
-            positive_tokens,
-            add_dict={"guidance": guidance, }
-        ),
-        clip.encode_from_tokens_scheduled(
-            negative_tokens,
-            add_dict={"guidance": guidance, }
-        )
-    )
+from ...lib.common import BasicGuider
+
+
+class Guider_Basic(S.CFGGuider):
+    def set_conds(self, positive):
+        self.inner_set_conds({"positive": positive})
 
 
 def encode_prompts_sdxl(clip, clip_l_positive, clip_g_positive, clip_l_negative, clip_g_negative,
                         ascore_positive, ascore_negative, width, height, target_width, target_height):
+    if clip is None:
+        raise ValueError("ERROR: CLIP is required for text encoder")
+
     positive_tokens = clip.tokenize(clip_g_positive)
     positive_tokens["l"] = clip.tokenize(clip_l_positive)["l"]
     negative_tokens = clip.tokenize(clip_g_negative)
@@ -47,3 +42,31 @@ def encode_prompts_sdxl(clip, clip_l_positive, clip_g_positive, clip_l_negative,
             }
         )
     )
+
+
+def encode_prompts_flux(clip, clip_l_positive, t5xxl_positive, clip_l_negative, t5xxl_negative, guidance):
+    if clip is None:
+        raise ValueError("ERROR: CLIP is required for text encoder")
+
+    positive_tokens = clip.tokenize(clip_l_positive)
+    positive_tokens["t5xxl"] = clip.tokenize(t5xxl_positive)["t5xxl"]
+    negative_tokens = clip.tokenize(clip_l_negative)
+    negative_tokens["t5xxl"] = clip.tokenize(t5xxl_negative)["t5xxl"]
+
+    return (
+        clip.encode_from_tokens_scheduled(positive_tokens, add_dict={"guidance": guidance, }),
+        clip.encode_from_tokens_scheduled(negative_tokens, add_dict={"guidance": guidance, })
+    )
+
+
+def encode_prompts_flux2(model, clip, prompt, lora_triggers, guidance):
+    if clip is None:
+        raise ValueError("ERROR: CLIP is required for text encoder")
+
+    full_prompt = f"{lora_triggers}, {prompt}" if lora_triggers.strip() else prompt
+    tokens = clip.tokenize(full_prompt)
+    conditioning = clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance, })
+    guider = BasicGuider(model)
+    guider.set_conds(conditioning)
+
+    return (guider,)

nodes/vae/vae_decode_context.py

@@ -1,4 +1,6 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+from .vae_helpers import vae_decode
+
 
 class TT_VaeDecodeContext:
     @classmethod
@@ -19,19 +21,11 @@ class TT_VaeDecodeContext:
         latent = context["latent"]
 
         if vae is None:
-            raise ValueError("VAE is required for decode")
+            raise ValueError("ERROR: VAE is required for decode")
         if latent is None:
-            raise ValueError("Latent image is required for decode")
-
-        samples = latent["samples"]
-        if samples.is_nested:
-            samples = samples.unbind()[0]
-
-        images = vae.decode(samples)
-
-        if len(images.shape) == 5:
-            images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1])
+            raise ValueError("ERROR: Latent image is required for decode")
 
+        images = vae_decode(latent, vae)
         context["image"] = images
 
         return (context, images,)

nodes/vae/vae_decode_tiled.py

@@ -1,6 +1,7 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
 
-import torch
+
+from .vae_helpers import vae_decode
 
 
 class TT_VaeDecodeTiled:
@@ -23,23 +24,4 @@ class TT_VaeDecodeTiled:
     CATEGORY = "TenserTensor/VAE"
 
     def vae_decode(self, latent, vae, tile_width, tile_height, overlap, circular):
-        samples = latent["samples"]
-        if samples.is_nested:
-            samples = samples.unbind()[0]
-
-        if circular == True:
-            for layer in [layer for layer in vae.first_stage_model.modules() if isinstance(layer, torch.nn.Conv2d)]:
-                layer.padding_mode = "circular"
-
-        compression = vae.spacial_compression_decode()
-        images = vae.decode_tiled(
-            samples,
-            tile_x=tile_width // compression,
-            tile_y=tile_height // compression,
-            overlap=overlap // compression
-        )
-
-        if len(images.shape) == 5:
-            images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1])
-
-        return (images,)
+        return (vae_decode(latent, vae, tile_width, tile_height, overlap, circular),)

nodes/vae/vae_encode_context.py

@@ -1,5 +1,8 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
 
+from .vae_helpers import vae_encode
+
+
 class TT_VaeEncodeContext:
     @classmethod
     def INPUT_TYPES(cls):
@@ -23,8 +26,7 @@ class TT_VaeEncodeContext:
         if image is None:
             raise ValueError("Pixel image is required for encode")
 
-        samples = vae.encode(image)
-        latent_dict = {"samples": samples}
-        context["latent"] = latent_dict
+        samples = vae_encode(image, vae)
+        context["latent"] = samples
 
-        return (context, latent_dict,)
+        return (context, samples,)

nodes/vae/vae_encode_tiled.py

@@ -1,5 +1,8 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
 
+from .vae_helpers import vae_encode
+
+
 class TT_VaeEncodeTiled:
     @classmethod
     def INPUT_TYPES(cls):
@@ -19,12 +22,4 @@ class TT_VaeEncodeTiled:
     CATEGORY = "TenserTensor/VAE"
 
     def vae_encode(self, image, vae, tile_width, tile_height, overlap):
-        samples = vae.encode_tiled(
-            image,
-            tile_x=tile_width,
-            tile_y=tile_height,
-            overlap=overlap
-        )
-        latent_dict = {"samples": samples}
-
-        return (latent_dict,)
+        return (vae_encode(image, vae, tile_width, tile_height, overlap),)

nodes/vae/vae_helpers.py

@@ -1,2 +1,38 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
 
+import torch
+
+
+def vae_encode(image, vae, tile_width=512, tile_height=512, overlap=64):
+    samples = vae.encode_tiled(
+        image,
+        tile_x=tile_width,
+        tile_y=tile_height,
+        overlap=overlap
+    )
+    latent_dict = {"samples": samples}
+
+    return latent_dict
+
+
+def vae_decode(latent, vae, tile_width=512, tile_height=512, overlap=64, circular=False):
+    samples = latent["samples"]
+    if samples.is_nested:
+        samples = samples.unbind()[0]
+
+    if circular == True:
+        for layer in [layer for layer in vae.first_stage_model.modules() if isinstance(layer, torch.nn.Conv2d)]:
+            layer.padding_mode = "circular"
+
+    compression = vae.spacial_compression_decode()
+    images = vae.decode_tiled(
+        samples,
+        tile_x=tile_width // compression,
+        tile_y=tile_height // compression,
+        overlap=overlap // compression
+    )
+
+    if len(images.shape) == 5:
+        images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1])
+
+    return images

nodes/workflow/flux2_workflow_settings.py

@@ -0,0 +1,67 @@
+# (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+
+import comfy.samplers as S
+
+from nodes import MAX_RESOLUTION
+from .workflow_settings_helpers import get_schedule
+
+
+class TT_Flux2WorkflowSettings:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "steps": ("INT", {"default": 25, "min": 1, "max": 10000}),
+                "cfg": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.1}),
+                "sampler_name": (S.KSampler.SAMPLERS,),
+                "width": ("INT", {"default": 1024, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
+                "height": ("INT", {"default": 1024, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
+                "guidance": ("FLOAT", {"default": 3.5, "min": 1.0, "max": 10.0, "step": 0.1})
+            }
+        }
+
+    RETURN_TYPES = (
+        "TT_WORKFLOW_CONFIG",
+        "INT",
+        "INT",
+        "FLOAT",
+        "SAMPLER",
+        S.KSampler.SAMPLERS,
+        "SIGMAS",
+        "INT",
+        "INT",
+        "FLOAT"
+    )
+    RETURN_NAMES = (
+        "WORKFLOW_CONFIG",
+        "SEED",
+        "STEPS",
+        "CFG",
+        "SAMPLER",
+        "SAMPLER_NAME",
+        "SCHEDULER",
+        "WIDTH",
+        "HEIGHT",
+        "GUIDANCE"
+    )
+    FUNCTION = "get_settings"
+    CATEGORY = "TenserTensor/Workflow/FLUX2"
+
+    def get_settings(self, seed, steps, cfg, sampler_name, width, height, guidance):
+        scheduler = get_schedule(steps, width, height)
+        sampler = S.sampler_object(sampler_name)
+
+        workflow_config = {
+            "seed": seed,
+            "steps": steps,
+            "cfg": cfg,
+            "sampler": sampler,
+            "sampler_name": sampler_name,
+            "scheduler": scheduler,
+            "width": width,
+            "height": height,
+            "guidance": guidance
+        }
+
+        return (workflow_config, seed, steps, cfg, sampler, sampler_name, scheduler, width, height, guidance)

nodes/workflow/flux2_workflow_settings_advanced.py

@@ -0,0 +1,75 @@
+# (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+
+import comfy.samplers as S
+
+from nodes import MAX_RESOLUTION
+from .workflow_settings_helpers import get_schedule
+
+
+class TT_Flux2WorkflowSettingsAdvanced:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "steps": ("INT", {"default": 25, "min": 1, "max": 10000}),
+                "cfg": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.1}),
+                "sampler_name": (S.KSampler.SAMPLERS,),
+                "width": ("INT", {"default": 1024, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
+                "height": ("INT", {"default": 1024, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
+                "prompt": ("STRING", {"multiline": True, "placeholder": "Prompt", "dynamicPrompts": True}),
+                "lora_triggers": ("STRING", {"multiline": True, "placeholder": "LoRA Triggers", "dynamicPrompts": True}),
+                "guidance": ("FLOAT", {"default": 3.5, "min": 1.0, "max": 10.0, "step": 0.1})
+            }
+        }
+
+    RETURN_TYPES = (
+        "TT_WORKFLOW_CONFIG",
+        "INT",
+        "INT",
+        "FLOAT",
+        S.KSampler.SAMPLERS,
+        "SAMPLER",
+        "SIGMAS",
+        "INT",
+        "INT",
+        "STRING",
+        "STRING",
+        "FLOAT"
+    )
+    RETURN_NAMES = (
+        "WORKFLOW_CONFIG",
+        "SEED",
+        "STEPS",
+        "CFG",
+        "SAMPLER_NAME",
+        "SAMPLER",
+        "SCHEDULER",
+        "WIDTH",
+        "HEIGHT",
+        "PROMPT",
+        "LORA_TRIGGERS",
+        "GUIDANCE"
+    )
+    FUNCTION = "get_settings"
+    CATEGORY = "TenserTensor/Workflow/FLUX2"
+
+    def get_settings(self, seed, steps, cfg, sampler_name, width, height, prompt, lora_triggers, guidance):
+        scheduler = get_schedule(steps, width, height)
+        sampler = S.sampler_object(sampler_name)
+
+        workflow_config = {
+            "seed": seed,
+            "steps": steps,
+            "cfg": cfg,
+            "sampler": sampler,
+            "sampler_name": sampler_name,
+            "scheduler": scheduler,
+            "width": width,
+            "height": height,
+            "prompt": prompt,
+            "lora_triggers": lora_triggers,
+            "guidance": guidance
+        }
+
+        return (workflow_config, seed, steps, cfg, sampler_name, sampler, scheduler, width, height, prompt, lora_triggers, guidance)

nodes/workflow/flux_workflow_settings.py

@@ -1,6 +1,6 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
 
-import comfy.samplers
+import comfy.samplers as S
 
 
 class TT_FluxWorkflowSettings:
@@ -11,14 +11,30 @@ class TT_FluxWorkflowSettings:
                 "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                 "steps": ("INT", {"default": 25, "min": 1, "max": 10000}),
                 "cfg": ("FLOAT", {"default": 1.5, "min": 0.0, "max": 100.0, "step": 0.1}),
-                "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
-                "scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
+                "sampler_name": (S.KSampler.SAMPLERS,),
+                "scheduler": (S.KSampler.SCHEDULERS,),
                 "guidance": ("FLOAT", {"default": 3.5, "min": 1.0, "max": 10.0, "step": 0.1})
             }
         }
 
-    RETURN_TYPES = ("TT_WORKFLOW_CONFIG", "INT", "INT", "FLOAT", "STRING", "STRING", "FLOAT")
-    RETURN_NAMES = ("WORKFLOW_CONFIG", "SEED", "STEPS", "CFG", "SAMPLER_NAME", "SCHEDULER", "GUIDANCE")
+    RETURN_TYPES = (
+        "TT_WORKFLOW_CONFIG",
+        "INT",
+        "INT",
+        "FLOAT",
+        S.KSampler.SAMPLERS,
+        S.KSampler.SCHEDULERS,
+        "FLOAT"
+    )
+    RETURN_NAMES = (
+        "WORKFLOW_CONFIG",
+        "SEED",
+        "STEPS",
+        "CFG",
+        "SAMPLER_NAME",
+        "SCHEDULER",
+        "GUIDANCE"
+    )
     FUNCTION = "get_settings"
     CATEGORY = "TenserTensor/Workflow/FLUX"
 

nodes/workflow/flux_workflow_settings_advanced.py

@@ -1,6 +1,6 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
 
-import comfy.samplers
+import comfy.samplers as S
 
 
 class TT_FluxWorkflowSettingsAdvanced:
@@ -11,24 +11,12 @@ class TT_FluxWorkflowSettingsAdvanced:
                 "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                 "steps": ("INT", {"default": 25, "min": 1, "max": 10000}),
                 "cfg": ("FLOAT", {"default": 1.5, "min": 0.0, "max": 100.0, "step": 0.1}),
-                "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
-                "scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
-                "clip_l_positive": (
-                    "STRING",
-                    {"multiline": True, "placeholder": "CLIP_L Positive", "dynamicPrompts": True}
-                ),
-                "t5xxl_positive": (
-                    "STRING",
-                    {"multiline": True, "placeholder": "T5XXL Positive", "dynamicPrompts": True}
-                ),
-                "clip_l_negative": (
-                    "STRING",
-                    {"multiline": True, "placeholder": "CLIP_L Negative", "dynamicPrompts": True}
-                ),
-                "t5xxl_negative": (
-                    "STRING",
-                    {"multiline": True, "placeholder": "CLIP_L Negative", "dynamicPrompts": True}
-                ),
+                "sampler_name": (S.KSampler.SAMPLERS,),
+                "scheduler": (S.KSampler.SCHEDULERS,),
+                "clip_l_positive": ("STRING", {"multiline": True, "placeholder": "CLIP_L Positive", "dynamicPrompts": True}),
+                "t5xxl_positive": ("STRING", {"multiline": True, "placeholder": "T5XXL Positive", "dynamicPrompts": True}),
+                "clip_l_negative": ("STRING", {"multiline": True, "placeholder": "CLIP_L Negative", "dynamicPrompts": True}),
+                "t5xxl_negative": ("STRING", {"multiline": True, "placeholder": "CLIP_L Negative", "dynamicPrompts": True}),
                 "guidance": ("FLOAT", {"default": 3.5, "min": 1.0, "max": 10.0, "step": 0.1})
             }
         }
@@ -38,8 +26,8 @@ class TT_FluxWorkflowSettingsAdvanced:
         "INT",
         "INT",
         "FLOAT",
-        "STRING",
-        "STRING",
+        S.KSampler.SAMPLERS,
+        S.KSampler.SCHEDULERS,
         "STRING",
         "STRING",
         "STRING",
@@ -62,8 +50,7 @@ class TT_FluxWorkflowSettingsAdvanced:
     FUNCTION = "get_settings"
     CATEGORY = "TenserTensor/Workflow/FLUX"
 
-    def get_settings(self, seed, steps, cfg, sampler_name, scheduler, guidance, clip_l_positive, t5xxl_positive,
-                     clip_l_negative, t5xxl_negative):
+    def get_settings(self, seed, steps, cfg, sampler_name, scheduler, guidance, clip_l_positive, t5xxl_positive, clip_l_negative, t5xxl_negative):
         workflow_config = {
             "seed": seed,
             "steps": steps,
@@ -77,5 +64,5 @@ class TT_FluxWorkflowSettingsAdvanced:
             "guidance": guidance
         }
 
-        return (workflow_config, seed, steps, cfg, sampler_name, scheduler, guidance,
-                clip_l_positive, t5xxl_positive, clip_l_negative, t5xxl_negative)
+        return (workflow_config, seed, steps, cfg, sampler_name, scheduler, guidance, clip_l_positive, t5xxl_positive, clip_l_negative,
+                t5xxl_negative)

nodes/workflow/sdxl_workflow_settings.py

@@ -1,6 +1,6 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
 
-import comfy.samplers
+import comfy.samplers as S
 
 
 class TT_SdxlWorkflowSettings:
@@ -11,8 +11,8 @@ class TT_SdxlWorkflowSettings:
                 "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                 "steps": ("INT", {"default": 30, "min": 1, "max": 10000}),
                 "cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step": 0.1}),
-                "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
-                "scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
+                "sampler_name": (S.KSampler.SAMPLERS,),
+                "scheduler": (S.KSampler.SCHEDULERS,),
             }
         }
 
@@ -21,8 +21,8 @@ class TT_SdxlWorkflowSettings:
         "INT",
         "INT",
         "FLOAT",
-        comfy.samplers.KSampler.SAMPLERS,
-        comfy.samplers.KSampler.SCHEDULERS,
+        S.KSampler.SAMPLERS,
+        S.KSampler.SCHEDULERS,
     )
 
     RETURN_NAMES = (

nodes/workflow/sdxl_workflow_settings_advanced.py

@@ -1,8 +1,8 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
 
-import comfy.samplers
+import comfy.samplers as S
 
-import nodes
+from nodes import MAX_RESOLUTION
 
 
 class TT_SdxlWorkflowSettingsAdvanced:
@@ -13,30 +13,18 @@ class TT_SdxlWorkflowSettingsAdvanced:
                 "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                 "steps": ("INT", {"default": 30, "min": 1, "max": 10000}),
                 "cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step": 0.1}),
-                "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
-                "scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
-                "clip_l_positive": (
-                    "STRING",
-                    {"multiline": True, "placeholder": "CLIP_L Positive", "dynamicPrompts": True}
-                ),
-                "clip_g_positive": (
-                    "STRING",
-                    {"multiline": True, "placeholder": "CLIP_G Positive", "dynamicPrompts": True}
-                ),
-                "clip_l_negative": (
-                    "STRING",
-                    {"multiline": True, "placeholder": "CLIP_L Negative", "dynamicPrompts": True}
-                ),
-                "clip_g_negative": (
-                    "STRING",
-                    {"multiline": True, "placeholder": "CLIP_G Negative", "dynamicPrompts": True}
-                ),
+                "sampler_name": (S.KSampler.SAMPLERS,),
+                "scheduler": (S.KSampler.SCHEDULERS,),
+                "clip_l_positive": ("STRING", {"multiline": True, "placeholder": "CLIP_L Positive", "dynamicPrompts": True}),
+                "clip_g_positive": ("STRING", {"multiline": True, "placeholder": "CLIP_G Positive", "dynamicPrompts": True}),
+                "clip_l_negative": ("STRING", {"multiline": True, "placeholder": "CLIP_L Negative", "dynamicPrompts": True}),
+                "clip_g_negative": ("STRING", {"multiline": True, "placeholder": "CLIP_G Negative", "dynamicPrompts": True}),
                 "ascore_positive": ("FLOAT", {"default": 9.0, "min": 0.0, "max": 1000.0, "step": 0.1}),
                 "ascore_negative": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.1}),
-                "width": ("INT", {"default": 512, "min": 0.0, "max": nodes.MAX_RESOLUTION}),
-                "height": ("INT", {"default": 512, "min": 0.0, "max": nodes.MAX_RESOLUTION}),
-                "target_width": ("INT", {"default": 512, "min": 0.0, "max": nodes.MAX_RESOLUTION}),
-                "target_height": ("INT", {"default": 512, "min": 0.0, "max": nodes.MAX_RESOLUTION}),
+                "width": ("INT", {"default": 512, "min": 0.0, "max": MAX_RESOLUTION}),
+                "height": ("INT", {"default": 512, "min": 0.0, "max": MAX_RESOLUTION}),
+                "target_width": ("INT", {"default": 512, "min": 0.0, "max": MAX_RESOLUTION}),
+                "target_height": ("INT", {"default": 512, "min": 0.0, "max": MAX_RESOLUTION}),
             }
         }
 
@@ -45,8 +33,8 @@ class TT_SdxlWorkflowSettingsAdvanced:
         "INT",
         "INT",
         "FLOAT",
-        "STRING",
-        "STRING",
+        S.KSampler.SAMPLERS,
+        S.KSampler.SCHEDULERS,
         "STRING",
         "STRING",
         "STRING",

nodes/workflow/workflow_settings_helpers.py

@@ -0,0 +1,36 @@
+# (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+
+import math
+
+import torch
+
+
+def _compute_empirical_mu(image_seq_len, num_steps):
+    a1, b1 = 8.73809524e-05, 1.89833333
+    a2, b2 = 0.00016927, 0.45666666
+
+    if image_seq_len > 4300:
+        mu = a2 * image_seq_len + b2
+        return float(mu)
+
+    m_200 = a2 * image_seq_len + b2
+    m_10 = a1 * image_seq_len + b1
+
+    a = (m_200 - m_10) / 190.0
+    b = m_200 - 200.0 * a
+    mu = a * num_steps + b
+
+    return float(mu)
+
+
+def _generalized_time_snr_shift(t, mu, sigma):
+    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+
+def get_schedule(num_steps, width, height):
+    image_seq_len = (width * height / (16 * 16))
+    mu = _compute_empirical_mu(image_seq_len, num_steps)
+    timesteps = torch.linspace(1, 0, num_steps + 1)
+    timesteps = _generalized_time_snr_shift(timesteps, mu, 1.0)
+
+    return timesteps

pyproject.toml

@@ -1,8 +1,8 @@
 # pyproject.toml
 [project]
 name = "ComfyUI-TenserTensor"
-description = "A collection of custom nodes for ComfyUI that streamline common workflows. The main goal is to reduce visual clutter by combining frequently used node chains into single, convenient loaders."
-version = "1.3.1"
+description = "A collection of custom nodes for ComfyUI that streamline common workflows. Includes all-in-one model loaders (checkpoint/unet, LoRA, CLIP, VAE), GGUF quantized model support (FLUX, FLUX2, SDXL), workflow settings nodes, latent factory (aspect ratio, megapixels, FLUX/SDXL), context nodes, CLIP text encoders, KSamplers (context, advanced, two-stage - draft/refine), VAE encoders/decoders (context, tiled), and post-production tools (brightness, contrast, LUT color grading, film grain, upscaler)."
+version = "1.4.7"
 license = { file = "LICENSE" }
 dependencies = ["gguf"]
 

web/docs/TT_ClipTextEncodeFlux2.md

@@ -0,0 +1,31 @@
+## TT Clip Text Encode Flux2 *TT_ClipTextEncodeFlux2*
+
+*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
+
+Text prompt encoder for FLUX2 models with LoRA trigger word support and built-in guidance configuration.
+
+### Inputs
+
+| Parameter       | Type   | Required | Description                                                  |
+|-----------------|--------|----------|--------------------------------------------------------------|
+| `model`         | MODEL  | Yes      | Diffusion model                                              |
+| `clip`          | CLIP   | Yes      | CLIP model for text encoding                                 |
+| `prompt`        | STRING | Yes      | Main generation prompt (multiline, supports dynamic prompts) |
+| `lora_triggers` | STRING | Yes      | LoRA trigger words (multiline, supports dynamic prompts)     |
+| `guidance`      | FLOAT  | Yes      | Guidance scale for generation (1.0–10.0, default: 3.5)       |
+
+### Outputs
+
+| Parameter | Type   | Description                                        |
+|-----------|--------|----------------------------------------------------|
+| `GUIDER`  | GUIDER | Configured guider with encoded prompt and guidance |
+
+### Usage
+
+Encodes text prompts specifically for FLUX2 models. Enter your main prompt in `prompt` and any LoRA-specific trigger
+words separately in `lora_triggers` — the node combines them internally. Adjust `guidance` to control how strongly
+the model follows the prompt (3.5 is a good starting point for FLUX2).
+
+---
+
+[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)

web/docs/TT_ClipTextEncodeFlux2Context.md

@@ -0,0 +1,29 @@
+## TT Clip Text Encode Flux2 (Context) *TT_ClipTextEncodeFlux2Context*
+
+*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
+
+Context-based text prompt encoder for FLUX2 models. Extracts model, CLIP, and prompt configuration directly from a TT_CONTEXT object.
+
+### Inputs
+
+| Parameter | Type       | Required | Description                                                                           |
+|-----------|------------|----------|---------------------------------------------------------------------------------------|
+| `context` | TT_CONTEXT | Yes      | Context object containing `model`, `clip`, and `workflow_config` with prompt settings |
+
+### Outputs
+
+| Parameter | Type       | Description                                        |
+|-----------|------------|----------------------------------------------------|
+| `CONTEXT` | TT_CONTEXT | Updated context with `guider` written back into it |
+| `GUIDER`  | GUIDER     | Configured guider with encoded prompt and guidance |
+
+### Usage
+
+Context-aware version of the FLUX2 text encoder. Instead of individual inputs, it reads `model`, `clip`, and
+`workflow_config` (containing `prompt`, `lora_triggers`, and `guidance`) from the passed `context`. The resulting
+guider is both returned as an output and stored back into the context under the `guider` key. Raises an error if
+any of the required fields are missing from the context.
+
+---
+
+[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)

web/docs/TT_ContextSetGuider.md

@@ -0,0 +1,27 @@
+## TT Context Set Guider *TT_ContextSetGuider*
+
+*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
+
+Writes a GUIDER object into the TT_CONTEXT, making it available for downstream context-aware nodes.
+
+### Inputs
+
+| Parameter | Type       | Required | Description                    |
+|-----------|------------|----------|--------------------------------|
+| `context` | TT_CONTEXT | Yes      | Context object to update       |
+| `guider`  | GUIDER     | Yes      | Guider to store in the context |
+
+### Outputs
+
+| Parameter | Type       | Description                                   |
+|-----------|------------|-----------------------------------------------|
+| `CONTEXT` | TT_CONTEXT | Updated context with `guider` written into it |
+
+### Usage
+
+Sets the `guider` field inside a TT_CONTEXT object. Use this node when you have a guider produced outside the
+context pipeline and need to inject it back in for use by subsequent context-aware nodes.
+
+---
+
+[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)

web/docs/TT_ContextSetImage.md

@@ -6,16 +6,16 @@ Adds or updates image in context object.
 
 ### Inputs
 
-| Parameter | Type       | Required | Description                    |
-|-----------|------------|----------|--------------------------------|
-| `context` | TT_CONTEXT | Yes      | Context to update              |
-| `image`   | IMAGE      | Yes      | Image to store in context      |
+| Parameter | Type       | Required | Description               |
+|-----------|------------|----------|---------------------------|
+| `context` | TT_CONTEXT | Yes      | Context to update         |
+| `image`   | IMAGE      | Yes      | Image to store in context |
 
 ### Outputs
 
-| Parameter | Type       | Description             |
-|-----------|------------|-------------------------|
-| `CONTEXT` | TT_CONTEXT | Updated context object  |
+| Parameter | Type       | Description            |
+|-----------|------------|------------------------|
+| `CONTEXT` | TT_CONTEXT | Updated context object |
 
 ### Usage
 

web/docs/TT_Flux2GgufModelsLoader.md

@@ -1,4 +1,4 @@
-## TT GGUF Models Loader *TT_GgufModelsLoader*
+## TT FLUX2 GGUF Models Loader *TT_GgufModelsLoader*
 
 *This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
 
@@ -14,15 +14,16 @@ Load GGUF-quantized diffusion model (UNet), CLIP text encoder, and VAE in a sing
 
 ### Outputs
 
-| Parameter | Type  | Description             |
-|-----------|-------|-------------------------|
-| `MODEL`   | MODEL | Loaded diffusion model  |
-| `CLIP`    | CLIP  | Loaded text encoder     |
-| `VAE`     | VAE   | Loaded VAE decoder      |
+| Parameter | Type  | Description            |
+|-----------|-------|------------------------|
+| `MODEL`   | MODEL | Loaded diffusion model |
+| `CLIP`    | CLIP  | Loaded text encoder    |
+| `VAE`     | VAE   | Loaded VAE decoder     |
 
 ### Usage
 
-All-in-one loader for GGUF-quantized models. Place your `.gguf` diffusion model files in the `diffusion_models` or `unet` directory, CLIP files in `text_encoders` or `clip` directory. VAE can be any standard ComfyUI-compatible VAE file.
+All-in-one loader for GGUF-quantized FLUX2 models. Place your `.gguf` diffusion model files in the `diffusion_models` or `unet` directory, CLIP files in
+`text_encoders` or `clip` directory. VAE can be any standard ComfyUI-compatible VAE file.
 
 ---
 

web/docs/TT_Flux2GgufModelsLoaderAdvanced.md

@@ -1,4 +1,4 @@
-## TT GGUF Models Loader (Advanced) *TT_GgufModelsLoaderAdvanced*
+## TT FLUX2 GGUF Models Loader (Advanced) *TT_GgufModelsLoaderAdvanced*
 
 *This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
 
@@ -6,22 +6,22 @@ All-in-one loader for GGUF-quantized models with extended control over quantizat
 
 ### Inputs
 
-| Parameter             | Type    | Required | Advanced | Description                                              |
-|-----------------------|---------|----------|----------|----------------------------------------------------------|
-| `unet_name`           | STRING  | Yes      |          | GGUF UNet/diffusion model filename                       |
+| Parameter             | Type    | Required | Advanced | Description                                                            |
+|-----------------------|---------|----------|----------|------------------------------------------------------------------------|
+| `unet_name`           | STRING  | Yes      |          | GGUF UNet/diffusion model filename                                     |
 | `dequant_dtype`       | STRING  | Yes      | Yes      | Data type for dequantization (default/target/float32/float16/bfloat16) |
-| `patch_dtype`         | STRING  | Yes      | Yes      | Data type for weight patching                            |
-| `apply_sampling`      | BOOLEAN | Yes      |          | Enable/disable flux sampling shift patch                 |
-| `base_sampling_shift` | FLOAT   | Yes      | Yes      | Base shift value for flux sampling (default: 0.5)        |
-| `max_sampling_shift`  | FLOAT   | Yes      | Yes      | Maximum shift value for flux sampling (default: 1.15)    |
-| `sampling_width`      | INT     | Yes      | Yes      | Reference width for sampling shift calculation           |
-| `sampling_height`     | INT     | Yes      | Yes      | Reference height for sampling shift calculation          |
-| `lora_name_1..4`      | STRING  | Yes      | Yes      | LoRA filename (None to skip)                             |
-| `strength_1..4`       | FLOAT   | Yes      | Yes      | LoRA blend strength (-10.0 to 10.0, default: 1.0)        |
-| `clip_name`           | STRING  | Yes      |          | GGUF CLIP text encoder filename                          |
-| `vae_name`            | STRING  | Yes      |          | VAE model filename                                       |
-| `vae_device`          | STRING  | Yes      | Yes      | Device for VAE (default/cpu)                             |
-| `vae_dtype`           | STRING  | Yes      | Yes      | Data type for VAE (bfloat16/float16/float32)             |
+| `patch_dtype`         | STRING  | Yes      | Yes      | Data type for weight patching                                          |
+| `apply_sampling`      | BOOLEAN | Yes      |          | Enable/disable flux sampling shift patch                               |
+| `base_sampling_shift` | FLOAT   | Yes      | Yes      | Base shift value for flux sampling (default: 0.5)                      |
+| `max_sampling_shift`  | FLOAT   | Yes      | Yes      | Maximum shift value for flux sampling (default: 1.15)                  |
+| `sampling_width`      | INT     | Yes      | Yes      | Reference width for sampling shift calculation                         |
+| `sampling_height`     | INT     | Yes      | Yes      | Reference height for sampling shift calculation                        |
+| `lora_name_1..4`      | STRING  | Yes      | Yes      | LoRA filename (None to skip)                                           |
+| `strength_1..4`       | FLOAT   | Yes      | Yes      | LoRA blend strength (-10.0 to 10.0, default: 1.0)                      |
+| `clip_name`           | STRING  | Yes      |          | GGUF CLIP text encoder filename                                        |
+| `vae_name`            | STRING  | Yes      |          | VAE model filename                                                     |
+| `vae_device`          | STRING  | Yes      | Yes      | Device for VAE (default/cpu)                                           |
+| `vae_dtype`           | STRING  | Yes      | Yes      | Data type for VAE (bfloat16/float16/float32)                           |
 
 ### Outputs
 
@@ -33,7 +33,9 @@ All-in-one loader for GGUF-quantized models with extended control over quantizat
 
 ### Usage
 
-Extended version of GGUF Models Loader. Supports up to 4 LoRAs loaded simultaneously. Enable `apply_sampling` to apply flux sampling shift patch — useful for improving quality at non-standard resolutions. Advanced parameters are hidden by default and can be revealed via the node's show/hide controls.
+Extended version of FLUX2 GGUF Models Loader. Supports up to 4 LoRAs loaded simultaneously. Enable `apply_sampling` to apply flux sampling shift patch —
+useful for improving quality at non-standard resolutions. Advanced parameters are hidden by default and can be revealed via the node's show/hide
+controls.
 
 ---
 

web/docs/TT_Flux2WorkflowSettings.md

@@ -0,0 +1,44 @@
+## TT Flux2 Workflow Settings *TT_Flux2WorkflowSettings*
+
+*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
+
+Central workflow configuration node for FLUX2 pipelines. Consolidates all sampling parameters into a single
+TT_WORKFLOW_CONFIG object while also exposing each value as an individual output.
+
+### Inputs
+
+| Parameter      | Type   | Required | Description                                                               |
+|----------------|--------|----------|---------------------------------------------------------------------------|
+| `seed`         | INT    | Yes      | Generation seed (0 – 18446744073709551615, default: 0)                    |
+| `steps`        | INT    | Yes      | Number of sampling steps (1–10000, default: 25)                           |
+| `cfg`          | FLOAT  | Yes      | CFG scale (0.0–100.0, default: 1.5)                                       |
+| `sampler_name` | STRING | Yes      | Sampler algorithm (KSampler sampler list)                                 |
+| `width`        | INT    | Yes      | Output image width in pixels (16–MAX_RESOLUTION, step: 8, default: 1024)  |
+| `height`       | INT    | Yes      | Output image height in pixels (16–MAX_RESOLUTION, step: 8, default: 1024) |
+| `guidance`     | FLOAT  | Yes      | Guidance scale for text encoder (1.0–10.0, default: 3.5)                  |
+
+### Outputs
+
+| Parameter         | Type               | Description                                          |
+|-------------------|--------------------|------------------------------------------------------|
+| `WORKFLOW_CONFIG` | TT_WORKFLOW_CONFIG | Full configuration object with all settings bundled  |
+| `SEED`            | INT                | Generation seed                                      |
+| `STEPS`           | INT                | Number of sampling steps                             |
+| `CFG`             | FLOAT              | CFG scale                                            |
+| `SAMPLER`         | SAMPLER            | Instantiated sampler object                          |
+| `SAMPLER_NAME`    | STRING             | Name of the selected sampler                         |
+| `SCHEDULER`       | SIGMAS             | Computed sigma schedule based on seed and resolution |
+| `WIDTH`           | INT                | Output image width                                   |
+| `HEIGHT`          | INT                | Output image height                                  |
+| `GUIDANCE`        | FLOAT              | Guidance scale                                       |
+
+### Usage
+
+Use this node as the single source of truth for FLUX2 workflow parameters. The `WORKFLOW_CONFIG` output is
+designed for context-aware nodes that consume a TT_WORKFLOW_CONFIG directly. Individual outputs are available
+for connecting to standard ComfyUI nodes. The sigma schedule (`SCHEDULER`) is computed automatically from
+the seed and resolution via `get_schedule`.
+
+---
+
+[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)

web/docs/TT_Flux2WorkflowSettingsAdvanced.md

@@ -0,0 +1,47 @@
+## TT Flux2 Workflow Settings (Advanced) *TT_Flux2WorkflowSettingsAdvanced*
+
+*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
+
+Extended workflow configuration node for FLUX2 pipelines. Includes all parameters from the base version plus
+built-in prompt and LoRA trigger fields, bundling everything into a single TT_WORKFLOW_CONFIG object.
+
+### Inputs
+
+| Parameter       | Type   | Required | Description                                                               |
+|-----------------|--------|----------|---------------------------------------------------------------------------|
+| `seed`          | INT    | Yes      | Generation seed (0 – 18446744073709551615, default: 0)                    |
+| `steps`         | INT    | Yes      | Number of sampling steps (1–10000, default: 25)                           |
+| `cfg`           | FLOAT  | Yes      | CFG scale (0.0–100.0, default: 1.5)                                       |
+| `sampler_name`  | STRING | Yes      | Sampler algorithm (KSampler sampler list)                                 |
+| `width`         | INT    | Yes      | Output image width in pixels (16–MAX_RESOLUTION, step: 8, default: 1024)  |
+| `height`        | INT    | Yes      | Output image height in pixels (16–MAX_RESOLUTION, step: 8, default: 1024) |
+| `prompt`        | STRING | Yes      | Main generation prompt (multiline, supports dynamic prompts)              |
+| `lora_triggers` | STRING | Yes      | LoRA trigger words (multiline, supports dynamic prompts)                  |
+| `guidance`      | FLOAT  | Yes      | Guidance scale for text encoder (1.0–10.0, default: 3.5)                  |
+
+### Outputs
+
+| Parameter         | Type               | Description                                          |
+|-------------------|--------------------|------------------------------------------------------|
+| `WORKFLOW_CONFIG` | TT_WORKFLOW_CONFIG | Full configuration object with all settings bundled  |
+| `SEED`            | INT                | Generation seed                                      |
+| `STEPS`           | INT                | Number of sampling steps                             |
+| `CFG`             | FLOAT              | CFG scale                                            |
+| `SAMPLER`         | SAMPLER            | Instantiated sampler object                          |
+| `SAMPLER_NAME`    | STRING             | Name of the selected sampler                         |
+| `SCHEDULER`       | SIGMAS             | Computed sigma schedule based on seed and resolution |
+| `WIDTH`           | INT                | Output image width                                   |
+| `HEIGHT`          | INT                | Output image height                                  |
+| `PROMPT`          | STRING             | Main generation prompt                               |
+| `GUIDANCE`        | FLOAT              | Guidance scale                                       |
+
+### Usage
+
+Advanced version of TT Flux2 Workflow Settings with `prompt` and `lora_triggers` fields included directly in the
+node. Both are stored in the `WORKFLOW_CONFIG` output alongside all other sampling parameters, making this node
+sufficient as the sole configuration source for full context-driven FLUX2 pipelines. Individual outputs remain
+available for connecting to standard ComfyUI nodes.
+
+---
+
+[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)

web/docs/TT_GuiderImageReference.md

@@ -0,0 +1,32 @@
+## TT Guider Image Reference *TT_GuiderImageReference*
+
+*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
+
+Encode a reference image into latent space and inject it into a Guider's positive conditioning for reference-based generation.
+
+### Inputs
+
+| Parameter        | Type   | Required | Description                                                               |
+|------------------|--------|----------|---------------------------------------------------------------------------|
+| `vae`            | VAE    | Yes      | VAE model used to encode the reference image                              |
+| `guider`         | GUIDER | Yes      | Guider to inject reference latent into                                    |
+| `megapixels`     | COMBO  | Yes      | Target resolution for the reference image before encoding                 |
+| `upscale_method` | STRING | Yes      | Resampling method for resizing (default: bicubic) - advanced              |
+| `dimension_step` | INT    | Yes      | Snaps image dimensions to multiples of this value (default: 1) - advanced |
+| `image`          | STRING | Yes      | Reference image from input directory                                      |
+
+### Outputs
+
+| Parameter | Type   | Description                           |
+|-----------|--------|---------------------------------------|
+| `VAE`     | VAE    | Passthrough VAE for chaining          |
+| `GUIDER`  | GUIDER | Guider with reference latent injected |
+
+### Usage
+
+Loads a reference image, resizes it to the target megapixel resolution, encodes it with VAE, and injects the resulting latent into the Guider's
+positive conditioning as `reference_latents`. Raises an error if the Guider has no positive conditioning.
+
+---
+
+[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)

web/docs/TT_ImagePreviewUpscaleSave.md

@@ -0,0 +1,35 @@
+## TT Image Preview / Upscale / Save *TT_ImagePreviewUpscaleSave*
+
+*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
+
+Combined preview, upscale, and save node with flexible output options.
+
+### Inputs
+
+| Parameter            | Type    | Required | Description                                                                                                                               |
+|----------------------|---------|----------|-------------------------------------------------------------------------------------------------------------------------------------------|
+| `image`              | IMAGE   | Yes      | Input image tensor to preview, upscale, or save                                                                                           |
+| `save_image`         | BOOLEAN | Yes      | Enable/disable saving to disk (default: Enabled)                                                                                          |
+| `filename_prefix`    | STRING  | Yes      | Prefix for output filename (default: "tenser-tensor")                                                                                     |
+| `filename_format`    | STRING  | Yes      | Naming pattern: "name-###" (prefix-00001), "date-name-###" (2026-02-16-prefix-00001), "name-datetime" (prefix-20260216-143045) - advanced |
+| `subfolder`          | STRING  | Yes      | Optional subfolder within output directory (empty = root output folder) - advanced                                                        |
+| `image_format`       | STRING  | Yes      | Output format: PNG, JPEG, or WEBP (default: PNG) - advanced                                                                               |
+| `image_quality`      | INT     | Yes      | Quality for JPEG and WEBP formats (0-100, default: 100). Ignored for PNG - advanced                                                       |
+| `compression_level`  | INT     | Yes      | Compression level for PNG format (0-9, default: 9). Ignored for JPEG and WEBP - advanced                                                  |
+| `upscale_image`      | BOOLEAN | Yes      | Enable/disable upscaling (default: Enabled)                                                                                               |
+| `upscaler_device`    | STRING  | Yes      | Device for upscaling: "default" or "cpu" - advanced                                                                                       |
+| `upscale_model_name` | STRING  | Yes      | Upscale model selected from available upscale_models - advanced                                                                           |
+| `tile`               | INT     | Yes      | Tile size for tiled upscaling (256-4096, step 64, default: 512) - advanced                                                                |
+| `overlap`            | INT     | Yes      | Overlap between tiles in pixels (0-256, step 8, default: 64) - advanced                                                                   |
+
+### Outputs
+
+This is an output node with no return values. Images are displayed in the UI and optionally upscaled and saved to disk.
+
+### Usage
+
+All-in-one node that can preview, upscale, and save images in a single step. Upscaling is applied before saving — the upscaled result is both displayed in the UI and written to disk. When `upscale_image` is disabled, the original image is used. When `save_image` is disabled, the image is only previewed in the UI without writing to disk.
+
+---
+
+[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)

web/docs/TT_KSamplerGuided.md

@@ -0,0 +1,31 @@
+## TT KSampler (Guided) *TT_KSamplerGuided*
+
+*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
+
+Guided latent sampler that takes a pre-configured guider, sigmas, sampler, and noise to produce denoised latent samples.
+
+### Inputs
+
+| Parameter      | Type    | Required | Description                             |
+|----------------|---------|----------|-----------------------------------------|
+| `latent`       | LATENT  | Yes      | Input latent to sample from             |
+| `guider`       | GUIDER  | Yes      | Configured guider with encoded prompt   |
+| `sigmas`       | SIGMAS  | Yes      | Sigma schedule for the sampling process |
+| `sampler`      | SAMPLER | Yes      | Instantiated sampler object             |
+| `random_noise` | NOISE   | Yes      | Noise source for the diffusion process  |
+
+### Outputs
+
+| Parameter | Type   | Description             |
+|-----------|--------|-------------------------|
+| `SAMPLES` | LATENT | Denoised latent samples |
+
+### Usage
+
+Runs guided diffusion sampling on the input latent. Connect a guider from a text encoder node, sigmas from a
+scheduler, and a sampler from a workflow settings node. `random_noise` controls stochasticity — use a seeded
+noise node for reproducible results.
+
+---
+
+[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)

web/docs/TT_LatentFactory.md

@@ -1,37 +1,43 @@
-# TT Latent Factory *TT_LatentFactory*
+## TT Latent Factory *TT_LatentFactory*
 
 *This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
 
-Generate empty latent images with precise dimension control based on aspect ratio and megapixel count.
-
+Generates empty latent images with precise dimension control based on aspect ratio and megapixel count.
 Automatically calculates correct dimensions for FLUX and SDXL models, plus separate CLIP conditioning sizes.
 
-## Parameters
+### Inputs
 
-| Parameter         | Type     | Description                                       |
-|-------------------|----------|---------------------------------------------------|
-| `aspect_ratio`    | DROPDOWN | Image proportions (1:1, 4:3, 3:2, 16:9, 21:9)     |
-| `seed`            | INT      | Seed for reproducible results (0 - 2^64-1)        |         
-| `megapixels`      | DROPDOWN | Total resolution (0.5 MP, 1 MP, 2 MP, 4 MP, 8 MP) |
-| `orientation`     | DROPDOWN | landscape or portrait                             |
-| `model_type`      | DROPDOWN | FLUX (16 channels) or SDXL (4 channels)           |
-| `clip_multiplier` | DROPDOWN | CLIP target size multiplier (1x, 2x, 4x)          |
-| `batch_size`      | INT      | Number of latents to generate (default: 1)        |
+| Parameter         | Type   | Required | Description                                                   |
+|-------------------|--------|----------|---------------------------------------------------------------|
+| `seed`            | INT    | Yes      | Seed for reproducible results (0 – 18446744073709551615)      |
+| `noise_seed`      | INT    | Yes      | Separate seed for noise generation (0 – 18446744073709551615) |
+| `aspect_ratio`    | STRING | Yes      | Image proportions (1:1, 4:3, 3:2, 16:9, 21:9)                 |
+| `megapixels`      | COMBO | Yes      | Total resolution (0.25 MP, 0.5 MP, 1 MP, 2 MP, 4 MP, 8 MP)    |
+| `orientation`     | STRING | Yes      | Image orientation (landscape / portrait)                      |
+| `model_type`      | STRING | Yes      | Target model architecture (FLUX1.D, FLUX2.D, SDXL)            |
+| `batch_size`      | INT    | Yes      | Number of latents to generate (1–64, default: 1) - advanced   |
+| `clip_multiplier` | STRING | Yes      | CLIP target size multiplier (1x, 2x, 4x) - advanced           |
 
-## Outputs
+### Outputs
 
-| Output          | Type   | Description                                         |
-|-----------------|--------|-----------------------------------------------------|
-| `LATENT`        | LATENT | Empty latent image tensor with random noise         |
-| `SEED`          | INT    | Seed value                                          |
-| `WIDTH`         | INT    | Calculated image width (rounded to multiple of 64)  |
-| `HEIGHT`        | INT    | Calculated image height (rounded to multiple of 64) |
-| `TARGET_WIDTH`  | INT    | CLIP conditioning target width                      |
-| `TARGET_HEIGHT` | INT    | CLIP conditioning target height                     |
+| Parameter       | Type   | Description                              |
+|-----------------|--------|------------------------------------------|
+| `LATENT`        | LATENT | Empty latent tensor ready for sampling   |
+| `RANDOM_NOISE`  | NOISE  | Noise object seeded with `noise_seed`    |
+| `SEED`          | INT    | Generation seed                          |
+| `NOISE_SEED`    | INT    | Noise seed                               |
+| `MEGAPIXELS`    | COMBO  | Selected megapixel value for chaining    |
+| `WIDTH`         | INT    | Calculated image width (multiple of 64)  |
+| `HEIGHT`        | INT    | Calculated image height (multiple of 64) |
+| `TARGET_WIDTH`  | INT    | CLIP conditioning target width           |
+| `TARGET_HEIGHT` | INT    | CLIP conditioning target height          |
 
-## Example
+### Usage
 
-Select 16:9, 2 MP, landscape, FLUX, 2x → generates 1920×1088 latent with 3840×2176 CLIP dimensions.
+Select aspect ratio, resolution, and orientation to automatically compute pixel dimensions suited for the chosen
+model. FLUX1.D uses 16-channel latents, FLUX2.D uses 128-channel, SDXL uses 4-channel. `clip_multiplier` scales the TARGET
+dimensions relative to the image dimensions — useful for multi-resolution CLIP conditioning. Connect `RANDOM_NOISE`
+directly to a sampler node for reproducible noise tied to `noise_seed`.
 
 ---
 

web/docs/TT_PostproductionAdvanced.md

@@ -41,7 +41,7 @@ Extended post-production node with image enhancement, LUT color grading, film gr
 
 | Parameter | Type  | Description                          |
 |-----------|-------|--------------------------------------|
-| `IMAGE`     | IMAGE | Processed image with applied effects |
+| `IMAGE`   | IMAGE | Processed image with applied effects |
 
 ### Usage