WIP: 6 new nodes, minor reformat and refactoring, added docs

2026-02-22 00:28:52 +03:00
parent 009a0f5728
commit 063e00adba
40 changed files with 852 additions and 274 deletions
@@ -36,6 +36,14 @@ 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.
@@ -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 (v1.4.0): {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}")
@@ -21,17 +21,17 @@ try:
    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
 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
@@ -41,7 +41,7 @@ try:
    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 +51,26 @@ 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 +78,17 @@ 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
@@ -119,11 +124,14 @@ 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 +142,8 @@ 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 = {
@@ -170,11 +180,14 @@ 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 +198,6 @@ 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)",
 }
@@ -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",
@@ -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,)
@@ -16,9 +16,11 @@ 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

@@ -1,40 +1,12 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)

-import torch
+from .latent_helpers import create_empty_latent

-ASPECT_RATIOS = [
-    "1:1",
-    "4:3",
-    "3:2",
-    "16:9",
-    "21:9"
-]
-
-MEGAPIXELS = [
-    "0.25 MP",
-    "0.5 MP",
-    "1 MP",
-    "2 MP",
-    "4 MP",
-    "8 MP"
-]
-
-ORIENTATIONS = [
-    "landscape",
-    "portrait"
-]
-
-MODEL_TYPES = [
-    "FLUX1.D",
-    "FLUX2.D",
-    "SDXL"
-]
-
-CLIP_MULTIPLIERS = [
-    "1x",
-    "2x",
-    "4x"
-]
+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"]


 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,),
                "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", "INT", "INT", "INT", "INT")
+    RETURN_NAMES = ("LATENT", "RANDOM_NOISE", "SEED", "NOISE_SEED", "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)
@@ -0,0 +1,73 @@
+# (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+
+import comfy.sample as S
+import torch
+
+
+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)
+
+
+def _calculate_dimensions(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_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, width, height, clip_width, clip_height,)
@@ -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,
@@ -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()

@@ -23,7 +23,7 @@ class TT_KSampler():
        }

    RETURN_TYPES = ("LATENT",)
-    RETURN_NAMES = ("LATENT",)
+    RETURN_NAMES = ("SAMPLES",)
    FUNCTION = "execute_sampling"
    CATEGORY = "TenserTensor/Sampling"

@@ -28,7 +28,7 @@ class TT_KSamplerAdvanced():
        }

    RETURN_TYPES = ("LATENT",)
-    RETURN_NAMES = ("LATENT",)
+    RETURN_NAMES = ("SAMPLES",)
    FUNCTION = "execute_sampling"
    CATEGORY = "TenserTensor/Sampling"

@@ -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"

@@ -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),)
@@ -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
@@ -28,7 +28,7 @@ class TT_KSamplerTwoStage():
        }

    RETURN_TYPES = ("LATENT",)
-    RETURN_NAMES = ("LATENT",)
+    RETURN_NAMES = ("SAMPLES",)
    FUNCTION = "execute_sampling"
    CATEGORY = "TenserTensor/Sampling"

@@ -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)
@@ -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)
@@ -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,)
@@ -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)
@@ -1,25 +1,18 @@
 # (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, }
-        )
-    )
+
+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 +40,36 @@ 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")
+
+    tokens = clip.tokenize(f"{lora_triggers} {prompt}")
+    conditioning = clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance, })
+    guider = Guider_Basic(model)
+    guider.set_conds(conditioning)
+
+    return (guider,)
@@ -1,2 +1 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
-
@@ -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": 1.5, "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(seed, 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)
@@ -0,0 +1,72 @@
+# (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": 1.5, "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",
+        "SAMPLER",
+        S.KSampler.SAMPLERS,
+        "SIGMAS",
+        "INT",
+        "INT",
+        "FLOAT"
+    )
+    RETURN_NAMES = (
+        "WORKFLOW_CONFIG",
+        "SEED",
+        "STEPS",
+        "CFG",
+        "SAMPLER",
+        "SAMPLER_NAME",
+        "SCHEDULER",
+        "WIDTH",
+        "HEIGHT",
+        "PROMPT",
+        "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(seed, 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, scheduler, width, height, prompt, lora_triggers, guidance)
@@ -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"

@@ -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)
@@ -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 = (
@@ -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",
@@ -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,)
@@ -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, LoRA, CLIP, VAE), GGUF quantized model support (Flux, Flux.2, SDXL), workflow settings nodes, latent factory (aspect ratio, megapixel, Flux/SDXL), context nodes, CLIP text encoders, KSamplers, VAE decoders (basic, tiled, two-pass), and post-production tools (brightness, contrast, LUT color grading, film grain, upscaler)."
+version = "1.4.0"
 license = { file = "LICENSE" }
 dependencies = ["gguf"]

@@ -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)
@@ -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)
@@ -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)
@@ -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

@@ -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)
@@ -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)
@@ -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 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.

 ---

@@ -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 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.

 ---

@@ -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)
@@ -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
				`@@ -1,2 +1 @@`
				`# (c) TenserTensor \|\| Apache-2.0 (apache.org/licenses/LICENSE-2.0)`