Automated PR - 2026-01-06

Author: sync-bot

.gitattributes

@@ -1,2 +1,10 @@
 **/*.png filter=lfs diff=lfs merge=lfs -text
 **/*.mp4 filter=lfs diff=lfs merge=lfs -text
+**/*.jpg filter=lfs diff=lfs merge=lfs -text
+
+example_workflows/low_level/end.jpg -filter -diff -merge -text
+example_workflows/low_level/fox.jpg -filter -diff -merge -text
+example_workflows/low_level/shrek2.jpg -filter -diff -merge -text
+example_workflows/low_level/start.jpg -filter -diff -merge -text
+example_workflows/tricks/shrek2.jpg -filter -diff -merge -text
+example_workflows/tricks/shrek3.jpg -filter -diff -merge -text

LICENSE

@@ -1,16 +1,33 @@
 from .decoder_noise import DecoderNoise
+from .dynamic_conditioning import DynamicConditioning
 from .easy_samplers import (
     LinearOverlapLatentTransition,
     LTXVBaseSampler,
     LTXVExtendSampler,
     LTXVInContextSampler,
 )
-from .film_grain import LTXVFilmGrain
+from .gemma_encoder import LTXVGemmaCLIPModelLoader, LTXVGemmaEnhancePrompt
+from .gemma_encoder_mgpu_loader import LTXVGemmaCLIPModelLoaderMGPU
 from .guide import LTXVAddGuideAdvanced
-from .latent_adain import LTXVAdainLatent
-from .latent_upsampler import LTXVLatentUpsampler
-from .latents import LTXVSelectLatents, LTXVSetVideoLatentNoiseMasks
+from .guiders import GuiderParametersNode, MultimodalGuiderNode
+from .latent_norm import (
+    LTXVAdainLatent,
+    LTXVPerStepAdainPatcher,
+    LTXVPerStepStatNormPatcher,
+    LTXVStatNormLatent,
+)
+from .latents import (
+    LTXVAddLatentGuide,
+    LTXVImgToVideoConditionOnly,
+    LTXVSelectLatents,
+    LTXVSetVideoLatentNoiseMasks,
+)
 from .looping_sampler import LTXVLoopingSampler, MultiPromptProvider
+from .low_vram_loaders import (
+    LowVRAMAudioVAELoader,
+    LowVRAMCheckpointLoader,
+    LowVRAMLatentUpscaleModelLoader,
+)
 from .masks import LTXVPreprocessMasks
 from .nodes_registry import NODE_CLASS_MAPPINGS as RUNTIME_NODE_CLASS_MAPPINGS
 from .nodes_registry import (
@@ -30,21 +47,22 @@
 from .tricks import NODE_CLASS_MAPPINGS as TRICKS_NODE_CLASS_MAPPINGS
 from .tricks import NODE_DISPLAY_NAME_MAPPINGS as TRICKS_NODE_DISPLAY_NAME_MAPPINGS
 from .utiltily_nodes import ImageToCPU
-from .vae_patcher.vae_patcher import LTXVPatcherVAE
+from .vae_patcher import LTXVPatcherVAE
 
 # Static node mappings, required for ComfyUI-Manager mapping to work
 NODE_CLASS_MAPPINGS = {
     "Set VAE Decoder Noise": DecoderNoise,
     "LTXVLinearOverlapLatentTransition": LinearOverlapLatentTransition,
     "LTXVAddGuideAdvanced": LTXVAddGuideAdvanced,
+    "LTXVAddLatentGuide": LTXVAddLatentGuide,
     "LTXVAdainLatent": LTXVAdainLatent,
+    "LTXVImgToVideoConditionOnly": LTXVImgToVideoConditionOnly,
+    "LTXVPerStepAdainPatcher": LTXVPerStepAdainPatcher,
     "LTXVApplySTG": LTXVApplySTG,
     "LTXVBaseSampler": LTXVBaseSampler,
     "LTXVInContextSampler": LTXVInContextSampler,
     "LTXVExtendSampler": LTXVExtendSampler,
-    "LTXVFilmGrain": LTXVFilmGrain,
     "LTXVPreprocessMasks": LTXVPreprocessMasks,
-    "LTXVLatentUpsampler": LTXVLatentUpsampler,
     "LTXVPatcherVAE": LTXVPatcherVAE,
     "LTXVPromptEnhancer": LTXVPromptEnhancer,
     "LTXVPromptEnhancerLoader": LTXVPromptEnhancerLoader,
@@ -55,11 +73,22 @@
     "LTXVTiledSampler": LTXVTiledSampler,
     "LTXVLoopingSampler": LTXVLoopingSampler,
     "LTXVTiledVAEDecode": LTXVTiledVAEDecode,
+    "MultimodalGuider": MultimodalGuiderNode,
+    "GuiderParameters": GuiderParametersNode,
     "STGAdvancedPresets": STGAdvancedPresetsNode,
     "STGGuiderAdvanced": STGGuiderAdvancedNode,
     "STGGuiderNode": STGGuiderNode,
     "LTXVMultiPromptProvider": MultiPromptProvider,
     "ImageToCPU": ImageToCPU,
+    "LTXVStatNormLatent": LTXVStatNormLatent,
+    "LTXVPerStepStatNormPatcher": LTXVPerStepStatNormPatcher,
+    "LTXVGemmaCLIPModelLoader": LTXVGemmaCLIPModelLoader,
+    "LTXVGemmaCLIPModelLoaderMGPU": LTXVGemmaCLIPModelLoaderMGPU,
+    "LTXVGemmaEnhancePrompt": LTXVGemmaEnhancePrompt,
+    "DynamicConditioning": DynamicConditioning,
+    "LowVRAMCheckpointLoader": LowVRAMCheckpointLoader,
+    "LowVRAMAudioVAELoader": LowVRAMAudioVAELoader,
+    "LowVRAMLatentUpscaleModelLoader": LowVRAMLatentUpscaleModelLoader,
 }
 
 # Consistent display names between static and dynamic node mappings in nodes_registry.py,

README.md

@@ -0,0 +1,57 @@
+import torch
+
+from .nodes_registry import comfy_node
+
+
+@comfy_node(name="DynamicConditioning")
+class DynamicConditioning:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "power": ("FLOAT", {"default": 1.3, "min": 1, "max": 2, "step": 0.01}),
+                "only_first_frame": ("BOOLEAN", {"default": True}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "apply"
+    CATEGORY = "lightricks/LTXV"
+    INIT = False
+
+    def apply(self, model, power, only_first_frame):
+        self.only_first_frame = only_first_frame
+        self.power = power
+        model = model.clone()
+        model.set_model_denoise_mask_function(self.forward)
+        return (model,)
+
+    def find_step(self, sigma: torch.Tensor, step_sigmas: torch.Tensor):
+        for i, step_sigma in enumerate(step_sigmas):
+            if step_sigma <= sigma:
+                return i
+        return len(step_sigmas) - 1
+
+    def forward(
+        self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict
+    ):
+        model = extra_options["model"]
+        step_sigmas = extra_options["sigmas"]
+        step = self.find_step(sigma, step_sigmas)
+        # In order to apply power multiple times, this is the same as applying power number of times equal to step
+        power = self.power**step
+        denoise_mask = denoise_mask.clone()
+        if self.only_first_frame:
+            num_channels = model.model_patcher.model.diffusion_model.in_channels
+            denoise_mask[:, :num_channels, :1] **= power
+        else:
+            denoise_mask **= power
+        # make sure to update the denoise mask in the model, to get correct timestep values for all tokens
+        for k in model.conds:
+            if "positive" in k or "negative" in k:
+                for cond in model.conds[k]:
+                    if "model_conds" in cond and "denoise_mask" in cond["model_conds"]:
+                        cond["model_conds"]["denoise_mask"].cond = denoise_mask
+        # print(f"DynamicConditioning: power: {power}, step: {step}, sigma: {sigma}, step_sigmas: {step_sigmas}")
+        return denoise_mask