Added TT_ImageLoaderResizerNode

node_mappings.py

@@ -35,6 +35,7 @@ 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
+    from .nodes_image import *
 
     NODES_COUNT += len(set(dir()) - before)
 except ImportError:
@@ -153,6 +154,8 @@ NODE_CLASS_MAPPINGS = {
     "TT_ImagePreviewSave": TT_ImagePreviewSave,
     "TT_ImagePreviewUpscaleSave": TT_ImagePreviewUpscaleSave,
     "TT_GuiderImageReference": TT_GuiderImageReference,
+    # Image V3 Nodes
+    "TT_ImageLoaderResizerNode": TT_ImageLoaderResizerNode,
     # Latent
     "TT_LatentFactory": TT_LatentFactory,
     # Latent V3 Nodes
@@ -229,6 +232,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "TT_ImagePreviewSave": "TT Image Preview / Save",
     "TT_ImagePreviewUpscaleSave": "TT Image Preview / Upscale / Save",
     "TT_GuiderImageReference": "TT Guider Image Reference",
+    # Image V3 Nodes
+    "TT_ImageLoaderResizerNode": "TT Image Loader / Resizer",
     # Latent
     "TT_LatentFactory": "TT Latent Factory (Deprecated)",
     # Latent V3 Nodes

nodes_context.py

@@ -315,7 +315,7 @@ class TT_ContextExtractImageNode(ContextNode):
 # V3 entrypoint — registers context nodes with ComfyUI
 # ==============================================================================
 
-class ContextExtension(ComfyExtension):
+class ContextNodesExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
         return [
@@ -334,8 +334,8 @@ class ContextExtension(ComfyExtension):
         ]
 
 
-async def comfy_entrypoint() -> ContextExtension:
-    return ContextExtension()
+async def comfy_entrypoint() -> ContextNodesExtension:
+    return ContextNodesExtension()
 
 
 # ==============================================================================

nodes_image.py

@@ -1 +1,200 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+import math
+import os
+from pathlib import Path
+from typing import override
+
+import numpy
+import torch
+from PIL import Image, ImageOps, ImageSequence
+
+import folder_paths
+from comfy import model_management, utils
+from comfy_api.latest import ComfyExtension, IO, ui
+from comfy_api.latest._io import UploadType
+from node_helpers import pillow
+from .nodes_latent import MEGAPIXELS
+
+CATEGORY = "TenserTensor/Image"
+RESIZE_METHODS = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"]
+
+
+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 utils.common_upscale(samples, int(to_width), int(to_height), upscale_method, crop).movedim(1, -1)
+
+
+def resize_image_to_megapixels(timage, resize_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 utils.common_upscale(samples, int(to_width), int(to_height), resize_method, "disabled").movedim(1, -1)
+
+
+def load_image(filename, create_mask, device=None):
+    tdevice = torch.device(device) if device is not None else model_management.get_torch_device()
+    path = folder_paths.get_annotated_filepath(filename)
+    pil_image = pillow(Image.open, 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
+
+        if create_mask:
+            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)
+            masks.append(tmask)
+
+        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)
+
+        if pil_image.format == "MPO":
+            break
+
+    if len(images) == 0:
+        raise ValueError(f"ERROR: No frames found in {path}")
+
+    o_image = torch.cat(images, dim=0) if len(images) > 1 else images[0]
+
+    match len(masks):
+        case 0:
+            o_mask = torch.zeros((1, height, width), dtype=torch.float32)
+        case 1:
+            o_mask = masks[0]
+        case _:
+            o_mask = torch.cat(masks, dim=0)
+
+    return o_image, o_mask
+
+
+class TT_ImageLoaderResizerNode(IO.ComfyNode):
+    @classmethod
+    def get_image_files(cls):
+        return sorted([
+            f.name for f in os.scandir(folder_paths.get_input_directory())
+            if f.is_file() and Path(f.name).suffix.lower() in (".png", ".jpg", ".webp", ".bmp")
+        ])
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="TT_ImageLoaderResizerNode",
+            display_name="TT Image Loader / Resizer",
+            category=CATEGORY,
+            description="",
+            inputs=[
+                IO.Boolean.Input("resize_image", default=False, label_on="Scale", label_off="Keep"),
+                IO.Combo.Input("resize_method", options=RESIZE_METHODS, advanced=True),
+                IO.Int.Input("dimension_step", default=1, min=1, max=256, advanced=True),
+                IO.Combo.Input("megapixels", options=MEGAPIXELS, advanced=True),
+                IO.Boolean.Input("create_mask", default=False, label_on="Mask", label_off="None"),
+                IO.Combo.Input("image", options=cls.get_image_files(), upload=UploadType.image)
+            ],
+            outputs=[
+                IO.Image.Output(display_name="IMAGE"),
+                IO.Mask.Output(display_name="MASK"),
+            ],
+        )
+
+    @classmethod
+    def fingerprint_inputs(cls, **kwargs):
+        path = folder_paths.get_annotated_filepath(kwargs.get("image"))
+        resize = kwargs.get("resize_image")
+        result = {
+            "image": path + str(os.path.getmtime(path)),
+            "resize_image": resize,
+            "resize_method": kwargs.get("resize_method"),
+            "dimension_step": kwargs.get("dimension_step"),
+            "create_mask": kwargs.get("create_mask"),
+        }
+
+        if resize:
+            result["megapixels"] = kwargs.get("megapixels")
+
+        return result
+
+    @classmethod
+    def validate_inputs(cls, **kwargs) -> bool | str:
+        path = folder_paths.get_annotated_filepath(kwargs.get("image"))
+        if not os.path.isfile(path):
+            return f"Image file not found: {path}"
+
+        return True
+
+    @classmethod
+    def execute(cls, **kwargs) -> IO.NodeOutput:
+        timage, tmask = load_image(kwargs.get("image"), kwargs.get("create_mask"))
+        if kwargs.get("resize_image"):
+            resize_method, megapixels, dimension_step = kwargs.get("resize_method"), kwargs.get("megapixels"), kwargs.get("dimension_step")
+            timage = resize_image_to_megapixels(
+                timage,
+                resize_method,
+                megapixels,
+                dimension_step
+            )
+            tmask = resize_image_to_megapixels(
+                tmask,
+                resize_method,
+                megapixels,
+                dimension_step
+            )
+
+        return IO.NodeOutput(timage, tmask, ui=ui.PreviewImage(timage, cls=cls))
+
+
+# ==============================================================================
+# V3 entrypoint — registers context nodes with ComfyUI
+# ==============================================================================
+
+class ImageNodesExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            TT_ImageLoaderResizerNode,
+        ]
+
+
+async def comfy_entrypoint() -> ImageNodesExtension:
+    return ImageNodesExtension()
+
+
+# ==============================================================================
+# Re-exports for backward compatibility
+# ==============================================================================
+
+__all__ = [
+    "TT_ImageLoaderResizerNode",
+]

nodes_latent.py

@@ -7,10 +7,11 @@ import torch
 
 import comfy.sample
 from comfy_api.latest import IO, ComfyExtension
-from .lib.common import CommonTypes
 
 CATEGORY = "TenserTensor/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"]
@@ -26,7 +27,6 @@ class RandomNoise:
         return comfy.sample.prepare_noise(latent_image, self.seed, batch_idx)
 
 
-
 class TT_LatentFactoryNode(IO.ComfyNode):
     @classmethod
     def define_schema(cls) -> IO.Schema:
@@ -39,7 +39,7 @@ class TT_LatentFactoryNode(IO.ComfyNode):
                 IO.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
                 IO.Int.Input("noise_seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True),
                 IO.Combo.Input("aspect_ratio", options=ASPECT_RATIOS),
-                IO.Combo.Input("megapixels", options=CommonTypes.MEGAPIXELS),
+                IO.Combo.Input("megapixels", options=MEGAPIXELS),
                 IO.Combo.Input("orientation", options=ORIENTATIONS),
                 IO.Combo.Input("model_type", options=MODEL_TYPES),
                 IO.Int.Input("batch_size", default=1, min=1, max=64, advanced=True),
@@ -133,7 +133,7 @@ class TT_LatentFactoryNode(IO.ComfyNode):
 # V3 entrypoint — registers context nodes with ComfyUI
 # ==============================================================================
 
-class ContextExtension(ComfyExtension):
+class LatentNodesExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
         return [
@@ -141,8 +141,8 @@ class ContextExtension(ComfyExtension):
         ]
 
 
-async def comfy_entrypoint() -> ContextExtension:
-    return ContextExtension()
+async def comfy_entrypoint() -> LatentNodesExtension:
+    return LatentNodesExtension()
 
 
 # ==============================================================================

nodes_loaders.py

@@ -1 +1,29 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+from typing import override
+
+from comfy_api.latest import ComfyExtension, IO
+
+
+# ==============================================================================
+# V3 entrypoint — registers context nodes with ComfyUI
+# ==============================================================================
+
+class LoaderNodesExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            # TT_Node,
+        ]
+
+
+async def comfy_entrypoint() -> LoaderNodesExtension:
+    return LoaderNodesExtension()
+
+
+# ==============================================================================
+# Re-exports for backward compatibility
+# ==============================================================================
+
+__all__ = [
+    # "TT_Node",
+]

nodes_postproduction.py

@@ -1 +1,30 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+
+from typing import override
+
+from comfy_api.latest import ComfyExtension, IO
+
+
+# ==============================================================================
+# V3 entrypoint — registers context nodes with ComfyUI
+# ==============================================================================
+
+class VaeNodesExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            # TT_Node,
+        ]
+
+
+async def comfy_entrypoint() -> VaeNodesExtension:
+    return VaeNodesExtension()
+
+
+# ==============================================================================
+# Re-exports for backward compatibility
+# ==============================================================================
+
+__all__ = [
+    # "TT_Node",
+]

nodes_sampling.py

@@ -1 +1,30 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+
+from typing import override
+
+from comfy_api.latest import ComfyExtension, IO
+
+
+# ==============================================================================
+# V3 entrypoint — registers context nodes with ComfyUI
+# ==============================================================================
+
+class LatentNodesExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            # TT_Node,
+        ]
+
+
+async def comfy_entrypoint() -> LatentNodesExtension:
+    return LatentNodesExtension()
+
+
+# ==============================================================================
+# Re-exports for backward compatibility
+# ==============================================================================
+
+__all__ = [
+    # "TT_Node",
+]

nodes_vae.py

@@ -1 +1,29 @@
 # (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+from typing import override
+
+from comfy_api.latest import IO, ComfyExtension
+
+
+# ==============================================================================
+# V3 entrypoint — registers context nodes with ComfyUI
+# ==============================================================================
+
+class VaeNodesExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            # TT_Node,
+        ]
+
+
+async def comfy_entrypoint() -> VaeNodesExtension:
+    return VaeNodesExtension()
+
+
+# ==============================================================================
+# Re-exports for backward compatibility
+# ==============================================================================
+
+__all__ = [
+    # "TT_Node",
+]

nodes_workflow.py

@@ -220,10 +220,7 @@ class TT_Flux2WorkflowSettingsAdvancedNode(IO.ComfyNode):
     @classmethod
     def execute(cls, **kwargs) -> IO.NodeOutput:
         kwargs["sampler"], kwargs["sigmas"] = build_sampler_sigmas(
-            kwargs.get("sampler_name"),
-            kwargs.get("steps"),
-            kwargs.get("width"),
-            kwargs.get("height"),
+            kwargs.get("sampler_name"), kwargs.get("steps"), kwargs.get("width"), kwargs.get("height"),
         )
 
         workflow_config = TTWorkflowSettings.create(**kwargs)
@@ -248,7 +245,7 @@ class TT_Flux2WorkflowSettingsAdvancedNode(IO.ComfyNode):
 # V3 entrypoint — registers context nodes with ComfyUI
 # ==============================================================================
 
-class ContextExtension(ComfyExtension):
+class WorkflowNodesExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
         return [
@@ -257,8 +254,8 @@ class ContextExtension(ComfyExtension):
         ]
 
 
-async def comfy_entrypoint() -> ContextExtension:
-    return ContextExtension()
+async def comfy_entrypoint() -> WorkflowNodesExtension:
+    return WorkflowNodesExtension()
 
 
 # ==============================================================================