Add VAE encode nodes and fix default parameters
- Add TT VAE Encode (Context) node with documentation - Add TT VAE Encode Tiled node with documentation - Fix default parameter types in TT_ImagePreviewSave (string to int) - Update inputs in TT_VaeDecodeTiled
This commit is contained in:
TenserTensor
@@ -26,6 +26,8 @@ from .nodes.text_encoders.clip_text_encode_sdxl import TT_ClipTextEncodeSdxl
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from .nodes.text_encoders.clip_text_encode_sdxl_context import TT_ClipTextEncodeSdxlContext
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from .nodes.vae.vae_decode_context import TT_VaeDecodeContext
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from .nodes.vae.vae_decode_tiled import TT_VaeDecodeTiled
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from .nodes.vae.vae_encode_context import TT_VaeEncodeContext
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from .nodes.vae.vae_encode_tiled import TT_VaeEncodeTiled
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from .nodes.workflow.flux_workfow_settings import TT_FluxWorkflowSettings
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from .nodes.workflow.flux_workfow_settings_advanced import TT_FluxWorkflowSettingsAdvanced
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from .nodes.workflow.sdxl_workfow_settings import TT_SdxlWorkflowSettings
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@@ -69,6 +71,8 @@ NODE_CLASS_MAPPINGS = {
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# VAE
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"TT_VaeDecodeContext": TT_VaeDecodeContext,
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"TT_VaeDecodeTiled": TT_VaeDecodeTiled,
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"TT_VaeEncodeContext": TT_VaeEncodeContext,
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"TT_VaeEncodeTiled": TT_VaeEncodeTiled,
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# Workflow
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"TT_FluxWorkflowSettings": TT_FluxWorkflowSettings,
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"TT_FluxWorkflowSettingsAdvanced": TT_FluxWorkflowSettingsAdvanced,
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@@ -113,6 +117,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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# VAE
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"TT_VaeDecodeContext": "TT VAE Decode (Context)",
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"TT_VaeDecodeTiled": "TT VAE Decode (Tiled)",
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"TT_VaeEncodeContext": "TT Vae Encode (Context)",
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"TT_VaeEncodeTiled": "TT Vae Encode (Tiled)",
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# Workflow
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"TT_FluxWorkflowSettings": "TT FLUX Workflow Settings",
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"TT_FluxWorkflowSettingsAdvanced": "TT FLUX Workflow Settings (Advanced)",
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@@ -14,8 +14,8 @@ def _save(
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output_path,
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filename_format="name-###",
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image_format="PNG",
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quality="50",
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compress_level="4"
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quality=50,
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compress_level=4
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):
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height, width = images[0].shape[:2]
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save_path, filename, counter, subfolder, _ = FP.get_save_image_path(
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@@ -8,9 +8,9 @@ class TT_VaeDecodeTiled:
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"required": {
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"vae": ("VAE",),
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"latent": ("LATENT",),
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"tile_width": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}),
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"tile_height": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}),
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"overlap": ("INT", {"default": 64, "min": 0, "max": 256, "step": 8}),
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"tile_width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
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"tile_height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
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"overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}),
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"circular": ("BOOLEAN", {"default": False, "label_on": "Enabled", "label_off": "Disabled"}),
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}
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}
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@@ -0,0 +1,28 @@
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class TT_VaeEncodeContext:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"context": ("TT_CONTEXT",),
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}
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}
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RETURN_TYPES = ("TT_CONTEXT", "LATENT",)
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RETURN_NAMES = ("CONTEXT", "LATENT",)
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FUNCTION = "vae_encode"
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CATEGORY = "TenserTensor/VAE"
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def vae_encode(self, context):
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vae = context["vae"]
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image = context["image"]
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if vae is None:
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raise ValueError("VAE is required for encode")
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if image is None:
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raise ValueError("Pixel image is required for encode")
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samples = vae.encode(image)
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latent_dict = {"samples": samples}
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context["latent"] = latent_dict
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return (context, latent_dict,)
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@@ -0,0 +1,28 @@
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class TT_VaeEncodeTiled:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"vae": ("VAE",),
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"image": ("IMAGE",),
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"tile_width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
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"tile_height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
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"overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}),
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}
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}
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RETURN_TYPES = ("LATENT",)
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RETURN_NAMES = ("LATENT",)
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FUNCTION = "vae_encode"
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CATEGORY = "TenserTensor/VAE"
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def vae_encode(self, image, vae, tile_width, tile_height, overlap):
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samples = vae.encode_tiled(
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image,
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tile_x=tile_width,
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tile_y=tile_height,
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overlap=overlap
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)
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latent_dict = {"samples": samples}
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return (latent_dict,)
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+1
-1
@@ -2,7 +2,7 @@
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[project]
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name = "ComfyUI-TenserTensor"
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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."
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version = "1.1.0"
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version = "1.2.0"
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license = { file = "LICENSE" }
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dependencies = []
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@@ -0,0 +1,29 @@
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## TT VAE Encode (Context) *TT_VaeEncodeContext*
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*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
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Encodes pixel image to latent space using VAE from context.
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### Inputs
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| Parameter | Type | Required | Description |
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|-----------|------------|----------|----------------------------------|
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| `context` | TT_CONTEXT | Yes | Context containing VAE and image |
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### Outputs
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| Parameter | Type | Description |
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|-----------|------------|-------------------------------------|
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| `CONTEXT` | TT_CONTEXT | Updated context with encoded latent |
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| `LATENT` | LATENT | Encoded latent representation |
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### Usage
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Encodes pixel-space images into latent representations for diffusion processing. Extracts VAE and image from context,
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performs encoding, and stores the result back in context.
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Requires context to contain both `vae` and `image` components. Raises error if either is missing.
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---
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[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)
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@@ -0,0 +1,34 @@
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## TT VAE Encode (Tiled) *TT_VaeEncodeTiled*
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*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
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Encodes large images to latent space using tiled processing to reduce VRAM usage.
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### Inputs
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| Parameter | Type | Required | Description |
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|---------------|-------|----------|-----------------------------------------------------|
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| `vae` | VAE | Yes | VAE model for encoding |
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| `image` | IMAGE | Yes | Input image to encode |
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| `tile_width` | INT | Yes | Width of processing tiles in pixels (default: 512) |
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| `tile_height` | INT | Yes | Height of processing tiles in pixels (default: 512) |
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| `overlap` | INT | Yes | Overlap between tiles in pixels (default: 64) |
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### Outputs
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| Parameter | Type | Description |
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|-----------|--------|-------------------------------|
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| `LATENT` | LATENT | Encoded latent representation |
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### Usage
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Processes large images in smaller tiles to reduce memory requirements during VAE encoding. Useful for encoding
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high-resolution images that would otherwise cause out-of-memory errors.
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Tile size should be set based on available VRAM (smaller tiles = less memory). Overlap prevents visible seams at tile
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boundaries - higher overlap produces smoother results but increases processing time. Recommended overlap is 10-15% of
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tile size.
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---
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[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)
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