mirror of
https://github.com/comfyanonymous/ComfyUI.git
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bbb8864778
* feat: Add search_aliases field to node schema
Adds `search_aliases` field to improve node discoverability. Users can define alternative search terms for nodes (e.g., "text concat" → StringConcatenate).
Changes:
- Add `search_aliases: list[str]` to V3 Schema
- Add `SEARCH_ALIASES` support for V1 nodes
- Include field in `/object_info` response
- Add aliases to high-priority core nodes
V1 usage:
```python
class MyNode:
SEARCH_ALIASES = ["alt name", "synonym"]
```
V3 usage:
```python
io.Schema(
node_id="MyNode",
search_aliases=["alt name", "synonym"],
...
)
```
## Related PRs
- Frontend: Comfy-Org/ComfyUI_frontend#XXXX (draft - merge after this)
- Docs: Comfy-Org/docs#XXXX (draft - merge after stable)
* Propagate search_aliases through V3 Schema.get_v1_info to NodeInfoV1
* feat: add SEARCH_ALIASES for core nodes (#12016)
Add search aliases to 22 core nodes in nodes.py to improve node discoverability:
- Checkpoint/model loaders: CheckpointLoader, DiffusersLoader
- Conditioning nodes: ConditioningAverage, ConditioningSetArea, ConditioningSetMask, ConditioningZeroOut
- Style nodes: StyleModelApply
- Image nodes: LoadImageMask, LoadImageOutput, ImageBatch, ImageInvert, ImagePadForOutpaint
- Latent nodes: LoadLatent, SaveLatent, LatentBlend, LatentComposite, LatentCrop, LatentFlip, LatentFromBatch, LatentUpscale, LatentUpscaleBy, RepeatLatentBatch
* feat: add SEARCH_ALIASES for image, mask, and string nodes (#12017)
Add search aliases to nodes in comfy_extras for better discoverability:
- nodes_mask.py: mask manipulation nodes
- nodes_images.py: image processing nodes
- nodes_post_processing.py: post-processing effect nodes
- nodes_string.py: string manipulation nodes
- nodes_compositing.py: compositing nodes
- nodes_morphology.py: morphological operation nodes
- nodes_latent.py: latent space nodes
Uses search_aliases parameter in io.Schema() for v3 nodes.
* feat: add SEARCH_ALIASES for audio and video nodes (#12018)
Add search aliases to audio and video nodes for better discoverability:
- nodes_audio.py: audio loading, saving, and processing nodes
- nodes_video.py: video loading and processing nodes
- nodes_wan.py: WAN model nodes
Uses search_aliases parameter in io.Schema() for v3 nodes.
* feat: add SEARCH_ALIASES for model and misc nodes (#12019)
Add search aliases to model-related and miscellaneous nodes:
- Model nodes: nodes_model_merging.py, nodes_model_advanced.py, nodes_lora_extract.py
- Sampler nodes: nodes_custom_sampler.py, nodes_align_your_steps.py
- Control nodes: nodes_controlnet.py, nodes_attention_multiply.py, nodes_hooks.py
- Training nodes: nodes_train.py, nodes_dataset.py
- Utility nodes: nodes_logic.py, nodes_canny.py, nodes_differential_diffusion.py
- Architecture-specific: nodes_sd3.py, nodes_pixart.py, nodes_lumina2.py, nodes_kandinsky5.py, nodes_hidream.py, nodes_fresca.py, nodes_hunyuan3d.py
- Media nodes: nodes_load_3d.py, nodes_webcam.py, nodes_preview_any.py, nodes_wanmove.py
Uses search_aliases parameter in io.Schema() for v3 nodes, SEARCH_ALIASES class attribute for legacy nodes.
227 lines
8.9 KiB
Python
227 lines
8.9 KiB
Python
import torch
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import comfy.utils
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from enum import Enum
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from typing_extensions import override
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from comfy_api.latest import ComfyExtension, io
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def resize_mask(mask, shape):
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return torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[0], shape[1]), mode="bilinear").squeeze(1)
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class PorterDuffMode(Enum):
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ADD = 0
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CLEAR = 1
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DARKEN = 2
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DST = 3
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DST_ATOP = 4
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DST_IN = 5
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DST_OUT = 6
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DST_OVER = 7
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LIGHTEN = 8
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MULTIPLY = 9
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OVERLAY = 10
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SCREEN = 11
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SRC = 12
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SRC_ATOP = 13
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SRC_IN = 14
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SRC_OUT = 15
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SRC_OVER = 16
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XOR = 17
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def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode):
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# convert mask to alpha
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src_alpha = 1 - src_alpha
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dst_alpha = 1 - dst_alpha
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# premultiply alpha
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src_image = src_image * src_alpha
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dst_image = dst_image * dst_alpha
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# composite ops below assume alpha-premultiplied images
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if mode == PorterDuffMode.ADD:
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out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1)
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out_image = torch.clamp(src_image + dst_image, 0, 1)
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elif mode == PorterDuffMode.CLEAR:
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out_alpha = torch.zeros_like(dst_alpha)
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out_image = torch.zeros_like(dst_image)
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elif mode == PorterDuffMode.DARKEN:
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out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
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out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image)
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elif mode == PorterDuffMode.DST:
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out_alpha = dst_alpha
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out_image = dst_image
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elif mode == PorterDuffMode.DST_ATOP:
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out_alpha = src_alpha
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out_image = src_alpha * dst_image + (1 - dst_alpha) * src_image
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elif mode == PorterDuffMode.DST_IN:
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out_alpha = src_alpha * dst_alpha
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out_image = dst_image * src_alpha
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elif mode == PorterDuffMode.DST_OUT:
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out_alpha = (1 - src_alpha) * dst_alpha
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out_image = (1 - src_alpha) * dst_image
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elif mode == PorterDuffMode.DST_OVER:
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out_alpha = dst_alpha + (1 - dst_alpha) * src_alpha
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out_image = dst_image + (1 - dst_alpha) * src_image
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elif mode == PorterDuffMode.LIGHTEN:
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out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
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out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.max(src_image, dst_image)
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elif mode == PorterDuffMode.MULTIPLY:
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out_alpha = src_alpha * dst_alpha
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out_image = src_image * dst_image
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elif mode == PorterDuffMode.OVERLAY:
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out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
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out_image = torch.where(2 * dst_image < dst_alpha, 2 * src_image * dst_image,
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src_alpha * dst_alpha - 2 * (dst_alpha - src_image) * (src_alpha - dst_image))
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elif mode == PorterDuffMode.SCREEN:
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out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
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out_image = src_image + dst_image - src_image * dst_image
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elif mode == PorterDuffMode.SRC:
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out_alpha = src_alpha
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out_image = src_image
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elif mode == PorterDuffMode.SRC_ATOP:
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out_alpha = dst_alpha
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out_image = dst_alpha * src_image + (1 - src_alpha) * dst_image
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elif mode == PorterDuffMode.SRC_IN:
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out_alpha = src_alpha * dst_alpha
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out_image = src_image * dst_alpha
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elif mode == PorterDuffMode.SRC_OUT:
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out_alpha = (1 - dst_alpha) * src_alpha
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out_image = (1 - dst_alpha) * src_image
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elif mode == PorterDuffMode.SRC_OVER:
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out_alpha = src_alpha + (1 - src_alpha) * dst_alpha
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out_image = src_image + (1 - src_alpha) * dst_image
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elif mode == PorterDuffMode.XOR:
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out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha
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out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image
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else:
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return None, None
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# back to non-premultiplied alpha
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out_image = torch.where(out_alpha > 1e-5, out_image / out_alpha, torch.zeros_like(out_image))
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out_image = torch.clamp(out_image, 0, 1)
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# convert alpha to mask
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out_alpha = 1 - out_alpha
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return out_image, out_alpha
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class PorterDuffImageComposite(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="PorterDuffImageComposite",
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search_aliases=["alpha composite", "blend modes", "layer blend", "transparency blend"],
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display_name="Porter-Duff Image Composite",
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category="mask/compositing",
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inputs=[
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io.Image.Input("source"),
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io.Mask.Input("source_alpha"),
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io.Image.Input("destination"),
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io.Mask.Input("destination_alpha"),
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io.Combo.Input("mode", options=[mode.name for mode in PorterDuffMode], default=PorterDuffMode.DST.name),
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],
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outputs=[
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io.Image.Output(),
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io.Mask.Output(),
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],
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)
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@classmethod
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def execute(cls, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode) -> io.NodeOutput:
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batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha))
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out_images = []
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out_alphas = []
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for i in range(batch_size):
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src_image = source[i]
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dst_image = destination[i]
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assert src_image.shape[2] == dst_image.shape[2] # inputs need to have same number of channels
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src_alpha = source_alpha[i].unsqueeze(2)
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dst_alpha = destination_alpha[i].unsqueeze(2)
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if dst_alpha.shape[:2] != dst_image.shape[:2]:
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upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2)
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upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
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dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
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if src_image.shape != dst_image.shape:
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upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2)
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upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
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src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0)
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if src_alpha.shape != dst_alpha.shape:
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upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2)
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upscale_output = comfy.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center')
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src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
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out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode])
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out_images.append(out_image)
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out_alphas.append(out_alpha.squeeze(2))
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return io.NodeOutput(torch.stack(out_images), torch.stack(out_alphas))
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class SplitImageWithAlpha(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="SplitImageWithAlpha",
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search_aliases=["extract alpha", "separate transparency", "remove alpha"],
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display_name="Split Image with Alpha",
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category="mask/compositing",
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inputs=[
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io.Image.Input("image"),
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],
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outputs=[
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io.Image.Output(),
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io.Mask.Output(),
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],
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)
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@classmethod
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def execute(cls, image: torch.Tensor) -> io.NodeOutput:
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out_images = [i[:,:,:3] for i in image]
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out_alphas = [i[:,:,3] if i.shape[2] > 3 else torch.ones_like(i[:,:,0]) for i in image]
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return io.NodeOutput(torch.stack(out_images), 1.0 - torch.stack(out_alphas))
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class JoinImageWithAlpha(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="JoinImageWithAlpha",
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search_aliases=["add transparency", "apply alpha", "composite alpha", "RGBA"],
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display_name="Join Image with Alpha",
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category="mask/compositing",
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inputs=[
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io.Image.Input("image"),
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io.Mask.Input("alpha"),
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],
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outputs=[io.Image.Output()],
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)
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@classmethod
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def execute(cls, image: torch.Tensor, alpha: torch.Tensor) -> io.NodeOutput:
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batch_size = min(len(image), len(alpha))
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out_images = []
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alpha = 1.0 - resize_mask(alpha, image.shape[1:])
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for i in range(batch_size):
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out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2))
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return io.NodeOutput(torch.stack(out_images))
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class CompositingExtension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[io.ComfyNode]]:
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return [
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PorterDuffImageComposite,
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SplitImageWithAlpha,
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JoinImageWithAlpha,
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]
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async def comfy_entrypoint() -> CompositingExtension:
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return CompositingExtension()
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