mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-07-27 08:16:44 +00:00
converted nodes files starting with "l" letter
This commit is contained in:
bigcat88
parent
e5cac06bbe
commit
991de5fc81
@ -657,9 +657,34 @@ class Accumulation(ComfyTypeIO):
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accum: list[Any]
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Type = AccumulationDict
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@comfytype(io_type="LOAD3D_CAMERA")
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class Load3DCamera(ComfyTypeIO):
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Type = Any # TODO: figure out type for this; in code, only described as image['camera_info'], gotten from a LOAD_3D or LOAD_3D_ANIMATION type
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class CameraInfo(TypedDict):
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position: dict[str, float | int]
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target: dict[str, float | int]
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zoom: int
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cameraType: str
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Type = CameraInfo
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@comfytype(io_type="LOAD_3D")
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class Load3D(ComfyTypeIO):
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"""3D models are stored as a dictionary."""
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class Model3DDict(TypedDict):
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image: str
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mask: str
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normal: str
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camera_info: Load3DCamera.CameraInfo
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recording: NotRequired[str]
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Type = Model3DDict
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@comfytype(io_type="LOAD_3D_ANIMATION")
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class Load3DAnimation(Load3D):
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...
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@comfytype(io_type="PHOTOMAKER")
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@ -479,7 +479,7 @@ class PreviewUI3D(_UIOutput):
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self.values = values
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def as_dict(self):
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return {"3d": self.values}
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return {"result": self.values}
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class PreviewText(_UIOutput):
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56
comfy_extras/v3/nodes_ip2p.py
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56
comfy_extras/v3/nodes_ip2p.py
Normal file
@ -0,0 +1,56 @@
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from __future__ import annotations
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import torch
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from comfy_api.v3 import io
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class InstructPixToPixConditioning(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="InstructPixToPixConditioning_V3",
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category="conditioning/instructpix2pix",
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inputs=[
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io.Conditioning.Input(id="positive"),
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io.Conditioning.Input(id="negative"),
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io.Vae.Input(id="vae"),
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io.Image.Input(id="pixels"),
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],
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outputs=[
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io.Conditioning.Output(display_name="positive"),
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io.Conditioning.Output(display_name="negative"),
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io.Latent.Output(display_name="latent"),
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],
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)
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@classmethod
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def execute(cls, positive, negative, pixels, vae):
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x = (pixels.shape[1] // 8) * 8
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y = (pixels.shape[2] // 8) * 8
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if pixels.shape[1] != x or pixels.shape[2] != y:
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x_offset = (pixels.shape[1] % 8) // 2
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y_offset = (pixels.shape[2] % 8) // 2
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pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
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concat_latent = vae.encode(pixels)
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out_latent = {}
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out_latent["samples"] = torch.zeros_like(concat_latent)
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out = []
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for conditioning in [positive, negative]:
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c = []
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for t in conditioning:
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d = t[1].copy()
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d["concat_latent_image"] = concat_latent
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n = [t[0], d]
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c.append(n)
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out.append(c)
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return io.NodeOutput(out[0], out[1], out_latent)
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NODES_LIST = [
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InstructPixToPixConditioning,
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]
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180
comfy_extras/v3/nodes_load_3d.py
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180
comfy_extras/v3/nodes_load_3d.py
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@ -0,0 +1,180 @@
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from __future__ import annotations
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import os
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from pathlib import Path
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import folder_paths
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import nodes
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from comfy_api.input_impl import VideoFromFile
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from comfy_api.v3 import io, ui
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def normalize_path(path):
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return path.replace("\\", "/")
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class Load3D(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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input_dir = os.path.join(folder_paths.get_input_directory(), "3d")
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os.makedirs(input_dir, exist_ok=True)
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input_path = Path(input_dir)
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base_path = Path(folder_paths.get_input_directory())
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files = [
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normalize_path(str(file_path.relative_to(base_path)))
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for file_path in input_path.rglob("*")
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if file_path.suffix.lower() in {".gltf", ".glb", ".obj", ".fbx", ".stl"}
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]
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return io.Schema(
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node_id="Load3D_V3",
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display_name="Load 3D _V3",
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category="3d",
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is_experimental=True,
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inputs=[
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io.Combo.Input(id="model_file", options=sorted(files), upload=io.UploadType.model),
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io.Load3D.Input(id="image"),
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io.Int.Input(id="width", default=1024, min=1, max=4096, step=1),
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io.Int.Input(id="height", default=1024, min=1, max=4096, step=1),
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],
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outputs=[
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io.Image.Output(display_name="image"),
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io.Mask.Output(display_name="mask"),
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io.String.Output(display_name="mesh_path"),
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io.Image.Output(display_name="normal"),
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io.Image.Output(display_name="lineart"),
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io.Load3DCamera.Output(display_name="camera_info"),
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io.Video.Output(display_name="recording_video"),
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],
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)
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@classmethod
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def execute(cls, model_file, image, **kwargs):
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image_path = folder_paths.get_annotated_filepath(image["image"])
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mask_path = folder_paths.get_annotated_filepath(image["mask"])
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normal_path = folder_paths.get_annotated_filepath(image["normal"])
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lineart_path = folder_paths.get_annotated_filepath(image["lineart"])
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load_image_node = nodes.LoadImage()
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output_image, ignore_mask = load_image_node.load_image(image=image_path)
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ignore_image, output_mask = load_image_node.load_image(image=mask_path)
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normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path)
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lineart_image, ignore_mask3 = load_image_node.load_image(image=lineart_path)
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video = None
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if image["recording"] != "":
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recording_video_path = folder_paths.get_annotated_filepath(image["recording"])
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video = VideoFromFile(recording_video_path)
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return io.NodeOutput(
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output_image, output_mask, model_file, normal_image, lineart_image, image["camera_info"], video
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)
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class Load3DAnimation(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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input_dir = os.path.join(folder_paths.get_input_directory(), "3d")
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os.makedirs(input_dir, exist_ok=True)
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input_path = Path(input_dir)
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base_path = Path(folder_paths.get_input_directory())
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files = [
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normalize_path(str(file_path.relative_to(base_path)))
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for file_path in input_path.rglob("*")
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if file_path.suffix.lower() in {".gltf", ".glb", ".fbx"}
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]
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return io.Schema(
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node_id="Load3DAnimation_V3",
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display_name="Load 3D - Animation _V3",
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category="3d",
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is_experimental=True,
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inputs=[
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io.Combo.Input(id="model_file", options=sorted(files), upload=io.UploadType.model),
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io.Load3DAnimation.Input(id="image"),
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io.Int.Input(id="width", default=1024, min=1, max=4096, step=1),
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io.Int.Input(id="height", default=1024, min=1, max=4096, step=1),
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],
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outputs=[
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io.Image.Output(display_name="image"),
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io.Mask.Output(display_name="mask"),
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io.String.Output(display_name="mesh_path"),
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io.Image.Output(display_name="normal"),
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io.Load3DCamera.Output(display_name="camera_info"),
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io.Video.Output(display_name="recording_video"),
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],
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)
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@classmethod
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def execute(cls, model_file, image, **kwargs):
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image_path = folder_paths.get_annotated_filepath(image["image"])
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mask_path = folder_paths.get_annotated_filepath(image["mask"])
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normal_path = folder_paths.get_annotated_filepath(image["normal"])
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load_image_node = nodes.LoadImage()
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output_image, ignore_mask = load_image_node.load_image(image=image_path)
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ignore_image, output_mask = load_image_node.load_image(image=mask_path)
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normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path)
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video = None
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if image['recording'] != "":
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recording_video_path = folder_paths.get_annotated_filepath(image["recording"])
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video = VideoFromFile(recording_video_path)
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return io.NodeOutput(output_image, output_mask, model_file, normal_image, image["camera_info"], video)
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class Preview3D(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="Preview3D_V3", # frontend expects "Preview3D" to work
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display_name="Preview 3D _V3",
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category="3d",
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is_experimental=True,
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is_output_node=True,
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inputs=[
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io.String.Input(id="model_file", default="", multiline=False),
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io.Load3DCamera.Input(id="camera_info", optional=True),
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],
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outputs=[],
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)
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@classmethod
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def execute(cls, model_file, camera_info=None):
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return io.NodeOutput(ui=ui.PreviewUI3D([model_file, camera_info], cls=cls))
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class Preview3DAnimation(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="Preview3DAnimation_V3", # frontend expects "Preview3DAnimation" to work
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display_name="Preview 3D - Animation _V3",
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category="3d",
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is_experimental=True,
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is_output_node=True,
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inputs=[
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io.String.Input(id="model_file", default="", multiline=False),
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io.Load3DCamera.Input(id="camera_info", optional=True),
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],
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outputs=[],
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)
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@classmethod
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def execute(cls, model_file, camera_info=None):
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return io.NodeOutput(ui=ui.PreviewUI3D([model_file, camera_info], cls=cls))
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NODES_LIST = [
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Load3D,
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Load3DAnimation,
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Preview3D,
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Preview3DAnimation,
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]
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138
comfy_extras/v3/nodes_lora_extract.py
Normal file
138
comfy_extras/v3/nodes_lora_extract.py
Normal file
@ -0,0 +1,138 @@
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from __future__ import annotations
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import logging
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import os
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from enum import Enum
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import torch
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import comfy.model_management
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import comfy.utils
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import folder_paths
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from comfy_api.v3 import io
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CLAMP_QUANTILE = 0.99
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def extract_lora(diff, rank):
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conv2d = (len(diff.shape) == 4)
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kernel_size = None if not conv2d else diff.size()[2:4]
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conv2d_3x3 = conv2d and kernel_size != (1, 1)
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out_dim, in_dim = diff.size()[0:2]
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rank = min(rank, in_dim, out_dim)
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if conv2d:
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if conv2d_3x3:
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diff = diff.flatten(start_dim=1)
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else:
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diff = diff.squeeze()
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U, S, Vh = torch.linalg.svd(diff.float())
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U = U[:, :rank]
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S = S[:rank]
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U = U @ torch.diag(S)
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Vh = Vh[:rank, :]
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dist = torch.cat([U.flatten(), Vh.flatten()])
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hi_val = torch.quantile(dist, CLAMP_QUANTILE)
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low_val = -hi_val
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U = U.clamp(low_val, hi_val)
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Vh = Vh.clamp(low_val, hi_val)
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if conv2d:
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U = U.reshape(out_dim, rank, 1, 1)
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Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1])
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return (U, Vh)
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class LORAType(Enum):
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STANDARD = 0
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FULL_DIFF = 1
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LORA_TYPES = {
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"standard": LORAType.STANDARD,
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"full_diff": LORAType.FULL_DIFF,
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}
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def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora_type, bias_diff=False):
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comfy.model_management.load_models_gpu([model_diff], force_patch_weights=True)
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sd = model_diff.model_state_dict(filter_prefix=prefix_model)
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for k in sd:
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if k.endswith(".weight"):
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weight_diff = sd[k]
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if lora_type == LORAType.STANDARD:
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if weight_diff.ndim < 2:
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if bias_diff:
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output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu()
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continue
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try:
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out = extract_lora(weight_diff, rank)
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output_sd["{}{}.lora_up.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[0].contiguous().half().cpu()
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output_sd["{}{}.lora_down.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[1].contiguous().half().cpu()
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except Exception:
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logging.warning("Could not generate lora weights for key {}, is the weight difference a zero?".format(k))
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elif lora_type == LORAType.FULL_DIFF:
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output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu()
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elif bias_diff and k.endswith(".bias"):
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output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = sd[k].contiguous().half().cpu()
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return output_sd
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class LoraSave(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="LoraSave_V3",
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display_name="Extract and Save Lora _V3",
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category="_for_testing",
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is_output_node=True,
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inputs=[
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io.String.Input(id="filename_prefix", default="loras/ComfyUI_extracted_lora"),
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io.Int.Input(id="rank", default=8, min=1, max=4096, step=1),
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io.Combo.Input(id="lora_type", options=list(LORA_TYPES.keys())),
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io.Boolean.Input(id="bias_diff", default=True),
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io.Model.Input(
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id="model_diff", optional=True, tooltip="The ModelSubtract output to be converted to a lora."
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),
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io.Clip.Input(
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id="text_encoder_diff", optional=True, tooltip="The CLIPSubtract output to be converted to a lora."
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),
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],
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outputs=[],
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is_experimental=True,
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)
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@classmethod
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def execute(cls, filename_prefix, rank, lora_type, bias_diff, model_diff=None, text_encoder_diff=None):
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if model_diff is None and text_encoder_diff is None:
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return io.NodeOutput()
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lora_type = LORA_TYPES.get(lora_type)
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full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
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filename_prefix, folder_paths.get_output_directory()
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)
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output_sd = {}
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if model_diff is not None:
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output_sd = calc_lora_model(
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model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd, lora_type, bias_diff=bias_diff
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)
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if text_encoder_diff is not None:
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output_sd = calc_lora_model(
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text_encoder_diff.patcher, rank, "", "text_encoders.", output_sd, lora_type, bias_diff=bias_diff
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)
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output_checkpoint = f"{filename}_{counter:05}_.safetensors"
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output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
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comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None)
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return io.NodeOutput()
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NODES_LIST = [
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LoraSave,
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]
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34
comfy_extras/v3/nodes_lotus.py
Normal file
34
comfy_extras/v3/nodes_lotus.py
Normal file
File diff suppressed because one or more lines are too long
116
comfy_extras/v3/nodes_lumina2.py
Normal file
116
comfy_extras/v3/nodes_lumina2.py
Normal file
@ -0,0 +1,116 @@
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from __future__ import annotations
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import torch
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from comfy_api.v3 import io
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class CLIPTextEncodeLumina2(io.ComfyNode):
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SYSTEM_PROMPT = {
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"superior": "You are an assistant designed to generate superior images with the superior "
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"degree of image-text alignment based on textual prompts or user prompts.",
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"alignment": "You are an assistant designed to generate high-quality images with the "
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"highest degree of image-text alignment based on textual prompts."
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}
|
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SYSTEM_PROMPT_TIP = "Lumina2 provide two types of system prompts:" \
|
||||
"Superior: You are an assistant designed to generate superior images with the superior "\
|
||||
"degree of image-text alignment based on textual prompts or user prompts. "\
|
||||
"Alignment: You are an assistant designed to generate high-quality images with the highest "\
|
||||
"degree of image-text alignment based on textual prompts."
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="CLIPTextEncodeLumina2_V3",
|
||||
display_name="CLIP Text Encode for Lumina2 _V3",
|
||||
category="conditioning",
|
||||
description="Encodes a system prompt and a user prompt using a CLIP model into an embedding "
|
||||
"that can be used to guide the diffusion model towards generating specific images.",
|
||||
inputs=[
|
||||
io.Combo.Input(id="system_prompt", options=list(cls.SYSTEM_PROMPT.keys()), tooltip=cls.SYSTEM_PROMPT_TIP),
|
||||
io.String.Input(id="user_prompt", multiline=True, dynamic_prompts=True, tooltip="The text to be encoded."),
|
||||
io.Clip.Input(id="clip", tooltip="The CLIP model used for encoding the text."),
|
||||
],
|
||||
outputs=[
|
||||
io.Conditioning.Output(tooltip="A conditioning containing the embedded text used to guide the diffusion model."),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, system_prompt, user_prompt, clip):
|
||||
if clip is None:
|
||||
raise RuntimeError(
|
||||
"ERROR: clip input is invalid: None\n\n"
|
||||
"If the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model."
|
||||
)
|
||||
system_prompt = cls.SYSTEM_PROMPT[system_prompt]
|
||||
prompt = f'{system_prompt} <Prompt Start> {user_prompt}'
|
||||
tokens = clip.tokenize(prompt)
|
||||
return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
|
||||
|
||||
|
||||
class RenormCFG(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="RenormCFG_V3",
|
||||
category="advanced/model",
|
||||
inputs=[
|
||||
io.Model.Input(id="model"),
|
||||
io.Float.Input(id="cfg_trunc", default=100, min=0.0, max=100.0, step=0.01),
|
||||
io.Float.Input(id="renorm_cfg", default=1.0, min=0.0, max=100.0, step=0.01),
|
||||
],
|
||||
outputs=[
|
||||
io.Model.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, model, cfg_trunc, renorm_cfg):
|
||||
def renorm_cfg_func(args):
|
||||
cond_denoised = args["cond_denoised"]
|
||||
uncond_denoised = args["uncond_denoised"]
|
||||
cond_scale = args["cond_scale"]
|
||||
timestep = args["timestep"]
|
||||
x_orig = args["input"]
|
||||
in_channels = model.model.diffusion_model.in_channels
|
||||
|
||||
if timestep[0] < cfg_trunc:
|
||||
cond_eps, uncond_eps = cond_denoised[:, :in_channels], uncond_denoised[:, :in_channels]
|
||||
cond_rest, _ = cond_denoised[:, in_channels:], uncond_denoised[:, in_channels:]
|
||||
half_eps = uncond_eps + cond_scale * (cond_eps - uncond_eps)
|
||||
half_rest = cond_rest
|
||||
|
||||
if float(renorm_cfg) > 0.0:
|
||||
ori_pos_norm = torch.linalg.vector_norm(
|
||||
cond_eps,
|
||||
dim=tuple(range(1, len(cond_eps.shape))),
|
||||
keepdim=True
|
||||
)
|
||||
max_new_norm = ori_pos_norm * float(renorm_cfg)
|
||||
new_pos_norm = torch.linalg.vector_norm(
|
||||
half_eps, dim=tuple(range(1, len(half_eps.shape))), keepdim=True
|
||||
)
|
||||
if new_pos_norm >= max_new_norm:
|
||||
half_eps = half_eps * (max_new_norm / new_pos_norm)
|
||||
else:
|
||||
cond_eps, uncond_eps = cond_denoised[:, :in_channels], uncond_denoised[:, :in_channels]
|
||||
cond_rest, _ = cond_denoised[:, in_channels:], uncond_denoised[:, in_channels:]
|
||||
half_eps = cond_eps
|
||||
half_rest = cond_rest
|
||||
|
||||
cfg_result = torch.cat([half_eps, half_rest], dim=1)
|
||||
|
||||
# cfg_result = uncond_denoised + (cond_denoised - uncond_denoised) * cond_scale
|
||||
|
||||
return x_orig - cfg_result
|
||||
|
||||
m = model.clone()
|
||||
m.set_model_sampler_cfg_function(renorm_cfg_func)
|
||||
return io.NodeOutput(m)
|
||||
|
||||
|
||||
NODES_LIST = [
|
||||
CLIPTextEncodeLumina2,
|
||||
RenormCFG,
|
||||
]
|
||||
5
nodes.py
5
nodes.py
@ -2321,8 +2321,13 @@ def init_builtin_extra_nodes():
|
||||
"v3/nodes_gits.py",
|
||||
"v3/nodes_hidream.py",
|
||||
"v3/nodes_images.py",
|
||||
"v3/nodes_ip2p.py",
|
||||
"v3/nodes_latent.py",
|
||||
"v3/nodes_load_3d.py",
|
||||
"v3/nodes_lora_extract.py",
|
||||
"v3/nodes_lotus.py",
|
||||
"v3/nodes_lt.py",
|
||||
"v3/nodes_lumina2.py",
|
||||
"v3/nodes_mask.py",
|
||||
"v3/nodes_mochi.py",
|
||||
"v3/nodes_model_advanced.py",
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user