mirror of
https://github.com/comfyanonymous/ComfyUI.git
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Merge pull request #8953 from bigcat88/v3/nodes/c-part1
[V3] wancamera, canny, clipsdxl, composite, ..
This commit is contained in:
Jedrzej Kosinski
GitHub
commit
de0901bd02
@ -439,6 +439,12 @@ class MultiCombo(ComfyTypeI):
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class Image(ComfyTypeIO):
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Type = torch.Tensor
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@comfytype(io_type="WAN_CAMERA_EMBEDDING")
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class WanCameraEmbedding(ComfyTypeIO):
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Type = torch.Tensor
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@comfytype(io_type="WEBCAM")
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class Webcam(ComfyTypeIO):
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Type = str
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217
comfy_extras/v3/nodes_camera_trajectory.py
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217
comfy_extras/v3/nodes_camera_trajectory.py
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@ -0,0 +1,217 @@
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from __future__ import annotations
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import numpy as np
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import torch
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from einops import rearrange
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import comfy.model_management
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import nodes
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from comfy_api.v3 import io
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CAMERA_DICT = {
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"base_T_norm": 1.5,
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"base_angle": np.pi / 3,
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"Static": {"angle": [0.0, 0.0, 0.0], "T": [0.0, 0.0, 0.0]},
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"Pan Up": {"angle": [0.0, 0.0, 0.0], "T": [0.0, -1.0, 0.0]},
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"Pan Down": {"angle": [0.0, 0.0, 0.0], "T": [0.0, 1.0, 0.0]},
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"Pan Left": {"angle": [0.0, 0.0, 0.0], "T": [-1.0, 0.0, 0.0]},
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"Pan Right": {"angle": [0.0, 0.0, 0.0], "T": [1.0, 0.0, 0.0]},
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"Zoom In": {"angle": [0.0, 0.0, 0.0], "T": [0.0, 0.0, 2.0]},
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"Zoom Out": {"angle": [0.0, 0.0, 0.0], "T": [0.0, 0.0, -2.0]},
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"Anti Clockwise (ACW)": {"angle": [0.0, 0.0, -1.0], "T": [0.0, 0.0, 0.0]},
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"ClockWise (CW)": {"angle": [0.0, 0.0, 1.0], "T": [0.0, 0.0, 0.0]},
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}
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def process_pose_params(cam_params, width=672, height=384, original_pose_width=1280, original_pose_height=720, device="cpu"):
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def get_relative_pose(cam_params):
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"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py"""
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abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
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abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
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cam_to_origin = 0
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target_cam_c2w = np.array([[1, 0, 0, 0], [0, 1, 0, -cam_to_origin], [0, 0, 1, 0], [0, 0, 0, 1]])
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abs2rel = target_cam_c2w @ abs_w2cs[0]
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ret_poses = [target_cam_c2w] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]]
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return np.array(ret_poses, dtype=np.float32)
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"""Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py"""
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cam_params = [Camera(cam_param) for cam_param in cam_params]
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sample_wh_ratio = width / height
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pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed
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if pose_wh_ratio > sample_wh_ratio:
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resized_ori_w = height * pose_wh_ratio
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for cam_param in cam_params:
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cam_param.fx = resized_ori_w * cam_param.fx / width
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else:
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resized_ori_h = width / pose_wh_ratio
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for cam_param in cam_params:
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cam_param.fy = resized_ori_h * cam_param.fy / height
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intrinsic = np.asarray(
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[[cam_param.fx * width, cam_param.fy * height, cam_param.cx * width, cam_param.cy * height] for cam_param in cam_params],
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dtype=np.float32,
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)
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K = torch.as_tensor(intrinsic)[None] # [1, 1, 4]
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c2ws = get_relative_pose(cam_params) # Assuming this function is defined elsewhere
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c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4]
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plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W
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plucker_embedding = plucker_embedding[None]
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return rearrange(plucker_embedding, "b f c h w -> b f h w c")[0]
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class Camera:
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"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py"""
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def __init__(self, entry):
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fx, fy, cx, cy = entry[1:5]
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self.fx = fx
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self.fy = fy
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self.cx = cx
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self.cy = cy
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c2w_mat = np.array(entry[7:]).reshape(4, 4)
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self.c2w_mat = c2w_mat
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self.w2c_mat = np.linalg.inv(c2w_mat)
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def ray_condition(K, c2w, H, W, device):
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"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py"""
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# c2w: B, V, 4, 4
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# K: B, V, 4
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B = K.shape[0]
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j, i = torch.meshgrid(
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torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
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torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype),
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indexing="ij",
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)
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i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW]
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j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW]
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fx, fy, cx, cy = K.chunk(4, dim=-1) # B,V, 1
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zs = torch.ones_like(i) # [B, HxW]
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xs = (i - cx) / fx * zs
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ys = (j - cy) / fy * zs
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zs = zs.expand_as(ys)
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directions = torch.stack((xs, ys, zs), dim=-1) # B, V, HW, 3
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directions = directions / directions.norm(dim=-1, keepdim=True) # B, V, HW, 3
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rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2) # B, V, 3, HW
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rays_o = c2w[..., :3, 3] # B, V, 3
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rays_o = rays_o[:, :, None].expand_as(rays_d) # B, V, 3, HW
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# c2w @ dirctions
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rays_dxo = torch.cross(rays_o, rays_d)
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plucker = torch.cat([rays_dxo, rays_d], dim=-1)
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plucker = plucker.reshape(B, c2w.shape[1], H, W, 6) # B, V, H, W, 6
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# plucker = plucker.permute(0, 1, 4, 2, 3)
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return plucker
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def get_camera_motion(angle, T, speed, n=81):
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def compute_R_form_rad_angle(angles):
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theta_x, theta_y, theta_z = angles
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Rx = np.array([[1, 0, 0], [0, np.cos(theta_x), -np.sin(theta_x)], [0, np.sin(theta_x), np.cos(theta_x)]])
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Ry = np.array([[np.cos(theta_y), 0, np.sin(theta_y)], [0, 1, 0], [-np.sin(theta_y), 0, np.cos(theta_y)]])
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Rz = np.array([[np.cos(theta_z), -np.sin(theta_z), 0], [np.sin(theta_z), np.cos(theta_z), 0], [0, 0, 1]])
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R = np.dot(Rz, np.dot(Ry, Rx))
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return R
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RT = []
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for i in range(n):
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_angle = (i / n) * speed * (CAMERA_DICT["base_angle"]) * angle
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R = compute_R_form_rad_angle(_angle)
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_T = (i / n) * speed * (CAMERA_DICT["base_T_norm"]) * (T.reshape(3, 1))
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_RT = np.concatenate([R, _T], axis=1)
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RT.append(_RT)
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RT = np.stack(RT)
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return RT
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class WanCameraEmbedding(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls):
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return io.SchemaV3(
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node_id="WanCameraEmbedding_V3",
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category="camera",
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inputs=[
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io.Combo.Input(
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"camera_pose",
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options=[
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"Static",
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"Pan Up",
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"Pan Down",
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"Pan Left",
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"Pan Right",
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"Zoom In",
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"Zoom Out",
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"Anti Clockwise (ACW)",
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"ClockWise (CW)",
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],
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default="Static",
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),
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io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
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io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
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io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
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io.Float.Input("speed", default=1.0, min=0, max=10.0, step=0.1, optional=True),
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io.Float.Input("fx", default=0.5, min=0, max=1, step=0.000000001, optional=True),
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io.Float.Input("fy", default=0.5, min=0, max=1, step=0.000000001, optional=True),
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io.Float.Input("cx", default=0.5, min=0, max=1, step=0.01, optional=True),
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io.Float.Input("cy", default=0.5, min=0, max=1, step=0.01, optional=True),
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],
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outputs=[
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io.WanCameraEmbedding.Output(display_name="camera_embedding"),
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io.Int.Output(display_name="width"),
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io.Int.Output(display_name="height"),
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io.Int.Output(display_name="length"),
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],
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)
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@classmethod
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def execute(cls, camera_pose, width, height, length, speed=1.0, fx=0.5, fy=0.5, cx=0.5, cy=0.5) -> io.NodeOutput:
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"""
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Use Camera trajectory as extrinsic parameters to calculate Plücker embeddings (Sitzmannet al., 2021)
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Adapted from https://github.com/aigc-apps/VideoX-Fun/blob/main/comfyui/comfyui_nodes.py
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"""
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motion_list = [camera_pose]
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speed = speed
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angle = np.array(CAMERA_DICT[motion_list[0]]["angle"])
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T = np.array(CAMERA_DICT[motion_list[0]]["T"])
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RT = get_camera_motion(angle, T, speed, length)
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trajs = []
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for cp in RT.tolist():
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traj = [fx, fy, cx, cy, 0, 0]
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traj.extend(cp[0])
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traj.extend(cp[1])
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traj.extend(cp[2])
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traj.extend([0, 0, 0, 1])
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trajs.append(traj)
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cam_params = np.array([[float(x) for x in pose] for pose in trajs])
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cam_params = np.concatenate([np.zeros_like(cam_params[:, :1]), cam_params], 1)
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control_camera_video = process_pose_params(cam_params, width=width, height=height)
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control_camera_video = control_camera_video.permute([3, 0, 1, 2]).unsqueeze(0).to(device=comfy.model_management.intermediate_device())
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control_camera_video = torch.concat(
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[torch.repeat_interleave(control_camera_video[:, :, 0:1], repeats=4, dim=2), control_camera_video[:, :, 1:]], dim=2
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).transpose(1, 2)
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# Reshape, transpose, and view into desired shape
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b, f, c, h, w = control_camera_video.shape
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control_camera_video = control_camera_video.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3)
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control_camera_video = control_camera_video.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2)
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return io.NodeOutput(control_camera_video, width, height, length)
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NODES_LIST = [
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WanCameraEmbedding,
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]
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32
comfy_extras/v3/nodes_canny.py
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32
comfy_extras/v3/nodes_canny.py
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@ -0,0 +1,32 @@
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from __future__ import annotations
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from kornia.filters import canny
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import comfy.model_management
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from comfy_api.v3 import io
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class Canny(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls):
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return io.SchemaV3(
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node_id="Canny_V3",
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category="image/preprocessors",
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inputs=[
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io.Image.Input("image"),
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io.Float.Input("low_threshold", default=0.4, min=0.01, max=0.99, step=0.01),
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io.Float.Input("high_threshold", default=0.8, min=0.01, max=0.99, step=0.01),
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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, low_threshold, high_threshold) -> io.NodeOutput:
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output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold)
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img_out = output[1].to(comfy.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1)
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return io.NodeOutput(img_out)
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NODES_LIST = [
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Canny,
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]
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88
comfy_extras/v3/nodes_cfg.py
Normal file
88
comfy_extras/v3/nodes_cfg.py
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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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def optimized_scale(positive, negative):
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positive_flat = positive.reshape(positive.shape[0], -1)
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negative_flat = negative.reshape(negative.shape[0], -1)
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# Calculate dot production
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dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
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# Squared norm of uncondition
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squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8
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# st_star = v_cond^T * v_uncond / ||v_uncond||^2
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st_star = dot_product / squared_norm
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return st_star.reshape([positive.shape[0]] + [1] * (positive.ndim - 1))
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class CFGNorm(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls) -> io.SchemaV3:
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return io.SchemaV3(
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node_id="CFGNorm_V3",
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category="advanced/guidance",
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inputs=[
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io.Model.Input("model"),
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io.Float.Input("strength", default=1.0, min=0.0, max=100.0, step=0.01),
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],
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outputs=[io.Model.Output("patched_model", display_name="patched_model")],
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is_experimental=True,
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)
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@classmethod
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def execute(cls, model, strength) -> io.NodeOutput:
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m = model.clone()
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def cfg_norm(args):
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cond_p = args['cond_denoised']
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pred_text_ = args["denoised"]
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norm_full_cond = torch.norm(cond_p, dim=1, keepdim=True)
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norm_pred_text = torch.norm(pred_text_, dim=1, keepdim=True)
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scale = (norm_full_cond / (norm_pred_text + 1e-8)).clamp(min=0.0, max=1.0)
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return pred_text_ * scale * strength
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m.set_model_sampler_post_cfg_function(cfg_norm)
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return io.NodeOutput(m)
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class CFGZeroStar(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls) -> io.SchemaV3:
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return io.SchemaV3(
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node_id="CFGZeroStar_V3",
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category="advanced/guidance",
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inputs=[
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io.Model.Input("model"),
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],
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outputs=[io.Model.Output("patched_model", display_name="patched_model")],
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)
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@classmethod
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def execute(cls, model) -> io.NodeOutput:
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m = model.clone()
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def cfg_zero_star(args):
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guidance_scale = args['cond_scale']
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x = args['input']
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cond_p = args['cond_denoised']
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uncond_p = args['uncond_denoised']
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out = args["denoised"]
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alpha = optimized_scale(x - cond_p, x - uncond_p)
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return out + uncond_p * (alpha - 1.0) + guidance_scale * uncond_p * (1.0 - alpha)
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m.set_model_sampler_post_cfg_function(cfg_zero_star)
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return io.NodeOutput(m)
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NODES_LIST = [
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CFGNorm,
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CFGZeroStar,
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]
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79
comfy_extras/v3/nodes_clip_sdxl.py
Normal file
79
comfy_extras/v3/nodes_clip_sdxl.py
Normal file
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from __future__ import annotations
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import nodes
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from comfy_api.v3 import io
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class CLIPTextEncodeSDXL(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls):
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return io.SchemaV3(
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node_id="CLIPTextEncodeSDXL_V3",
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category="advanced/conditioning",
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inputs=[
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io.Clip.Input("clip"),
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io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
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io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
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io.Int.Input("crop_w", default=0, min=0, max=nodes.MAX_RESOLUTION),
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io.Int.Input("crop_h", default=0, min=0, max=nodes.MAX_RESOLUTION),
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io.Int.Input("target_width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
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io.Int.Input("target_height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
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io.String.Input("text_g", multiline=True, dynamic_prompts=True),
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io.String.Input("text_l", multiline=True, dynamic_prompts=True),
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],
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outputs=[io.Conditioning.Output()],
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)
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|
||||
@classmethod
|
||||
def execute(cls, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l) -> io.NodeOutput:
|
||||
tokens = clip.tokenize(text_g)
|
||||
tokens["l"] = clip.tokenize(text_l)["l"]
|
||||
if len(tokens["l"]) != len(tokens["g"]):
|
||||
empty = clip.tokenize("")
|
||||
while len(tokens["l"]) < len(tokens["g"]):
|
||||
tokens["l"] += empty["l"]
|
||||
while len(tokens["l"]) > len(tokens["g"]):
|
||||
tokens["g"] += empty["g"]
|
||||
conditioning = clip.encode_from_tokens_scheduled(
|
||||
tokens,
|
||||
add_dict={
|
||||
"width": width,
|
||||
"height": height,
|
||||
"crop_w": crop_w,
|
||||
"crop_h": crop_h,
|
||||
"target_width": target_width,
|
||||
"target_height": target_height,
|
||||
},
|
||||
)
|
||||
return io.NodeOutput(conditioning)
|
||||
|
||||
|
||||
class CLIPTextEncodeSDXLRefiner(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="CLIPTextEncodeSDXLRefiner_V3",
|
||||
category="advanced/conditioning",
|
||||
inputs=[
|
||||
io.Float.Input("ascore", default=6.0, min=0.0, max=1000.0, step=0.01),
|
||||
io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
|
||||
io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
|
||||
io.String.Input("text", multiline=True, dynamic_prompts=True),
|
||||
io.Clip.Input("clip"),
|
||||
],
|
||||
outputs=[io.Conditioning.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, ascore, width, height, text, clip) -> io.NodeOutput:
|
||||
tokens = clip.tokenize(text)
|
||||
conditioning = clip.encode_from_tokens_scheduled(
|
||||
tokens, add_dict={"aesthetic_score": ascore, "width": width, "height": height}
|
||||
)
|
||||
return io.NodeOutput(conditioning)
|
||||
|
||||
|
||||
NODES_LIST = [
|
||||
CLIPTextEncodeSDXL,
|
||||
CLIPTextEncodeSDXLRefiner,
|
||||
]
|
||||
226
comfy_extras/v3/nodes_compositing.py
Normal file
226
comfy_extras/v3/nodes_compositing.py
Normal file
@ -0,0 +1,226 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from enum import Enum
|
||||
|
||||
import torch
|
||||
|
||||
import comfy.utils
|
||||
from comfy_api.v3 import io
|
||||
|
||||
|
||||
def resize_mask(mask, shape):
|
||||
return torch.nn.functional.interpolate(
|
||||
mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[0], shape[1]), mode="bilinear"
|
||||
).squeeze(1)
|
||||
|
||||
|
||||
class PorterDuffMode(Enum):
|
||||
ADD = 0
|
||||
CLEAR = 1
|
||||
DARKEN = 2
|
||||
DST = 3
|
||||
DST_ATOP = 4
|
||||
DST_IN = 5
|
||||
DST_OUT = 6
|
||||
DST_OVER = 7
|
||||
LIGHTEN = 8
|
||||
MULTIPLY = 9
|
||||
OVERLAY = 10
|
||||
SCREEN = 11
|
||||
SRC = 12
|
||||
SRC_ATOP = 13
|
||||
SRC_IN = 14
|
||||
SRC_OUT = 15
|
||||
SRC_OVER = 16
|
||||
XOR = 17
|
||||
|
||||
|
||||
def porter_duff_composite(
|
||||
src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode
|
||||
):
|
||||
# convert mask to alpha
|
||||
src_alpha = 1 - src_alpha
|
||||
dst_alpha = 1 - dst_alpha
|
||||
# premultiply alpha
|
||||
src_image = src_image * src_alpha
|
||||
dst_image = dst_image * dst_alpha
|
||||
|
||||
# composite ops below assume alpha-premultiplied images
|
||||
if mode == PorterDuffMode.ADD:
|
||||
out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1)
|
||||
out_image = torch.clamp(src_image + dst_image, 0, 1)
|
||||
elif mode == PorterDuffMode.CLEAR:
|
||||
out_alpha = torch.zeros_like(dst_alpha)
|
||||
out_image = torch.zeros_like(dst_image)
|
||||
elif mode == PorterDuffMode.DARKEN:
|
||||
out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
|
||||
out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image)
|
||||
elif mode == PorterDuffMode.DST:
|
||||
out_alpha = dst_alpha
|
||||
out_image = dst_image
|
||||
elif mode == PorterDuffMode.DST_ATOP:
|
||||
out_alpha = src_alpha
|
||||
out_image = src_alpha * dst_image + (1 - dst_alpha) * src_image
|
||||
elif mode == PorterDuffMode.DST_IN:
|
||||
out_alpha = src_alpha * dst_alpha
|
||||
out_image = dst_image * src_alpha
|
||||
elif mode == PorterDuffMode.DST_OUT:
|
||||
out_alpha = (1 - src_alpha) * dst_alpha
|
||||
out_image = (1 - src_alpha) * dst_image
|
||||
elif mode == PorterDuffMode.DST_OVER:
|
||||
out_alpha = dst_alpha + (1 - dst_alpha) * src_alpha
|
||||
out_image = dst_image + (1 - dst_alpha) * src_image
|
||||
elif mode == PorterDuffMode.LIGHTEN:
|
||||
out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
|
||||
out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.max(src_image, dst_image)
|
||||
elif mode == PorterDuffMode.MULTIPLY:
|
||||
out_alpha = src_alpha * dst_alpha
|
||||
out_image = src_image * dst_image
|
||||
elif mode == PorterDuffMode.OVERLAY:
|
||||
out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
|
||||
out_image = torch.where(2 * dst_image < dst_alpha, 2 * src_image * dst_image,
|
||||
src_alpha * dst_alpha - 2 * (dst_alpha - src_image) * (src_alpha - dst_image))
|
||||
elif mode == PorterDuffMode.SCREEN:
|
||||
out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
|
||||
out_image = src_image + dst_image - src_image * dst_image
|
||||
elif mode == PorterDuffMode.SRC:
|
||||
out_alpha = src_alpha
|
||||
out_image = src_image
|
||||
elif mode == PorterDuffMode.SRC_ATOP:
|
||||
out_alpha = dst_alpha
|
||||
out_image = dst_alpha * src_image + (1 - src_alpha) * dst_image
|
||||
elif mode == PorterDuffMode.SRC_IN:
|
||||
out_alpha = src_alpha * dst_alpha
|
||||
out_image = src_image * dst_alpha
|
||||
elif mode == PorterDuffMode.SRC_OUT:
|
||||
out_alpha = (1 - dst_alpha) * src_alpha
|
||||
out_image = (1 - dst_alpha) * src_image
|
||||
elif mode == PorterDuffMode.SRC_OVER:
|
||||
out_alpha = src_alpha + (1 - src_alpha) * dst_alpha
|
||||
out_image = src_image + (1 - src_alpha) * dst_image
|
||||
elif mode == PorterDuffMode.XOR:
|
||||
out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha
|
||||
out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image
|
||||
else:
|
||||
return None, None
|
||||
|
||||
# back to non-premultiplied alpha
|
||||
out_image = torch.where(out_alpha > 1e-5, out_image / out_alpha, torch.zeros_like(out_image))
|
||||
out_image = torch.clamp(out_image, 0, 1)
|
||||
# convert alpha to mask
|
||||
out_alpha = 1 - out_alpha
|
||||
return out_image, out_alpha
|
||||
|
||||
|
||||
class JoinImageWithAlpha(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="JoinImageWithAlpha_V3",
|
||||
display_name="Join Image with Alpha _V3",
|
||||
category="mask/compositing",
|
||||
inputs=[
|
||||
io.Image.Input("image"),
|
||||
io.Mask.Input("alpha"),
|
||||
],
|
||||
outputs=[io.Image.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, image: torch.Tensor, alpha: torch.Tensor) -> io.NodeOutput:
|
||||
batch_size = min(len(image), len(alpha))
|
||||
out_images = []
|
||||
|
||||
alpha = 1.0 - resize_mask(alpha, image.shape[1:])
|
||||
for i in range(batch_size):
|
||||
out_images.append(torch.cat((image[i][:, :, :3], alpha[i].unsqueeze(2)), dim=2))
|
||||
|
||||
return io.NodeOutput(torch.stack(out_images))
|
||||
|
||||
|
||||
class PorterDuffImageComposite(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="PorterDuffImageComposite_V3",
|
||||
display_name="Porter-Duff Image Composite _V3",
|
||||
category="mask/compositing",
|
||||
inputs=[
|
||||
io.Image.Input("source"),
|
||||
io.Mask.Input("source_alpha"),
|
||||
io.Image.Input("destination"),
|
||||
io.Mask.Input("destination_alpha"),
|
||||
io.Combo.Input("mode", options=[mode.name for mode in PorterDuffMode], default=PorterDuffMode.DST.name),
|
||||
],
|
||||
outputs=[io.Image.Output(), io.Mask.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(
|
||||
cls, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode
|
||||
) -> io.NodeOutput:
|
||||
batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha))
|
||||
out_images = []
|
||||
out_alphas = []
|
||||
|
||||
for i in range(batch_size):
|
||||
src_image = source[i]
|
||||
dst_image = destination[i]
|
||||
|
||||
assert src_image.shape[2] == dst_image.shape[2] # inputs need to have same number of channels
|
||||
|
||||
src_alpha = source_alpha[i].unsqueeze(2)
|
||||
dst_alpha = destination_alpha[i].unsqueeze(2)
|
||||
|
||||
if dst_alpha.shape[:2] != dst_image.shape[:2]:
|
||||
upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2)
|
||||
upscale_output = comfy.utils.common_upscale(
|
||||
upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center'
|
||||
)
|
||||
dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
|
||||
if src_image.shape != dst_image.shape:
|
||||
upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2)
|
||||
upscale_output = comfy.utils.common_upscale(
|
||||
upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center'
|
||||
)
|
||||
src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0)
|
||||
if src_alpha.shape != dst_alpha.shape:
|
||||
upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2)
|
||||
upscale_output = comfy.utils.common_upscale(
|
||||
upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center'
|
||||
)
|
||||
src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
|
||||
|
||||
out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode])
|
||||
|
||||
out_images.append(out_image)
|
||||
out_alphas.append(out_alpha.squeeze(2))
|
||||
|
||||
return io.NodeOutput(torch.stack(out_images), torch.stack(out_alphas))
|
||||
|
||||
|
||||
class SplitImageWithAlpha(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="SplitImageWithAlpha_V3",
|
||||
display_name="Split Image with Alpha _V3",
|
||||
category="mask/compositing",
|
||||
inputs=[
|
||||
io.Image.Input("image"),
|
||||
],
|
||||
outputs=[io.Image.Output(), io.Mask.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, image: torch.Tensor) -> io.NodeOutput:
|
||||
out_images = [i[:, :, :3] for i in image]
|
||||
out_alphas = [i[:, :, 3] if i.shape[2] > 3 else torch.ones_like(i[:, :, 0]) for i in image]
|
||||
return io.NodeOutput(torch.stack(out_images), 1.0 - torch.stack(out_alphas))
|
||||
|
||||
|
||||
NODES_LIST = [
|
||||
JoinImageWithAlpha,
|
||||
PorterDuffImageComposite,
|
||||
SplitImageWithAlpha,
|
||||
]
|
||||
60
comfy_extras/v3/nodes_cond.py
Normal file
60
comfy_extras/v3/nodes_cond.py
Normal file
@ -0,0 +1,60 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from comfy_api.v3 import io
|
||||
|
||||
|
||||
class CLIPTextEncodeControlnet(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.SchemaV3:
|
||||
return io.SchemaV3(
|
||||
node_id="CLIPTextEncodeControlnet_V3",
|
||||
category="_for_testing/conditioning",
|
||||
inputs=[
|
||||
io.Clip.Input("clip"),
|
||||
io.Conditioning.Input("conditioning"),
|
||||
io.String.Input("text", multiline=True, dynamic_prompts=True),
|
||||
],
|
||||
outputs=[io.Conditioning.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, clip, conditioning, text) -> io.NodeOutput:
|
||||
tokens = clip.tokenize(text)
|
||||
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
|
||||
c = []
|
||||
for t in conditioning:
|
||||
n = [t[0], t[1].copy()]
|
||||
n[1]['cross_attn_controlnet'] = cond
|
||||
n[1]['pooled_output_controlnet'] = pooled
|
||||
c.append(n)
|
||||
return io.NodeOutput(c)
|
||||
|
||||
|
||||
class T5TokenizerOptions(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.SchemaV3:
|
||||
return io.SchemaV3(
|
||||
node_id="T5TokenizerOptions_V3",
|
||||
category="_for_testing/conditioning",
|
||||
inputs=[
|
||||
io.Clip.Input("clip"),
|
||||
io.Int.Input("min_padding", default=0, min=0, max=10000, step=1),
|
||||
io.Int.Input("min_length", default=0, min=0, max=10000, step=1),
|
||||
],
|
||||
outputs=[io.Clip.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, clip, min_padding, min_length) -> io.NodeOutput:
|
||||
clip = clip.clone()
|
||||
for t5_type in ["t5xxl", "pile_t5xl", "t5base", "mt5xl", "umt5xxl"]:
|
||||
clip.set_tokenizer_option("{}_min_padding".format(t5_type), min_padding)
|
||||
clip.set_tokenizer_option("{}_min_length".format(t5_type), min_length)
|
||||
|
||||
return io.NodeOutput(clip)
|
||||
|
||||
|
||||
NODES_LIST = [
|
||||
CLIPTextEncodeControlnet,
|
||||
T5TokenizerOptions,
|
||||
]
|
||||
146
comfy_extras/v3/nodes_cosmos.py
Normal file
146
comfy_extras/v3/nodes_cosmos.py
Normal file
@ -0,0 +1,146 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import torch
|
||||
|
||||
import comfy.latent_formats
|
||||
import comfy.model_management
|
||||
import comfy.utils
|
||||
import nodes
|
||||
from comfy_api.v3 import io
|
||||
|
||||
|
||||
def vae_encode_with_padding(vae, image, width, height, length, padding=0):
|
||||
pixels = comfy.utils.common_upscale(image[..., :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
|
||||
pixel_len = min(pixels.shape[0], length)
|
||||
padded_length = min(length, (((pixel_len - 1) // 8) + 1 + padding) * 8 - 7)
|
||||
padded_pixels = torch.ones((padded_length, height, width, 3)) * 0.5
|
||||
padded_pixels[:pixel_len] = pixels[:pixel_len]
|
||||
latent_len = ((pixel_len - 1) // 8) + 1
|
||||
latent_temp = vae.encode(padded_pixels)
|
||||
return latent_temp[:, :, :latent_len]
|
||||
|
||||
|
||||
class CosmosImageToVideoLatent(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.SchemaV3:
|
||||
return io.SchemaV3(
|
||||
node_id="CosmosImageToVideoLatent_V3",
|
||||
category="conditioning/inpaint",
|
||||
inputs=[
|
||||
io.Vae.Input("vae"),
|
||||
io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=8),
|
||||
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
||||
io.Image.Input("start_image", optional=True),
|
||||
io.Image.Input("end_image", optional=True),
|
||||
],
|
||||
outputs=[io.Latent.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, vae, width, height, length, batch_size, start_image=None, end_image=None) -> io.NodeOutput:
|
||||
latent = torch.zeros([1, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
|
||||
if start_image is None and end_image is None:
|
||||
out_latent = {}
|
||||
out_latent["samples"] = latent
|
||||
return io.NodeOutput(out_latent)
|
||||
|
||||
mask = torch.ones(
|
||||
[latent.shape[0], 1, ((length - 1) // 8) + 1, latent.shape[-2], latent.shape[-1]],
|
||||
device=comfy.model_management.intermediate_device(),
|
||||
)
|
||||
|
||||
if start_image is not None:
|
||||
latent_temp = vae_encode_with_padding(vae, start_image, width, height, length, padding=1)
|
||||
latent[:, :, :latent_temp.shape[-3]] = latent_temp
|
||||
mask[:, :, :latent_temp.shape[-3]] *= 0.0
|
||||
|
||||
if end_image is not None:
|
||||
latent_temp = vae_encode_with_padding(vae, end_image, width, height, length, padding=0)
|
||||
latent[:, :, -latent_temp.shape[-3]:] = latent_temp
|
||||
mask[:, :, -latent_temp.shape[-3]:] *= 0.0
|
||||
|
||||
out_latent = {}
|
||||
out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1))
|
||||
out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1))
|
||||
return io.NodeOutput(out_latent)
|
||||
|
||||
|
||||
class CosmosPredict2ImageToVideoLatent(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.SchemaV3:
|
||||
return io.SchemaV3(
|
||||
node_id="CosmosPredict2ImageToVideoLatent_V3",
|
||||
category="conditioning/inpaint",
|
||||
inputs=[
|
||||
io.Vae.Input("vae"),
|
||||
io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input("length", default=93, min=1, max=nodes.MAX_RESOLUTION, step=4),
|
||||
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
||||
io.Image.Input("start_image", optional=True),
|
||||
io.Image.Input("end_image", optional=True),
|
||||
],
|
||||
outputs=[io.Latent.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, vae, width, height, length, batch_size, start_image=None, end_image=None) -> io.NodeOutput:
|
||||
latent = torch.zeros([1, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
|
||||
if start_image is None and end_image is None:
|
||||
out_latent = {}
|
||||
out_latent["samples"] = latent
|
||||
return io.NodeOutput(out_latent)
|
||||
|
||||
mask = torch.ones(
|
||||
[latent.shape[0], 1, ((length - 1) // 4) + 1, latent.shape[-2], latent.shape[-1]],
|
||||
device=comfy.model_management.intermediate_device(),
|
||||
)
|
||||
|
||||
if start_image is not None:
|
||||
latent_temp = vae_encode_with_padding(vae, start_image, width, height, length, padding=1)
|
||||
latent[:, :, :latent_temp.shape[-3]] = latent_temp
|
||||
mask[:, :, :latent_temp.shape[-3]] *= 0.0
|
||||
|
||||
if end_image is not None:
|
||||
latent_temp = vae_encode_with_padding(vae, end_image, width, height, length, padding=0)
|
||||
latent[:, :, -latent_temp.shape[-3]:] = latent_temp
|
||||
mask[:, :, -latent_temp.shape[-3]:] *= 0.0
|
||||
|
||||
out_latent = {}
|
||||
latent_format = comfy.latent_formats.Wan21()
|
||||
latent = latent_format.process_out(latent) * mask + latent * (1.0 - mask)
|
||||
out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1))
|
||||
out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1))
|
||||
return io.NodeOutput(out_latent)
|
||||
|
||||
|
||||
class EmptyCosmosLatentVideo(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.SchemaV3:
|
||||
return io.SchemaV3(
|
||||
node_id="EmptyCosmosLatentVideo_V3",
|
||||
category="latent/video",
|
||||
inputs=[
|
||||
io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=8),
|
||||
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
||||
],
|
||||
outputs=[io.Latent.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, width, height, length, batch_size) -> io.NodeOutput:
|
||||
latent = torch.zeros(
|
||||
[batch_size, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()
|
||||
)
|
||||
return io.NodeOutput({"samples": latent})
|
||||
|
||||
|
||||
NODES_LIST = [
|
||||
CosmosImageToVideoLatent,
|
||||
CosmosPredict2ImageToVideoLatent,
|
||||
EmptyCosmosLatentVideo,
|
||||
]
|
||||
50
comfy_extras/v3/nodes_differential_diffusion.py
Normal file
50
comfy_extras/v3/nodes_differential_diffusion.py
Normal file
@ -0,0 +1,50 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import torch
|
||||
|
||||
from comfy_api.v3 import io
|
||||
|
||||
|
||||
class DifferentialDiffusion(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="DifferentialDiffusion_V3",
|
||||
display_name="Differential Diffusion _V3",
|
||||
category="_for_testing",
|
||||
inputs=[
|
||||
io.Model.Input(id="model"),
|
||||
],
|
||||
outputs=[
|
||||
io.Model.Output(),
|
||||
],
|
||||
is_experimental=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, model):
|
||||
model = model.clone()
|
||||
model.set_model_denoise_mask_function(cls.forward)
|
||||
return io.NodeOutput(model)
|
||||
|
||||
@classmethod
|
||||
def forward(cls, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict):
|
||||
model = extra_options["model"]
|
||||
step_sigmas = extra_options["sigmas"]
|
||||
sigma_to = model.inner_model.model_sampling.sigma_min
|
||||
if step_sigmas[-1] > sigma_to:
|
||||
sigma_to = step_sigmas[-1]
|
||||
sigma_from = step_sigmas[0]
|
||||
|
||||
ts_from = model.inner_model.model_sampling.timestep(sigma_from)
|
||||
ts_to = model.inner_model.model_sampling.timestep(sigma_to)
|
||||
current_ts = model.inner_model.model_sampling.timestep(sigma[0])
|
||||
|
||||
threshold = (current_ts - ts_to) / (ts_from - ts_to)
|
||||
|
||||
return (denoise_mask >= threshold).to(denoise_mask.dtype)
|
||||
|
||||
|
||||
NODES_LIST = [
|
||||
DifferentialDiffusion,
|
||||
]
|
||||
125
comfy_extras/v3/nodes_flux.py
Normal file
125
comfy_extras/v3/nodes_flux.py
Normal file
@ -0,0 +1,125 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import comfy.utils
|
||||
import node_helpers
|
||||
from comfy_api.v3 import io
|
||||
|
||||
PREFERED_KONTEXT_RESOLUTIONS = [
|
||||
(672, 1568),
|
||||
(688, 1504),
|
||||
(720, 1456),
|
||||
(752, 1392),
|
||||
(800, 1328),
|
||||
(832, 1248),
|
||||
(880, 1184),
|
||||
(944, 1104),
|
||||
(1024, 1024),
|
||||
(1104, 944),
|
||||
(1184, 880),
|
||||
(1248, 832),
|
||||
(1328, 800),
|
||||
(1392, 752),
|
||||
(1456, 720),
|
||||
(1504, 688),
|
||||
(1568, 672),
|
||||
]
|
||||
|
||||
|
||||
class CLIPTextEncodeFlux(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="CLIPTextEncodeFlux_V3",
|
||||
category="advanced/conditioning/flux",
|
||||
inputs=[
|
||||
io.Clip.Input(id="clip"),
|
||||
io.String.Input(id="clip_l", multiline=True, dynamic_prompts=True),
|
||||
io.String.Input(id="t5xxl", multiline=True, dynamic_prompts=True),
|
||||
io.Float.Input(id="guidance", default=3.5, min=0.0, max=100.0, step=0.1),
|
||||
],
|
||||
outputs=[
|
||||
io.Conditioning.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, clip, clip_l, t5xxl, guidance):
|
||||
tokens = clip.tokenize(clip_l)
|
||||
tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
|
||||
|
||||
return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance}))
|
||||
|
||||
|
||||
class FluxDisableGuidance(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="FluxDisableGuidance_V3",
|
||||
category="advanced/conditioning/flux",
|
||||
description="This node completely disables the guidance embed on Flux and Flux like models",
|
||||
inputs=[
|
||||
io.Conditioning.Input(id="conditioning"),
|
||||
],
|
||||
outputs=[
|
||||
io.Conditioning.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, conditioning):
|
||||
c = node_helpers.conditioning_set_values(conditioning, {"guidance": None})
|
||||
return io.NodeOutput(c)
|
||||
|
||||
|
||||
class FluxGuidance(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="FluxGuidance_V3",
|
||||
category="advanced/conditioning/flux",
|
||||
inputs=[
|
||||
io.Conditioning.Input(id="conditioning"),
|
||||
io.Float.Input(id="guidance", default=3.5, min=0.0, max=100.0, step=0.1),
|
||||
],
|
||||
outputs=[
|
||||
io.Conditioning.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, conditioning, guidance):
|
||||
c = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance})
|
||||
return io.NodeOutput(c)
|
||||
|
||||
|
||||
class FluxKontextImageScale(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="FluxKontextImageScale_V3",
|
||||
category="advanced/conditioning/flux",
|
||||
description="This node resizes the image to one that is more optimal for flux kontext.",
|
||||
inputs=[
|
||||
io.Image.Input(id="image"),
|
||||
],
|
||||
outputs=[
|
||||
io.Image.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, image):
|
||||
width = image.shape[2]
|
||||
height = image.shape[1]
|
||||
aspect_ratio = width / height
|
||||
_, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS)
|
||||
image = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1)
|
||||
return io.NodeOutput(image)
|
||||
|
||||
|
||||
NODES_LIST = [
|
||||
CLIPTextEncodeFlux,
|
||||
FluxDisableGuidance,
|
||||
FluxGuidance,
|
||||
FluxKontextImageScale,
|
||||
]
|
||||
131
comfy_extras/v3/nodes_freelunch.py
Normal file
131
comfy_extras/v3/nodes_freelunch.py
Normal file
@ -0,0 +1,131 @@
|
||||
#code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License)
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
|
||||
import torch
|
||||
|
||||
from comfy_api.v3 import io
|
||||
|
||||
|
||||
def Fourier_filter(x, threshold, scale):
|
||||
# FFT
|
||||
x_freq = torch.fft.fftn(x.float(), dim=(-2, -1))
|
||||
x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1))
|
||||
|
||||
B, C, H, W = x_freq.shape
|
||||
mask = torch.ones((B, C, H, W), device=x.device)
|
||||
|
||||
crow, ccol = H // 2, W //2
|
||||
mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale
|
||||
x_freq = x_freq * mask
|
||||
|
||||
# IFFT
|
||||
x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1))
|
||||
x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real
|
||||
|
||||
return x_filtered.to(x.dtype)
|
||||
|
||||
|
||||
class FreeU(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="FreeU_V3",
|
||||
category="model_patches/unet",
|
||||
inputs=[
|
||||
io.Model.Input(id="model"),
|
||||
io.Float.Input(id="b1", default=1.1, min=0.0, max=10.0, step=0.01),
|
||||
io.Float.Input(id="b2", default=1.2, min=0.0, max=10.0, step=0.01),
|
||||
io.Float.Input(id="s1", default=0.9, min=0.0, max=10.0, step=0.01),
|
||||
io.Float.Input(id="s2", default=0.2, min=0.0, max=10.0, step=0.01),
|
||||
],
|
||||
outputs=[
|
||||
io.Model.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, model, b1, b2, s1, s2):
|
||||
model_channels = model.model.model_config.unet_config["model_channels"]
|
||||
scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
|
||||
on_cpu_devices = {}
|
||||
|
||||
def output_block_patch(h, hsp, transformer_options):
|
||||
scale = scale_dict.get(int(h.shape[1]), None)
|
||||
if scale is not None:
|
||||
h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0]
|
||||
if hsp.device not in on_cpu_devices:
|
||||
try:
|
||||
hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
|
||||
except Exception:
|
||||
logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
|
||||
on_cpu_devices[hsp.device] = True
|
||||
hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
|
||||
else:
|
||||
hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
|
||||
|
||||
return h, hsp
|
||||
|
||||
m = model.clone()
|
||||
m.set_model_output_block_patch(output_block_patch)
|
||||
return io.NodeOutput(m)
|
||||
|
||||
|
||||
class FreeU_V2(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="FreeU_V2_V3",
|
||||
category="model_patches/unet",
|
||||
inputs=[
|
||||
io.Model.Input(id="model"),
|
||||
io.Float.Input(id="b1", default=1.3, min=0.0, max=10.0, step=0.01),
|
||||
io.Float.Input(id="b2", default=1.4, min=0.0, max=10.0, step=0.01),
|
||||
io.Float.Input(id="s1", default=0.9, min=0.0, max=10.0, step=0.01),
|
||||
io.Float.Input(id="s2", default=0.2, min=0.0, max=10.0, step=0.01),
|
||||
],
|
||||
outputs=[
|
||||
io.Model.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, model, b1, b2, s1, s2):
|
||||
model_channels = model.model.model_config.unet_config["model_channels"]
|
||||
scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
|
||||
on_cpu_devices = {}
|
||||
|
||||
def output_block_patch(h, hsp, transformer_options):
|
||||
scale = scale_dict.get(int(h.shape[1]), None)
|
||||
if scale is not None:
|
||||
hidden_mean = h.mean(1).unsqueeze(1)
|
||||
B = hidden_mean.shape[0]
|
||||
hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True)
|
||||
hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True)
|
||||
hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3)
|
||||
|
||||
h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * ((scale[0] - 1 ) * hidden_mean + 1)
|
||||
|
||||
if hsp.device not in on_cpu_devices:
|
||||
try:
|
||||
hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
|
||||
except Exception:
|
||||
logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
|
||||
on_cpu_devices[hsp.device] = True
|
||||
hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
|
||||
else:
|
||||
hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
|
||||
|
||||
return h, hsp
|
||||
|
||||
m = model.clone()
|
||||
m.set_model_output_block_patch(output_block_patch)
|
||||
return io.NodeOutput(m)
|
||||
|
||||
|
||||
NODES_LIST = [
|
||||
FreeU,
|
||||
FreeU_V2,
|
||||
]
|
||||
110
comfy_extras/v3/nodes_fresca.py
Normal file
110
comfy_extras/v3/nodes_fresca.py
Normal file
@ -0,0 +1,110 @@
|
||||
# Code based on https://github.com/WikiChao/FreSca (MIT License)
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import torch
|
||||
import torch.fft as fft
|
||||
|
||||
from comfy_api.v3 import io
|
||||
|
||||
|
||||
def Fourier_filter(x, scale_low=1.0, scale_high=1.5, freq_cutoff=20):
|
||||
"""
|
||||
Apply frequency-dependent scaling to an image tensor using Fourier transforms.
|
||||
|
||||
Parameters:
|
||||
x: Input tensor of shape (B, C, H, W)
|
||||
scale_low: Scaling factor for low-frequency components (default: 1.0)
|
||||
scale_high: Scaling factor for high-frequency components (default: 1.5)
|
||||
freq_cutoff: Number of frequency indices around center to consider as low-frequency (default: 20)
|
||||
|
||||
Returns:
|
||||
x_filtered: Filtered version of x in spatial domain with frequency-specific scaling applied.
|
||||
"""
|
||||
# Preserve input dtype and device
|
||||
dtype, device = x.dtype, x.device
|
||||
|
||||
# Convert to float32 for FFT computations
|
||||
x = x.to(torch.float32)
|
||||
|
||||
# 1) Apply FFT and shift low frequencies to center
|
||||
x_freq = fft.fftn(x, dim=(-2, -1))
|
||||
x_freq = fft.fftshift(x_freq, dim=(-2, -1))
|
||||
|
||||
# Initialize mask with high-frequency scaling factor
|
||||
mask = torch.ones(x_freq.shape, device=device) * scale_high
|
||||
m = mask
|
||||
for d in range(len(x_freq.shape) - 2):
|
||||
dim = d + 2
|
||||
cc = x_freq.shape[dim] // 2
|
||||
f_c = min(freq_cutoff, cc)
|
||||
m = m.narrow(dim, cc - f_c, f_c * 2)
|
||||
|
||||
# Apply low-frequency scaling factor to center region
|
||||
m[:] = scale_low
|
||||
|
||||
# 3) Apply frequency-specific scaling
|
||||
x_freq = x_freq * mask
|
||||
|
||||
# 4) Convert back to spatial domain
|
||||
x_freq = fft.ifftshift(x_freq, dim=(-2, -1))
|
||||
x_filtered = fft.ifftn(x_freq, dim=(-2, -1)).real
|
||||
|
||||
# 5) Restore original dtype
|
||||
x_filtered = x_filtered.to(dtype)
|
||||
|
||||
return x_filtered
|
||||
|
||||
|
||||
class FreSca(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="FreSca_V3",
|
||||
display_name="FreSca _V3",
|
||||
category="_for_testing",
|
||||
description="Applies frequency-dependent scaling to the guidance",
|
||||
inputs=[
|
||||
io.Model.Input(id="model"),
|
||||
io.Float.Input(id="scale_low", default=1.0, min=0, max=10, step=0.01,
|
||||
tooltip="Scaling factor for low-frequency components"),
|
||||
io.Float.Input(id="scale_high", default=1.25, min=0, max=10, step=0.01,
|
||||
tooltip="Scaling factor for high-frequency components"),
|
||||
io.Int.Input(id="freq_cutoff", default=20, min=1, max=10000, step=1,
|
||||
tooltip="Number of frequency indices around center to consider as low-frequency"),
|
||||
],
|
||||
outputs=[
|
||||
io.Model.Output(),
|
||||
],
|
||||
is_experimental=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, model, scale_low, scale_high, freq_cutoff):
|
||||
def custom_cfg_function(args):
|
||||
conds_out = args["conds_out"]
|
||||
if len(conds_out) <= 1 or None in args["conds"][:2]:
|
||||
return conds_out
|
||||
cond = conds_out[0]
|
||||
uncond = conds_out[1]
|
||||
|
||||
guidance = cond - uncond
|
||||
filtered_guidance = Fourier_filter(
|
||||
guidance,
|
||||
scale_low=scale_low,
|
||||
scale_high=scale_high,
|
||||
freq_cutoff=freq_cutoff,
|
||||
)
|
||||
filtered_cond = filtered_guidance + uncond
|
||||
|
||||
return [filtered_cond, uncond] + conds_out[2:]
|
||||
|
||||
m = model.clone()
|
||||
m.set_model_sampler_pre_cfg_function(custom_cfg_function)
|
||||
|
||||
return io.NodeOutput(m)
|
||||
|
||||
|
||||
NODES_LIST = [
|
||||
FreSca,
|
||||
]
|
||||
376
comfy_extras/v3/nodes_gits.py
Normal file
376
comfy_extras/v3/nodes_gits.py
Normal file
@ -0,0 +1,376 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from comfy_api.v3 import io
|
||||
|
||||
|
||||
def loglinear_interp(t_steps, num_steps):
|
||||
"""Performs log-linear interpolation of a given array of decreasing numbers."""
|
||||
xs = np.linspace(0, 1, len(t_steps))
|
||||
ys = np.log(t_steps[::-1])
|
||||
|
||||
new_xs = np.linspace(0, 1, num_steps)
|
||||
new_ys = np.interp(new_xs, xs, ys)
|
||||
|
||||
return np.exp(new_ys)[::-1].copy()
|
||||
|
||||
|
||||
NOISE_LEVELS = {
|
||||
0.80: [
|
||||
[14.61464119, 7.49001646, 0.02916753],
|
||||
[14.61464119, 11.54541874, 6.77309084, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.75677586, 2.84484982, 1.78698075, 0.803307, 0.02916753],
|
||||
],
|
||||
0.85: [
|
||||
[14.61464119, 7.49001646, 0.02916753],
|
||||
[14.61464119, 7.49001646, 1.84880662, 0.02916753],
|
||||
[14.61464119, 11.54541874, 6.77309084, 1.56271636, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.11996698, 3.07277966, 1.24153244, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753],
|
||||
[14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753],
|
||||
[14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.60512662, 2.6383388, 1.56271636, 0.72133851, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
|
||||
],
|
||||
0.90: [
|
||||
[14.61464119, 6.77309084, 0.02916753],
|
||||
[14.61464119, 7.49001646, 1.56271636, 0.02916753],
|
||||
[14.61464119, 7.49001646, 3.07277966, 0.95350921, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 2.54230714, 0.89115214, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 4.86714602, 2.54230714, 0.89115214, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.09240818, 3.07277966, 1.61558151, 0.69515091, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.11996698, 4.86714602, 3.07277966, 1.61558151, 0.69515091, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 2.95596409, 1.61558151, 0.69515091, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.19988537, 1.24153244, 0.57119018, 0.02916753],
|
||||
[14.61464119, 12.96784878, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.19988537, 1.24153244, 0.57119018, 0.02916753],
|
||||
[14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.19988537, 1.24153244, 0.57119018, 0.02916753],
|
||||
[14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.75677586, 2.84484982, 1.84880662, 1.08895338, 0.52423614, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.75677586, 2.84484982, 1.84880662, 1.08895338, 0.52423614, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.44769001, 5.58536053, 4.45427561, 3.32507086, 2.45070267, 1.61558151, 0.95350921, 0.45573691, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.44769001, 5.58536053, 4.45427561, 3.32507086, 2.45070267, 1.61558151, 0.95350921, 0.45573691, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.91689563, 3.07277966, 2.27973175, 1.56271636, 0.95350921, 0.45573691, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.91689563, 3.07277966, 2.27973175, 1.56271636, 0.95350921, 0.45573691, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.91689563, 3.07277966, 2.27973175, 1.56271636, 0.95350921, 0.45573691, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 5.09240818, 4.45427561, 3.60512662, 2.95596409, 2.19988537, 1.51179266, 0.89115214, 0.43325692, 0.02916753],
|
||||
],
|
||||
0.95: [
|
||||
[14.61464119, 6.77309084, 0.02916753],
|
||||
[14.61464119, 6.77309084, 1.56271636, 0.02916753],
|
||||
[14.61464119, 7.49001646, 2.84484982, 0.89115214, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 2.36326075, 0.803307, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 2.95596409, 1.56271636, 0.64427125, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 4.86714602, 2.95596409, 1.56271636, 0.64427125, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 4.86714602, 3.07277966, 1.91321158, 1.08895338, 0.50118381, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.07277966, 1.91321158, 1.08895338, 0.50118381, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 3.07277966, 1.91321158, 1.08895338, 0.50118381, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.19988537, 1.41535246, 0.803307, 0.38853383, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.65472794, 3.46139455, 2.6383388, 1.84880662, 1.24153244, 0.72133851, 0.34370604, 0.02916753],
|
||||
[14.61464119, 12.96784878, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 4.65472794, 3.46139455, 2.6383388, 1.84880662, 1.24153244, 0.72133851, 0.34370604, 0.02916753],
|
||||
[14.61464119, 12.96784878, 10.90732002, 8.75849152, 7.49001646, 6.14220476, 4.86714602, 3.75677586, 2.95596409, 2.19988537, 1.56271636, 1.05362725, 0.64427125, 0.32104823, 0.02916753],
|
||||
[14.61464119, 12.96784878, 10.90732002, 8.75849152, 7.49001646, 6.44769001, 5.58536053, 4.65472794, 3.60512662, 2.95596409, 2.19988537, 1.56271636, 1.05362725, 0.64427125, 0.32104823, 0.02916753],
|
||||
[14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 6.44769001, 5.58536053, 4.65472794, 3.60512662, 2.95596409, 2.19988537, 1.56271636, 1.05362725, 0.64427125, 0.32104823, 0.02916753],
|
||||
[14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 6.44769001, 5.58536053, 4.65472794, 3.75677586, 3.07277966, 2.45070267, 1.78698075, 1.24153244, 0.83188516, 0.50118381, 0.22545385, 0.02916753],
|
||||
[14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 5.09240818, 4.45427561, 3.60512662, 2.95596409, 2.36326075, 1.72759056, 1.24153244, 0.83188516, 0.50118381, 0.22545385, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 5.09240818, 4.45427561, 3.60512662, 2.95596409, 2.36326075, 1.72759056, 1.24153244, 0.83188516, 0.50118381, 0.22545385, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 5.09240818, 4.45427561, 3.75677586, 3.07277966, 2.45070267, 1.91321158, 1.46270394, 1.05362725, 0.72133851, 0.43325692, 0.19894916, 0.02916753],
|
||||
],
|
||||
1.00: [
|
||||
[14.61464119, 1.56271636, 0.02916753],
|
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|
||||
[14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.57119018, 0.4783645, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.59516323, 0.50118381, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
],
|
||||
1.50: [
|
||||
[14.61464119, 0.54755926, 0.02916753],
|
||||
[14.61464119, 0.803307, 0.25053367, 0.02916753],
|
||||
[14.61464119, 0.86115354, 0.32104823, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.61558151, 0.83188516, 0.52423614, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.38853383, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.41087446, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.84880662, 0.95350921, 0.61951244, 0.43325692, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.32549286, 0.86115354, 0.64427125, 0.50118381, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.36964464, 0.92192322, 0.69515091, 0.54755926, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.41535246, 0.95350921, 0.72133851, 0.57119018, 0.4783645, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
class GITSScheduler(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="GITSScheduler_V3",
|
||||
category="sampling/custom_sampling/schedulers",
|
||||
inputs=[
|
||||
io.Float.Input(id="coeff", default=1.20, min=0.80, max=1.50, step=0.05),
|
||||
io.Int.Input(id="steps", default=10, min=2, max=1000),
|
||||
io.Float.Input(id="denoise", default=1.0, min=0.0, max=1.0, step=0.01),
|
||||
],
|
||||
outputs=[
|
||||
io.Sigmas.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, coeff, steps, denoise):
|
||||
total_steps = steps
|
||||
if denoise < 1.0:
|
||||
if denoise <= 0.0:
|
||||
return io.NodeOutput(torch.FloatTensor([]))
|
||||
total_steps = round(steps * denoise)
|
||||
|
||||
if steps <= 20:
|
||||
sigmas = NOISE_LEVELS[round(coeff, 2)][steps-2][:]
|
||||
else:
|
||||
sigmas = NOISE_LEVELS[round(coeff, 2)][-1][:]
|
||||
sigmas = loglinear_interp(sigmas, steps + 1)
|
||||
|
||||
sigmas = sigmas[-(total_steps + 1):]
|
||||
sigmas[-1] = 0
|
||||
return io.NodeOutput(torch.FloatTensor(sigmas))
|
||||
|
||||
|
||||
NODES_LIST = [
|
||||
GITSScheduler,
|
||||
]
|
||||
148
comfy_extras/v3/nodes_rebatch.py
Normal file
148
comfy_extras/v3/nodes_rebatch.py
Normal file
@ -0,0 +1,148 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import torch
|
||||
|
||||
from comfy_api.v3 import io
|
||||
|
||||
|
||||
class ImageRebatch(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="RebatchImages_V3",
|
||||
display_name="Rebatch Images _V3",
|
||||
category="image/batch",
|
||||
is_input_list=True,
|
||||
inputs=[
|
||||
io.Image.Input("images"),
|
||||
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
||||
],
|
||||
outputs=[
|
||||
io.Image.Output("IMAGE", display_name="IMAGE", is_output_list=True),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, images, batch_size):
|
||||
batch_size = batch_size[0]
|
||||
|
||||
output_list = []
|
||||
all_images = []
|
||||
for img in images:
|
||||
for i in range(img.shape[0]):
|
||||
all_images.append(img[i:i+1])
|
||||
|
||||
for i in range(0, len(all_images), batch_size):
|
||||
output_list.append(torch.cat(all_images[i:i+batch_size], dim=0))
|
||||
|
||||
return io.NodeOutput(output_list)
|
||||
|
||||
|
||||
class LatentRebatch(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="RebatchLatents_V3",
|
||||
display_name="Rebatch Latents _V3",
|
||||
category="latent/batch",
|
||||
is_input_list=True,
|
||||
inputs=[
|
||||
io.Latent.Input("latents"),
|
||||
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
||||
],
|
||||
outputs=[
|
||||
io.Latent.Output(is_output_list=True),
|
||||
],
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def get_batch(latents, list_ind, offset):
|
||||
"""prepare a batch out of the list of latents"""
|
||||
samples = latents[list_ind]['samples']
|
||||
shape = samples.shape
|
||||
mask = latents[list_ind]['noise_mask'] if 'noise_mask' in latents[list_ind] else torch.ones((shape[0], 1, shape[2]*8, shape[3]*8), device='cpu')
|
||||
if mask.shape[-1] != shape[-1] * 8 or mask.shape[-2] != shape[-2]:
|
||||
torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[-2]*8, shape[-1]*8), mode="bilinear")
|
||||
if mask.shape[0] < samples.shape[0]:
|
||||
mask = mask.repeat((shape[0] - 1) // mask.shape[0] + 1, 1, 1, 1)[:shape[0]]
|
||||
if 'batch_index' in latents[list_ind]:
|
||||
batch_inds = latents[list_ind]['batch_index']
|
||||
else:
|
||||
batch_inds = [x+offset for x in range(shape[0])]
|
||||
return samples, mask, batch_inds
|
||||
|
||||
@staticmethod
|
||||
def get_slices(indexable, num, batch_size):
|
||||
"""divides an indexable object into num slices of length batch_size, and a remainder"""
|
||||
slices = []
|
||||
for i in range(num):
|
||||
slices.append(indexable[i*batch_size:(i+1)*batch_size])
|
||||
if num * batch_size < len(indexable):
|
||||
return slices, indexable[num * batch_size:]
|
||||
else:
|
||||
return slices, None
|
||||
|
||||
@staticmethod
|
||||
def slice_batch(batch, num, batch_size):
|
||||
result = [LatentRebatch.get_slices(x, num, batch_size) for x in batch]
|
||||
return list(zip(*result))
|
||||
|
||||
@staticmethod
|
||||
def cat_batch(batch1, batch2):
|
||||
if batch1[0] is None:
|
||||
return batch2
|
||||
result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)]
|
||||
return result
|
||||
|
||||
@classmethod
|
||||
def execute(cls, latents, batch_size):
|
||||
batch_size = batch_size[0]
|
||||
|
||||
output_list = []
|
||||
current_batch = (None, None, None)
|
||||
processed = 0
|
||||
|
||||
for i in range(len(latents)):
|
||||
# fetch new entry of list
|
||||
#samples, masks, indices = self.get_batch(latents, i)
|
||||
next_batch = cls.get_batch(latents, i, processed)
|
||||
processed += len(next_batch[2])
|
||||
# set to current if current is None
|
||||
if current_batch[0] is None:
|
||||
current_batch = next_batch
|
||||
# add previous to list if dimensions do not match
|
||||
elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]:
|
||||
sliced, _ = cls.slice_batch(current_batch, 1, batch_size)
|
||||
output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]})
|
||||
current_batch = next_batch
|
||||
# cat if everything checks out
|
||||
else:
|
||||
current_batch = cls.cat_batch(current_batch, next_batch)
|
||||
|
||||
# add to list if dimensions gone above target batch size
|
||||
if current_batch[0].shape[0] > batch_size:
|
||||
num = current_batch[0].shape[0] // batch_size
|
||||
sliced, remainder = cls.slice_batch(current_batch, num, batch_size)
|
||||
|
||||
for i in range(num):
|
||||
output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]})
|
||||
|
||||
current_batch = remainder
|
||||
|
||||
#add remainder
|
||||
if current_batch[0] is not None:
|
||||
sliced, _ = cls.slice_batch(current_batch, 1, batch_size)
|
||||
output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]})
|
||||
|
||||
#get rid of empty masks
|
||||
for s in output_list:
|
||||
if s['noise_mask'].mean() == 1.0:
|
||||
del s['noise_mask']
|
||||
|
||||
return io.NodeOutput(output_list)
|
||||
|
||||
|
||||
NODES_LIST = [
|
||||
ImageRebatch,
|
||||
LatentRebatch,
|
||||
]
|
||||
15
nodes.py
15
nodes.py
@ -2302,14 +2302,27 @@ def init_builtin_extra_nodes():
|
||||
"v3/nodes_ace.py",
|
||||
"v3/nodes_advanced_samplers.py",
|
||||
"v3/nodes_align_your_steps.py",
|
||||
"v3/nodes_audio.py",
|
||||
"v3/nodes_apg.py",
|
||||
"v3/nodes_attention_multiply.py",
|
||||
"v3/nodes_audio.py",
|
||||
"v3/nodes_camera_trajectory.py",
|
||||
"v3/nodes_canny.py",
|
||||
"v3/nodes_cfg.py",
|
||||
"v3/nodes_clip_sdxl.py",
|
||||
"v3/nodes_compositing.py",
|
||||
"v3/nodes_cond.py",
|
||||
"v3/nodes_controlnet.py",
|
||||
"v3/nodes_cosmos.py",
|
||||
"v3/nodes_differential_diffusion.py",
|
||||
"v3/nodes_flux.py",
|
||||
"v3/nodes_freelunch.py",
|
||||
"v3/nodes_fresca.py",
|
||||
"v3/nodes_gits.py",
|
||||
"v3/nodes_images.py",
|
||||
"v3/nodes_mask.py",
|
||||
"v3/nodes_preview_any.py",
|
||||
"v3/nodes_primitive.py",
|
||||
"v3/nodes_rebatch.py",
|
||||
"v3/nodes_stable_cascade.py",
|
||||
"v3/nodes_webcam.py",
|
||||
]
|
||||
|
||||
@ -23,9 +23,8 @@ lint.select = [
|
||||
# See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f
|
||||
"F",
|
||||
]
|
||||
lint.ignore = ["E501"] # disable line-length checking
|
||||
exclude = ["*.ipynb"]
|
||||
line-length = 144
|
||||
lint.pycodestyle.ignore-overlong-task-comments = true
|
||||
|
||||
[tool.ruff.lint.per-file-ignores]
|
||||
"!comfy_extras/v3/*" = ["E", "I"] # enable these rules only for V3 nodes
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user