nodes/viperyl-ComfyUI-RGT
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Xu Yang
2024-06-01 11:44:58 +08:00
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.gitignore
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.DS_Store
README.md
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# ComfyUI-RGT
This repo cast **Recursive Generalization Transformer for Image Super-Resolution** to ComfyUI, the original [paper link](https://arxiv.org/abs/2303.06373) and [github link]([zhengchen1999/RGT: PyTorch code for our ICLR 2024 paper "Recursive Generalization Transformer for Image Super-Resolution" (github.com)](https://github.com/zhengchen1999/RGT))
# Usage
## Install
```
cd comfyui/custom_nodes
git clone https://github.com/viperyl/ComfyUI-RGT.git
cd ComfyUI-RGT
pip install -r requirements.txt
```
model download from huggingface
soon
## Parameters
```
use_chop: process image with tiled operation, save lots vram
```
__init__.py
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import os
import folder_paths
from .node import NODE_CLASS_MAPPINGS as NODE_CLASS_MAPPINGS_0, NODE_DISPLAY_NAME_MAPPINGS as NODE_DISPLAY_NAME_MAPPINGS_0
NODE_CLASS_MAPPINGS = {
**NODE_CLASS_MAPPINGS_0,
}
NODE_DISPLAY_NAME_MAPPINGS = {
**NODE_DISPLAY_NAME_MAPPINGS_0,
}
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
model.py
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import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from torch import Tensor
from torch.nn import functional as F
from torchvision.utils import make_grid
from timm.models.layers import DropPath, trunc_normal_
from einops.layers.torch import Rearrange
from einops import rearrange, repeat
import math
import numpy as np
import random
from typing import Union
import cv2
from loguru import logger
def img2windows(img, H_sp, W_sp):
"""
Input: Image (B, C, H, W)
Output: Window Partition (B', N, C)
"""
B, C, H, W = img.shape
img_reshape = img.view(B, C, H // H_sp, H_sp, W // W_sp, W_sp)
img_perm = img_reshape.permute(0, 2, 4, 3, 5, 1).contiguous().reshape(-1, H_sp* W_sp, C)
return img_perm
def windows2img(img_splits_hw, H_sp, W_sp, H, W):
"""
Input: Window Partition (B', N, C)
Output: Image (B, H, W, C)
"""
B = int(img_splits_hw.shape[0] / (H * W / H_sp / W_sp))
img = img_splits_hw.view(B, H // H_sp, W // W_sp, H_sp, W_sp, -1)
img = img.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
return img
class Gate(nn.Module):
def __init__(self, dim):
super().__init__()
self.norm = nn.LayerNorm(dim)
self.conv = nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim) # DW Conv
def forward(self, x, H, W):
# Split
x1, x2 = x.chunk(2, dim = -1)
B, N, C = x.shape
x2 = self.conv(self.norm(x2).transpose(1, 2).contiguous().view(B, C//2, H, W)).flatten(2).transpose(-1, -2).contiguous()
return x1 * x2
class MLP(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.sg = Gate(hidden_features//2)
self.fc2 = nn.Linear(hidden_features//2, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x, H, W):
"""
Input: x: (B, H*W, C), H, W
Output: x: (B, H*W, C)
"""
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.sg(x, H, W)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class DynamicPosBias(nn.Module):
# The implementation builds on Crossformer code https://github.com/cheerss/CrossFormer/blob/main/models/crossformer.py
""" Dynamic Relative Position Bias.
Args:
dim (int): Number of input channels.
num_heads (int): Number of attention heads.
residual (bool): If True, use residual strage to connect conv.
"""
def __init__(self, dim, num_heads, residual):
super().__init__()
self.residual = residual
self.num_heads = num_heads
self.pos_dim = dim // 4
self.pos_proj = nn.Linear(2, self.pos_dim)
self.pos1 = nn.Sequential(
nn.LayerNorm(self.pos_dim),
nn.ReLU(inplace=True),
nn.Linear(self.pos_dim, self.pos_dim),
)
self.pos2 = nn.Sequential(
nn.LayerNorm(self.pos_dim),
nn.ReLU(inplace=True),
nn.Linear(self.pos_dim, self.pos_dim)
)
self.pos3 = nn.Sequential(
nn.LayerNorm(self.pos_dim),
nn.ReLU(inplace=True),
nn.Linear(self.pos_dim, self.num_heads)
)
def forward(self, biases):
if self.residual:
pos = self.pos_proj(biases) # 2Gh-1 * 2Gw-1, heads
pos = pos + self.pos1(pos)
pos = pos + self.pos2(pos)
pos = self.pos3(pos)
else:
pos = self.pos3(self.pos2(self.pos1(self.pos_proj(biases))))
return pos
class WindowAttention(nn.Module):
def __init__(self, dim, idx, split_size=[8,8], dim_out=None, num_heads=6, attn_drop=0., proj_drop=0., qk_scale=None, position_bias=True):
super().__init__()
self.dim = dim
self.dim_out = dim_out or dim
self.split_size = split_size
self.num_heads = num_heads
self.idx = idx
self.position_bias = position_bias
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
if idx == 0:
H_sp, W_sp = self.split_size[0], self.split_size[1]
elif idx == 1:
W_sp, H_sp = self.split_size[0], self.split_size[1]
else:
print ("ERROR MODE", idx)
exit(0)
self.H_sp = H_sp
self.W_sp = W_sp
if self.position_bias:
self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False)
# generate mother-set
position_bias_h = torch.arange(1 - self.H_sp, self.H_sp)
position_bias_w = torch.arange(1 - self.W_sp, self.W_sp)
biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w]))
biases = biases.flatten(1).transpose(0, 1).contiguous().float()
self.register_buffer('rpe_biases', biases)
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(self.H_sp)
coords_w = torch.arange(self.W_sp)
coords = torch.stack(torch.meshgrid([coords_h, coords_w]))
coords_flatten = torch.flatten(coords, 1)
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
relative_coords = relative_coords.permute(1, 2, 0).contiguous()
relative_coords[:, :, 0] += self.H_sp - 1
relative_coords[:, :, 1] += self.W_sp - 1
relative_coords[:, :, 0] *= 2 * self.W_sp - 1
relative_position_index = relative_coords.sum(-1)
self.register_buffer('relative_position_index', relative_position_index)
self.attn_drop = nn.Dropout(attn_drop)
def im2win(self, x, H, W):
B, N, C = x.shape
x = x.transpose(-2,-1).contiguous().view(B, C, H, W)
x = img2windows(x, self.H_sp, self.W_sp)
x = x.reshape(-1, self.H_sp* self.W_sp, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3).contiguous()
return x
def forward(self, qkv, H, W, mask=None):
"""
Input: qkv: (B, 3*L, C), H, W, mask: (B, N, N), N is the window size
Output: x (B, H, W, C)
"""
q,k,v = qkv[0], qkv[1], qkv[2]
B, L, C = q.shape
assert L == H * W, "flatten img_tokens has wrong size"
# partition the q,k,v, image to window
q = self.im2win(q, H, W)
k = self.im2win(k, H, W)
v = self.im2win(v, H, W)
q = q * self.scale
attn = (q @ k.transpose(-2, -1)) # B head N C @ B head C N --> B head N N
# calculate drpe
if self.position_bias:
pos = self.pos(self.rpe_biases)
# select position bias
relative_position_bias = pos[self.relative_position_index.view(-1)].view(
self.H_sp * self.W_sp, self.H_sp * self.W_sp, -1)
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
attn = attn + relative_position_bias.unsqueeze(0)
N = attn.shape[3]
# use mask for shift window
if mask is not None:
nW = mask.shape[0]
attn = attn.view(B, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
attn = attn.view(-1, self.num_heads, N, N)
attn = nn.functional.softmax(attn, dim=-1, dtype=attn.dtype)
attn = self.attn_drop(attn)
x = (attn @ v)
x = x.transpose(1, 2).reshape(-1, self.H_sp* self.W_sp, C) # B head N N @ B head N C
# merge the window, window to image
x = windows2img(x, self.H_sp, self.W_sp, H, W) # B H' W' C
return x
class L_SA(nn.Module):
# The implementation builds on CAT code https://github.com/zhengchen1999/CAT/blob/main/basicsr/archs/cat_arch.py
def __init__(self, dim, num_heads,
split_size=[2,4], shift_size=[1,2], qkv_bias=False, qk_scale=None,
drop=0., attn_drop=0., idx=0, reso=64, rs_id=0):
super().__init__()
self.dim = dim
self.num_heads = num_heads
self.split_size = split_size
self.shift_size = shift_size
self.idx = idx
self.rs_id = rs_id
self.patches_resolution = reso
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
assert 0 <= self.shift_size[0] < self.split_size[0], "shift_size must in 0-split_size0"
assert 0 <= self.shift_size[1] < self.split_size[1], "shift_size must in 0-split_size1"
self.branch_num = 2
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(drop)
self.attns = nn.ModuleList([
WindowAttention(
dim//2, idx = i,
split_size=split_size, num_heads=num_heads//2, dim_out=dim//2,
qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, position_bias=True)
for i in range(self.branch_num)])
if (self.rs_id % 2 == 0 and self.idx > 0 and (self.idx - 2) % 4 == 0) or (self.rs_id % 2 != 0 and self.idx % 4 == 0):
attn_mask = self.calculate_mask(self.patches_resolution, self.patches_resolution)
self.register_buffer("attn_mask_0", attn_mask[0])
self.register_buffer("attn_mask_1", attn_mask[1])
else:
attn_mask = None
self.register_buffer("attn_mask_0", None)
self.register_buffer("attn_mask_1", None)
self.get_v = nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1,groups=dim) # DW Conv
def calculate_mask(self, H, W):
# The implementation builds on Swin Transformer code https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py
# calculate attention mask for Rwin
img_mask_0 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=0
img_mask_1 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=1
h_slices_0 = (slice(0, -self.split_size[0]),
slice(-self.split_size[0], -self.shift_size[0]),
slice(-self.shift_size[0], None))
w_slices_0 = (slice(0, -self.split_size[1]),
slice(-self.split_size[1], -self.shift_size[1]),
slice(-self.shift_size[1], None))
h_slices_1 = (slice(0, -self.split_size[1]),
slice(-self.split_size[1], -self.shift_size[1]),
slice(-self.shift_size[1], None))
w_slices_1 = (slice(0, -self.split_size[0]),
slice(-self.split_size[0], -self.shift_size[0]),
slice(-self.shift_size[0], None))
cnt = 0
for h in h_slices_0:
for w in w_slices_0:
img_mask_0[:, h, w, :] = cnt
cnt += 1
cnt = 0
for h in h_slices_1:
for w in w_slices_1:
img_mask_1[:, h, w, :] = cnt
cnt += 1
# calculate mask for H-Shift
img_mask_0 = img_mask_0.view(1, H // self.split_size[0], self.split_size[0], W // self.split_size[1], self.split_size[1], 1)
img_mask_0 = img_mask_0.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, self.split_size[0], self.split_size[1], 1) # nW, sw[0], sw[1], 1
mask_windows_0 = img_mask_0.view(-1, self.split_size[0] * self.split_size[1])
attn_mask_0 = mask_windows_0.unsqueeze(1) - mask_windows_0.unsqueeze(2)
attn_mask_0 = attn_mask_0.masked_fill(attn_mask_0 != 0, float(-100.0)).masked_fill(attn_mask_0 == 0, float(0.0))
# calculate mask for V-Shift
img_mask_1 = img_mask_1.view(1, H // self.split_size[1], self.split_size[1], W // self.split_size[0], self.split_size[0], 1)
img_mask_1 = img_mask_1.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, self.split_size[1], self.split_size[0], 1) # nW, sw[1], sw[0], 1
mask_windows_1 = img_mask_1.view(-1, self.split_size[1] * self.split_size[0])
attn_mask_1 = mask_windows_1.unsqueeze(1) - mask_windows_1.unsqueeze(2)
attn_mask_1 = attn_mask_1.masked_fill(attn_mask_1 != 0, float(-100.0)).masked_fill(attn_mask_1 == 0, float(0.0))
return attn_mask_0, attn_mask_1
def forward(self, x, H, W):
"""
Input: x: (B, H*W, C), x_size: (H, W)
Output: x: (B, H*W, C)
"""
B, L, C = x.shape
assert L == H * W, "flatten img_tokens has wrong size"
qkv = self.qkv(x).reshape(B, -1, 3, C).permute(2, 0, 1, 3) # 3, B, HW, C
# v without partition
v = qkv[2].transpose(-2,-1).contiguous().view(B, C, H, W)
max_split_size = max(self.split_size[0], self.split_size[1])
pad_l = pad_t = 0
pad_r = (max_split_size - W % max_split_size) % max_split_size
pad_b = (max_split_size - H % max_split_size) % max_split_size
qkv = qkv.reshape(3*B, H, W, C).permute(0, 3, 1, 2) # 3B C H W
qkv = F.pad(qkv, (pad_l, pad_r, pad_t, pad_b)).reshape(3, B, C, -1).transpose(-2, -1) # l r t b
_H = pad_b + H
_W = pad_r + W
_L = _H * _W
if (self.rs_id % 2 == 0 and self.idx > 0 and (self.idx - 2) % 4 == 0) or (self.rs_id % 2 != 0 and self.idx % 4 == 0):
qkv = qkv.view(3, B, _H, _W, C)
# H-Shift
qkv_0 = torch.roll(qkv[:,:,:,:,:C//2], shifts=(-self.shift_size[0], -self.shift_size[1]), dims=(2, 3))
qkv_0 = qkv_0.view(3, B, _L, C//2)
# V-Shift
qkv_1 = torch.roll(qkv[:,:,:,:,C//2:], shifts=(-self.shift_size[1], -self.shift_size[0]), dims=(2, 3))
qkv_1 = qkv_1.view(3, B, _L, C//2)
if self.patches_resolution != _H or self.patches_resolution != _W:
mask_tmp = self.calculate_mask(_H, _W)
# H-Rwin
x1_shift = self.attns[0](qkv_0, _H, _W, mask=mask_tmp[0].to(x.device))
# V-Rwin
x2_shift = self.attns[1](qkv_1, _H, _W, mask=mask_tmp[1].to(x.device))
else:
# H-Rwin
x1_shift = self.attns[0](qkv_0, _H, _W, mask=self.attn_mask_0)
# V-Rwin
x2_shift = self.attns[1](qkv_1, _H, _W, mask=self.attn_mask_1)
x1 = torch.roll(x1_shift, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2))
x2 = torch.roll(x2_shift, shifts=(self.shift_size[1], self.shift_size[0]), dims=(1, 2))
x1 = x1[:, :H, :W, :].reshape(B, L, C//2)
x2 = x2[:, :H, :W, :].reshape(B, L, C//2)
# Concat
attened_x = torch.cat([x1,x2], dim=2)
else:
# V-Rwin
x1 = self.attns[0](qkv[:,:,:,:C//2], _H, _W)[:, :H, :W, :].reshape(B, L, C//2)
# H-Rwin
x2 = self.attns[1](qkv[:,:,:,C//2:], _H, _W)[:, :H, :W, :].reshape(B, L, C//2)
# Concat
attened_x = torch.cat([x1,x2], dim=2)
# mix
lcm = self.get_v(v)
lcm = lcm.permute(0, 2, 3, 1).contiguous().view(B, L, C)
x = attened_x + lcm
x = self.proj(x)
x = self.proj_drop(x)
return x
class RG_SA(nn.Module):
"""
Recursive-Generalization Self-Attention (RG-SA).
Args:
dim (int): Number of input channels.
num_heads (int): Number of attention heads.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
c_ratio (float): channel adjustment factor.
"""
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., c_ratio=0.5):
super(RG_SA, self).__init__()
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
self.num_heads = num_heads
head_dim = dim // num_heads
self.cr = int(dim * c_ratio) # scaled channel dimension
# self.scale = qk_scale or head_dim ** -0.5
self.scale = qk_scale or (head_dim * c_ratio) ** -0.5
# RGM
self.reduction1 = nn.Conv2d(dim, dim, kernel_size=4, stride=4, groups=dim)
self.dwconv = nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1,groups=dim)
self.conv = nn.Conv2d(dim, self.cr, kernel_size=1, stride=1)
self.norm_act = nn.Sequential(
nn.LayerNorm(self.cr),
nn.GELU())
# CA
self.q = nn.Linear(dim, self.cr, bias=qkv_bias)
self.k = nn.Linear(self.cr, self.cr, bias=qkv_bias)
self.v = nn.Linear(self.cr, dim, bias=qkv_bias)
# CPE
self.cpe = nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim)
self.proj = nn.Linear(dim, dim)
self.attn_drop = nn.Dropout(attn_drop)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, H, W):
B, N, C = x.shape
_scale = 1
# reduction
_x = x.permute(0, 2, 1).reshape(B, C, H, W).contiguous()
if self.training:
_time = max(int(math.log(H//4, 4)), int(math.log(W//4, 4)))
else:
_time = max(int(math.log(H//16, 4)), int(math.log(W//16, 4)))
if _time < 2: _time = 2 # testing _time must equal or larger than training _time (2)
_scale = 4 ** _time
# Recursion xT
for _ in range(_time):
_x = self.reduction1(_x)
_x = self.conv(self.dwconv(_x)).reshape(B, self.cr, -1).permute(0, 2, 1).contiguous() # shape=(B, N', C')
_x = self.norm_act(_x)
# q, k, v, where q_shape=(B, N, C'), k_shape=(B, N', C'), v_shape=(B, N', C)
q = self.q(x).reshape(B, N, self.num_heads, int(self.cr / self.num_heads)).permute(0, 2, 1, 3)
k = self.k(_x).reshape(B, -1, self.num_heads, int(self.cr / self.num_heads)).permute(0, 2, 1, 3)
v = self.v(_x).reshape(B, -1, self.num_heads, int(C / self.num_heads)).permute(0, 2, 1, 3)
# corss-attention
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
# CPE
# v_shape=(B, H, N', C//H)
v = v + self.cpe(v.transpose(1, 2).reshape(B, -1, C).transpose(1, 2).contiguous().view(B, C, H // _scale, W // _scale)).view(B, C, -1).view(B, self.num_heads, int(C / self.num_heads), -1).transpose(-1, -2)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0.,
attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, idx=0,
rs_id=0, split_size=[2,4], shift_size=[1,2], reso=64, c_ratio=0.5, layerscale_value=1e-4):
super().__init__()
self.norm1 = norm_layer(dim)
if idx % 2 == 0:
self.attn = L_SA(
dim, split_size=split_size, shift_size=shift_size, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
drop=drop, idx=idx, reso=reso, rs_id=rs_id
)
else:
self.attn = RG_SA(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
proj_drop=drop, c_ratio=c_ratio
)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, out_features=dim, act_layer=act_layer)
self.norm2 = norm_layer(dim)
# HAI
self.gamma = nn.Parameter(layerscale_value * torch.ones((dim)), requires_grad=True)
def forward(self, x, x_size):
H , W = x_size
res = x
x = x + self.drop_path(self.attn(self.norm1(x), H, W))
x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
# HAI
x = x + (res * self.gamma)
return x
class ResidualGroup(nn.Module):
def __init__( self,
dim,
reso,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
qk_scale=None,
drop=0.,
attn_drop=0.,
drop_paths=None,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
depth=2,
use_chk=False,
resi_connection='1conv',
rs_id=0,
split_size=[8,8],
c_ratio = 0.5):
super().__init__()
self.use_chk = use_chk
self.reso = reso
self.blocks = nn.ModuleList([
Block(
dim=dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop,
attn_drop=attn_drop,
drop_path=drop_paths[i],
act_layer=act_layer,
norm_layer=norm_layer,
idx = i,
rs_id = rs_id,
split_size = split_size,
shift_size = [split_size[0]//2, split_size[1]//2],
c_ratio = c_ratio,
)for i in range(depth)])
if resi_connection == '1conv':
self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
elif resi_connection == '3conv':
self.conv = nn.Sequential(
nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(dim // 4, dim, 3, 1, 1))
def forward(self, x, x_size):
"""
Input: x: (B, H*W, C), x_size: (H, W)
Output: x: (B, H*W, C)
"""
H, W = x_size
res = x
for blk in self.blocks:
if self.use_chk:
x = checkpoint.checkpoint(blk, x, x_size)
else:
x = blk(x, x_size)
x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W)
x = self.conv(x)
x = rearrange(x, "b c h w -> b (h w) c")
x = res + x
return x
class Upsample(nn.Sequential):
"""Upsample module.
Args:
scale (int): Scale factor. Supported scales: 2^n and 3.
num_feat (int): Channel number of intermediate features.
"""
def __init__(self, scale, num_feat):
m = []
if (scale & (scale - 1)) == 0: # scale = 2^n
for _ in range(int(math.log(scale, 2))):
m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
m.append(nn.PixelShuffle(2))
elif scale == 3:
m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
m.append(nn.PixelShuffle(3))
else:
raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
super(Upsample, self).__init__(*m)
class RGT(nn.Module):
def __init__(self,
img_size=64,
in_chans=3,
embed_dim=180,
depth=[2,2,2,2],
num_heads=[2,2,2,2],
mlp_ratio=4.,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.1,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
use_chk=False,
upscale=2,
img_range=1.,
resi_connection='1conv',
split_size=[8,8],
c_ratio=0.5,
**kwargs):
super().__init__()
num_in_ch = in_chans
num_out_ch = in_chans
num_feat = 64
self.img_range = img_range
if in_chans == 3:
rgb_mean = (0.4488, 0.4371, 0.4040)
self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
else:
self.mean = torch.zeros(1, 1, 1, 1)
self.upscale = upscale
# ------------------------- 1, Shallow Feature Extraction ------------------------- #
self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
# ------------------------- 2, Deep Feature Extraction ------------------------- #
self.num_layers = len(depth)
self.use_chk = use_chk
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
heads=num_heads
self.before_RG = nn.Sequential(
Rearrange('b c h w -> b (h w) c'),
nn.LayerNorm(embed_dim)
)
curr_dim = embed_dim
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, np.sum(depth))] # stochastic depth decay rule
self.layers = nn.ModuleList()
for i in range(self.num_layers):
layer = ResidualGroup(
dim=embed_dim,
num_heads=heads[i],
reso=img_size,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_paths=dpr[sum(depth[:i]):sum(depth[:i + 1])],
act_layer=act_layer,
norm_layer=norm_layer,
depth=depth[i],
use_chk=use_chk,
resi_connection=resi_connection,
rs_id=i,
split_size = split_size,
c_ratio = c_ratio
)
self.layers.append(layer)
self.norm = norm_layer(curr_dim)
# build the last conv layer in deep feature extraction
if resi_connection == '1conv':
self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
elif resi_connection == '3conv':
# to save parameters and memory
self.conv_after_body = nn.Sequential(
nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
# ------------------------- 3, Reconstruction ------------------------- #
self.conv_before_upsample = nn.Sequential(
nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True))
self.upsample = Upsample(upscale, num_feat)
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
def forward_features(self, x):
_, _, H, W = x.shape
x_size = [H, W]
x = self.before_RG(x)
for layer in self.layers:
x = layer(x, x_size)
x = self.norm(x)
x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W)
return x
def forward(self, x):
"""
Input: x: (B, C, H, W)
"""
self.mean = self.mean.type_as(x)
x = (x - self.mean) * self.img_range
x = self.conv_first(x)
x = self.conv_after_body(self.forward_features(x)) + x
x = self.conv_before_upsample(x)
x = self.conv_last(self.upsample(x))
x = x / self.img_range + self.mean
return x
def img2tensor(imgs, bgr2rgb=True, float32=True):
"""Numpy array to tensor.
Args:
imgs (list[ndarray] | ndarray): Input images.
bgr2rgb (bool): Whether to change bgr to rgb.
float32 (bool): Whether to change to float32.
Returns:
list[tensor] | tensor: Tensor images. If returned results only have
one element, just return tensor.
"""
def _totensor(img, bgr2rgb, float32):
if img.shape[2] == 3 and bgr2rgb:
if img.dtype == 'float64':
img = img.astype('float32')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = torch.from_numpy(img.transpose(2, 0, 1))
if float32:
img = img.float()
return img
if isinstance(imgs, list):
return [_totensor(img, bgr2rgb, float32) for img in imgs]
else:
return _totensor(imgs, bgr2rgb, float32)
def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
"""Convert torch Tensors into image numpy arrays.
After clamping to [min, max], values will be normalized to [0, 1].
Args:
tensor (Tensor or list[Tensor]): Accept shapes:
1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
2) 3D Tensor of shape (3/1 x H x W);
3) 2D Tensor of shape (H x W).
Tensor channel should be in RGB order.
rgb2bgr (bool): Whether to change rgb to bgr.
out_type (numpy type): output types. If ``np.uint8``, transform outputs
to uint8 type with range [0, 255]; otherwise, float type with
range [0, 1]. Default: ``np.uint8``.
min_max (tuple[int]): min and max values for clamp.
Returns:
(Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of
shape (H x W). The channel order is BGR.
"""
if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')
if torch.is_tensor(tensor):
tensor = [tensor]
result = []
for _tensor in tensor:
_tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
_tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])
n_dim = _tensor.dim()
if n_dim == 4:
img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()
img_np = img_np.transpose(1, 2, 0)
if rgb2bgr:
img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
elif n_dim == 3:
img_np = _tensor.numpy()
img_np = img_np.transpose(1, 2, 0)
if img_np.shape[2] == 1: # gray image
img_np = np.squeeze(img_np, axis=2)
else:
if rgb2bgr:
img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
elif n_dim == 2:
img_np = _tensor.numpy()
else:
raise TypeError(f'Only support 4D, 3D or 2D tensor. But received with dimension: {n_dim}')
if out_type == np.uint8:
# Unlike MATLAB, numpy.unit8() WILL NOT round by default.
img_np = (img_np * 255.0).round()
img_np = img_np.astype(out_type)
result.append(img_np)
if len(result) == 1:
result = result[0]
return result
def data_pre_preprocess(image):
img = cv2.imread(image)
img = img.astype(np.float32) / 255.
img_lq = img2tensor(img, bgr2rgb=True, float32=True)
return img_lq
class RGTModel:
def __init__(self, name:str, upscale:int, device:str, model_path:str):
self.name = None
self.upscale = None
self. v = None
self.device = None
self.load_model(name, upscale, device, model_path)
def load_model(self, name, upscale, device, model_path):
if self.name == name and self.upscale == upscale and self.device == upscale:
logger.debug(f"Nothing changed, continued use loaded model")
return
# init model by config
if name == "RGT":
num_heads = depth = [6,6,6,6,6,6,6,6]
elif name == "RGT_S":
num_heads = depth = [6,6,6,6,6,6]
else:
raise ValueError(f"model name wrong, should be RGT or RGT-S, but your input is: {name}")
self.device = device
self.upscale = upscale
self.net_g = RGT(
img_size=64,
in_chans=3,
embed_dim=180,
depth=depth,
num_heads=num_heads,
mlp_ratio=2,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.1,
use_chk=False,
upscale=upscale,
img_range=1.,
split_size=[8,32]
)
logger.debug(f"checkpoints path:{model_path}")
checkpoints = torch.load(model_path)
self.net_g.load_state_dict(checkpoints["params"])
self.net_g.to(torch.device(device))
self.net_g.eval()
def __call__(self, image: Union[str, torch.Tensor], use_chop: bool = True):
if isinstance(image, str):
image = data_pre_preprocess(image)
logger.debug(f"image: {image.shape}")
image = image.to(self.device)
if use_chop:
_, C, h, w = image.size()
split_token_h = h // 200 + 1 # number of horizontal cut sections
split_token_w = w // 200 + 1 # number of vertical cut sections
patch_size_tmp_h = split_token_h
patch_size_tmp_w = split_token_w
# padding
mod_pad_h, mod_pad_w = 0, 0
if h % patch_size_tmp_h != 0:
mod_pad_h = patch_size_tmp_h - h % patch_size_tmp_h
if w % patch_size_tmp_w != 0:
mod_pad_w = patch_size_tmp_w - w % patch_size_tmp_w
img = image
img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, :h+mod_pad_h, :]
img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, :w+mod_pad_w]
_, _, H, W = img.size()
split_h = H // split_token_h # height of each partition
split_w = W // split_token_w # width of each partition
# overlapping
shave_h = 16
shave_w = 16
scale = self.upscale
ral = H // split_h
row = W // split_w
slices = [] # list of partition borders
for i in range(ral):
for j in range(row):
if i == 0 and i == ral - 1:
top = slice(i * split_h, (i + 1) * split_h)
elif i == 0:
top = slice(i*split_h, (i+1)*split_h+shave_h)
elif i == ral - 1:
top = slice(i*split_h-shave_h, (i+1)*split_h)
else:
top = slice(i*split_h-shave_h, (i+1)*split_h+shave_h)
if j == 0 and j == row - 1:
left = slice(j*split_w, (j+1)*split_w)
elif j == 0:
left = slice(j*split_w, (j+1)*split_w+shave_w)
elif j == row - 1:
left = slice(j*split_w-shave_w, (j+1)*split_w)
else:
left = slice(j*split_w-shave_w, (j+1)*split_w+shave_w)
temp = (top, left)
slices.append(temp)
img_chops = [] # list of partitions
for temp in slices:
top, left = temp
img_chops.append(img[..., top, left])
with torch.no_grad():
outputs = []
for chop in img_chops:
out = self.net_g(chop) # image processing of each partition
outputs.append(out)
_img = torch.zeros(1, C, H * scale, W * scale)
# merge
for i in range(ral):
for j in range(row):
top = slice(i * split_h * scale, (i + 1) * split_h * scale)
left = slice(j * split_w * scale, (j + 1) * split_w * scale)
if i == 0:
_top = slice(0, split_h * scale)
else:
_top = slice(shave_h * scale, (shave_h + split_h) * scale)
if j == 0:
_left = slice(0, split_w * scale)
else:
_left = slice(shave_w * scale, (shave_w + split_w) * scale)
_img[..., top, left] = outputs[i * row + j][..., _top, _left]
output = _img
self.net_g.train()
_, _, h, w = output.size()
output = output[:, :, 0:h - mod_pad_h * scale, 0:w - mod_pad_w * scale]
else:
with torch.no_grad():
output = self.net_g(image)
return output
if __name__ == "__main__":
model_path = "/root/code/ComfyUI/models/RGT/RGT/RGT_x2.pth"
rgt_model = RGTModel(name="RGT", upscale=2, device="cuda:0", model_path=model_path)
res = rgt_model(image="/root/code/RGT/datasets/single/test_4.png", use_chop=True)
# index = 0
# for name in ["RGT", "RGT_S"]:
# for upsacle in [2, 3, 4]:
# rgt_model = RGTModel(name=name, upscale=upsacle, device="cuda:0")
# res = rgt_model(image="/root/code/RGT/datasets/single/test_4.png", use_chop=True)
# cv2.imwrite(f"/root/code/RGT/datasets/single/res_{index}.png", res)
# index += 1
node.py
+67
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@@ -0,0 +1,67 @@
import os
import cv2
import torch
import numpy as np
from comfy.model_management import cast_to_device, get_torch_device
from loguru import logger
from .model import RGTModel
from folder_paths import models_dir
class RGT_Upscale:
def __init__(self):
self.model = None
@classmethod
def INPUT_TYPES(cls):
# Example structure, adjust according to your actual input requirements
return {
"required": {
"model_type": (["RGT", "RGT_S"], ),
"upscale": (["x2", "x3", "x4"], ),
"use_chop": (["enable", "disable"], ),
"image": ("IMAGE", )
},
}
RETURN_TYPES = ("IMAGE",)
CATEGORY = "114514"
FUNCTION = "load_model"
def load_model(self, model_type, upscale, use_chop, image):
logger.debug(f"image: {image.shape}")
model_path = os.path.join(models_dir, "RGT",f"{model_type}", f"{model_type}_{upscale}.pth")
image = torch.permute(image, (0, 3, 1, 2))
if self.model is None:
self.model = RGTModel(
name=model_type,
upscale=int(upscale[-1]),
device=get_torch_device(),
model_path=model_path
)
else:
self.model.load_model(
model_type,
int(upscale[-1]),
get_torch_device(),
model_path
)
output = self.model(image, use_chop=True if use_chop == "enable" else False)
output = torch.permute(output, (0, 2, 3, 1))
logger.debug(f"output: {output.shape}")
return (output, )
NODE_CLASS_MAPPINGS = {
"RGT_Upscale": RGT_Upscale,
}
# A dictionary that contains the friendly/humanly readable titles for the nodes
NODE_DISPLAY_NAME_MAPPINGS = {
"RGT Upscale": "RGT Upscale"
}
requirements.txt
View File