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Commit f0a2b81c authored by comfyanonymous's avatar comfyanonymous
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Cleanup: Remove a bunch of useless files.

parent 74297f5f
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comfy/cldm/cldm.py
View file @ f0a2b81c
...@@ -14,8 +14,7 @@ from ..ldm.modules.diffusionmodules.util import ( ...@@ -14,8 +14,7 @@ from ..ldm.modules.diffusionmodules.util import (
from ..ldm.modules.attention import SpatialTransformer from ..ldm.modules.attention import SpatialTransformer
from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock
from ..ldm.models.diffusion.ddpm import LatentDiffusion from ..ldm.util import exists
from ..ldm.util import log_txt_as_img, exists, instantiate_from_config
class ControlledUnetModel(UNetModel): class ControlledUnetModel(UNetModel):
......
comfy/diffusers_load.py
View file @ f0a2b81c
...@@ -3,7 +3,6 @@ import os ...@@ -3,7 +3,6 @@ import os
import yaml import yaml
import folder_paths import folder_paths
from comfy.ldm.util import instantiate_from_config
from comfy.sd import ModelPatcher, load_model_weights, CLIP, VAE, load_checkpoint from comfy.sd import ModelPatcher, load_model_weights, CLIP, VAE, load_checkpoint
import os.path as osp import os.path as osp
import re import re
......
comfy/ldm/data/util.py deleted 100644 → 0
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import torch
from ldm.modules.midas.api import load_midas_transform
class AddMiDaS(object):
def __init__(self, model_type):
super().__init__()
self.transform = load_midas_transform(model_type)
def pt2np(self, x):
x = ((x + 1.0) * .5).detach().cpu().numpy()
return x
def np2pt(self, x):
x = torch.from_numpy(x) * 2 - 1.
return x
def __call__(self, sample):
# sample['jpg'] is tensor hwc in [-1, 1] at this point
x = self.pt2np(sample['jpg'])
x = self.transform({"image": x})["image"]
sample['midas_in'] = x
return sample
\ No newline at end of file
comfy/ldm/models/diffusion/ddpm.py deleted 100644 → 0
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comfy/ldm/modules/encoders/kornia_functions.py deleted 100644 → 0
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from typing import List, Tuple, Union
import torch
import torch.nn as nn
#from: https://github.com/kornia/kornia/blob/master/kornia/enhance/normalize.py
def enhance_normalize(data: torch.Tensor, mean: torch.Tensor, std: torch.Tensor) -> torch.Tensor:
r"""Normalize an image/video tensor with mean and standard deviation.
.. math::
\text{input[channel] = (input[channel] - mean[channel]) / std[channel]}
Where `mean` is :math:`(M_1, ..., M_n)` and `std` :math:`(S_1, ..., S_n)` for `n` channels,
Args:
data: Image tensor of size :math:`(B, C, *)`.
mean: Mean for each channel.
std: Standard deviations for each channel.
Return:
Normalised tensor with same size as input :math:`(B, C, *)`.
Examples:
>>> x = torch.rand(1, 4, 3, 3)
>>> out = normalize(x, torch.tensor([0.0]), torch.tensor([255.]))
>>> out.shape
torch.Size([1, 4, 3, 3])
>>> x = torch.rand(1, 4, 3, 3)
>>> mean = torch.zeros(4)
>>> std = 255. * torch.ones(4)
>>> out = normalize(x, mean, std)
>>> out.shape
torch.Size([1, 4, 3, 3])
"""
shape = data.shape
if len(mean.shape) == 0 or mean.shape[0] == 1:
mean = mean.expand(shape[1])
if len(std.shape) == 0 or std.shape[0] == 1:
std = std.expand(shape[1])
# Allow broadcast on channel dimension
if mean.shape and mean.shape[0] != 1:
if mean.shape[0] != data.shape[1] and mean.shape[:2] != data.shape[:2]:
raise ValueError(f"mean length and number of channels do not match. Got {mean.shape} and {data.shape}.")
# Allow broadcast on channel dimension
if std.shape and std.shape[0] != 1:
if std.shape[0] != data.shape[1] and std.shape[:2] != data.shape[:2]:
raise ValueError(f"std length and number of channels do not match. Got {std.shape} and {data.shape}.")
mean = torch.as_tensor(mean, device=data.device, dtype=data.dtype)
std = torch.as_tensor(std, device=data.device, dtype=data.dtype)
if mean.shape:
mean = mean[..., :, None]
if std.shape:
std = std[..., :, None]
out: torch.Tensor = (data.view(shape[0], shape[1], -1) - mean) / std
return out.view(shape)
comfy/ldm/modules/encoders/modules.py deleted 100644 → 0
View file @ 74297f5f
import torch
import torch.nn as nn
from . import kornia_functions
from torch.utils.checkpoint import checkpoint
from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
import open_clip
from ldm.util import default, count_params
class AbstractEncoder(nn.Module):
def __init__(self):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
class IdentityEncoder(AbstractEncoder):
def encode(self, x):
return x
class ClassEmbedder(nn.Module):
def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1):
super().__init__()
self.key = key
self.embedding = nn.Embedding(n_classes, embed_dim)
self.n_classes = n_classes
self.ucg_rate = ucg_rate
def forward(self, batch, key=None, disable_dropout=False):
if key is None:
key = self.key
# this is for use in crossattn
c = batch[key][:, None]
if self.ucg_rate > 0. and not disable_dropout:
mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
c = mask * c + (1 - mask) * torch.ones_like(c) * (self.n_classes - 1)
c = c.long()
c = self.embedding(c)
return c
def get_unconditional_conditioning(self, bs, device="cuda"):
uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
uc = torch.ones((bs,), device=device) * uc_class
uc = {self.key: uc}
return uc
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
class FrozenT5Embedder(AbstractEncoder):
"""Uses the T5 transformer encoder for text"""
def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77,
freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
super().__init__()
self.tokenizer = T5Tokenizer.from_pretrained(version)
self.transformer = T5EncoderModel.from_pretrained(version)
self.device = device
self.max_length = max_length # TODO: typical value?
if freeze:
self.freeze()
def freeze(self):
self.transformer = self.transformer.eval()
# self.train = disabled_train
for param in self.parameters():
param.requires_grad = False
def forward(self, text):
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
tokens = batch_encoding["input_ids"].to(self.device)
outputs = self.transformer(input_ids=tokens)
z = outputs.last_hidden_state
return z
def encode(self, text):
return self(text)
class FrozenCLIPEmbedder(AbstractEncoder):
"""Uses the CLIP transformer encoder for text (from huggingface)"""
LAYERS = [
"last",
"pooled",
"hidden"
]
def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77,
freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32
super().__init__()
assert layer in self.LAYERS
self.tokenizer = CLIPTokenizer.from_pretrained(version)
self.transformer = CLIPTextModel.from_pretrained(version)
self.device = device
self.max_length = max_length
if freeze:
self.freeze()
self.layer = layer
self.layer_idx = layer_idx
if layer == "hidden":
assert layer_idx is not None
assert 0 <= abs(layer_idx) <= 12
def freeze(self):
self.transformer = self.transformer.eval()
# self.train = disabled_train
for param in self.parameters():
param.requires_grad = False
def forward(self, text):
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
tokens = batch_encoding["input_ids"].to(self.device)
outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer == "hidden")
if self.layer == "last":
z = outputs.last_hidden_state
elif self.layer == "pooled":
z = outputs.pooler_output[:, None, :]
else:
z = outputs.hidden_states[self.layer_idx]
return z
def encode(self, text):
return self(text)
class ClipImageEmbedder(nn.Module):
def __init__(
self,
model,
jit=False,
device='cuda' if torch.cuda.is_available() else 'cpu',
antialias=True,
ucg_rate=0.
):
super().__init__()
from clip import load as load_clip
self.model, _ = load_clip(name=model, device=device, jit=jit)
self.antialias = antialias
self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
self.ucg_rate = ucg_rate
def preprocess(self, x):
# normalize to [0,1]
# x = kornia_functions.geometry_resize(x, (224, 224),
# interpolation='bicubic', align_corners=True,
# antialias=self.antialias)
x = torch.nn.functional.interpolate(x, size=(224, 224), mode='bicubic', align_corners=True, antialias=True)
x = (x + 1.) / 2.
# re-normalize according to clip
x = kornia_functions.enhance_normalize(x, self.mean, self.std)
return x
def forward(self, x, no_dropout=False):
# x is assumed to be in range [-1,1]
out = self.model.encode_image(self.preprocess(x))
out = out.to(x.dtype)
if self.ucg_rate > 0. and not no_dropout:
out = torch.bernoulli((1. - self.ucg_rate) * torch.ones(out.shape[0], device=out.device))[:, None] * out
return out
class FrozenOpenCLIPEmbedder(AbstractEncoder):
"""
Uses the OpenCLIP transformer encoder for text
"""
LAYERS = [
# "pooled",
"last",
"penultimate"
]
def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
freeze=True, layer="last"):
super().__init__()
assert layer in self.LAYERS
model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
del model.visual
self.model = model
self.device = device
self.max_length = max_length
if freeze:
self.freeze()
self.layer = layer
if self.layer == "last":
self.layer_idx = 0
elif self.layer == "penultimate":
self.layer_idx = 1
else:
raise NotImplementedError()
def freeze(self):
self.model = self.model.eval()
for param in self.parameters():
param.requires_grad = False
def forward(self, text):
tokens = open_clip.tokenize(text)
z = self.encode_with_transformer(tokens.to(self.device))
return z
def encode_with_transformer(self, text):
x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
x = x + self.model.positional_embedding
x = x.permute(1, 0, 2) # NLD -> LND
x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
x = x.permute(1, 0, 2) # LND -> NLD
x = self.model.ln_final(x)
return x
def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
for i, r in enumerate(self.model.transformer.resblocks):
if i == len(self.model.transformer.resblocks) - self.layer_idx:
break
if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint(r, x, attn_mask)
else:
x = r(x, attn_mask=attn_mask)
return x
def encode(self, text):
return self(text)
class FrozenOpenCLIPImageEmbedder(AbstractEncoder):
"""
Uses the OpenCLIP vision transformer encoder for images
"""
def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
freeze=True, layer="pooled", antialias=True, ucg_rate=0.):
super().__init__()
model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'),
pretrained=version, )
del model.transformer
self.model = model
self.device = device
self.max_length = max_length
if freeze:
self.freeze()
self.layer = layer
if self.layer == "penultimate":
raise NotImplementedError()
self.layer_idx = 1
self.antialias = antialias
self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
self.ucg_rate = ucg_rate
def preprocess(self, x):
# normalize to [0,1]
# x = kornia.geometry.resize(x, (224, 224),
# interpolation='bicubic', align_corners=True,
# antialias=self.antialias)
x = torch.nn.functional.interpolate(x, size=(224, 224), mode='bicubic', align_corners=True, antialias=True)
x = (x + 1.) / 2.
# renormalize according to clip
x = kornia_functions.enhance_normalize(x, self.mean, self.std)
return x
def freeze(self):
self.model = self.model.eval()
for param in self.parameters():
param.requires_grad = False
def forward(self, image, no_dropout=False):
z = self.encode_with_vision_transformer(image)
if self.ucg_rate > 0. and not no_dropout:
z = torch.bernoulli((1. - self.ucg_rate) * torch.ones(z.shape[0], device=z.device))[:, None] * z
return z
def encode_with_vision_transformer(self, img):
img = self.preprocess(img)
x = self.model.visual(img)
return x
def encode(self, text):
return self(text)
class FrozenCLIPT5Encoder(AbstractEncoder):
def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda",
clip_max_length=77, t5_max_length=77):
super().__init__()
self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, "
f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params.")
def encode(self, text):
return self(text)
def forward(self, text):
clip_z = self.clip_encoder.encode(text)
t5_z = self.t5_encoder.encode(text)
return [clip_z, t5_z]
comfy/ldm/modules/midas/api.py deleted 100644 → 0
View file @ 74297f5f
# based on https://github.com/isl-org/MiDaS
import cv2
import torch
import torch.nn as nn
from torchvision.transforms import Compose
from ldm.modules.midas.midas.dpt_depth import DPTDepthModel
from ldm.modules.midas.midas.midas_net import MidasNet
from ldm.modules.midas.midas.midas_net_custom import MidasNet_small
from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet
ISL_PATHS = {
"dpt_large": "midas_models/dpt_large-midas-2f21e586.pt",
"dpt_hybrid": "midas_models/dpt_hybrid-midas-501f0c75.pt",
"midas_v21": "",
"midas_v21_small": "",
}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def load_midas_transform(model_type):
# https://github.com/isl-org/MiDaS/blob/master/run.py
# load transform only
if model_type == "dpt_large": # DPT-Large
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_hybrid": # DPT-Hybrid
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "midas_v21":
net_w, net_h = 384, 384
resize_mode = "upper_bound"
normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
elif model_type == "midas_v21_small":
net_w, net_h = 256, 256
resize_mode = "upper_bound"
normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
else:
assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
transform = Compose(
[
Resize(
net_w,
net_h,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method=resize_mode,
image_interpolation_method=cv2.INTER_CUBIC,
),
normalization,
PrepareForNet(),
]
)
return transform
def load_model(model_type):
# https://github.com/isl-org/MiDaS/blob/master/run.py
# load network
model_path = ISL_PATHS[model_type]
if model_type == "dpt_large": # DPT-Large
model = DPTDepthModel(
path=model_path,
backbone="vitl16_384",
non_negative=True,
)
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_hybrid": # DPT-Hybrid
model = DPTDepthModel(
path=model_path,
backbone="vitb_rn50_384",
non_negative=True,
)
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "midas_v21":
model = MidasNet(model_path, non_negative=True)
net_w, net_h = 384, 384
resize_mode = "upper_bound"
normalization = NormalizeImage(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
elif model_type == "midas_v21_small":
model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
non_negative=True, blocks={'expand': True})
net_w, net_h = 256, 256
resize_mode = "upper_bound"
normalization = NormalizeImage(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
else:
print(f"model_type '{model_type}' not implemented, use: --model_type large")
assert False
transform = Compose(
[
Resize(
net_w,
net_h,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method=resize_mode,
image_interpolation_method=cv2.INTER_CUBIC,
),
normalization,
PrepareForNet(),
]
)
return model.eval(), transform
class MiDaSInference(nn.Module):
MODEL_TYPES_TORCH_HUB = [
"DPT_Large",
"DPT_Hybrid",
"MiDaS_small"
]
MODEL_TYPES_ISL = [
"dpt_large",
"dpt_hybrid",
"midas_v21",
"midas_v21_small",
]
def __init__(self, model_type):
super().__init__()
assert (model_type in self.MODEL_TYPES_ISL)
model, _ = load_model(model_type)
self.model = model
self.model.train = disabled_train
def forward(self, x):
# x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array
# NOTE: we expect that the correct transform has been called during dataloading.
with torch.no_grad():
prediction = self.model(x)
prediction = torch.nn.functional.interpolate(
prediction.unsqueeze(1),
size=x.shape[2:],
mode="bicubic",
align_corners=False,
)
assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3])
return prediction
comfy/ldm/modules/midas/midas/base_model.py deleted 100644 → 0
View file @ 74297f5f
import torch
class BaseModel(torch.nn.Module):
def load(self, path):
"""Load model from file.
Args:
path (str): file path
"""
parameters = torch.load(path, map_location=torch.device('cpu'))
if "optimizer" in parameters:
parameters = parameters["model"]
self.load_state_dict(parameters)
comfy/ldm/modules/midas/midas/blocks.py deleted 100644 → 0
View file @ 74297f5f
import torch
import torch.nn as nn
from .vit import (
_make_pretrained_vitb_rn50_384,
_make_pretrained_vitl16_384,
_make_pretrained_vitb16_384,
forward_vit,
)
def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",):
if backbone == "vitl16_384":
pretrained = _make_pretrained_vitl16_384(
use_pretrained, hooks=hooks, use_readout=use_readout
)
scratch = _make_scratch(
[256, 512, 1024, 1024], features, groups=groups, expand=expand
) # ViT-L/16 - 85.0% Top1 (backbone)
elif backbone == "vitb_rn50_384":
pretrained = _make_pretrained_vitb_rn50_384(
use_pretrained,
hooks=hooks,
use_vit_only=use_vit_only,
use_readout=use_readout,
)
scratch = _make_scratch(
[256, 512, 768, 768], features, groups=groups, expand=expand
) # ViT-H/16 - 85.0% Top1 (backbone)
elif backbone == "vitb16_384":
pretrained = _make_pretrained_vitb16_384(
use_pretrained, hooks=hooks, use_readout=use_readout
)
scratch = _make_scratch(
[96, 192, 384, 768], features, groups=groups, expand=expand
) # ViT-B/16 - 84.6% Top1 (backbone)
elif backbone == "resnext101_wsl":
pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand) # efficientnet_lite3
elif backbone == "efficientnet_lite3":
pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable)
scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand) # efficientnet_lite3
else:
print(f"Backbone '{backbone}' not implemented")
assert False
return pretrained, scratch
def _make_scratch(in_shape, out_shape, groups=1, expand=False):
scratch = nn.Module()
out_shape1 = out_shape
out_shape2 = out_shape
out_shape3 = out_shape
out_shape4 = out_shape
if expand==True:
out_shape1 = out_shape
out_shape2 = out_shape*2
out_shape3 = out_shape*4
out_shape4 = out_shape*8
scratch.layer1_rn = nn.Conv2d(
in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
)
scratch.layer2_rn = nn.Conv2d(
in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
)
scratch.layer3_rn = nn.Conv2d(
in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
)
scratch.layer4_rn = nn.Conv2d(
in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
)
return scratch
def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
efficientnet = torch.hub.load(
"rwightman/gen-efficientnet-pytorch",
"tf_efficientnet_lite3",
pretrained=use_pretrained,
exportable=exportable
)
return _make_efficientnet_backbone(efficientnet)
def _make_efficientnet_backbone(effnet):
pretrained = nn.Module()
pretrained.layer1 = nn.Sequential(
effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
)
pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])
return pretrained
def _make_resnet_backbone(resnet):
pretrained = nn.Module()
pretrained.layer1 = nn.Sequential(
resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
)
pretrained.layer2 = resnet.layer2
pretrained.layer3 = resnet.layer3
pretrained.layer4 = resnet.layer4
return pretrained
def _make_pretrained_resnext101_wsl(use_pretrained):
resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
return _make_resnet_backbone(resnet)
class Interpolate(nn.Module):
"""Interpolation module.
"""
def __init__(self, scale_factor, mode, align_corners=False):
"""Init.
Args:
scale_factor (float): scaling
mode (str): interpolation mode
"""
super(Interpolate, self).__init__()
self.interp = nn.functional.interpolate
self.scale_factor = scale_factor
self.mode = mode
self.align_corners = align_corners
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: interpolated data
"""
x = self.interp(
x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners
)
return x
class ResidualConvUnit(nn.Module):
"""Residual convolution module.
"""
def __init__(self, features):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.conv1 = nn.Conv2d(
features, features, kernel_size=3, stride=1, padding=1, bias=True
)
self.conv2 = nn.Conv2d(
features, features, kernel_size=3, stride=1, padding=1, bias=True
)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: output
"""
out = self.relu(x)
out = self.conv1(out)
out = self.relu(out)
out = self.conv2(out)
return out + x
class FeatureFusionBlock(nn.Module):
"""Feature fusion block.
"""
def __init__(self, features):
"""Init.
Args:
features (int): number of features
"""
super(FeatureFusionBlock, self).__init__()
self.resConfUnit1 = ResidualConvUnit(features)
self.resConfUnit2 = ResidualConvUnit(features)
def forward(self, *xs):
"""Forward pass.
Returns:
tensor: output
"""
output = xs[0]
if len(xs) == 2:
output += self.resConfUnit1(xs[1])
output = self.resConfUnit2(output)
output = nn.functional.interpolate(
output, scale_factor=2, mode="bilinear", align_corners=True
)
return output
class ResidualConvUnit_custom(nn.Module):
"""Residual convolution module.
"""
def __init__(self, features, activation, bn):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.bn = bn
self.groups=1
self.conv1 = nn.Conv2d(
features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
)
self.conv2 = nn.Conv2d(
features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
)
if self.bn==True:
self.bn1 = nn.BatchNorm2d(features)
self.bn2 = nn.BatchNorm2d(features)
self.activation = activation
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: output
"""
out = self.activation(x)
out = self.conv1(out)
if self.bn==True:
out = self.bn1(out)
out = self.activation(out)
out = self.conv2(out)
if self.bn==True:
out = self.bn2(out)
if self.groups > 1:
out = self.conv_merge(out)
return self.skip_add.add(out, x)
# return out + x
class FeatureFusionBlock_custom(nn.Module):
"""Feature fusion block.
"""
def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True):
"""Init.
Args:
features (int): number of features
"""
super(FeatureFusionBlock_custom, self).__init__()
self.deconv = deconv
self.align_corners = align_corners
self.groups=1
self.expand = expand
out_features = features
if self.expand==True:
out_features = features//2
self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, *xs):
"""Forward pass.
Returns:
tensor: output
"""
output = xs[0]
if len(xs) == 2:
res = self.resConfUnit1(xs[1])
output = self.skip_add.add(output, res)
# output += res
output = self.resConfUnit2(output)
output = nn.functional.interpolate(
output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
)
output = self.out_conv(output)
return output
comfy/ldm/modules/midas/midas/dpt_depth.py deleted 100644 → 0
View file @ 74297f5f
import torch
import torch.nn as nn
import torch.nn.functional as F
from .base_model import BaseModel
from .blocks import (
FeatureFusionBlock,
FeatureFusionBlock_custom,
Interpolate,
_make_encoder,
forward_vit,
)
def _make_fusion_block(features, use_bn):
return FeatureFusionBlock_custom(
features,
nn.ReLU(False),
deconv=False,
bn=use_bn,
expand=False,
align_corners=True,
)
class DPT(BaseModel):
def __init__(
self,
head,
features=256,
backbone="vitb_rn50_384",
readout="project",
channels_last=False,
use_bn=False,
):
super(DPT, self).__init__()
self.channels_last = channels_last
hooks = {
"vitb_rn50_384": [0, 1, 8, 11],
"vitb16_384": [2, 5, 8, 11],
"vitl16_384": [5, 11, 17, 23],
}
# Instantiate backbone and reassemble blocks
self.pretrained, self.scratch = _make_encoder(
backbone,
features,
False, # Set to true of you want to train from scratch, uses ImageNet weights
groups=1,
expand=False,
exportable=False,
hooks=hooks[backbone],
use_readout=readout,
)
self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
self.scratch.output_conv = head
def forward(self, x):
if self.channels_last == True:
x.contiguous(memory_format=torch.channels_last)
layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
layer_1_rn = self.scratch.layer1_rn(layer_1)
layer_2_rn = self.scratch.layer2_rn(layer_2)
layer_3_rn = self.scratch.layer3_rn(layer_3)
layer_4_rn = self.scratch.layer4_rn(layer_4)
path_4 = self.scratch.refinenet4(layer_4_rn)
path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
out = self.scratch.output_conv(path_1)
return out
class DPTDepthModel(DPT):
def __init__(self, path=None, non_negative=True, **kwargs):
features = kwargs["features"] if "features" in kwargs else 256
head = nn.Sequential(
nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
nn.ReLU(True),
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
nn.ReLU(True) if non_negative else nn.Identity(),
nn.Identity(),
)
super().__init__(head, **kwargs)
if path is not None:
self.load(path)
def forward(self, x):
return super().forward(x).squeeze(dim=1)
comfy/ldm/modules/midas/midas/midas_net.py deleted 100644 → 0
View file @ 74297f5f
"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from .base_model import BaseModel
from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
class MidasNet(BaseModel):
"""Network for monocular depth estimation.
"""
def __init__(self, path=None, features=256, non_negative=True):
"""Init.
Args:
path (str, optional): Path to saved model. Defaults to None.
features (int, optional): Number of features. Defaults to 256.
backbone (str, optional): Backbone network for encoder. Defaults to resnet50
"""
print("Loading weights: ", path)
super(MidasNet, self).__init__()
use_pretrained = False if path is None else True
self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
self.scratch.refinenet4 = FeatureFusionBlock(features)
self.scratch.refinenet3 = FeatureFusionBlock(features)
self.scratch.refinenet2 = FeatureFusionBlock(features)
self.scratch.refinenet1 = FeatureFusionBlock(features)
self.scratch.output_conv = nn.Sequential(
nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
Interpolate(scale_factor=2, mode="bilinear"),
nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
nn.ReLU(True),
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
nn.ReLU(True) if non_negative else nn.Identity(),
)
if path:
self.load(path)
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input data (image)
Returns:
tensor: depth
"""
layer_1 = self.pretrained.layer1(x)
layer_2 = self.pretrained.layer2(layer_1)
layer_3 = self.pretrained.layer3(layer_2)
layer_4 = self.pretrained.layer4(layer_3)
layer_1_rn = self.scratch.layer1_rn(layer_1)
layer_2_rn = self.scratch.layer2_rn(layer_2)
layer_3_rn = self.scratch.layer3_rn(layer_3)
layer_4_rn = self.scratch.layer4_rn(layer_4)
path_4 = self.scratch.refinenet4(layer_4_rn)
path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
out = self.scratch.output_conv(path_1)
return torch.squeeze(out, dim=1)
comfy/ldm/modules/midas/midas/midas_net_custom.py deleted 100644 → 0
View file @ 74297f5f
"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from .base_model import BaseModel
from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
class MidasNet_small(BaseModel):
"""Network for monocular depth estimation.
"""
def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
blocks={'expand': True}):
"""Init.
Args:
path (str, optional): Path to saved model. Defaults to None.
features (int, optional): Number of features. Defaults to 256.
backbone (str, optional): Backbone network for encoder. Defaults to resnet50
"""
print("Loading weights: ", path)
super(MidasNet_small, self).__init__()
use_pretrained = False if path else True
self.channels_last = channels_last
self.blocks = blocks
self.backbone = backbone
self.groups = 1
features1=features
features2=features
features3=features
features4=features
self.expand = False
if "expand" in self.blocks and self.blocks['expand'] == True:
self.expand = True
features1=features
features2=features*2
features3=features*4
features4=features*8
self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
self.scratch.activation = nn.ReLU(False)
self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
self.scratch.output_conv = nn.Sequential(
nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
Interpolate(scale_factor=2, mode="bilinear"),
nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
self.scratch.activation,
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
nn.ReLU(True) if non_negative else nn.Identity(),
nn.Identity(),
)
if path:
self.load(path)
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input data (image)
Returns:
tensor: depth
"""
if self.channels_last==True:
print("self.channels_last = ", self.channels_last)
x.contiguous(memory_format=torch.channels_last)
layer_1 = self.pretrained.layer1(x)
layer_2 = self.pretrained.layer2(layer_1)
layer_3 = self.pretrained.layer3(layer_2)
layer_4 = self.pretrained.layer4(layer_3)
layer_1_rn = self.scratch.layer1_rn(layer_1)
layer_2_rn = self.scratch.layer2_rn(layer_2)
layer_3_rn = self.scratch.layer3_rn(layer_3)
layer_4_rn = self.scratch.layer4_rn(layer_4)
path_4 = self.scratch.refinenet4(layer_4_rn)
path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
out = self.scratch.output_conv(path_1)
return torch.squeeze(out, dim=1)
def fuse_model(m):
prev_previous_type = nn.Identity()
prev_previous_name = ''
previous_type = nn.Identity()
previous_name = ''
for name, module in m.named_modules():
if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
# print("FUSED ", prev_previous_name, previous_name, name)
torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
# print("FUSED ", prev_previous_name, previous_name)
torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
# elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
# print("FUSED ", previous_name, name)
# torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
prev_previous_type = previous_type
prev_previous_name = previous_name
previous_type = type(module)
previous_name = name
\ No newline at end of file
comfy/ldm/modules/midas/midas/transforms.py deleted 100644 → 0
View file @ 74297f5f
import numpy as np
import cv2
import math
def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
"""Rezise the sample to ensure the given size. Keeps aspect ratio.
Args:
sample (dict): sample
size (tuple): image size
Returns:
tuple: new size
"""
shape = list(sample["disparity"].shape)
if shape[0] >= size[0] and shape[1] >= size[1]:
return sample
scale = [0, 0]
scale[0] = size[0] / shape[0]
scale[1] = size[1] / shape[1]
scale = max(scale)
shape[0] = math.ceil(scale * shape[0])
shape[1] = math.ceil(scale * shape[1])
# resize
sample["image"] = cv2.resize(
sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
)
sample["disparity"] = cv2.resize(
sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
)
sample["mask"] = cv2.resize(
sample["mask"].astype(np.float32),
tuple(shape[::-1]),
interpolation=cv2.INTER_NEAREST,
)
sample["mask"] = sample["mask"].astype(bool)
return tuple(shape)
class Resize(object):
"""Resize sample to given size (width, height).
"""
def __init__(
self,
width,
height,
resize_target=True,
keep_aspect_ratio=False,
ensure_multiple_of=1,
resize_method="lower_bound",
image_interpolation_method=cv2.INTER_AREA,
):
"""Init.
Args:
width (int): desired output width
height (int): desired output height
resize_target (bool, optional):
True: Resize the full sample (image, mask, target).
False: Resize image only.
Defaults to True.
keep_aspect_ratio (bool, optional):
True: Keep the aspect ratio of the input sample.
Output sample might not have the given width and height, and
resize behaviour depends on the parameter 'resize_method'.
Defaults to False.
ensure_multiple_of (int, optional):
Output width and height is constrained to be multiple of this parameter.
Defaults to 1.
resize_method (str, optional):
"lower_bound": Output will be at least as large as the given size.
"upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
"minimal": Scale as least as possible. (Output size might be smaller than given size.)
Defaults to "lower_bound".
"""
self.__width = width
self.__height = height
self.__resize_target = resize_target
self.__keep_aspect_ratio = keep_aspect_ratio
self.__multiple_of = ensure_multiple_of
self.__resize_method = resize_method
self.__image_interpolation_method = image_interpolation_method
def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
if max_val is not None and y > max_val:
y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
if y < min_val:
y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
return y
def get_size(self, width, height):
# determine new height and width
scale_height = self.__height / height
scale_width = self.__width / width
if self.__keep_aspect_ratio:
if self.__resize_method == "lower_bound":
# scale such that output size is lower bound
if scale_width > scale_height:
# fit width
scale_height = scale_width
else:
# fit height
scale_width = scale_height
elif self.__resize_method == "upper_bound":
# scale such that output size is upper bound
if scale_width < scale_height:
# fit width
scale_height = scale_width
else:
# fit height
scale_width = scale_height
elif self.__resize_method == "minimal":
# scale as least as possbile
if abs(1 - scale_width) < abs(1 - scale_height):
# fit width
scale_height = scale_width
else:
# fit height
scale_width = scale_height
else:
raise ValueError(
f"resize_method {self.__resize_method} not implemented"
)
if self.__resize_method == "lower_bound":
new_height = self.constrain_to_multiple_of(
scale_height * height, min_val=self.__height
)
new_width = self.constrain_to_multiple_of(
scale_width * width, min_val=self.__width
)
elif self.__resize_method == "upper_bound":
new_height = self.constrain_to_multiple_of(
scale_height * height, max_val=self.__height
)
new_width = self.constrain_to_multiple_of(
scale_width * width, max_val=self.__width
)
elif self.__resize_method == "minimal":
new_height = self.constrain_to_multiple_of(scale_height * height)
new_width = self.constrain_to_multiple_of(scale_width * width)
else:
raise ValueError(f"resize_method {self.__resize_method} not implemented")
return (new_width, new_height)
def __call__(self, sample):
width, height = self.get_size(
sample["image"].shape[1], sample["image"].shape[0]
)
# resize sample
sample["image"] = cv2.resize(
sample["image"],
(width, height),
interpolation=self.__image_interpolation_method,
)
if self.__resize_target:
if "disparity" in sample:
sample["disparity"] = cv2.resize(
sample["disparity"],
(width, height),
interpolation=cv2.INTER_NEAREST,
)
if "depth" in sample:
sample["depth"] = cv2.resize(
sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
)
sample["mask"] = cv2.resize(
sample["mask"].astype(np.float32),
(width, height),
interpolation=cv2.INTER_NEAREST,
)
sample["mask"] = sample["mask"].astype(bool)
return sample
class NormalizeImage(object):
"""Normlize image by given mean and std.
"""
def __init__(self, mean, std):
self.__mean = mean
self.__std = std
def __call__(self, sample):
sample["image"] = (sample["image"] - self.__mean) / self.__std
return sample
class PrepareForNet(object):
"""Prepare sample for usage as network input.
"""
def __init__(self):
pass
def __call__(self, sample):
image = np.transpose(sample["image"], (2, 0, 1))
sample["image"] = np.ascontiguousarray(image).astype(np.float32)
if "mask" in sample:
sample["mask"] = sample["mask"].astype(np.float32)
sample["mask"] = np.ascontiguousarray(sample["mask"])
if "disparity" in sample:
disparity = sample["disparity"].astype(np.float32)
sample["disparity"] = np.ascontiguousarray(disparity)
if "depth" in sample:
depth = sample["depth"].astype(np.float32)
sample["depth"] = np.ascontiguousarray(depth)
return sample
comfy/ldm/modules/midas/midas/vit.py deleted 100644 → 0
View file @ 74297f5f
import torch
import torch.nn as nn
import timm
import types
import math
import torch.nn.functional as F
class Slice(nn.Module):
def __init__(self, start_index=1):
super(Slice, self).__init__()
self.start_index = start_index
def forward(self, x):
return x[:, self.start_index :]
class AddReadout(nn.Module):
def __init__(self, start_index=1):
super(AddReadout, self).__init__()
self.start_index = start_index
def forward(self, x):
if self.start_index == 2:
readout = (x[:, 0] + x[:, 1]) / 2
else:
readout = x[:, 0]
return x[:, self.start_index :] + readout.unsqueeze(1)
class ProjectReadout(nn.Module):
def __init__(self, in_features, start_index=1):
super(ProjectReadout, self).__init__()
self.start_index = start_index
self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
def forward(self, x):
readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
features = torch.cat((x[:, self.start_index :], readout), -1)
return self.project(features)
class Transpose(nn.Module):
def __init__(self, dim0, dim1):
super(Transpose, self).__init__()
self.dim0 = dim0
self.dim1 = dim1
def forward(self, x):
x = x.transpose(self.dim0, self.dim1)
return x
def forward_vit(pretrained, x):
b, c, h, w = x.shape
glob = pretrained.model.forward_flex(x)
layer_1 = pretrained.activations["1"]
layer_2 = pretrained.activations["2"]
layer_3 = pretrained.activations["3"]
layer_4 = pretrained.activations["4"]
layer_1 = pretrained.act_postprocess1[0:2](layer_1)
layer_2 = pretrained.act_postprocess2[0:2](layer_2)
layer_3 = pretrained.act_postprocess3[0:2](layer_3)
layer_4 = pretrained.act_postprocess4[0:2](layer_4)
unflatten = nn.Sequential(
nn.Unflatten(
2,
torch.Size(
[
h // pretrained.model.patch_size[1],
w // pretrained.model.patch_size[0],
]
),
)
)
if layer_1.ndim == 3:
layer_1 = unflatten(layer_1)
if layer_2.ndim == 3:
layer_2 = unflatten(layer_2)
if layer_3.ndim == 3:
layer_3 = unflatten(layer_3)
if layer_4.ndim == 3:
layer_4 = unflatten(layer_4)
layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
return layer_1, layer_2, layer_3, layer_4
def _resize_pos_embed(self, posemb, gs_h, gs_w):
posemb_tok, posemb_grid = (
posemb[:, : self.start_index],
posemb[0, self.start_index :],
)
gs_old = int(math.sqrt(len(posemb_grid)))
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
return posemb
def forward_flex(self, x):
b, c, h, w = x.shape
pos_embed = self._resize_pos_embed(
self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
)
B = x.shape[0]
if hasattr(self.patch_embed, "backbone"):
x = self.patch_embed.backbone(x)
if isinstance(x, (list, tuple)):
x = x[-1] # last feature if backbone outputs list/tuple of features
x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
if getattr(self, "dist_token", None) is not None:
cls_tokens = self.cls_token.expand(
B, -1, -1
) # stole cls_tokens impl from Phil Wang, thanks
dist_token = self.dist_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, dist_token, x), dim=1)
else:
cls_tokens = self.cls_token.expand(
B, -1, -1
) # stole cls_tokens impl from Phil Wang, thanks
x = torch.cat((cls_tokens, x), dim=1)
x = x + pos_embed
x = self.pos_drop(x)
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
return x
activations = {}
def get_activation(name):
def hook(model, input, output):
activations[name] = output
return hook
def get_readout_oper(vit_features, features, use_readout, start_index=1):
if use_readout == "ignore":
readout_oper = [Slice(start_index)] * len(features)
elif use_readout == "add":
readout_oper = [AddReadout(start_index)] * len(features)
elif use_readout == "project":
readout_oper = [
ProjectReadout(vit_features, start_index) for out_feat in features
]
else:
assert (
False
), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
return readout_oper
def _make_vit_b16_backbone(
model,
features=[96, 192, 384, 768],
size=[384, 384],
hooks=[2, 5, 8, 11],
vit_features=768,
use_readout="ignore",
start_index=1,
):
pretrained = nn.Module()
pretrained.model = model
pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
pretrained.activations = activations
readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
# 32, 48, 136, 384
pretrained.act_postprocess1 = nn.Sequential(
readout_oper[0],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[0],
kernel_size=1,
stride=1,
padding=0,
),
nn.ConvTranspose2d(
in_channels=features[0],
out_channels=features[0],
kernel_size=4,
stride=4,
padding=0,
bias=True,
dilation=1,
groups=1,
),
)
pretrained.act_postprocess2 = nn.Sequential(
readout_oper[1],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[1],
kernel_size=1,
stride=1,
padding=0,
),
nn.ConvTranspose2d(
in_channels=features[1],
out_channels=features[1],
kernel_size=2,
stride=2,
padding=0,
bias=True,
dilation=1,
groups=1,
),
)
pretrained.act_postprocess3 = nn.Sequential(
readout_oper[2],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[2],
kernel_size=1,
stride=1,
padding=0,
),
)
pretrained.act_postprocess4 = nn.Sequential(
readout_oper[3],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[3],
kernel_size=1,
stride=1,
padding=0,
),
nn.Conv2d(
in_channels=features[3],
out_channels=features[3],
kernel_size=3,
stride=2,
padding=1,
),
)
pretrained.model.start_index = start_index
pretrained.model.patch_size = [16, 16]
# We inject this function into the VisionTransformer instances so that
# we can use it with interpolated position embeddings without modifying the library source.
pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
pretrained.model._resize_pos_embed = types.MethodType(
_resize_pos_embed, pretrained.model
)
return pretrained
def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
hooks = [5, 11, 17, 23] if hooks == None else hooks
return _make_vit_b16_backbone(
model,
features=[256, 512, 1024, 1024],
hooks=hooks,
vit_features=1024,
use_readout=use_readout,
)
def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
hooks = [2, 5, 8, 11] if hooks == None else hooks
return _make_vit_b16_backbone(
model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
)
def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
hooks = [2, 5, 8, 11] if hooks == None else hooks
return _make_vit_b16_backbone(
model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
)
def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model(
"vit_deit_base_distilled_patch16_384", pretrained=pretrained
)
hooks = [2, 5, 8, 11] if hooks == None else hooks
return _make_vit_b16_backbone(
model,
features=[96, 192, 384, 768],
hooks=hooks,
use_readout=use_readout,
start_index=2,
)
def _make_vit_b_rn50_backbone(
model,
features=[256, 512, 768, 768],
size=[384, 384],
hooks=[0, 1, 8, 11],
vit_features=768,
use_vit_only=False,
use_readout="ignore",
start_index=1,
):
pretrained = nn.Module()
pretrained.model = model
if use_vit_only == True:
pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
else:
pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
get_activation("1")
)
pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
get_activation("2")
)
pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
pretrained.activations = activations
readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
if use_vit_only == True:
pretrained.act_postprocess1 = nn.Sequential(
readout_oper[0],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[0],
kernel_size=1,
stride=1,
padding=0,
),
nn.ConvTranspose2d(
in_channels=features[0],
out_channels=features[0],
kernel_size=4,
stride=4,
padding=0,
bias=True,
dilation=1,
groups=1,
),
)
pretrained.act_postprocess2 = nn.Sequential(
readout_oper[1],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[1],
kernel_size=1,
stride=1,
padding=0,
),
nn.ConvTranspose2d(
in_channels=features[1],
out_channels=features[1],
kernel_size=2,
stride=2,
padding=0,
bias=True,
dilation=1,
groups=1,
),
)
else:
pretrained.act_postprocess1 = nn.Sequential(
nn.Identity(), nn.Identity(), nn.Identity()
)
pretrained.act_postprocess2 = nn.Sequential(
nn.Identity(), nn.Identity(), nn.Identity()
)
pretrained.act_postprocess3 = nn.Sequential(
readout_oper[2],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[2],
kernel_size=1,
stride=1,
padding=0,
),
)
pretrained.act_postprocess4 = nn.Sequential(
readout_oper[3],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[3],
kernel_size=1,
stride=1,
padding=0,
),
nn.Conv2d(
in_channels=features[3],
out_channels=features[3],
kernel_size=3,
stride=2,
padding=1,
),
)
pretrained.model.start_index = start_index
pretrained.model.patch_size = [16, 16]
# We inject this function into the VisionTransformer instances so that
# we can use it with interpolated position embeddings without modifying the library source.
pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
# We inject this function into the VisionTransformer instances so that
# we can use it with interpolated position embeddings without modifying the library source.
pretrained.model._resize_pos_embed = types.MethodType(
_resize_pos_embed, pretrained.model
)
return pretrained
def _make_pretrained_vitb_rn50_384(
pretrained, use_readout="ignore", hooks=None, use_vit_only=False
):
model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
hooks = [0, 1, 8, 11] if hooks == None else hooks
return _make_vit_b_rn50_backbone(
model,
features=[256, 512, 768, 768],
size=[384, 384],
hooks=hooks,
use_vit_only=use_vit_only,
use_readout=use_readout,
)
comfy/ldm/modules/midas/utils.py deleted 100644 → 0
View file @ 74297f5f
"""Utils for monoDepth."""
import sys
import re
import numpy as np
import cv2
import torch
def read_pfm(path):
"""Read pfm file.
Args:
path (str): path to file
Returns:
tuple: (data, scale)
"""
with open(path, "rb") as file:
color = None
width = None
height = None
scale = None
endian = None
header = file.readline().rstrip()
if header.decode("ascii") == "PF":
color = True
elif header.decode("ascii") == "Pf":
color = False
else:
raise Exception("Not a PFM file: " + path)
dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
if dim_match:
width, height = list(map(int, dim_match.groups()))
else:
raise Exception("Malformed PFM header.")
scale = float(file.readline().decode("ascii").rstrip())
if scale < 0:
# little-endian
endian = "<"
scale = -scale
else:
# big-endian
endian = ">"
data = np.fromfile(file, endian + "f")
shape = (height, width, 3) if color else (height, width)
data = np.reshape(data, shape)
data = np.flipud(data)
return data, scale
def write_pfm(path, image, scale=1):
"""Write pfm file.
Args:
path (str): pathto file
image (array): data
scale (int, optional): Scale. Defaults to 1.
"""
with open(path, "wb") as file:
color = None
if image.dtype.name != "float32":
raise Exception("Image dtype must be float32.")
image = np.flipud(image)
if len(image.shape) == 3 and image.shape[2] == 3: # color image
color = True
elif (
len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1
): # greyscale
color = False
else:
raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
file.write("PF\n" if color else "Pf\n".encode())
file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
endian = image.dtype.byteorder
if endian == "<" or endian == "=" and sys.byteorder == "little":
scale = -scale
file.write("%f\n".encode() % scale)
image.tofile(file)
def read_image(path):
"""Read image and output RGB image (0-1).
Args:
path (str): path to file
Returns:
array: RGB image (0-1)
"""
img = cv2.imread(path)
if img.ndim == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
return img
def resize_image(img):
"""Resize image and make it fit for network.
Args:
img (array): image
Returns:
tensor: data ready for network
"""
height_orig = img.shape[0]
width_orig = img.shape[1]
if width_orig > height_orig:
scale = width_orig / 384
else:
scale = height_orig / 384
height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
width = (np.ceil(width_orig / scale / 32) * 32).astype(int)
img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
img_resized = (
torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
)
img_resized = img_resized.unsqueeze(0)
return img_resized
def resize_depth(depth, width, height):
"""Resize depth map and bring to CPU (numpy).
Args:
depth (tensor): depth
width (int): image width
height (int): image height
Returns:
array: processed depth
"""
depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
depth_resized = cv2.resize(
depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
)
return depth_resized
def write_depth(path, depth, bits=1):
"""Write depth map to pfm and png file.
Args:
path (str): filepath without extension
depth (array): depth
"""
write_pfm(path + ".pfm", depth.astype(np.float32))
depth_min = depth.min()
depth_max = depth.max()
max_val = (2**(8*bits))-1
if depth_max - depth_min > np.finfo("float").eps:
out = max_val * (depth - depth_min) / (depth_max - depth_min)
else:
out = np.zeros(depth.shape, dtype=depth.type)
if bits == 1:
cv2.imwrite(path + ".png", out.astype("uint8"))
elif bits == 2:
cv2.imwrite(path + ".png", out.astype("uint16"))
return
comfy/sd.py
View file @ f0a2b81c
...@@ -1111,7 +1111,6 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o ...@@ -1111,7 +1111,6 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
unet_config["context_dim"] = sd['model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight'].shape[1] unet_config["context_dim"] = sd['model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight'].shape[1]
sd_config["unet_config"] = {"target": "comfy.ldm.modules.diffusionmodules.openaimodel.UNetModel", "params": unet_config} sd_config["unet_config"] = {"target": "comfy.ldm.modules.diffusionmodules.openaimodel.UNetModel", "params": unet_config}
model_config = {"target": "comfy.ldm.models.diffusion.ddpm.LatentDiffusion", "params": sd_config}
unclip_model = False unclip_model = False
inpaint_model = False inpaint_model = False
...@@ -1121,11 +1120,9 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o ...@@ -1121,11 +1120,9 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
sd_config["embedding_dropout"] = 0.25 sd_config["embedding_dropout"] = 0.25
sd_config["conditioning_key"] = 'crossattn-adm' sd_config["conditioning_key"] = 'crossattn-adm'
unclip_model = True unclip_model = True
model_config["target"] = "comfy.ldm.models.diffusion.ddpm.ImageEmbeddingConditionedLatentDiffusion"
elif unet_config["in_channels"] > 4: #inpainting model elif unet_config["in_channels"] > 4: #inpainting model
sd_config["conditioning_key"] = "hybrid" sd_config["conditioning_key"] = "hybrid"
sd_config["finetune_keys"] = None sd_config["finetune_keys"] = None
model_config["target"] = "comfy.ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
inpaint_model = True inpaint_model = True
else: else:
sd_config["conditioning_key"] = "crossattn" sd_config["conditioning_key"] = "crossattn"
......
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