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DiT/conf.py
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"""
List of all DiT model types / settings
"""
dit_conf = {
"XL/2": { # DiT_XL_2
"depth" : 28,
"num_heads" : 16,
"patch_size" : 2,
"hidden_size" : 1152,
},
"XL/4": { # DiT_XL_4
"depth" : 28,
"num_heads" : 16,
"patch_size" : 4,
"hidden_size" : 1152,
},
"XL/8": { # DiT_XL_8
"depth" : 28,
"num_heads" : 16,
"patch_size" : 8,
"hidden_size" : 1152,
},
"L/2": { # DiT_L_2
"depth" : 24,
"num_heads" : 16,
"patch_size" : 2,
"hidden_size" : 1024,
},
"L/4": { # DiT_L_4
"depth" : 24,
"num_heads" : 16,
"patch_size" : 4,
"hidden_size" : 1024,
},
"L/8": { # DiT_L_8
"depth" : 24,
"num_heads" : 16,
"patch_size" : 8,
"hidden_size" : 1024,
},
"B/2": { # DiT_B_2
"depth" : 12,
"num_heads" : 12,
"patch_size" : 2,
"hidden_size" : 768,
},
"B/4": { # DiT_B_4
"depth" : 12,
"num_heads" : 12,
"patch_size" : 4,
"hidden_size" : 768,
},
"B/8": { # DiT_B_8
"depth" : 12,
"num_heads" : 12,
"patch_size" : 8,
"hidden_size" : 768,
},
"S/2": { # DiT_S_2
"depth" : 12,
"num_heads" : 6,
"patch_size" : 2,
"hidden_size" : 384,
},
"S/4": { # DiT_S_4
"depth" : 12,
"num_heads" : 6,
"patch_size" : 4,
"hidden_size" : 384,
},
"S/8": { # DiT_S_8
"depth" : 12,
"num_heads" : 6,
"patch_size" : 8,
"hidden_size" : 384,
},
}
DiT/labels/imagenet1000.json
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DiT/loader.py
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import comfy.supported_models_base
import comfy.supported_models
import comfy.latent_formats
import comfy.model_patcher
import comfy.model_base
import comfy.utils
import torch
from comfy import model_management
from .model import DiT
class EXMDiT(comfy.supported_models.SD15):
unet_config = {}
unet_extra_config = {}
def model_type(self, state_dict, prefix=""):
return comfy.model_base.ModelType.EPS
def load_dit(model_path, model_conf):
state_dict = comfy.utils.load_torch_file(model_path)
state_dict = state_dict.get("model", state_dict)
parameters = comfy.utils.calculate_parameters(state_dict)
unet_dtype = model_management.unet_dtype(model_params=parameters)
offload_device = model_management.unet_offload_device()
model = comfy.model_base.BaseModel(
EXMDiT({"disable_unet_model_creation" : True }),
model_type=comfy.model_base.ModelType.EPS,
device=model_management.get_torch_device()
)
model_conf["num_classes"] = state_dict["y_embedder.embedding_table.weight"].shape[0] - 1 # adj. for empty
model.dit_config = model_conf
model.diffusion_model = DiT(**model_conf).eval()
model.diffusion_model.load_state_dict(state_dict)
model.diffusion_model.eval()
model.diffusion_model.dtype = unet_dtype
model.diffusion_model.to(unet_dtype)
model_patcher = comfy.model_patcher.ModelPatcher(
model,
load_device=comfy.model_management.get_torch_device(),
offload_device=comfy.model_management.unet_offload_device(),
current_device="cpu"
)
return model_patcher
DiT/model.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# GLIDE: https://github.com/openai/glide-text2im
# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
# --------------------------------------------------------
import torch
import torch.nn as nn
import numpy as np
import math
from timm.models.vision_transformer import PatchEmbed, Attention, Mlp
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
#################################################################################
# Embedding Layers for Timesteps and Class Labels #
#################################################################################
class TimestepEmbedder(nn.Module):
"""
Embeds scalar timesteps into vector representations.
"""
def __init__(self, hidden_size, frequency_embedding_size=256):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param t: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an (N, D) Tensor of positional embeddings.
"""
# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=t.device)
args = t[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding.to(dtype=t.dtype)
def forward(self, t):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
t_emb = self.mlp(t_freq)
return t_emb
class LabelEmbedder(nn.Module):
"""
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
"""
def __init__(self, num_classes, hidden_size, dropout_prob):
super().__init__()
use_cfg_embedding = dropout_prob > 0
self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
self.num_classes = num_classes
self.dropout_prob = dropout_prob
def token_drop(self, labels, force_drop_ids=None):
"""
Drops labels to enable classifier-free guidance.
"""
if force_drop_ids is None:
drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
else:
drop_ids = force_drop_ids == 1
labels = torch.where(drop_ids, self.num_classes, labels)
return labels
def forward(self, labels, train, force_drop_ids=None):
use_dropout = self.dropout_prob > 0
if (train and use_dropout) or (force_drop_ids is not None):
labels = self.token_drop(labels, force_drop_ids)
embeddings = self.embedding_table(labels)
return embeddings
#################################################################################
# Core DiT Model #
#################################################################################
class DiTBlock(nn.Module):
"""
A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
"""
def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
super().__init__()
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(hidden_size, 6 * hidden_size, bias=True)
)
def forward(self, x, c):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa))
x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
return x
class FinalLayer(nn.Module):
"""
The final layer of DiT.
"""
def __init__(self, hidden_size, patch_size, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(hidden_size, 2 * hidden_size, bias=True)
)
def forward(self, x, c):
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
x = modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class DiT(nn.Module):
"""
Diffusion model with a Transformer backbone.
"""
def __init__(
self,
input_size=32,
patch_size=2,
in_channels=4,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4.0,
class_dropout_prob=0.1,
num_classes=1000,
learn_sigma=True,
):
super().__init__()
self.learn_sigma = learn_sigma
self.in_channels = in_channels
self.out_channels = in_channels * 2 if learn_sigma else in_channels
self.patch_size = patch_size
self.num_heads = num_heads
self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True)
self.t_embedder = TimestepEmbedder(hidden_size)
self.y_embedder = LabelEmbedder(num_classes, hidden_size, class_dropout_prob)
num_patches = self.x_embedder.num_patches
# Will use fixed sin-cos embedding:
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False)
self.blocks = nn.ModuleList([
DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio) for _ in range(depth)
])
self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels)
self.initialize_weights()
def initialize_weights(self):
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
# Initialize (and freeze) pos_embed by sin-cos embedding:
pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches ** 0.5))
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
w = self.x_embedder.proj.weight.data
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
nn.init.constant_(self.x_embedder.proj.bias, 0)
# Initialize label embedding table:
nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
# Initialize timestep embedding MLP:
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
# Zero-out adaLN modulation layers in DiT blocks:
for block in self.blocks:
nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
# Zero-out output layers:
nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
def unpatchify(self, x):
"""
x: (N, T, patch_size**2 * C)
imgs: (N, H, W, C)
"""
c = self.out_channels
p = self.x_embedder.patch_size[0]
h = w = int(x.shape[1] ** 0.5)
assert h * w == x.shape[1]
x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
x = torch.einsum('nhwpqc->nchpwq', x)
imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
return imgs
def forward(self, x, timesteps, context, y, **kwargs):
"""
Forward pass of DiT.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
y: (N,) tensor of class labels
"""
# adapt to comfy inputs
t = timesteps
y = context.to(torch.int)
y = y[:, 0]
x = self.x_embedder(x) + self.pos_embed # (N, T, D), where T = H * W / patch_size ** 2
t = self.t_embedder(t) # (N, D) + change to model dtype
y = self.y_embedder(y, self.training) # (N, D)
c = t + y # (N, D)
for block in self.blocks:
x = block(x, c) # (N, T, D)
x = self.final_layer(x, c) # (N, T, patch_size ** 2 * out_channels)
x = self.unpatchify(x) # (N, out_channels, H, W)
# adapt to comfy outputs
x = x.to(torch.float)
eps, rest = x[:, :self.in_channels], x[:, self.in_channels:]
return eps
def forward_with_cfg(self, x, t, y, cfg_scale):
"""
Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance.
"""
# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
half = x[: len(x) // 2]
combined = torch.cat([half, half], dim=0)
model_out = self.forward(combined, t, y)
# For exact reproducibility reasons, we apply classifier-free guidance on only
# three channels by default. The standard approach to cfg applies it to all channels.
# This can be done by uncommenting the following line and commenting-out the line following that.
eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
# eps, rest = model_out[:, :3], model_out[:, 3:]
cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
eps = torch.cat([half_eps, half_eps], dim=0)
return torch.cat([eps, rest], dim=1)
#################################################################################
# Sine/Cosine Positional Embedding Functions #
#################################################################################
# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token and extra_tokens > 0:
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.
omega = 1. / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
DiT/nodes.py
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import os
import json
import torch
import folder_paths
from .conf import dit_conf
from .loader import load_dit
# initialize custom folder path
# TODO: integrate with `extra_model_paths.yaml`
os.makedirs(
os.path.join(folder_paths.models_dir,"dit"),
exist_ok = True,
)
folder_paths.folder_names_and_paths["dit"] = (
[os.path.join(folder_paths.models_dir,"dit")],
folder_paths.supported_pt_extensions
)
class DitCheckpointLoader:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"ckpt_name": (folder_paths.get_filename_list("dit"),),
"model": (list(dit_conf.keys()),),
"image_size": ([256, 512],),
# "num_classes": ("INT", {"default": 1000, "min": 0,}),
}
}
RETURN_TYPES = ("MODEL",)
RETURN_NAMES = ("model",)
FUNCTION = "load_checkpoint"
CATEGORY = "ExtraModels/DiT"
TITLE = "DitCheckpointLoader"
def load_checkpoint(self, ckpt_name, model, image_size):
ckpt_path = folder_paths.get_full_path("dit", ckpt_name)
model_conf = dit_conf[model]
model_conf["input_size"] = image_size // 8
# model_conf["num_classes"] = num_classes
dit = load_dit(
model_path = ckpt_path,
model_conf = model_conf,
)
return (dit,)
# todo: this needs frontend code to display properly
def get_label_data(label_file="labels/imagenet1000.json"):
label_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
label_file,
)
label_data = {0: "None"}
with open(label_path, "r") as f:
label_data = json.loads(f.read())
return label_data
label_data = get_label_data()
class DiTCondLabelSelect:
@classmethod
def INPUT_TYPES(s):
global label_data
return {
"required": {
"model" : ("MODEL",),
"label_name": (list(label_data.values()),),
}
}
RETURN_TYPES = ("CONDITIONING",)
RETURN_NAMES = ("class",)
FUNCTION = "cond_label"
CATEGORY = "ExtraModels/DiT"
TITLE = "DiTCondLabelSelect"
def cond_label(self, model, label_name):
global label_data
class_labels = [int(k) for k,v in label_data.items() if v == label_name]
y = torch.tensor([[class_labels[0]]]).to(torch.int)
return ([[y, {"pooled_output": []}]], )
class DiTCondLabelEmpty:
@classmethod
def INPUT_TYPES(s):
global label_data
return {
"required": {
"model" : ("MODEL",),
}
}
RETURN_TYPES = ("CONDITIONING",)
RETURN_NAMES = ("empty",)
FUNCTION = "cond_empty"
CATEGORY = "ExtraModels/DiT"
TITLE = "DiTCondLabelEmpty"
def cond_empty(self, model):
# [ID of last class + 1] == [num_classes]
y_null = model.model.dit_config["num_classes"]
y = torch.tensor([[y_null]]).to(torch.int)
return ([[y, {"pooled_output": []}]], )
NODE_CLASS_MAPPINGS = {
"DitCheckpointLoader" : DitCheckpointLoader,
"DiTCondLabelSelect" : DiTCondLabelSelect,
"DiTCondLabelEmpty" : DiTCondLabelEmpty,
}
__init__.py
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# only import if running as a custom node
try:
import comfy.utils
except ImportError:
pass
else:
NODE_CLASS_MAPPINGS = {}
# Deci Diffusion
# from .DeciDiffusion.nodes import NODE_CLASS_MAPPINGS as DeciDiffusion_Nodes
# NODE_CLASS_MAPPINGS.update(DeciDiffusion_Nodes)
# DiT
from .DiT.nodes import NODE_CLASS_MAPPINGS as DiT_Nodes
NODE_CLASS_MAPPINGS.update(DiT_Nodes)
NODE_DISPLAY_NAME_MAPPINGS = {k:v.TITLE for k,v in NODE_CLASS_MAPPINGS.items()}
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']