Unverified Commit 41360440 authored by XCL's avatar XCL Committed by GitHub
Browse files

Tencent Hunyuan Team: add HunyuanDiT related updates (#8240)



* Hunyuan Team: add HunyuanDiT related updates


---------
Co-authored-by: default avatarXCLiu <liuxc1996@gmail.com>
Co-authored-by: default avataryiyixuxu <yixu310@gmail.com>
parent bc108e15
......@@ -83,6 +83,7 @@ else:
"ControlNetModel",
"ControlNetXSAdapter",
"DiTTransformer2DModel",
"HunyuanDiT2DModel",
"I2VGenXLUNet",
"Kandinsky3UNet",
"ModelMixin",
......@@ -229,6 +230,7 @@ else:
"BlipDiffusionPipeline",
"CLIPImageProjection",
"CycleDiffusionPipeline",
"HunyuanDiTPipeline",
"I2VGenXLPipeline",
"IFImg2ImgPipeline",
"IFImg2ImgSuperResolutionPipeline",
......@@ -487,6 +489,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
ControlNetModel,
ControlNetXSAdapter,
DiTTransformer2DModel,
HunyuanDiT2DModel,
I2VGenXLUNet,
Kandinsky3UNet,
ModelMixin,
......@@ -611,6 +614,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
AudioLDMPipeline,
CLIPImageProjection,
CycleDiffusionPipeline,
HunyuanDiTPipeline,
I2VGenXLPipeline,
IFImg2ImgPipeline,
IFImg2ImgSuperResolutionPipeline,
......
......@@ -38,6 +38,7 @@ if is_torch_available():
_import_structure["embeddings"] = ["ImageProjection"]
_import_structure["modeling_utils"] = ["ModelMixin"]
_import_structure["transformers.dit_transformer_2d"] = ["DiTTransformer2DModel"]
_import_structure["transformers.hunyuan_transformer_2d"] = ["HunyuanDiT2DModel"]
_import_structure["transformers.pixart_transformer_2d"] = ["PixArtTransformer2DModel"]
_import_structure["transformers.prior_transformer"] = ["PriorTransformer"]
_import_structure["transformers.t5_film_transformer"] = ["T5FilmDecoder"]
......@@ -78,6 +79,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
from .transformers import (
DiTTransformer2DModel,
DualTransformer2DModel,
HunyuanDiT2DModel,
PixArtTransformer2DModel,
PriorTransformer,
T5FilmDecoder,
......
......@@ -50,6 +50,18 @@ def get_activation(act_fn: str) -> nn.Module:
raise ValueError(f"Unsupported activation function: {act_fn}")
class FP32SiLU(nn.Module):
r"""
SiLU activation function with input upcasted to torch.float32.
"""
def __init__(self):
super().__init__()
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
return F.silu(inputs.float(), inplace=False).to(inputs.dtype)
class GELU(nn.Module):
r"""
GELU activation function with tanh approximation support with `approximate="tanh"`.
......
......@@ -103,6 +103,7 @@ class Attention(nn.Module):
upcast_softmax: bool = False,
cross_attention_norm: Optional[str] = None,
cross_attention_norm_num_groups: int = 32,
qk_norm: Optional[str] = None,
added_kv_proj_dim: Optional[int] = None,
norm_num_groups: Optional[int] = None,
spatial_norm_dim: Optional[int] = None,
......@@ -161,6 +162,15 @@ class Attention(nn.Module):
else:
self.spatial_norm = None
if qk_norm is None:
self.norm_q = None
self.norm_k = None
elif qk_norm == "layer_norm":
self.norm_q = nn.LayerNorm(dim_head, eps=eps)
self.norm_k = nn.LayerNorm(dim_head, eps=eps)
else:
raise ValueError(f"unknown qk_norm: {qk_norm}. Should be None or 'layer_norm'")
if cross_attention_norm is None:
self.norm_cross = None
elif cross_attention_norm == "layer_norm":
......@@ -1426,6 +1436,104 @@ class AttnProcessor2_0:
return hidden_states
class HunyuanAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on query and key vector.
"""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
) -> torch.Tensor:
from .embeddings import apply_rotary_emb
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# Apply RoPE if needed
if image_rotary_emb is not None:
query = apply_rotary_emb(query, image_rotary_emb)
if not attn.is_cross_attention:
key = apply_rotary_emb(key, image_rotary_emb)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
class FusedAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). It uses
......
......@@ -16,10 +16,11 @@ from typing import List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import deprecate
from .activations import get_activation
from .activations import FP32SiLU, get_activation
from .attention_processor import Attention
......@@ -135,6 +136,7 @@ class PatchEmbed(nn.Module):
flatten=True,
bias=True,
interpolation_scale=1,
pos_embed_type="sincos",
):
super().__init__()
......@@ -156,10 +158,18 @@ class PatchEmbed(nn.Module):
self.height, self.width = height // patch_size, width // patch_size
self.base_size = height // patch_size
self.interpolation_scale = interpolation_scale
pos_embed = get_2d_sincos_pos_embed(
embed_dim, int(num_patches**0.5), base_size=self.base_size, interpolation_scale=self.interpolation_scale
)
self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=False)
if pos_embed_type is None:
self.pos_embed = None
elif pos_embed_type == "sincos":
pos_embed = get_2d_sincos_pos_embed(
embed_dim,
int(num_patches**0.5),
base_size=self.base_size,
interpolation_scale=self.interpolation_scale,
)
self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=False)
else:
raise ValueError(f"Unsupported pos_embed_type: {pos_embed_type}")
def forward(self, latent):
height, width = latent.shape[-2] // self.patch_size, latent.shape[-1] // self.patch_size
......@@ -169,6 +179,8 @@ class PatchEmbed(nn.Module):
latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC
if self.layer_norm:
latent = self.norm(latent)
if self.pos_embed is None:
return latent.to(latent.dtype)
# Interpolate positional embeddings if needed.
# (For PixArt-Alpha: https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L162C151-L162C160)
......@@ -187,6 +199,113 @@ class PatchEmbed(nn.Module):
return (latent + pos_embed).to(latent.dtype)
def get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size, use_real=True):
"""
RoPE for image tokens with 2d structure.
Args:
embed_dim: (`int`):
The embedding dimension size
crops_coords (`Tuple[int]`)
The top-left and bottom-right coordinates of the crop.
grid_size (`Tuple[int]`):
The grid size of the positional embedding.
use_real (`bool`):
If True, return real part and imaginary part separately. Otherwise, return complex numbers.
Returns:
`torch.Tensor`: positional embdding with shape `( grid_size * grid_size, embed_dim/2)`.
"""
start, stop = crops_coords
grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32)
grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0) # [2, W, H]
grid = grid.reshape([2, 1, *grid.shape[1:]])
pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real)
return pos_embed
def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False):
assert embed_dim % 4 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real) # (H*W, D/4)
emb_w = get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real) # (H*W, D/4)
if use_real:
cos = torch.cat([emb_h[0], emb_w[0]], dim=1) # (H*W, D/2)
sin = torch.cat([emb_h[1], emb_w[1]], dim=1) # (H*W, D/2)
return cos, sin
else:
emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2)
return emb
def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False):
"""
Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' and the end
index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in complex64
data type.
Args:
dim (`int`): Dimension of the frequency tensor.
pos (`np.ndarray` or `int`): Position indices for the frequency tensor. [S] or scalar
theta (`float`, *optional*, defaults to 10000.0):
Scaling factor for frequency computation. Defaults to 10000.0.
use_real (`bool`, *optional*):
If True, return real part and imaginary part separately. Otherwise, return complex numbers.
Returns:
`torch.Tensor`: Precomputed frequency tensor with complex exponentials. [S, D/2]
"""
if isinstance(pos, int):
pos = np.arange(pos)
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # [D/2]
t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S]
freqs = torch.outer(t, freqs).float() # type: ignore # [S, D/2]
if use_real:
freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D]
freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D]
return freqs_cos, freqs_sin
else:
freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2]
return freqs_cis
def apply_rotary_emb(
x: torch.Tensor,
freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings
to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are
reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting
tensors contain rotary embeddings and are returned as real tensors.
Args:
x (`torch.Tensor`):
Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply
freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],)
Returns:
Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
"""
cos, sin = freqs_cis # [S, D]
cos = cos[None, None]
sin = sin[None, None]
cos, sin = cos.to(x.device), sin.to(x.device)
x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2]
x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
return out
class TimestepEmbedding(nn.Module):
def __init__(
self,
......@@ -507,6 +626,88 @@ class CombinedTimestepLabelEmbeddings(nn.Module):
return conditioning
class HunyuanDiTAttentionPool(nn.Module):
# Copied from https://github.com/Tencent/HunyuanDiT/blob/cb709308d92e6c7e8d59d0dff41b74d35088db6a/hydit/modules/poolers.py#L6
def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
super().__init__()
self.positional_embedding = nn.Parameter(torch.randn(spacial_dim + 1, embed_dim) / embed_dim**0.5)
self.k_proj = nn.Linear(embed_dim, embed_dim)
self.q_proj = nn.Linear(embed_dim, embed_dim)
self.v_proj = nn.Linear(embed_dim, embed_dim)
self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
self.num_heads = num_heads
def forward(self, x):
x = x.permute(1, 0, 2) # NLC -> LNC
x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (L+1)NC
x = x + self.positional_embedding[:, None, :].to(x.dtype) # (L+1)NC
x, _ = F.multi_head_attention_forward(
query=x[:1],
key=x,
value=x,
embed_dim_to_check=x.shape[-1],
num_heads=self.num_heads,
q_proj_weight=self.q_proj.weight,
k_proj_weight=self.k_proj.weight,
v_proj_weight=self.v_proj.weight,
in_proj_weight=None,
in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
bias_k=None,
bias_v=None,
add_zero_attn=False,
dropout_p=0,
out_proj_weight=self.c_proj.weight,
out_proj_bias=self.c_proj.bias,
use_separate_proj_weight=True,
training=self.training,
need_weights=False,
)
return x.squeeze(0)
class HunyuanCombinedTimestepTextSizeStyleEmbedding(nn.Module):
def __init__(self, embedding_dim, pooled_projection_dim=1024, seq_len=256, cross_attention_dim=2048):
super().__init__()
self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
self.pooler = HunyuanDiTAttentionPool(
seq_len, cross_attention_dim, num_heads=8, output_dim=pooled_projection_dim
)
# Here we use a default learned embedder layer for future extension.
self.style_embedder = nn.Embedding(1, embedding_dim)
extra_in_dim = 256 * 6 + embedding_dim + pooled_projection_dim
self.extra_embedder = PixArtAlphaTextProjection(
in_features=extra_in_dim,
hidden_size=embedding_dim * 4,
out_features=embedding_dim,
act_fn="silu_fp32",
)
def forward(self, timestep, encoder_hidden_states, image_meta_size, style, hidden_dtype=None):
timesteps_proj = self.time_proj(timestep)
timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, 256)
# extra condition1: text
pooled_projections = self.pooler(encoder_hidden_states) # (N, 1024)
# extra condition2: image meta size embdding
image_meta_size = get_timestep_embedding(image_meta_size.view(-1), 256, True, 0)
image_meta_size = image_meta_size.to(dtype=hidden_dtype)
image_meta_size = image_meta_size.view(-1, 6 * 256) # (N, 1536)
# extra condition3: style embedding
style_embedding = self.style_embedder(style) # (N, embedding_dim)
# Concatenate all extra vectors
extra_cond = torch.cat([pooled_projections, image_meta_size, style_embedding], dim=1)
conditioning = timesteps_emb + self.extra_embedder(extra_cond) # [B, D]
return conditioning
class TextTimeEmbedding(nn.Module):
def __init__(self, encoder_dim: int, time_embed_dim: int, num_heads: int = 64):
super().__init__()
......@@ -793,11 +994,18 @@ class PixArtAlphaTextProjection(nn.Module):
Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
"""
def __init__(self, in_features, hidden_size, num_tokens=120):
def __init__(self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh"):
super().__init__()
if out_features is None:
out_features = hidden_size
self.linear_1 = nn.Linear(in_features=in_features, out_features=hidden_size, bias=True)
self.act_1 = nn.GELU(approximate="tanh")
self.linear_2 = nn.Linear(in_features=hidden_size, out_features=hidden_size, bias=True)
if act_fn == "gelu_tanh":
self.act_1 = nn.GELU(approximate="tanh")
elif act_fn == "silu_fp32":
self.act_1 = FP32SiLU()
else:
raise ValueError(f"Unknown activation function: {act_fn}")
self.linear_2 = nn.Linear(in_features=hidden_size, out_features=out_features, bias=True)
def forward(self, caption):
hidden_states = self.linear_1(caption)
......
......@@ -176,7 +176,8 @@ class AdaLayerNormContinuous(nn.Module):
raise ValueError(f"unknown norm_type {norm_type}")
def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:
emb = self.linear(self.silu(conditioning_embedding))
# convert back to the original dtype in case `conditioning_embedding`` is upcasted to float32 (needed for hunyuanDiT)
emb = self.linear(self.silu(conditioning_embedding).to(x.dtype))
scale, shift = torch.chunk(emb, 2, dim=1)
x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
return x
......
......@@ -4,6 +4,7 @@ from ...utils import is_torch_available
if is_torch_available():
from .dit_transformer_2d import DiTTransformer2DModel
from .dual_transformer_2d import DualTransformer2DModel
from .hunyuan_transformer_2d import HunyuanDiT2DModel
from .pixart_transformer_2d import PixArtTransformer2DModel
from .prior_transformer import PriorTransformer
from .t5_film_transformer import T5FilmDecoder
......
# Copyright 2024 HunyuanDiT Authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
import torch
import torch.nn.functional as F
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...utils import logging
from ...utils.torch_utils import maybe_allow_in_graph
from ..attention import FeedForward
from ..attention_processor import Attention, HunyuanAttnProcessor2_0
from ..embeddings import (
HunyuanCombinedTimestepTextSizeStyleEmbedding,
PatchEmbed,
PixArtAlphaTextProjection,
)
from ..modeling_outputs import Transformer2DModelOutput
from ..modeling_utils import ModelMixin
from ..normalization import AdaLayerNormContinuous
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class FP32LayerNorm(nn.LayerNorm):
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
origin_dtype = inputs.dtype
return F.layer_norm(
inputs.float(), self.normalized_shape, self.weight.float(), self.bias.float(), self.eps
).to(origin_dtype)
class AdaLayerNormShift(nn.Module):
r"""
Norm layer modified to incorporate timestep embeddings.
Parameters:
embedding_dim (`int`): The size of each embedding vector.
num_embeddings (`int`): The size of the embeddings dictionary.
"""
def __init__(self, embedding_dim: int, elementwise_affine=True, eps=1e-6):
super().__init__()
self.silu = nn.SiLU()
self.linear = nn.Linear(embedding_dim, embedding_dim)
self.norm = FP32LayerNorm(embedding_dim, elementwise_affine=elementwise_affine, eps=eps)
def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor:
shift = self.linear(self.silu(emb.to(torch.float32)).to(emb.dtype))
x = self.norm(x) + shift.unsqueeze(dim=1)
return x
@maybe_allow_in_graph
class HunyuanDiTBlock(nn.Module):
r"""
Transformer block used in Hunyuan-DiT model (https://github.com/Tencent/HunyuanDiT). Allow skip connection and
QKNorm
Parameters:
dim (`int`):
The number of channels in the input and output.
num_attention_heads (`int`):
The number of headsto use for multi-head attention.
cross_attention_dim (`int`,*optional*):
The size of the encoder_hidden_states vector for cross attention.
dropout(`float`, *optional*, defaults to 0.0):
The dropout probability to use.
activation_fn (`str`,*optional*, defaults to `"geglu"`):
Activation function to be used in feed-forward. .
norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
Whether to use learnable elementwise affine parameters for normalization.
norm_eps (`float`, *optional*, defaults to 1e-6):
A small constant added to the denominator in normalization layers to prevent division by zero.
final_dropout (`bool` *optional*, defaults to False):
Whether to apply a final dropout after the last feed-forward layer.
ff_inner_dim (`int`, *optional*):
The size of the hidden layer in the feed-forward block. Defaults to `None`.
ff_bias (`bool`, *optional*, defaults to `True`):
Whether to use bias in the feed-forward block.
skip (`bool`, *optional*, defaults to `False`):
Whether to use skip connection. Defaults to `False` for down-blocks and mid-blocks.
qk_norm (`bool`, *optional*, defaults to `True`):
Whether to use normalization in QK calculation. Defaults to `True`.
"""
def __init__(
self,
dim: int,
num_attention_heads: int,
cross_attention_dim: int = 1024,
dropout=0.0,
activation_fn: str = "geglu",
norm_elementwise_affine: bool = True,
norm_eps: float = 1e-6,
final_dropout: bool = False,
ff_inner_dim: Optional[int] = None,
ff_bias: bool = True,
skip: bool = False,
qk_norm: bool = True,
):
super().__init__()
# Define 3 blocks. Each block has its own normalization layer.
# NOTE: when new version comes, check norm2 and norm 3
# 1. Self-Attn
self.norm1 = AdaLayerNormShift(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
self.attn1 = Attention(
query_dim=dim,
cross_attention_dim=None,
dim_head=dim // num_attention_heads,
heads=num_attention_heads,
qk_norm="layer_norm" if qk_norm else None,
eps=1e-6,
bias=True,
processor=HunyuanAttnProcessor2_0(),
)
# 2. Cross-Attn
self.norm2 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine)
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim,
dim_head=dim // num_attention_heads,
heads=num_attention_heads,
qk_norm="layer_norm" if qk_norm else None,
eps=1e-6,
bias=True,
processor=HunyuanAttnProcessor2_0(),
)
# 3. Feed-forward
self.norm3 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine)
self.ff = FeedForward(
dim,
dropout=dropout, ### 0.0
activation_fn=activation_fn, ### approx GeLU
final_dropout=final_dropout, ### 0.0
inner_dim=ff_inner_dim, ### int(dim * mlp_ratio)
bias=ff_bias,
)
# 4. Skip Connection
if skip:
self.skip_norm = FP32LayerNorm(2 * dim, norm_eps, elementwise_affine=True)
self.skip_linear = nn.Linear(2 * dim, dim)
else:
self.skip_linear = None
# let chunk size default to None
self._chunk_size = None
self._chunk_dim = 0
def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
# Sets chunk feed-forward
self._chunk_size = chunk_size
self._chunk_dim = dim
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
image_rotary_emb=None,
skip=None,
) -> torch.Tensor:
# Notice that normalization is always applied before the real computation in the following blocks.
# 0. Long Skip Connection
if self.skip_linear is not None:
cat = torch.cat([hidden_states, skip], dim=-1)
cat = self.skip_norm(cat)
hidden_states = self.skip_linear(cat)
# 1. Self-Attention
norm_hidden_states = self.norm1(hidden_states, temb) ### checked: self.norm1 is correct
attn_output = self.attn1(
norm_hidden_states,
image_rotary_emb=image_rotary_emb,
)
hidden_states = hidden_states + attn_output
# 2. Cross-Attention
hidden_states = hidden_states + self.attn2(
self.norm2(hidden_states),
encoder_hidden_states=encoder_hidden_states,
image_rotary_emb=image_rotary_emb,
)
# FFN Layer ### TODO: switch norm2 and norm3 in the state dict
mlp_inputs = self.norm3(hidden_states)
hidden_states = hidden_states + self.ff(mlp_inputs)
return hidden_states
class HunyuanDiT2DModel(ModelMixin, ConfigMixin):
"""
HunYuanDiT: Diffusion model with a Transformer backbone.
Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.
Parameters:
num_attention_heads (`int`, *optional*, defaults to 16):
The number of heads to use for multi-head attention.
attention_head_dim (`int`, *optional*, defaults to 88):
The number of channels in each head.
in_channels (`int`, *optional*):
The number of channels in the input and output (specify if the input is **continuous**).
patch_size (`int`, *optional*):
The size of the patch to use for the input.
activation_fn (`str`, *optional*, defaults to `"geglu"`):
Activation function to use in feed-forward.
sample_size (`int`, *optional*):
The width of the latent images. This is fixed during training since it is used to learn a number of
position embeddings.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability to use.
cross_attention_dim (`int`, *optional*):
The number of dimension in the clip text embedding.
hidden_size (`int`, *optional*):
The size of hidden layer in the conditioning embedding layers.
num_layers (`int`, *optional*, defaults to 1):
The number of layers of Transformer blocks to use.
mlp_ratio (`float`, *optional*, defaults to 4.0):
The ratio of the hidden layer size to the input size.
learn_sigma (`bool`, *optional*, defaults to `True`):
Whether to predict variance.
cross_attention_dim_t5 (`int`, *optional*):
The number dimensions in t5 text embedding.
pooled_projection_dim (`int`, *optional*):
The size of the pooled projection.
text_len (`int`, *optional*):
The length of the clip text embedding.
text_len_t5 (`int`, *optional*):
The length of the T5 text embedding.
"""
@register_to_config
def __init__(
self,
num_attention_heads: int = 16,
attention_head_dim: int = 88,
in_channels: Optional[int] = None,
patch_size: Optional[int] = None,
activation_fn: str = "gelu-approximate",
sample_size=32,
hidden_size=1152,
num_layers: int = 28,
mlp_ratio: float = 4.0,
learn_sigma: bool = True,
cross_attention_dim: int = 1024,
norm_type: str = "layer_norm",
cross_attention_dim_t5: int = 2048,
pooled_projection_dim: int = 1024,
text_len: int = 77,
text_len_t5: int = 256,
):
super().__init__()
self.out_channels = in_channels * 2 if learn_sigma else in_channels
self.num_heads = num_attention_heads
self.inner_dim = num_attention_heads * attention_head_dim
self.text_embedder = PixArtAlphaTextProjection(
in_features=cross_attention_dim_t5,
hidden_size=cross_attention_dim_t5 * 4,
out_features=cross_attention_dim,
act_fn="silu_fp32",
)
self.text_embedding_padding = nn.Parameter(
torch.randn(text_len + text_len_t5, cross_attention_dim, dtype=torch.float32)
)
self.pos_embed = PatchEmbed(
height=sample_size,
width=sample_size,
in_channels=in_channels,
embed_dim=hidden_size,
patch_size=patch_size,
pos_embed_type=None,
)
self.time_extra_emb = HunyuanCombinedTimestepTextSizeStyleEmbedding(
hidden_size,
pooled_projection_dim=pooled_projection_dim,
seq_len=text_len_t5,
cross_attention_dim=cross_attention_dim_t5,
)
# HunyuanDiT Blocks
self.blocks = nn.ModuleList(
[
HunyuanDiTBlock(
dim=self.inner_dim,
num_attention_heads=self.config.num_attention_heads,
activation_fn=activation_fn,
ff_inner_dim=int(self.inner_dim * mlp_ratio),
cross_attention_dim=cross_attention_dim,
qk_norm=True, # See http://arxiv.org/abs/2302.05442 for details.
skip=layer > num_layers // 2,
)
for layer in range(num_layers)
]
)
self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)
self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)
def forward(
self,
hidden_states,
timestep,
encoder_hidden_states=None,
text_embedding_mask=None,
encoder_hidden_states_t5=None,
text_embedding_mask_t5=None,
image_meta_size=None,
style=None,
image_rotary_emb=None,
return_dict=True,
):
"""
The [`HunyuanDiT2DModel`] forward method.
Args:
hidden_states (`torch.Tensor` of shape `(batch size, dim, height, width)`):
The input tensor.
timestep ( `torch.LongTensor`, *optional*):
Used to indicate denoising step.
encoder_hidden_states ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
Conditional embeddings for cross attention layer. This is the output of `BertModel`.
text_embedding_mask: torch.Tensor
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. This is the output
of `BertModel`.
encoder_hidden_states_t5 ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
Conditional embeddings for cross attention layer. This is the output of T5 Text Encoder.
text_embedding_mask_t5: torch.Tensor
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. This is the output
of T5 Text Encoder.
image_meta_size (torch.Tensor):
Conditional embedding indicate the image sizes
style: torch.Tensor:
Conditional embedding indicate the style
image_rotary_emb (`torch.Tensor`):
The image rotary embeddings to apply on query and key tensors during attention calculation.
return_dict: bool
Whether to return a dictionary.
"""
height, width = hidden_states.shape[-2:]
hidden_states = self.pos_embed(hidden_states)
temb = self.time_extra_emb(
timestep, encoder_hidden_states_t5, image_meta_size, style, hidden_dtype=timestep.dtype
) # [B, D]
# text projection
batch_size, sequence_length, _ = encoder_hidden_states_t5.shape
encoder_hidden_states_t5 = self.text_embedder(
encoder_hidden_states_t5.view(-1, encoder_hidden_states_t5.shape[-1])
)
encoder_hidden_states_t5 = encoder_hidden_states_t5.view(batch_size, sequence_length, -1)
encoder_hidden_states = torch.cat([encoder_hidden_states, encoder_hidden_states_t5], dim=1)
text_embedding_mask = torch.cat([text_embedding_mask, text_embedding_mask_t5], dim=-1)
text_embedding_mask = text_embedding_mask.unsqueeze(2).bool()
encoder_hidden_states = torch.where(text_embedding_mask, encoder_hidden_states, self.text_embedding_padding)
skips = []
for layer, block in enumerate(self.blocks):
if layer > self.config.num_layers // 2:
skip = skips.pop()
hidden_states = block(
hidden_states,
temb=temb,
encoder_hidden_states=encoder_hidden_states,
image_rotary_emb=image_rotary_emb,
skip=skip,
) # (N, L, D)
else:
hidden_states = block(
hidden_states,
temb=temb,
encoder_hidden_states=encoder_hidden_states,
image_rotary_emb=image_rotary_emb,
) # (N, L, D)
if layer < (self.config.num_layers // 2 - 1):
skips.append(hidden_states)
# final layer
hidden_states = self.norm_out(hidden_states, temb.to(torch.float32))
hidden_states = self.proj_out(hidden_states)
# (N, L, patch_size ** 2 * out_channels)
# unpatchify: (N, out_channels, H, W)
patch_size = self.pos_embed.patch_size
height = height // patch_size
width = width // patch_size
hidden_states = hidden_states.reshape(
shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels)
)
hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
output = hidden_states.reshape(
shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size)
)
if not return_dict:
return (output,)
return Transformer2DModelOutput(sample=output)
......@@ -150,6 +150,7 @@ else:
"IFPipeline",
"IFSuperResolutionPipeline",
]
_import_structure["hunyuandit"] = ["HunyuanDiTPipeline"]
_import_structure["kandinsky"] = [
"KandinskyCombinedPipeline",
"KandinskyImg2ImgCombinedPipeline",
......@@ -418,6 +419,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
VersatileDiffusionTextToImagePipeline,
VQDiffusionPipeline,
)
from .hunyuandit import HunyuanDiTPipeline
from .i2vgen_xl import I2VGenXLPipeline
from .kandinsky import (
KandinskyCombinedPipeline,
......
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils import dummy_torch_and_transformers_objects # noqa F403
_dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
else:
_import_structure["pipeline_hunyuandit"] = ["HunyuanDiTPipeline"]
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import *
else:
from .pipeline_hunyuandit import HunyuanDiTPipeline
else:
import sys
sys.modules[__name__] = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
module_spec=__spec__,
)
for name, value in _dummy_objects.items():
setattr(sys.modules[__name__], name, value)
This diff is collapsed.
......@@ -122,6 +122,21 @@ class DiTTransformer2DModel(metaclass=DummyObject):
requires_backends(cls, ["torch"])
class HunyuanDiT2DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
class I2VGenXLUNet(metaclass=DummyObject):
_backends = ["torch"]
......
......@@ -212,6 +212,21 @@ class CycleDiffusionPipeline(metaclass=DummyObject):
requires_backends(cls, ["torch", "transformers"])
class HunyuanDiTPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class I2VGenXLPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
......
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import tempfile
import unittest
import numpy as np
import torch
from transformers import AutoTokenizer, BertModel, T5EncoderModel
from diffusers import (
AutoencoderKL,
DDPMScheduler,
HunyuanDiT2DModel,
HunyuanDiTPipeline,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, to_np
enable_full_determinism()
class HunyuanDiTPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = HunyuanDiTPipeline
params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
required_optional_params = PipelineTesterMixin.required_optional_params
def get_dummy_components(self):
torch.manual_seed(0)
transformer = HunyuanDiT2DModel(
sample_size=16,
num_layers=2,
patch_size=2,
attention_head_dim=8,
num_attention_heads=3,
in_channels=4,
cross_attention_dim=32,
cross_attention_dim_t5=32,
pooled_projection_dim=16,
hidden_size=24,
activation_fn="gelu-approximate",
)
torch.manual_seed(0)
vae = AutoencoderKL()
scheduler = DDPMScheduler()
text_encoder = BertModel.from_pretrained("hf-internal-testing/tiny-random-BertModel")
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel")
text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
components = {
"transformer": transformer.eval(),
"vae": vae.eval(),
"scheduler": scheduler,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"output_type": "np",
"use_resolution_binning": False,
}
return inputs
def test_inference(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
self.assertEqual(image.shape, (1, 16, 16, 3))
expected_slice = np.array(
[0.56939435, 0.34541583, 0.35915792, 0.46489206, 0.38775963, 0.45004836, 0.5957267, 0.59481275, 0.33287364]
)
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_sequential_cpu_offload_forward_pass(self):
# TODO(YiYi) need to fix later
pass
def test_sequential_offload_forward_pass_twice(self):
# TODO(YiYi) need to fix later
pass
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(
expected_max_diff=1e-3,
)
def test_save_load_optional_components(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
prompt = inputs["prompt"]
generator = inputs["generator"]
num_inference_steps = inputs["num_inference_steps"]
output_type = inputs["output_type"]
(
prompt_embeds,
negative_prompt_embeds,
prompt_attention_mask,
negative_prompt_attention_mask,
) = pipe.encode_prompt(prompt, device=torch_device, dtype=torch.float32, text_encoder_index=0)
(
prompt_embeds_2,
negative_prompt_embeds_2,
prompt_attention_mask_2,
negative_prompt_attention_mask_2,
) = pipe.encode_prompt(
prompt,
device=torch_device,
dtype=torch.float32,
text_encoder_index=1,
)
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"prompt_attention_mask": prompt_attention_mask,
"negative_prompt_embeds": negative_prompt_embeds,
"negative_prompt_attention_mask": negative_prompt_attention_mask,
"prompt_embeds_2": prompt_embeds_2,
"prompt_attention_mask_2": prompt_attention_mask_2,
"negative_prompt_embeds_2": negative_prompt_embeds_2,
"negative_prompt_attention_mask_2": negative_prompt_attention_mask_2,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
"use_resolution_binning": False,
}
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(pipe, optional_component, None)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(pipe_loaded, optional_component) is None,
f"`{optional_component}` did not stay set to None after loading.",
)
inputs = self.get_dummy_inputs(torch_device)
generator = inputs["generator"]
num_inference_steps = inputs["num_inference_steps"]
output_type = inputs["output_type"]
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"prompt_attention_mask": prompt_attention_mask,
"negative_prompt_embeds": negative_prompt_embeds,
"negative_prompt_attention_mask": negative_prompt_attention_mask,
"prompt_embeds_2": prompt_embeds_2,
"prompt_attention_mask_2": prompt_attention_mask_2,
"negative_prompt_embeds_2": negative_prompt_embeds_2,
"negative_prompt_attention_mask_2": negative_prompt_attention_mask_2,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
"use_resolution_binning": False,
}
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(max_diff, 1e-4)
@slow
@require_torch_gpu
class HunyuanDiTPipelineIntegrationTests(unittest.TestCase):
prompt = "一个宇航员在骑马"
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_hunyuan_dit_1024(self):
generator = torch.Generator("cpu").manual_seed(0)
pipe = HunyuanDiTPipeline.from_pretrained(
"XCLiu/HunyuanDiT-0523", revision="refs/pr/2", torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()
prompt = self.prompt
image = pipe(
prompt=prompt, height=1024, width=1024, generator=generator, num_inference_steps=2, output_type="np"
).images
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array(
[0.48388672, 0.33789062, 0.30737305, 0.47875977, 0.25097656, 0.30029297, 0.4440918, 0.26953125, 0.30078125]
)
max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice)
assert max_diff < 1e-3, f"Max diff is too high. got {image_slice.flatten()}"
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