wehub-resource-sync
565 lines
20 KiB
Python
Executable File
565 lines
20 KiB
Python
Executable File
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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# This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
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import os
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import torch
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import torch.nn as nn
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from timm.models.layers import DropPath
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from diffusion.model.builder import MODELS
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from diffusion.model.nets.basic_modules import DWMlp, GLUMBConv, Mlp
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from diffusion.model.nets.basic_modules_linear import GLUMBConvLinear
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from diffusion.model.nets.sana import Sana, get_2d_sincos_pos_embed
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from diffusion.model.nets.sana_blocks import (
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Attention,
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CaptionEmbedder,
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FlashAttention,
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LiteLA,
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MultiHeadCrossAttention,
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MultiHeadCrossVallinaAttention,
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PatchEmbedMS,
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RopePosEmbed,
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T2IFinalLayer,
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t2i_modulate,
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)
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from diffusion.model.utils import auto_grad_checkpoint
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from diffusion.utils.import_utils import is_triton_module_available, is_xformers_available
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_triton_modules_available = False
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if is_triton_module_available():
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from diffusion.model.nets.fastlinear.modules import TritonLiteMLA, TritonMBConvPreGLU
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_triton_modules_available = True
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_xformers_available = False if os.environ.get("DISABLE_XFORMERS", "0") == "1" else is_xformers_available()
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if _xformers_available:
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import xformers.ops
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class SanaMSBlock(nn.Module):
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"""
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A Sana block with global shared adaptive layer norm zero (adaLN-Zero) conditioning.
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"""
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def __init__(
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self,
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hidden_size,
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num_heads,
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mlp_ratio=4.0,
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drop_path=0.0,
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qk_norm=False,
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attn_type="flash",
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ffn_type="mlp",
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mlp_acts=("silu", "silu", None),
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linear_head_dim=32,
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cross_norm=False,
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cross_attn_type="flash",
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**block_kwargs,
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):
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super().__init__()
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self.hidden_size = hidden_size
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self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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if attn_type == "flash":
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# flash self attention
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self.attn = FlashAttention(
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hidden_size,
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num_heads=num_heads,
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qkv_bias=True,
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qk_norm=qk_norm,
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**block_kwargs,
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)
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elif attn_type == "linear":
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# linear self attention
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# TODO: Here the num_heads set to 36 for tmp used
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self_num_heads = hidden_size // linear_head_dim
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self.attn = LiteLA(hidden_size, hidden_size, heads=self_num_heads, eps=1e-8, qk_norm=qk_norm)
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elif attn_type == "triton_linear":
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if not _triton_modules_available:
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raise ValueError(
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f"{attn_type} type is not available due to _triton_modules_available={_triton_modules_available}."
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)
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# linear self attention with triton kernel fusion
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self_num_heads = hidden_size // linear_head_dim
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self.attn = TritonLiteMLA(hidden_size, num_heads=self_num_heads, eps=1e-8)
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elif attn_type == "vanilla":
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# vanilla self attention
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self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True)
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else:
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self.attn = None
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if cross_attn_type in ["flash", "linear"]:
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self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, qk_norm=cross_norm, **block_kwargs)
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elif cross_attn_type == "vanilla":
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self.cross_attn = MultiHeadCrossVallinaAttention(hidden_size, num_heads, qk_norm=cross_norm, **block_kwargs)
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else:
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raise ValueError(f"{cross_attn_type} type is not defined.")
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self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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if ffn_type == "dwmlp":
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approx_gelu = lambda: nn.GELU(approximate="tanh")
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self.mlp = DWMlp(
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in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0
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)
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elif ffn_type == "glumbconv":
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self.mlp = GLUMBConv(
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in_features=hidden_size,
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hidden_features=int(hidden_size * mlp_ratio),
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use_bias=(True, True, False),
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norm=(None, None, None),
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act=mlp_acts,
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)
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elif ffn_type == "glumbconv_linear":
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self.mlp = GLUMBConvLinear(
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in_features=hidden_size,
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hidden_features=int(hidden_size * mlp_ratio),
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use_bias=(True, True, False),
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norm=(None, None, None),
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act=mlp_acts,
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)
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elif ffn_type == "mlp":
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approx_gelu = lambda: nn.GELU(approximate="tanh")
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self.mlp = Mlp(
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in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0
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)
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else:
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self.mlp = None
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self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
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self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size**0.5)
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def forward(self, x, y, t, mask=None, HW=None, image_rotary_emb=None, **kwargs):
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B, N, C = x.shape
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
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self.scale_shift_table[None] + t.reshape(B, 6, -1)
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).chunk(6, dim=1)
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x = x + self.drop_path(
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gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW, rotary_emb=image_rotary_emb)
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)
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x = x + self.cross_attn(x, y, mask)
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x = x + self.drop_path(gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp), HW=HW))
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return x
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#############################################################################
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# Core Sana Model #
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#################################################################################
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@MODELS.register_module()
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class SanaMS(Sana):
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"""
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Diffusion model with a Transformer backbone.
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"""
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def __init__(
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self,
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input_size=32,
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patch_size=2,
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in_channels=4,
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hidden_size=1152,
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depth=28,
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num_heads=16,
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mlp_ratio=4.0,
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class_dropout_prob=0.1,
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learn_sigma=True,
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pred_sigma=True,
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drop_path: float = 0.0,
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caption_channels=2304,
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pe_interpolation=1.0,
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config=None,
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model_max_length=300,
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qk_norm=False,
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y_norm=False,
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norm_eps=1e-5,
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attn_type="flash",
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ffn_type="mlp",
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use_pe=True,
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y_norm_scale_factor=1.0,
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patch_embed_kernel=None,
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mlp_acts=("silu", "silu", None),
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linear_head_dim=32,
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cross_norm=False,
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cross_attn_type="flash",
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logvar=False,
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logvar_scale_factor=1.0,
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cfg_embed=False,
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cfg_embed_scale=1.0,
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lr_scale=None,
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timestep_norm_scale_factor=1.0,
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**kwargs,
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):
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super().__init__(
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input_size=input_size,
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patch_size=patch_size,
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in_channels=in_channels,
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hidden_size=hidden_size,
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depth=depth,
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num_heads=num_heads,
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mlp_ratio=mlp_ratio,
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class_dropout_prob=class_dropout_prob,
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learn_sigma=learn_sigma,
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pred_sigma=pred_sigma,
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drop_path=drop_path,
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caption_channels=caption_channels,
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pe_interpolation=pe_interpolation,
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config=config,
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model_max_length=model_max_length,
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qk_norm=qk_norm,
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y_norm=y_norm,
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norm_eps=norm_eps,
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attn_type=attn_type,
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ffn_type=ffn_type,
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use_pe=use_pe,
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y_norm_scale_factor=y_norm_scale_factor,
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patch_embed_kernel=patch_embed_kernel,
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mlp_acts=mlp_acts,
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linear_head_dim=linear_head_dim,
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cross_norm=cross_norm,
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cross_attn_type=cross_attn_type,
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cfg_embed=cfg_embed,
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timestep_norm_scale_factor=timestep_norm_scale_factor,
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**kwargs,
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)
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self.h = self.w = 0
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approx_gelu = lambda: nn.GELU(approximate="tanh")
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self.t_block = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
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self.pos_embed_ms = None
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self.cfg_embed_scale = cfg_embed_scale
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kernel_size = patch_embed_kernel or patch_size
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self.x_embedder = PatchEmbedMS(patch_size, in_channels, hidden_size, kernel_size=kernel_size, bias=True)
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self.y_embedder = CaptionEmbedder(
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in_channels=caption_channels,
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hidden_size=hidden_size,
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uncond_prob=class_dropout_prob,
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act_layer=approx_gelu,
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token_num=model_max_length,
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)
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drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule
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self.blocks = nn.ModuleList(
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[
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SanaMSBlock(
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hidden_size,
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num_heads,
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mlp_ratio=mlp_ratio,
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drop_path=drop_path[i],
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qk_norm=qk_norm,
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attn_type=attn_type,
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ffn_type=ffn_type,
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mlp_acts=mlp_acts,
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linear_head_dim=linear_head_dim,
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cross_norm=cross_norm,
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cross_attn_type=cross_attn_type,
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)
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for i in range(depth)
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]
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)
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self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
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self.logvar_linear = None
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if logvar:
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self.logvar_scale_factor = logvar_scale_factor
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self.logvar_linear = nn.Linear(hidden_size, 1)
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self.lr_scale = lr_scale
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self.initialize()
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def _apply_positional_embedding(self, x, bs):
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"""Apply positional embedding to input tensor.
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Args:
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x: Input tensor (N, T, D)
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bs: Batch size
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Returns:
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x with positional embedding added
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image_pos_embed for flux_rope type (or None)
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"""
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image_pos_embed = None
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if self.pos_embed_type == "sincos":
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if self.pos_embed_ms is None or self.pos_embed_ms.shape[1:] != x.shape[1:]:
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self.pos_embed_ms = (
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torch.from_numpy(
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get_2d_sincos_pos_embed(
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self.pos_embed.shape[-1],
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(self.h, self.w),
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pe_interpolation=self.pe_interpolation,
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base_size=self.base_size,
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)
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)
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.unsqueeze(0)
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.to(x.device)
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.to(self.dtype)
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)
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x = x + self.pos_embed_ms # (N, T, D), where T = H * W / patch_size ** 2
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elif self.pos_embed_type == "flux_rope":
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self.pos_embed_ms = RopePosEmbed(theta=10000, axes_dim=[0, 16, 16])
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latent_image_ids = self.pos_embed_ms._prepare_latent_image_ids(bs, self.h, self.w, x.device, x.dtype)
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image_pos_embed = self.pos_embed_ms(latent_image_ids)
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x = x + image_pos_embed
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else:
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raise ValueError(f"Unknown pos_embed_type: {self.pos_embed_type}")
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return x, image_pos_embed
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def forward(self, x, timestep, y, mask=None, data_info=None, return_logvar=False, jvp=False, **kwargs):
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"""
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Forward pass of Sana.
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x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
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t: (N,) tensor of diffusion timesteps
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y: (N, 1, 120, C) tensor of class labels
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"""
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bs = x.shape[0]
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x = x.to(self.dtype)
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if self.timestep_norm_scale_factor != 1.0:
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timestep = (timestep.float() / self.timestep_norm_scale_factor).to(torch.float32)
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else:
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timestep = timestep.long().to(torch.float32)
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y = y.to(self.dtype)
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self.h, self.w = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size
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x = self.x_embedder(x)
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image_pos_embed = None
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if self.use_pe:
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x, image_pos_embed = self._apply_positional_embedding(x, bs)
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t = self.t_embedder(timestep) # (N, D)
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if self.cfg_embedder:
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cfg_embed = self.cfg_embedder(data_info["cfg_scale"] * self.cfg_embed_scale)
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t += cfg_embed
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t0 = self.t_block(t)
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y = self.y_embedder(y, self.training, mask=mask) # (N, D)
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if self.y_norm:
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y = self.attention_y_norm(y)
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if mask is not None:
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mask = mask.to(torch.int16)
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mask = mask.repeat(y.shape[0] // mask.shape[0], 1) if mask.shape[0] != y.shape[0] else mask
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mask = mask.squeeze(1).squeeze(1)
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if _xformers_available:
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y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
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y_lens = mask.sum(dim=1).tolist()
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else:
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y_lens = mask
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elif _xformers_available:
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y_lens = [y.shape[2]] * y.shape[0]
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y = y.squeeze(1).view(1, -1, x.shape[-1])
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else:
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raise ValueError(f"Attention type is not available due to _xformers_available={_xformers_available}.")
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for block in self.blocks:
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if jvp:
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x = block(x, y, t0, y_lens, (self.h, self.w), image_pos_embed, **kwargs)
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# gradient checkpointing is not supported for JVP
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else:
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x = auto_grad_checkpoint(
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block,
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x,
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y,
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t0,
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y_lens,
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(self.h, self.w),
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image_pos_embed,
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**kwargs,
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use_reentrant=False,
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) # (N, T, D) #support grad checkpoint
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x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels)
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x = self.unpatchify(x) # (N, out_channels, H, W)
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if return_logvar and self.logvar_linear is not None:
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logvar = self.logvar_linear(t) * self.logvar_scale_factor
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return x, logvar
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return x
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def __call__(self, *args, **kwargs):
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"""
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This method allows the object to be called like a function.
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It simply calls the forward method.
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"""
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return self.forward(*args, **kwargs)
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def forward_with_dpmsolver(self, x, timestep, y, data_info, **kwargs):
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"""
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dpm solver donnot need variance prediction
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"""
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# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
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model_out = self.forward(x, timestep, y, data_info=data_info, **kwargs)
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return model_out.chunk(2, dim=1)[0] if self.pred_sigma else model_out
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def unpatchify(self, x):
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"""
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x: (N, T, patch_size**2 * C)
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imgs: (N, H, W, C)
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"""
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c = self.out_channels
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p = self.x_embedder.patch_size[0]
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assert self.h * self.w == x.shape[1]
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x = x.reshape(shape=(x.shape[0], self.h, self.w, p, p, c))
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x = torch.einsum("nhwpqc->nchpwq", x)
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imgs = x.reshape(shape=(x.shape[0], c, self.h * p, self.w * p))
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return imgs
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def initialize(self):
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super().initialize_weights()
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# Initialize transformer layers:
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def _basic_init(module):
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if isinstance(module, nn.Linear):
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torch.nn.init.xavier_uniform_(module.weight)
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if module.bias is not None:
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nn.init.constant_(module.bias, 0)
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self.apply(_basic_init)
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# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
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w = self.x_embedder.proj.weight.data
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nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
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# Initialize timestep embedding MLP:
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nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
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nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
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nn.init.normal_(self.t_block[1].weight, std=0.02)
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# Initialize caption embedding MLP:
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nn.init.normal_(self.y_embedder.y_proj.fc1.weight, std=0.02)
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nn.init.normal_(self.y_embedder.y_proj.fc2.weight, std=0.02)
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# Initialize cfg embedder
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if self.cfg_embedder:
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nn.init.normal_(self.cfg_embedder.mlp[0].weight, std=0.02)
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nn.init.zeros_(self.cfg_embedder.mlp[2].weight)
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if hasattr(self.cfg_embedder.mlp[2], "bias") and self.cfg_embedder.mlp[2].bias is not None:
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nn.init.zeros_(self.cfg_embedder.mlp[2].bias)
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class SanaMSCM(SanaMS):
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def forward(self, x, timestep, y, data_info=None, return_logvar=False, **kwargs):
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# TrigFlow --> Flow Transformation
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# the input now is [0, np.pi/2], arctan(N(P_mean, P_std))
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t = torch.sin(timestep) / (torch.cos(timestep) + torch.sin(timestep))
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pretrain_timestep = t * 1000 # stabilize large resolution training
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t = t.view(-1, 1, 1, 1)
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x = x * torch.sqrt(t**2 + (1 - t) ** 2)
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# forward in original flow
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if return_logvar:
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model_out, logvar = super().forward(
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x, pretrain_timestep, y, data_info=data_info, return_logvar=return_logvar, **kwargs
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)
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else:
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model_out = super().forward(x, pretrain_timestep, y, data_info=data_info, **kwargs)
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# Flow --> TrigFlow Transformation
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trigflow_model_out = ((1 - 2 * t) * x + (1 - 2 * t + 2 * t**2) * model_out) / torch.sqrt(t**2 + (1 - t) ** 2)
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if return_logvar:
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return trigflow_model_out, logvar
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else:
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return trigflow_model_out
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@MODELS.register_module()
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class SanaMSLinearFFN(SanaMS):
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"""SanaMS variant using GLUMBConvLinear in the FFN blocks."""
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def __init__(self, *args, **kwargs):
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ffn_type = kwargs.pop("ffn_type", "glumbconv_linear")
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if ffn_type != "glumbconv_linear":
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raise ValueError(f"SanaMSLinearFFN expects ffn_type='glumbconv_linear', got {ffn_type!r}")
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super().__init__(*args, ffn_type="glumbconv_linear", **kwargs)
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|
|
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#################################################################################
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# Sana Multi-scale Configs #
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#################################################################################
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@MODELS.register_module()
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def SanaMS_600M_P1_D28(**kwargs):
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return SanaMS(depth=28, hidden_size=1152, patch_size=1, num_heads=16, **kwargs)
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|
|
|
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@MODELS.register_module()
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|
def SanaMS_600M_P2_D28(**kwargs):
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|
return SanaMS(depth=28, hidden_size=1152, patch_size=2, num_heads=16, **kwargs)
|
|
|
|
|
|
@MODELS.register_module()
|
|
def SanaMS_600M_P4_D28(**kwargs):
|
|
return SanaMS(depth=28, hidden_size=1152, patch_size=4, num_heads=16, **kwargs)
|
|
|
|
|
|
@MODELS.register_module()
|
|
def SanaMS_1600M_P1_D20(**kwargs):
|
|
# 20 layers, 1648.48M
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|
return SanaMS(depth=20, hidden_size=2240, patch_size=1, num_heads=20, **kwargs)
|
|
|
|
|
|
@MODELS.register_module()
|
|
def SanaMS_1600M_P2_D20(**kwargs):
|
|
# 28 layers, 1648.48M
|
|
return SanaMS(depth=20, hidden_size=2240, patch_size=2, num_heads=20, **kwargs)
|
|
|
|
|
|
@MODELS.register_module()
|
|
def SanaMSLinearFFN_1600M_P1_D20(**kwargs):
|
|
# 20 layers, 1648.48M
|
|
return SanaMSLinearFFN(depth=20, hidden_size=2240, patch_size=1, num_heads=20, **kwargs)
|
|
|
|
|
|
@MODELS.register_module()
|
|
def SanaMS_2400M_P1_D30(**kwargs):
|
|
return SanaMS(depth=30, hidden_size=2240, patch_size=1, num_heads=20, **kwargs)
|
|
|
|
|
|
@MODELS.register_module()
|
|
def SanaMS_3200M_P1_D40(**kwargs):
|
|
return SanaMS(depth=40, hidden_size=2240, patch_size=1, num_heads=20, **kwargs)
|
|
|
|
|
|
@MODELS.register_module()
|
|
def SanaMS_4800M_P1_D60(**kwargs):
|
|
# 60 layers, 4800M
|
|
return SanaMS(depth=60, hidden_size=2240, patch_size=1, num_heads=20, **kwargs)
|
|
|
|
|
|
# TrigFlow/sCM model
|
|
@MODELS.register_module()
|
|
def SanaMSCM_600M_P1_D28(**kwargs):
|
|
return SanaMSCM(depth=28, hidden_size=1152, patch_size=1, num_heads=16, **kwargs)
|
|
|
|
|
|
@MODELS.register_module()
|
|
def SanaMSCM_1600M_P1_D20(**kwargs):
|
|
return SanaMSCM(depth=20, hidden_size=2240, patch_size=1, num_heads=20, **kwargs)
|
|
|
|
|
|
@MODELS.register_module()
|
|
def SanaMSCM_2400M_P1_D30(**kwargs):
|
|
# 30 layers, 2400M
|
|
return SanaMSCM(depth=30, hidden_size=2240, patch_size=1, num_heads=20, **kwargs)
|