diffusion/model/nets/sana_multi_scale_controlnet.py

# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# 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.
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# SPDX-License-Identifier: Apache-2.0

from copy import deepcopy
from typing import Optional, Tuple

# This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
import torch
import torch.nn as nn
from timm.models.layers import DropPath
from torch.nn import Linear, Module, init

from diffusion.model.builder import MODELS
from diffusion.model.nets.basic_modules import DWMlp, GLUMBConv, MBConvPreGLU, Mlp
from diffusion.model.nets.fastlinear.modules import TritonLiteMLA, TritonMBConvPreGLU
from diffusion.model.nets.sana import Sana, get_2d_sincos_pos_embed
from diffusion.model.nets.sana_blocks import (
    Attention,
    CaptionEmbedder,
    FlashAttention,
    LiteLA,
    MultiHeadCrossAttention,
    PatchEmbedMS,
    T2IFinalLayer,
    t2i_modulate,
)
from diffusion.model.nets.sana_multi_scale import SanaMS, SanaMSBlock
from diffusion.model.utils import auto_grad_checkpoint, to_2tuple

#############################################################################
#                                 Core Sana Model                                #
#################################################################################


class ControlSanaMSBlock(Module):
    def __init__(self, base_block: SanaMSBlock, block_index: int) -> None:
        super().__init__()
        self.copied_block = deepcopy(base_block)
        self.block_index = block_index
        self.hidden_size = hidden_size = base_block.hidden_size
        if self.block_index == 0:
            self.before_proj = Linear(hidden_size, hidden_size)

        self.after_proj = Linear(hidden_size, hidden_size)

    def initialize_all_and_copy_from_base(self, base_block):
        for name, param in self.named_parameters():
            param.requires_grad_(True)

        self.copied_block.load_state_dict(base_block.state_dict())
        self.train()

        if self.block_index == 0:
            init.zeros_(self.before_proj.weight)
            init.zeros_(self.before_proj.bias)

        init.zeros_(self.after_proj.weight)
        init.zeros_(self.after_proj.bias)

    def forward(self, x, y, t, control_signal, mask=None, HW=None):
        if self.block_index == 0:
            # the first block
            control_signal = self.before_proj(control_signal)
            control_signal = self.copied_block(x + control_signal, y, t, mask, HW)
            control_signal_skip = self.after_proj(control_signal)
        else:
            # load from previous control_signal and produce the control_signal for skip connection
            control_signal = self.copied_block(control_signal, y, t, mask, HW)
            control_signal_skip = self.after_proj(control_signal)

        return control_signal, control_signal_skip


@MODELS.register_module()
class SanaMSControlNet(SanaMS):
    """
    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,
        learn_sigma=True,
        pred_sigma=True,
        drop_path: float = 0.0,
        caption_channels=4096,
        pe_interpolation=1.0,
        config=None,
        model_max_length=300,
        qk_norm=False,
        y_norm=False,
        norm_eps=1e-5,
        attn_type="flash",
        ffn_type="mlp",
        use_pe=True,
        y_norm_scale_factor=1.0,
        patch_embed_kernel=None,
        mlp_acts=("silu", "gelu", None),
        linear_head_dim=32,
        copy_blocks_num=7,
        **kwargs,
    ):
        super().__init__(
            input_size=input_size,
            patch_size=patch_size,
            in_channels=in_channels,
            hidden_size=hidden_size,
            depth=depth,
            num_heads=num_heads,
            mlp_ratio=mlp_ratio,
            class_dropout_prob=class_dropout_prob,
            learn_sigma=learn_sigma,
            pred_sigma=pred_sigma,
            drop_path=drop_path,
            caption_channels=caption_channels,
            pe_interpolation=pe_interpolation,
            config=config,
            model_max_length=model_max_length,
            qk_norm=qk_norm,
            y_norm=y_norm,
            norm_eps=norm_eps,
            attn_type=attn_type,
            ffn_type=ffn_type,
            use_pe=use_pe,
            y_norm_scale_factor=y_norm_scale_factor,
            patch_embed_kernel=patch_embed_kernel,
            mlp_acts=mlp_acts,
            linear_head_dim=linear_head_dim,
            **kwargs,
        )
        self.h = self.w = 0
        approx_gelu = lambda: nn.GELU(approximate="tanh")
        self.t_block = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
        self.pos_embed_ms = None

        kernel_size = patch_embed_kernel or patch_size
        self.x_embedder = PatchEmbedMS(patch_size, in_channels, hidden_size, kernel_size=kernel_size, bias=True)
        self.y_embedder = CaptionEmbedder(
            in_channels=caption_channels,
            hidden_size=hidden_size,
            uncond_prob=class_dropout_prob,
            act_layer=approx_gelu,
            token_num=model_max_length,
        )
        drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)]  # stochastic depth decay rule
        self.blocks = nn.ModuleList(
            [
                SanaMSBlock(
                    hidden_size,
                    num_heads,
                    mlp_ratio=mlp_ratio,
                    drop_path=drop_path[i],
                    input_size=(input_size // patch_size, input_size // patch_size),
                    qk_norm=qk_norm,
                    attn_type=attn_type,
                    ffn_type=ffn_type,
                    mlp_acts=mlp_acts,
                    linear_head_dim=linear_head_dim,
                )
                for i in range(depth)
            ]
        )

        self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)

        # define controlnet
        self.copy_blocks_num = copy_blocks_num
        self.controlnet = nn.ModuleList([ControlSanaMSBlock(self.blocks[i], i) for i in range(copy_blocks_num)])

    def load_pretrain_and_initialize(self, model_path):
        missing, unexpected = self.load_state_dict(torch.load(model_path)["state_dict"], strict=False)
        self.initialize_all()
        return missing, unexpected

    def initialize_all(self):
        # freeze all the parameters
        for p in self.parameters():
            p.requires_grad_(False)
        # self.eval()

        for i, block in enumerate(self.controlnet):
            block.initialize_all_and_copy_from_base(self.blocks[i])

    def forward_controlnet(self, control_signal):
        if self.use_pe:
            control_signal = self.x_embedder(control_signal) + self.pos_embed_ms
        else:
            control_signal = self.x_embedder(control_signal)
        return control_signal

    def forward(self, x, timestep, control_signal, y, mask=None, data_info=None, **kwargs):
        """
        Forward pass of Sana.
        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
        t: (N,) tensor of diffusion timesteps
        y: (N, 1, 120, C) tensor of class labels
        """

        bs = x.shape[0]
        x = x.to(self.dtype)
        timestep = timestep.to(self.dtype)
        y = y.to(self.dtype)

        self.h, self.w = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size
        if self.use_pe:
            x = self.x_embedder(x)
            if self.pos_embed_ms is None or self.pos_embed_ms.shape[1:] != x.shape[1:]:
                self.pos_embed_ms = (
                    torch.from_numpy(
                        get_2d_sincos_pos_embed(
                            self.pos_embed.shape[-1],
                            (self.h, self.w),
                            pe_interpolation=self.pe_interpolation,
                            base_size=self.base_size,
                        )
                    )
                    .unsqueeze(0)
                    .to(x.device)
                    .to(self.dtype)
                )
            x += self.pos_embed_ms  # (N, T, D), where T = H * W / patch_size ** 2
        else:
            x = self.x_embedder(x)

        # control signal branch
        control_signal = control_signal.to(self.dtype)
        control_signal = self.forward_controlnet(control_signal)

        t = self.t_embedder(timestep)  # (N, D)
        t0 = self.t_block(t)
        y = self.y_embedder(y, self.training, mask=mask)  # (N, D)
        if self.y_norm:
            y = self.attention_y_norm(y)

        if mask is not None:
            if mask.shape[0] != y.shape[0]:
                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
            mask = mask.squeeze(1).squeeze(1)
            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
            y_lens = mask.sum(dim=1).tolist()
        else:
            y_lens = [y.shape[2]] * y.shape[0]
            y = y.squeeze(1).view(1, -1, x.shape[-1])

        x = auto_grad_checkpoint(self.blocks[0], x, y, t0, y_lens, (self.h, self.w), **kwargs)

        for i in range(1, self.copy_blocks_num + 1):
            control_signal, control_signal_skip = auto_grad_checkpoint(
                self.controlnet[i - 1], x, y, t0, control_signal, y_lens, (self.h, self.w), **kwargs
            )
            x = auto_grad_checkpoint(self.blocks[i], x + control_signal_skip, y, t0, y_lens, (self.h, self.w), **kwargs)

        for i in range(self.copy_blocks_num + 1, len(self.blocks)):
            x = auto_grad_checkpoint(self.blocks[i], x, y, t0, y_lens, (self.h, self.w), **kwargs)

        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
        x = self.unpatchify(x)  # (N, out_channels, H, W)

        return x

    def __call__(self, *args, **kwargs):
        """
        This method allows the object to be called like a function.
        It simply calls the forward method.
        """
        return self.forward(*args, **kwargs)

    def forward_with_dpmsolver(self, x, timestep, y, data_info, **kwargs):
        """
        dpm solver donnot need variance prediction
        """
        control_signal = kwargs.pop("control_signal", None)
        assert control_signal is not None, "control_signal is required for dpm solver"
        assert control_signal.dim() == 4, "control_signal should be a 4D tensor"

        if x.shape[0] != control_signal.shape[0]:
            control_signal = control_signal.repeat(x.shape[0] // control_signal.shape[0], 1, 1, 1)

        assert control_signal.shape[0] == x.shape[0], "control_signal and x should have the same batch size"

        # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
        model_out = self.forward(x, timestep, control_signal, y, data_info=data_info, **kwargs)
        return model_out.chunk(2, dim=1)[0] if self.pred_sigma else model_out


#################################################################################
#                                   Sana Multi-scale Configs                              #
#################################################################################


@MODELS.register_module()
def SanaMSControlNet_600M_P1_D28(**kwargs):
    return SanaMSControlNet(depth=28, hidden_size=1152, patch_size=1, num_heads=16, **kwargs)


@MODELS.register_module()
def SanaMSControlNet_1600M_P1_D20(**kwargs):
    return SanaMSControlNet(depth=20, hidden_size=2240, patch_size=1, num_heads=20, **kwargs)