wehub-resource-sync
301 lines
12 KiB
Python
301 lines
12 KiB
Python
# 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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from copy import deepcopy
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# This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
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import torch
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import torch.nn as nn
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from torch.nn import Linear, Module, init
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from diffusion.model.builder import MODELS
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from diffusion.model.nets.sana import get_2d_sincos_pos_embed
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from diffusion.model.nets.sana_blocks import RopePosEmbed
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from diffusion.model.nets.sana_multi_scale import SanaMS, SanaMSBlock
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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
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if is_xformers_available():
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_xformers_available = True
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class ControlSanaMSBlock(Module):
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def __init__(self, base_block: SanaMSBlock, block_index: int) -> None:
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super().__init__()
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self.copied_block = deepcopy(base_block)
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self.block_index = block_index
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self.hidden_size = hidden_size = base_block.hidden_size
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if self.block_index == 0:
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self.before_proj = Linear(hidden_size, hidden_size)
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self.after_proj = Linear(hidden_size, hidden_size)
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def initialize_all_and_copy_from_base(self, base_block):
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for name, param in self.named_parameters():
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param.requires_grad_(True)
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self.copied_block.load_state_dict(base_block.state_dict())
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self.train()
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if self.block_index == 0:
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init.zeros_(self.before_proj.weight)
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init.zeros_(self.before_proj.bias)
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init.zeros_(self.after_proj.weight)
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init.zeros_(self.after_proj.bias)
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def forward(self, x, y, t, control_signal, mask=None, HW=None, image_rotary_emb=None):
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if self.block_index == 0:
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# the first block
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control_signal = self.before_proj(control_signal)
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control_signal = self.copied_block(x + control_signal, y, t, mask, HW, image_rotary_emb)
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control_signal_skip = self.after_proj(control_signal)
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else:
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# load from previous control_signal and produce the control_signal for skip connection
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control_signal = self.copied_block(control_signal, y, t, mask, HW, image_rotary_emb)
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control_signal_skip = self.after_proj(control_signal)
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return control_signal, control_signal_skip
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@MODELS.register_module()
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class SanaMSControlNet(SanaMS):
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"""
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Sana with ControlNet
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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=4096,
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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", "gelu", None),
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linear_head_dim=32,
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copy_blocks_num=7,
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cross_norm=False,
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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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timestep_norm_scale_factor=timestep_norm_scale_factor,
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**kwargs,
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)
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# define controlnet
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self.copy_blocks_num = copy_blocks_num
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self.controlnet = nn.ModuleList([ControlSanaMSBlock(self.blocks[i], i) for i in range(copy_blocks_num)])
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def load_pretrain_and_initialize(self, model_path):
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missing, unexpected = self.load_state_dict(
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torch.load(model_path, map_location="cpu")["state_dict"], strict=False
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)
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self.initialize_all()
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return missing, unexpected
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def initialize_all(self):
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# freeze all the parameters
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for p in self.parameters():
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p.requires_grad_(False)
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for i, block in enumerate(self.controlnet):
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block.initialize_all_and_copy_from_base(self.blocks[i])
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def forward_controlnet(self, control_signal, pos_embed_ms=None):
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if self.use_pe and pos_embed_ms:
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control_signal = self.x_embedder(control_signal) + pos_embed_ms
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else:
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control_signal = self.x_embedder(control_signal)
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return control_signal
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def forward(self, x, timestep, y, mask=None, data_info=None, **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(self.dtype)
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else:
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timestep = timestep.long().to(self.dtype)
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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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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 += self.pos_embed_ms # (N, T, D), where T = H * W / patch_size ** 2
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elif self.pos_embed_type == "3d_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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else:
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raise ValueError(f"Unknown pos_embed_type: {self.pos_embed_type}")
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# control signal branch
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control_signal = data_info["control_signal"].to(self.dtype)
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control_signal = self.forward_controlnet(control_signal, pos_embed_ms=image_pos_embed)
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t = self.t_embedder(timestep) # (N, D)
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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.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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x = auto_grad_checkpoint(self.blocks[0], x, y, t0, y_lens, (self.h, self.w), image_pos_embed, **kwargs)
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for i in range(1, self.copy_blocks_num + 1):
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control_signal, control_signal_skip = auto_grad_checkpoint(
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self.controlnet[i - 1], x, y, t0, control_signal, y_lens, (self.h, self.w), image_pos_embed, **kwargs
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)
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x = auto_grad_checkpoint(
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self.blocks[i], x + control_signal_skip, y, t0, y_lens, (self.h, self.w), image_pos_embed, **kwargs
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)
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for i in range(self.copy_blocks_num + 1, len(self.blocks)):
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x = auto_grad_checkpoint(self.blocks[i], x, y, t0, y_lens, (self.h, self.w), image_pos_embed, **kwargs)
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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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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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control_signal = data_info["control_signal"]
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assert control_signal is not None, "control_signal is required for dpm solver"
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assert control_signal.dim() == 4, "control_signal should be a 4D tensor"
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if x.shape[0] != control_signal.shape[0]:
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control_signal = control_signal.repeat(x.shape[0] // control_signal.shape[0], 1, 1, 1)
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assert control_signal.shape[0] == x.shape[0], "control_signal and x should have the same batch size"
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data_info["control_signal"] = control_signal
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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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#################################################################################
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# Sana Multi-scale Configs #
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#################################################################################
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@MODELS.register_module()
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def SanaMSControlNet_600M_P1_D28(**kwargs):
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return SanaMSControlNet(depth=28, hidden_size=1152, patch_size=1, num_heads=16, **kwargs)
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@MODELS.register_module()
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def SanaMSControlNet_1600M_P1_D20(**kwargs):
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return SanaMSControlNet(depth=20, hidden_size=2240, patch_size=1, num_heads=20, **kwargs)
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