transformer_minimax_remover.py

import math
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils import logging
from diffusers.models.attention import FeedForward
from diffusers.models.attention_processor import Attention
from diffusers.models.embeddings import PixArtAlphaTextProjection, TimestepEmbedding, Timesteps, get_1d_rotary_pos_embed
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import FP32LayerNorm

class AttnProcessor2_0:
    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,
        rotary_emb: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None
    ) -> torch.Tensor:

        encoder_hidden_states = hidden_states
        query = attn.to_q(hidden_states)
        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        if attn.norm_q is not None:
            query = attn.norm_q(query)
        if attn.norm_k is not None:
            key = attn.norm_k(key)

        query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2)
        key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2)
        value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2)
        
        if rotary_emb is not None:

            def apply_rotary_emb(hidden_states: torch.Tensor, freqs: torch.Tensor):
                x_rotated = torch.view_as_complex(hidden_states.to(torch.float64).unflatten(3, (-1, 2)))
                x_out = torch.view_as_real(x_rotated * freqs).flatten(3, 4)
                return x_out.type_as(hidden_states)

            query = apply_rotary_emb(query, rotary_emb)
            key = apply_rotary_emb(key, rotary_emb)

        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False
        )
        hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)
        hidden_states = hidden_states.type_as(query)

        hidden_states = attn.to_out[0](hidden_states)
        hidden_states = attn.to_out[1](hidden_states)
        return hidden_states

class TimeEmbedding(nn.Module):
    def __init__(
        self,
        dim: int,
        time_freq_dim: int,
        time_proj_dim: int
    ):
        super().__init__()

        self.timesteps_proj = Timesteps(num_channels=time_freq_dim, flip_sin_to_cos=True, downscale_freq_shift=0)
        self.time_embedder = TimestepEmbedding(in_channels=time_freq_dim, time_embed_dim=dim)

        self.act_fn = nn.SiLU()
        self.time_proj = nn.Linear(dim, time_proj_dim)

    def forward(
        self,
        timestep: torch.Tensor,
    ):
        timestep = self.timesteps_proj(timestep)

        time_embedder_dtype = next(iter(self.time_embedder.parameters())).dtype
        if timestep.dtype != time_embedder_dtype and time_embedder_dtype != torch.int8:
            timestep = timestep.to(time_embedder_dtype)
        temb = self.time_embedder(timestep).type_as(self.time_proj.weight.data)
        timestep_proj = self.time_proj(self.act_fn(temb))
        
        return temb, timestep_proj


class RotaryPosEmbed(nn.Module):
    def __init__(
        self, attention_head_dim: int, patch_size: Tuple[int, int, int], max_seq_len: int, theta: float = 10000.0
    ):
        super().__init__()

        self.attention_head_dim = attention_head_dim
        self.patch_size = patch_size
        self.max_seq_len = max_seq_len

        h_dim = w_dim = 2 * (attention_head_dim // 6)
        t_dim = attention_head_dim - h_dim - w_dim

        freqs = []
        for dim in [t_dim, h_dim, w_dim]:
            freq = get_1d_rotary_pos_embed(
                dim, max_seq_len, theta, use_real=False, repeat_interleave_real=False, freqs_dtype=torch.float64
            )
            freqs.append(freq)
        self.freqs = torch.cat(freqs, dim=1)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        batch_size, num_channels, num_frames, height, width = hidden_states.shape
        p_t, p_h, p_w = self.patch_size
        ppf, pph, ppw = num_frames // p_t, height // p_h, width // p_w

        self.freqs = self.freqs.to(hidden_states.device)
        freqs = self.freqs.split_with_sizes(
            [
                self.attention_head_dim // 2 - 2 * (self.attention_head_dim // 6),
                self.attention_head_dim // 6,
                self.attention_head_dim // 6,
            ],
            dim=1,
        )

        freqs_f = freqs[0][:ppf].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1)
        freqs_h = freqs[1][:pph].view(1, pph, 1, -1).expand(ppf, pph, ppw, -1)
        freqs_w = freqs[2][:ppw].view(1, 1, ppw, -1).expand(ppf, pph, ppw, -1)
        freqs = torch.cat([freqs_f, freqs_h, freqs_w], dim=-1).reshape(1, 1, ppf * pph * ppw, -1)
        return freqs


class TransformerBlock(nn.Module):
    def __init__(
        self,
        dim: int,
        ffn_dim: int,
        num_heads: int,
        qk_norm: str = "rms_norm_across_heads",
        cross_attn_norm: bool = False,
        eps: float = 1e-6,
        added_kv_proj_dim: Optional[int] = None,
    ):
        super().__init__()

        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
        self.attn1 = Attention(
            query_dim=dim,
            heads=num_heads,
            kv_heads=num_heads,
            dim_head=dim // num_heads,
            qk_norm=qk_norm,
            eps=eps,
            bias=True,
            cross_attention_dim=None,
            out_bias=True,
            processor=AttnProcessor2_0(),
        )

        self.ffn = FeedForward(dim, inner_dim=ffn_dim, activation_fn="gelu-approximate")
        self.norm2 = FP32LayerNorm(dim, eps, elementwise_affine=False)

        self.scale_shift_table = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)

    def forward(
        self,
        hidden_states: torch.Tensor,
        temb: torch.Tensor,
        rotary_emb: torch.Tensor,
    ) -> torch.Tensor:
        shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
            self.scale_shift_table + temb.float()
        ).chunk(6, dim=1)

        norm_hidden_states = (self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa).type_as(hidden_states)
        attn_output = self.attn1(hidden_states=norm_hidden_states, rotary_emb=rotary_emb)
        hidden_states = (hidden_states.float() + attn_output * gate_msa).type_as(hidden_states)

        norm_hidden_states = (self.norm2(hidden_states.float()) * (1 + c_scale_msa) + c_shift_msa).type_as(
            hidden_states
        )
        ff_output = self.ffn(norm_hidden_states)
        hidden_states = (hidden_states.float() + ff_output.float() * c_gate_msa).type_as(hidden_states)

        return hidden_states


class Transformer3DModel(ModelMixin, ConfigMixin):

    _skip_layerwise_casting_patterns = ["patch_embedding", "condition_embedder", "norm"]
    _no_split_modules = ["TransformerBlock"]
    _keep_in_fp32_modules = ["time_embedder", "scale_shift_table", "norm1", "norm2"]

    @register_to_config
    def __init__(
        self,
        patch_size: Tuple[int] = (1, 2, 2),
        num_attention_heads: int = 40,
        attention_head_dim: int = 128,
        in_channels: int = 16,
        out_channels: int = 16,
        freq_dim: int = 256,
        ffn_dim: int = 13824,
        num_layers: int = 40,
        cross_attn_norm: bool = True,
        qk_norm: Optional[str] = "rms_norm_across_heads",
        eps: float = 1e-6,
        added_kv_proj_dim: Optional[int] = None,
        rope_max_seq_len: int = 1024
    ) -> None:
        super().__init__()

        inner_dim = num_attention_heads * attention_head_dim
        out_channels = out_channels or in_channels

        # 1. Patch & position embedding
        self.rope = RotaryPosEmbed(attention_head_dim, patch_size, rope_max_seq_len)
        self.patch_embedding = nn.Conv3d(in_channels, inner_dim, kernel_size=patch_size, stride=patch_size)

        # 2. Condition embeddings
        self.condition_embedder = TimeEmbedding(
            dim=inner_dim,
            time_freq_dim=freq_dim,
            time_proj_dim=inner_dim * 6,
        )

        # 3. Transformer blocks
        self.blocks = nn.ModuleList(
            [
                TransformerBlock(
                    inner_dim, ffn_dim, num_attention_heads, qk_norm, cross_attn_norm, eps, added_kv_proj_dim
                )
                for _ in range(num_layers)
            ]
        )

        # 4. Output norm & projection
        self.norm_out = FP32LayerNorm(inner_dim, eps, elementwise_affine=False)
        self.proj_out = nn.Linear(inner_dim, out_channels * math.prod(patch_size))
        self.scale_shift_table = nn.Parameter(torch.randn(1, 2, inner_dim) / inner_dim**0.5)

    def forward(
        self,
        hidden_states: torch.Tensor,
        timestep: torch.LongTensor
    ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
        batch_size, num_channels, num_frames, height, width = hidden_states.shape
        p_t, p_h, p_w = self.config.patch_size
        post_patch_num_frames = num_frames // p_t
        post_patch_height = height // p_h
        post_patch_width = width // p_w
        
        rotary_emb = self.rope(hidden_states)

        hidden_states = self.patch_embedding(hidden_states)
        hidden_states = hidden_states.flatten(2).transpose(1, 2)

        temb, timestep_proj = self.condition_embedder(
            timestep
        )
        timestep_proj = timestep_proj.unflatten(1, (6, -1))

        for block in self.blocks:
            hidden_states = block(hidden_states, timestep_proj, rotary_emb)

        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)
        hidden_states = (self.norm_out(hidden_states.float()) * (1 + scale) + shift).type_as(hidden_states)
        hidden_states = self.proj_out(hidden_states)

        hidden_states = hidden_states.reshape(
            batch_size, post_patch_num_frames, post_patch_height, post_patch_width, p_t, p_h, p_w, -1
        )
        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)

        return Transformer2DModelOutput(sample=output)