478 lines
20 KiB
Python
478 lines
20 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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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import math
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from typing import Any, Dict, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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#from diffusers import WanTransformer3DModel
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from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
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from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
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from diffusers.models.attention import FeedForward
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from diffusers.models.attention_processor import Attention
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from diffusers.models.cache_utils import CacheMixin
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from diffusers.models.embeddings import PixArtAlphaTextProjection, TimestepEmbedding, Timesteps, get_1d_rotary_pos_embed
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from diffusers.models.modeling_outputs import Transformer2DModelOutput
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from diffusers.models.modeling_utils import ModelMixin
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from diffusers.models.normalization import FP32LayerNorm
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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class ChronoEditAttnProcessor2_0:
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def __init__(self):
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if not hasattr(F, "scaled_dot_product_attention"):
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raise ImportError("ChronoEditAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0.")
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def __call__(
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self,
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attn: Attention,
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hidden_states: torch.Tensor,
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encoder_hidden_states: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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rotary_emb: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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encoder_hidden_states_img = None
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if attn.add_k_proj is not None:
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encoder_hidden_states_img = encoder_hidden_states[:, :257]
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encoder_hidden_states = encoder_hidden_states[:, 257:]
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if encoder_hidden_states is None:
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encoder_hidden_states = hidden_states
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query = attn.to_q(hidden_states)
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key = attn.to_k(encoder_hidden_states)
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value = attn.to_v(encoder_hidden_states)
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if attn.norm_q is not None:
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query = attn.norm_q(query)
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if attn.norm_k is not None:
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key = attn.norm_k(key)
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query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2)
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key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2)
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value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2)
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if rotary_emb is not None:
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def apply_rotary_emb(hidden_states: torch.Tensor, freqs: torch.Tensor):
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x_rotated = torch.view_as_complex(hidden_states.to(torch.float64).unflatten(3, (-1, 2)))
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x_out = torch.view_as_real(x_rotated * freqs).flatten(3, 4)
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return x_out.type_as(hidden_states)
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query = apply_rotary_emb(query, rotary_emb)
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key = apply_rotary_emb(key, rotary_emb)
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# I2V task
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hidden_states_img = None
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if encoder_hidden_states_img is not None:
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key_img = attn.add_k_proj(encoder_hidden_states_img)
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key_img = attn.norm_added_k(key_img)
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value_img = attn.add_v_proj(encoder_hidden_states_img)
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key_img = key_img.unflatten(2, (attn.heads, -1)).transpose(1, 2)
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value_img = value_img.unflatten(2, (attn.heads, -1)).transpose(1, 2)
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hidden_states_img = F.scaled_dot_product_attention(
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query, key_img, value_img, attn_mask=None, dropout_p=0.0, is_causal=False
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)
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hidden_states_img = hidden_states_img.transpose(1, 2).flatten(2, 3)
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hidden_states_img = hidden_states_img.type_as(query)
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hidden_states = F.scaled_dot_product_attention(
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query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
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)
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hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)
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hidden_states = hidden_states.type_as(query)
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if hidden_states_img is not None:
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hidden_states = hidden_states + hidden_states_img
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hidden_states = attn.to_out[0](hidden_states)
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hidden_states = attn.to_out[1](hidden_states)
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return hidden_states
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class ChronoEditImageEmbedding(torch.nn.Module):
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def __init__(self, in_features: int, out_features: int):
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super().__init__()
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self.norm1 = FP32LayerNorm(in_features)
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self.ff = FeedForward(in_features, out_features, mult=1, activation_fn="gelu")
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self.norm2 = FP32LayerNorm(out_features)
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def forward(self, encoder_hidden_states_image: torch.Tensor) -> torch.Tensor:
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hidden_states = self.norm1(encoder_hidden_states_image)
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hidden_states = self.ff(hidden_states)
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hidden_states = self.norm2(hidden_states)
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return hidden_states
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class ChronoEditTimeTextImageEmbedding(nn.Module):
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def __init__(
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self,
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dim: int,
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time_freq_dim: int,
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time_proj_dim: int,
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text_embed_dim: int,
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image_embed_dim: Optional[int] = None,
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):
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super().__init__()
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self.timesteps_proj = Timesteps(num_channels=time_freq_dim, flip_sin_to_cos=True, downscale_freq_shift=0)
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self.time_embedder = TimestepEmbedding(in_channels=time_freq_dim, time_embed_dim=dim)
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self.act_fn = nn.SiLU()
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self.time_proj = nn.Linear(dim, time_proj_dim)
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self.text_embedder = PixArtAlphaTextProjection(text_embed_dim, dim, act_fn="gelu_tanh")
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self.image_embedder = None
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if image_embed_dim is not None:
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self.image_embedder = ChronoEditImageEmbedding(image_embed_dim, dim)
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def forward(
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self,
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timestep: torch.Tensor,
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encoder_hidden_states: torch.Tensor,
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encoder_hidden_states_image: Optional[torch.Tensor] = None,
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):
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timestep = self.timesteps_proj(timestep)
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time_embedder_dtype = next(iter(self.time_embedder.parameters())).dtype
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if timestep.dtype != time_embedder_dtype and time_embedder_dtype != torch.int8:
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timestep = timestep.to(time_embedder_dtype)
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temb = self.time_embedder(timestep).type_as(encoder_hidden_states)
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timestep_proj = self.time_proj(self.act_fn(temb))
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encoder_hidden_states = self.text_embedder(encoder_hidden_states)
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if encoder_hidden_states_image is not None:
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encoder_hidden_states_image = self.image_embedder(encoder_hidden_states_image)
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return temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image
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class ChronoEditRotaryPosEmbed(nn.Module):
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def __init__(
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self, attention_head_dim: int, patch_size: Tuple[int, int, int], max_seq_len: int, theta: float = 10000.0, temporal_skip_len: int = 8
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):
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super().__init__()
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self.attention_head_dim = attention_head_dim
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self.patch_size = patch_size
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self.max_seq_len = max_seq_len
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self.temporal_skip_len = temporal_skip_len
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h_dim = w_dim = 2 * (attention_head_dim // 6)
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t_dim = attention_head_dim - h_dim - w_dim
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freqs = []
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for dim in [t_dim, h_dim, w_dim]:
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freq = get_1d_rotary_pos_embed(
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dim, max_seq_len, theta, use_real=False, repeat_interleave_real=False, freqs_dtype=torch.float64
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)
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freqs.append(freq)
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self.freqs = torch.cat(freqs, dim=1)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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batch_size, num_channels, num_frames, height, width = hidden_states.shape
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p_t, p_h, p_w = self.patch_size
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ppf, pph, ppw = num_frames // p_t, height // p_h, width // p_w
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self.freqs = self.freqs.to(hidden_states.device)
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freqs = self.freqs.split_with_sizes(
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[
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self.attention_head_dim // 2 - 2 * (self.attention_head_dim // 6),
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self.attention_head_dim // 6,
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self.attention_head_dim // 6,
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],
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dim=1,
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)
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assert num_frames == 2 or num_frames == self.temporal_skip_len, f"num_frames must be 2 or {self.temporal_skip_len}, but got {num_frames}"
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if num_frames == 2:
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freqs_f = freqs[0][:self.temporal_skip_len][[0, -1]].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1)
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else:
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freqs_f = freqs[0][:ppf].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1)
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freqs_h = freqs[1][:pph].view(1, pph, 1, -1).expand(ppf, pph, ppw, -1)
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freqs_w = freqs[2][:ppw].view(1, 1, ppw, -1).expand(ppf, pph, ppw, -1)
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freqs = torch.cat([freqs_f, freqs_h, freqs_w], dim=-1).reshape(1, 1, ppf * pph * ppw, -1)
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return freqs
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class ChronoEditTransformerBlock(nn.Module):
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def __init__(
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self,
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dim: int,
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ffn_dim: int,
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num_heads: int,
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qk_norm: str = "rms_norm_across_heads",
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cross_attn_norm: bool = False,
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eps: float = 1e-6,
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added_kv_proj_dim: Optional[int] = None,
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):
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super().__init__()
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# 1. Self-attention
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self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
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self.attn1 = Attention(
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query_dim=dim,
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heads=num_heads,
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kv_heads=num_heads,
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dim_head=dim // num_heads,
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qk_norm=qk_norm,
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eps=eps,
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bias=True,
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cross_attention_dim=None,
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out_bias=True,
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processor=ChronoEditAttnProcessor2_0(),
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)
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# 2. Cross-attention
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self.attn2 = Attention(
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query_dim=dim,
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heads=num_heads,
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kv_heads=num_heads,
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dim_head=dim // num_heads,
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qk_norm=qk_norm,
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eps=eps,
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bias=True,
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cross_attention_dim=None,
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out_bias=True,
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added_kv_proj_dim=added_kv_proj_dim,
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added_proj_bias=True,
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processor=ChronoEditAttnProcessor2_0(),
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)
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self.norm2 = FP32LayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()
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# 3. Feed-forward
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self.ffn = FeedForward(dim, inner_dim=ffn_dim, activation_fn="gelu-approximate")
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self.norm3 = FP32LayerNorm(dim, eps, elementwise_affine=False)
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self.scale_shift_table = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
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def forward(
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self,
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hidden_states: torch.Tensor,
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encoder_hidden_states: torch.Tensor,
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temb: torch.Tensor,
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rotary_emb: torch.Tensor,
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) -> torch.Tensor:
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shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
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self.scale_shift_table + temb.float()
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).chunk(6, dim=1)
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# 1. Self-attention
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norm_hidden_states = (self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa).type_as(hidden_states)
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attn_output = self.attn1(hidden_states=norm_hidden_states, rotary_emb=rotary_emb)
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hidden_states = (hidden_states.float() + attn_output * gate_msa).type_as(hidden_states)
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# 2. Cross-attention
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norm_hidden_states = self.norm2(hidden_states.float()).type_as(hidden_states)
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attn_output = self.attn2(hidden_states=norm_hidden_states, encoder_hidden_states=encoder_hidden_states)
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hidden_states = hidden_states + attn_output
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# 3. Feed-forward
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norm_hidden_states = (self.norm3(hidden_states.float()) * (1 + c_scale_msa) + c_shift_msa).type_as(
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hidden_states
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)
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ff_output = self.ffn(norm_hidden_states)
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hidden_states = (hidden_states.float() + ff_output.float() * c_gate_msa).type_as(hidden_states)
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return hidden_states
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class WanTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin):
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r"""
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A Transformer model for video-like data used in the ChronoEdit model.
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Args:
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patch_size (`Tuple[int]`, defaults to `(1, 2, 2)`):
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3D patch dimensions for video embedding (t_patch, h_patch, w_patch).
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num_attention_heads (`int`, defaults to `40`):
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Fixed length for text embeddings.
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attention_head_dim (`int`, defaults to `128`):
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The number of channels in each head.
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in_channels (`int`, defaults to `16`):
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The number of channels in the input.
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out_channels (`int`, defaults to `16`):
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The number of channels in the output.
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text_dim (`int`, defaults to `512`):
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Input dimension for text embeddings.
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freq_dim (`int`, defaults to `256`):
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Dimension for sinusoidal time embeddings.
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ffn_dim (`int`, defaults to `13824`):
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Intermediate dimension in feed-forward network.
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num_layers (`int`, defaults to `40`):
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The number of layers of transformer blocks to use.
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window_size (`Tuple[int]`, defaults to `(-1, -1)`):
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Window size for local attention (-1 indicates global attention).
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cross_attn_norm (`bool`, defaults to `True`):
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Enable cross-attention normalization.
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qk_norm (`bool`, defaults to `True`):
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Enable query/key normalization.
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eps (`float`, defaults to `1e-6`):
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Epsilon value for normalization layers.
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add_img_emb (`bool`, defaults to `False`):
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Whether to use img_emb.
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added_kv_proj_dim (`int`, *optional*, defaults to `None`):
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The number of channels to use for the added key and value projections. If `None`, no projection is used.
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"""
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_supports_gradient_checkpointing = True
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_skip_layerwise_casting_patterns = ["patch_embedding", "condition_embedder", "norm"]
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_no_split_modules = ["ChronoEditTransformerBlock"]
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_keep_in_fp32_modules = ["time_embedder", "scale_shift_table", "norm1", "norm2", "norm3"]
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_keys_to_ignore_on_load_unexpected = ["norm_added_q"]
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_repeated_blocks = ["WanTransformerBlock"]
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@register_to_config
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def __init__(
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self,
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patch_size: Tuple[int] = (1, 2, 2),
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num_attention_heads: int = 40,
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attention_head_dim: int = 128,
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in_channels: int = 16,
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out_channels: int = 16,
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text_dim: int = 4096,
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freq_dim: int = 256,
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ffn_dim: int = 13824,
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num_layers: int = 40,
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cross_attn_norm: bool = True,
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qk_norm: Optional[str] = "rms_norm_across_heads",
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eps: float = 1e-6,
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image_dim: Optional[int] = None,
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added_kv_proj_dim: Optional[int] = None,
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rope_max_seq_len: int = 1024,
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rope_temporal_skip_len: int = 8,
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) -> None:
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super().__init__()
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inner_dim = num_attention_heads * attention_head_dim
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out_channels = out_channels or in_channels
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# 1. Patch & position embedding
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self.rope = ChronoEditRotaryPosEmbed(attention_head_dim, patch_size, rope_max_seq_len, temporal_skip_len=rope_temporal_skip_len)
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self.patch_embedding = nn.Conv3d(in_channels, inner_dim, kernel_size=patch_size, stride=patch_size)
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# 2. Condition embeddings
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# image_embedding_dim=1280 for I2V model
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self.condition_embedder = ChronoEditTimeTextImageEmbedding(
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dim=inner_dim,
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time_freq_dim=freq_dim,
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time_proj_dim=inner_dim * 6,
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text_embed_dim=text_dim,
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image_embed_dim=image_dim,
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)
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# 3. Transformer blocks
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self.blocks = nn.ModuleList(
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[
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ChronoEditTransformerBlock(
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inner_dim, ffn_dim, num_attention_heads, qk_norm, cross_attn_norm, eps, added_kv_proj_dim
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)
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for _ in range(num_layers)
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]
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)
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# 4. Output norm & projection
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self.norm_out = FP32LayerNorm(inner_dim, eps, elementwise_affine=False)
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self.proj_out = nn.Linear(inner_dim, out_channels * math.prod(patch_size))
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self.scale_shift_table = nn.Parameter(torch.randn(1, 2, inner_dim) / inner_dim**0.5)
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self.gradient_checkpointing = False
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def forward(
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self,
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hidden_states: torch.Tensor,
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timestep: torch.LongTensor,
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encoder_hidden_states: torch.Tensor,
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encoder_hidden_states_image: Optional[torch.Tensor] = None,
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return_dict: bool = True,
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attention_kwargs: Optional[Dict[str, Any]] = None,
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) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
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if attention_kwargs is not None:
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attention_kwargs = attention_kwargs.copy()
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lora_scale = attention_kwargs.pop("scale", 1.0)
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else:
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lora_scale = 1.0
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if USE_PEFT_BACKEND:
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# weight the lora layers by setting `lora_scale` for each PEFT layer
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scale_lora_layers(self, lora_scale)
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else:
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if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
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logger.warning(
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"Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
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)
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batch_size, num_channels, num_frames, height, width = hidden_states.shape
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p_t, p_h, p_w = self.config.patch_size
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post_patch_num_frames = num_frames // p_t
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post_patch_height = height // p_h
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post_patch_width = width // p_w
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|
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rotary_emb = self.rope(hidden_states)
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|
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hidden_states = self.patch_embedding(hidden_states)
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hidden_states = hidden_states.flatten(2).transpose(1, 2)
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|
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temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
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timestep, encoder_hidden_states, encoder_hidden_states_image
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)
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timestep_proj = timestep_proj.unflatten(1, (6, -1))
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|
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|
if encoder_hidden_states_image is not None:
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encoder_hidden_states = torch.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1)
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|
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|
# 4. Transformer blocks
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|
if torch.is_grad_enabled() and self.gradient_checkpointing:
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|
for block in self.blocks:
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|
hidden_states = self._gradient_checkpointing_func(
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|
block, hidden_states, encoder_hidden_states, timestep_proj, rotary_emb
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|
)
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|
else:
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|
for block in self.blocks:
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|
hidden_states = block(hidden_states, encoder_hidden_states, timestep_proj, rotary_emb)
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|
|
|
# 5. Output norm, projection & unpatchify
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|
shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)
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|
|
|
# Move the shift and scale tensors to the same device as hidden_states.
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|
# When using multi-GPU inference via accelerate these will be on the
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|
# first device rather than the last device, which hidden_states ends up
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|
# on.
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|
shift = shift.to(hidden_states.device)
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|
scale = scale.to(hidden_states.device)
|
|
|
|
hidden_states = (self.norm_out(hidden_states.float()) * (1 + scale) + shift).type_as(hidden_states)
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|
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
|
|
)
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|
hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
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|
output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
|
|
|
|
if USE_PEFT_BACKEND:
|
|
# remove `lora_scale` from each PEFT layer
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|
unscale_lora_layers(self, lora_scale)
|
|
|
|
if not return_dict:
|
|
return (output,)
|
|
|
|
return Transformer2DModelOutput(sample=output)
|