595 lines
19 KiB
Python
595 lines
19 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
#
|
|
# 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.
|
|
|
|
# Modified from ``https://github.com/openai/CLIP'' and ``https://github.com/mlfoundations/open_clip''
|
|
# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
|
|
|
|
import math
|
|
from typing import Dict, List, Optional
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import torchvision.transforms as T
|
|
|
|
from chronoedit._ext.imaginaire.utils import distributed, log
|
|
from chronoedit._ext.imaginaire.utils.easy_io import easy_io
|
|
from chronoedit._src.modules.conditioner import AbstractEmbModel
|
|
from chronoedit._src.modules.umt5 import HuggingfaceTokenizer
|
|
from chronoedit._src.modules.attention import attention
|
|
from chronoedit._src.modules.xlm_roberta import XLMRoberta
|
|
|
|
__all__ = [
|
|
"CLIPModel",
|
|
]
|
|
|
|
|
|
class QuickGELU(nn.Module):
|
|
def forward(self, x):
|
|
return x * torch.sigmoid(1.702 * x)
|
|
|
|
|
|
class LayerNorm(nn.LayerNorm):
|
|
def forward(self, x):
|
|
return super().forward(x.float()).type_as(x)
|
|
|
|
|
|
class SelfAttention(nn.Module):
|
|
def __init__(self, dim, num_heads, causal=False, attn_dropout=0.0, proj_dropout=0.0):
|
|
assert dim % num_heads == 0
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.num_heads = num_heads
|
|
self.head_dim = dim // num_heads
|
|
self.causal = causal
|
|
self.attn_dropout = attn_dropout
|
|
self.proj_dropout = proj_dropout
|
|
|
|
# layers
|
|
self.to_qkv = nn.Linear(dim, dim * 3)
|
|
self.proj = nn.Linear(dim, dim)
|
|
|
|
def forward(self, x):
|
|
"""
|
|
x: [B, L, C].
|
|
"""
|
|
b, s, c, n, d = *x.size(), self.num_heads, self.head_dim
|
|
|
|
# compute query, key, value
|
|
q, k, v = self.to_qkv(x).view(b, s, 3, n, d).unbind(2)
|
|
|
|
# compute attention
|
|
p = self.attn_dropout if self.training else 0.0
|
|
x = attention(q, k, v, dropout_p=p, causal=self.causal)
|
|
x = x.reshape(b, s, c)
|
|
|
|
# output
|
|
x = self.proj(x)
|
|
x = F.dropout(x, self.proj_dropout, self.training)
|
|
return x
|
|
|
|
|
|
class SwiGLU(nn.Module):
|
|
def __init__(self, dim, mid_dim):
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.mid_dim = mid_dim
|
|
|
|
# layers
|
|
self.fc1 = nn.Linear(dim, mid_dim)
|
|
self.fc2 = nn.Linear(dim, mid_dim)
|
|
self.fc3 = nn.Linear(mid_dim, dim)
|
|
|
|
def forward(self, x):
|
|
x = F.silu(self.fc1(x)) * self.fc2(x)
|
|
x = self.fc3(x)
|
|
return x
|
|
|
|
|
|
class AttentionBlock(nn.Module):
|
|
def __init__(
|
|
self,
|
|
dim,
|
|
mlp_ratio,
|
|
num_heads,
|
|
post_norm=False,
|
|
causal=False,
|
|
activation="quick_gelu",
|
|
attn_dropout=0.0,
|
|
proj_dropout=0.0,
|
|
norm_eps=1e-5,
|
|
):
|
|
assert activation in ["quick_gelu", "gelu", "swi_glu"]
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.mlp_ratio = mlp_ratio
|
|
self.num_heads = num_heads
|
|
self.post_norm = post_norm
|
|
self.causal = causal
|
|
self.norm_eps = norm_eps
|
|
|
|
# layers
|
|
self.norm1 = LayerNorm(dim, eps=norm_eps)
|
|
self.attn = SelfAttention(dim, num_heads, causal, attn_dropout, proj_dropout)
|
|
self.norm2 = LayerNorm(dim, eps=norm_eps)
|
|
if activation == "swi_glu":
|
|
self.mlp = SwiGLU(dim, int(dim * mlp_ratio))
|
|
else:
|
|
self.mlp = nn.Sequential(
|
|
nn.Linear(dim, int(dim * mlp_ratio)),
|
|
QuickGELU() if activation == "quick_gelu" else nn.GELU(),
|
|
nn.Linear(int(dim * mlp_ratio), dim),
|
|
nn.Dropout(proj_dropout),
|
|
)
|
|
|
|
def forward(self, x):
|
|
if self.post_norm:
|
|
x = x + self.norm1(self.attn(x))
|
|
x = x + self.norm2(self.mlp(x))
|
|
else:
|
|
x = x + self.attn(self.norm1(x))
|
|
x = x + self.mlp(self.norm2(x))
|
|
return x
|
|
|
|
|
|
class AttentionPool(nn.Module):
|
|
def __init__(self, dim, mlp_ratio, num_heads, activation="gelu", proj_dropout=0.0, norm_eps=1e-5):
|
|
assert dim % num_heads == 0
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.mlp_ratio = mlp_ratio
|
|
self.num_heads = num_heads
|
|
self.head_dim = dim // num_heads
|
|
self.proj_dropout = proj_dropout
|
|
self.norm_eps = norm_eps
|
|
|
|
# layers
|
|
gain = 1.0 / math.sqrt(dim)
|
|
self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim))
|
|
self.to_q = nn.Linear(dim, dim)
|
|
self.to_kv = nn.Linear(dim, dim * 2)
|
|
self.proj = nn.Linear(dim, dim)
|
|
self.norm = LayerNorm(dim, eps=norm_eps)
|
|
self.mlp = nn.Sequential(
|
|
nn.Linear(dim, int(dim * mlp_ratio)),
|
|
QuickGELU() if activation == "quick_gelu" else nn.GELU(),
|
|
nn.Linear(int(dim * mlp_ratio), dim),
|
|
nn.Dropout(proj_dropout),
|
|
)
|
|
|
|
def forward(self, x):
|
|
"""
|
|
x: [B, L, C].
|
|
"""
|
|
b, s, c, n, d = *x.size(), self.num_heads, self.head_dim
|
|
|
|
# compute query, key, value
|
|
q = self.to_q(self.cls_embedding).view(1, 1, n, d).expand(b, -1, -1, -1)
|
|
k, v = self.to_kv(x).view(b, s, 2, n, d).unbind(2)
|
|
|
|
# compute attention
|
|
x = attention(q, k, v)
|
|
x = x.reshape(b, 1, c)
|
|
|
|
# output
|
|
x = self.proj(x)
|
|
x = F.dropout(x, self.proj_dropout, self.training)
|
|
|
|
# mlp
|
|
x = x + self.mlp(self.norm(x))
|
|
return x[:, 0]
|
|
|
|
|
|
class VisionTransformer(nn.Module):
|
|
def __init__(
|
|
self,
|
|
image_size=224,
|
|
patch_size=16,
|
|
dim=768,
|
|
mlp_ratio=4,
|
|
out_dim=512,
|
|
num_heads=12,
|
|
num_layers=12,
|
|
pool_type="token",
|
|
pre_norm=True,
|
|
post_norm=False,
|
|
activation="quick_gelu",
|
|
attn_dropout=0.0,
|
|
proj_dropout=0.0,
|
|
embedding_dropout=0.0,
|
|
norm_eps=1e-5,
|
|
):
|
|
if image_size % patch_size != 0:
|
|
print("[WARNING] image_size is not divisible by patch_size", flush=True)
|
|
assert pool_type in ("token", "token_fc", "attn_pool")
|
|
out_dim = out_dim or dim
|
|
super().__init__()
|
|
self.image_size = image_size
|
|
self.patch_size = patch_size
|
|
self.num_patches = (image_size // patch_size) ** 2
|
|
self.dim = dim
|
|
self.mlp_ratio = mlp_ratio
|
|
self.out_dim = out_dim
|
|
self.num_heads = num_heads
|
|
self.num_layers = num_layers
|
|
self.pool_type = pool_type
|
|
self.post_norm = post_norm
|
|
self.norm_eps = norm_eps
|
|
|
|
# embeddings
|
|
gain = 1.0 / math.sqrt(dim)
|
|
self.patch_embedding = nn.Conv2d(3, dim, kernel_size=patch_size, stride=patch_size, bias=not pre_norm)
|
|
if pool_type in ("token", "token_fc"):
|
|
self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim))
|
|
self.pos_embedding = nn.Parameter(
|
|
gain * torch.randn(1, self.num_patches + (1 if pool_type in ("token", "token_fc") else 0), dim)
|
|
)
|
|
self.dropout = nn.Dropout(embedding_dropout)
|
|
|
|
# transformer
|
|
self.pre_norm = LayerNorm(dim, eps=norm_eps) if pre_norm else None
|
|
self.transformer = nn.Sequential(
|
|
*[
|
|
AttentionBlock(
|
|
dim, mlp_ratio, num_heads, post_norm, False, activation, attn_dropout, proj_dropout, norm_eps
|
|
)
|
|
for _ in range(num_layers)
|
|
]
|
|
)
|
|
self.post_norm = LayerNorm(dim, eps=norm_eps)
|
|
|
|
# head
|
|
if pool_type == "token":
|
|
self.head = nn.Parameter(gain * torch.randn(dim, out_dim))
|
|
elif pool_type == "token_fc":
|
|
self.head = nn.Linear(dim, out_dim)
|
|
elif pool_type == "attn_pool":
|
|
self.head = AttentionPool(dim, mlp_ratio, num_heads, activation, proj_dropout, norm_eps)
|
|
|
|
def forward(self, x, interpolation=False, use_31_block=False):
|
|
b = x.size(0)
|
|
|
|
# embeddings
|
|
x = self.patch_embedding(x).flatten(2).permute(0, 2, 1)
|
|
if self.pool_type in ("token", "token_fc"):
|
|
x = torch.cat([self.cls_embedding.expand(b, -1, -1), x], dim=1)
|
|
if interpolation:
|
|
e = pos_interpolate(self.pos_embedding, x.size(1))
|
|
else:
|
|
e = self.pos_embedding
|
|
x = self.dropout(x + e)
|
|
if self.pre_norm is not None:
|
|
x = self.pre_norm(x)
|
|
|
|
# transformer
|
|
if use_31_block:
|
|
x = self.transformer[:-1](x)
|
|
return x
|
|
else:
|
|
x = self.transformer(x)
|
|
return x
|
|
|
|
|
|
class XLMRobertaWithHead(XLMRoberta):
|
|
def __init__(self, **kwargs):
|
|
self.out_dim = kwargs.pop("out_dim")
|
|
super().__init__(**kwargs)
|
|
|
|
# head
|
|
mid_dim = (self.dim + self.out_dim) // 2
|
|
self.head = nn.Sequential(
|
|
nn.Linear(self.dim, mid_dim, bias=False), nn.GELU(), nn.Linear(mid_dim, self.out_dim, bias=False)
|
|
)
|
|
|
|
def forward(self, ids):
|
|
# xlm-roberta
|
|
x = super().forward(ids)
|
|
|
|
# average pooling
|
|
mask = ids.ne(self.pad_id).unsqueeze(-1).to(x)
|
|
x = (x * mask).sum(dim=1) / mask.sum(dim=1)
|
|
|
|
# head
|
|
x = self.head(x)
|
|
return x
|
|
|
|
|
|
class XLMRobertaCLIP(nn.Module):
|
|
def __init__(
|
|
self,
|
|
embed_dim=1024,
|
|
image_size=224,
|
|
patch_size=14,
|
|
vision_dim=1280,
|
|
vision_mlp_ratio=4,
|
|
vision_heads=16,
|
|
vision_layers=32,
|
|
vision_pool="token",
|
|
vision_pre_norm=True,
|
|
vision_post_norm=False,
|
|
activation="gelu",
|
|
vocab_size=250002,
|
|
max_text_len=514,
|
|
type_size=1,
|
|
pad_id=1,
|
|
text_dim=1024,
|
|
text_heads=16,
|
|
text_layers=24,
|
|
text_post_norm=True,
|
|
text_dropout=0.1,
|
|
attn_dropout=0.0,
|
|
proj_dropout=0.0,
|
|
embedding_dropout=0.0,
|
|
norm_eps=1e-5,
|
|
):
|
|
super().__init__()
|
|
self.embed_dim = embed_dim
|
|
self.image_size = image_size
|
|
self.patch_size = patch_size
|
|
self.vision_dim = vision_dim
|
|
self.vision_mlp_ratio = vision_mlp_ratio
|
|
self.vision_heads = vision_heads
|
|
self.vision_layers = vision_layers
|
|
self.vision_pre_norm = vision_pre_norm
|
|
self.vision_post_norm = vision_post_norm
|
|
self.activation = activation
|
|
self.vocab_size = vocab_size
|
|
self.max_text_len = max_text_len
|
|
self.type_size = type_size
|
|
self.pad_id = pad_id
|
|
self.text_dim = text_dim
|
|
self.text_heads = text_heads
|
|
self.text_layers = text_layers
|
|
self.text_post_norm = text_post_norm
|
|
self.norm_eps = norm_eps
|
|
|
|
# models
|
|
self.visual = VisionTransformer(
|
|
image_size=image_size,
|
|
patch_size=patch_size,
|
|
dim=vision_dim,
|
|
mlp_ratio=vision_mlp_ratio,
|
|
out_dim=embed_dim,
|
|
num_heads=vision_heads,
|
|
num_layers=vision_layers,
|
|
pool_type=vision_pool,
|
|
pre_norm=vision_pre_norm,
|
|
post_norm=vision_post_norm,
|
|
activation=activation,
|
|
attn_dropout=attn_dropout,
|
|
proj_dropout=proj_dropout,
|
|
embedding_dropout=embedding_dropout,
|
|
norm_eps=norm_eps,
|
|
)
|
|
self.textual = XLMRobertaWithHead(
|
|
vocab_size=vocab_size,
|
|
max_seq_len=max_text_len,
|
|
type_size=type_size,
|
|
pad_id=pad_id,
|
|
dim=text_dim,
|
|
out_dim=embed_dim,
|
|
num_heads=text_heads,
|
|
num_layers=text_layers,
|
|
post_norm=text_post_norm,
|
|
dropout=text_dropout,
|
|
)
|
|
self.log_scale = nn.Parameter(math.log(1 / 0.07) * torch.ones([]))
|
|
|
|
def forward(self, imgs, txt_ids):
|
|
"""
|
|
imgs: [B, 3, H, W] of torch.float32.
|
|
- mean: [0.48145466, 0.4578275, 0.40821073]
|
|
- std: [0.26862954, 0.26130258, 0.27577711]
|
|
txt_ids: [B, L] of torch.long.
|
|
Encoded by data.CLIPTokenizer.
|
|
"""
|
|
xi = self.visual(imgs)
|
|
xt = self.textual(txt_ids)
|
|
return xi, xt
|
|
|
|
def param_groups(self):
|
|
groups = [
|
|
{
|
|
"params": [p for n, p in self.named_parameters() if "norm" in n or n.endswith("bias")],
|
|
"weight_decay": 0.0,
|
|
},
|
|
{"params": [p for n, p in self.named_parameters() if not ("norm" in n or n.endswith("bias"))]},
|
|
]
|
|
return groups
|
|
|
|
|
|
def _clip(
|
|
pretrained=False,
|
|
pretrained_name=None,
|
|
model_cls=XLMRobertaCLIP,
|
|
return_transforms=False,
|
|
return_tokenizer=False,
|
|
tokenizer_padding="eos",
|
|
dtype=torch.float32,
|
|
device="cpu",
|
|
**kwargs,
|
|
):
|
|
# init a model on device
|
|
with torch.device(device):
|
|
model = model_cls(**kwargs)
|
|
|
|
# set device
|
|
model = model.to(dtype=dtype, device=device)
|
|
output = (model,)
|
|
|
|
# init transforms
|
|
if return_transforms:
|
|
# mean and std
|
|
if "siglip" in pretrained_name.lower():
|
|
mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]
|
|
else:
|
|
mean = [0.48145466, 0.4578275, 0.40821073]
|
|
std = [0.26862954, 0.26130258, 0.27577711]
|
|
|
|
# transforms
|
|
transforms = T.Compose(
|
|
[
|
|
T.Resize((model.image_size, model.image_size), interpolation=T.InterpolationMode.BICUBIC),
|
|
T.ToTensor(),
|
|
T.Normalize(mean=mean, std=std),
|
|
]
|
|
)
|
|
output += (transforms,)
|
|
return output[0] if len(output) == 1 else output
|
|
|
|
|
|
def clip_xlm_roberta_vit_h_14(pretrained=False, pretrained_name="open-clip-xlm-roberta-large-vit-huge-14", **kwargs):
|
|
cfg = dict(
|
|
embed_dim=1024,
|
|
image_size=224,
|
|
patch_size=14,
|
|
vision_dim=1280,
|
|
vision_mlp_ratio=4,
|
|
vision_heads=16,
|
|
vision_layers=32,
|
|
vision_pool="token",
|
|
activation="gelu",
|
|
vocab_size=250002,
|
|
max_text_len=514,
|
|
type_size=1,
|
|
pad_id=1,
|
|
text_dim=1024,
|
|
text_heads=16,
|
|
text_layers=24,
|
|
text_post_norm=True,
|
|
text_dropout=0.1,
|
|
attn_dropout=0.0,
|
|
proj_dropout=0.0,
|
|
embedding_dropout=0.0,
|
|
)
|
|
cfg.update(**kwargs)
|
|
return _clip(pretrained, pretrained_name, XLMRobertaCLIP, **cfg)
|
|
|
|
|
|
def load_model_torch(model, ckpt_path, credential_path: Optional[str] = None):
|
|
log.info(f"loading weights from {ckpt_path}")
|
|
if distributed.is_rank0():
|
|
if ckpt_path.startswith("s3://"):
|
|
backend_key = "_clip_model"
|
|
easy_io.set_s3_backend(
|
|
key=backend_key,
|
|
backend_args={
|
|
"backend": "s3",
|
|
"s3_credential_path": credential_path,
|
|
},
|
|
)
|
|
else:
|
|
backend_key = None
|
|
|
|
ckpt = easy_io.load(
|
|
ckpt_path, backend_key=backend_key, map_location="cpu", fast_backend=False, weights_only=True
|
|
)
|
|
model.load_state_dict(ckpt)
|
|
|
|
distributed.sync_model_states(model, src=0)
|
|
return model
|
|
|
|
|
|
class CLIPModel:
|
|
def __init__(
|
|
self,
|
|
dtype=torch.float16,
|
|
device="cuda",
|
|
checkpoint_path="./checkpoints/ChronoEdit-14B/models_clip_open-clip-xlm-roberta-large-vit-huge-14_fp16.pth", #TODO: make sure it's downloaded
|
|
tokenizer_path="xlm-roberta-large",
|
|
credential_path: Optional[str] = "credentials/s3_training.secret",
|
|
):
|
|
self.dtype = dtype
|
|
self.device = device
|
|
self.checkpoint_path = checkpoint_path
|
|
self.tokenizer_path = tokenizer_path
|
|
|
|
# init model
|
|
self.model, self.transforms = clip_xlm_roberta_vit_h_14(
|
|
pretrained=False, return_transforms=True, return_tokenizer=False, dtype=dtype, device=device
|
|
)
|
|
self.model = self.model.cuda().eval().requires_grad_(False)
|
|
self.model = load_model_torch(self.model, checkpoint_path, credential_path=credential_path)
|
|
|
|
# init tokenizer
|
|
self.tokenizer = HuggingfaceTokenizer(
|
|
name=tokenizer_path, seq_len=self.model.max_text_len - 2, clean="whitespace"
|
|
)
|
|
|
|
def visual(self, videos_B_C_H_W_n1_p1):
|
|
# preprocess
|
|
size = (self.model.image_size,) * 2
|
|
videos = F.interpolate(videos_B_C_H_W_n1_p1, size=size, mode="bicubic", align_corners=False)
|
|
videos = self.transforms.transforms[-1](videos.mul_(0.5).add_(0.5))
|
|
|
|
# forward
|
|
with torch.amp.autocast('cuda', dtype=self.dtype):
|
|
out = self.model.visual(videos, use_31_block=True)
|
|
return out
|
|
|
|
|
|
class Wan2pt1CLIPEmb(AbstractEmbModel):
|
|
def __init__(
|
|
self,
|
|
input_key: List[str],
|
|
dropout_rate: Optional[float] = 0.0,
|
|
num_token: int = 257,
|
|
dtype: str = "bfloat16",
|
|
):
|
|
super().__init__()
|
|
self.num_token = num_token
|
|
self.model_dim = 1280
|
|
self.clip_model = CLIPModel()
|
|
|
|
self._input_key = input_key
|
|
self._output_key = None
|
|
self._dropout_rate = dropout_rate
|
|
self.dtype = {
|
|
"bfloat16": torch.bfloat16,
|
|
"float16": torch.float16,
|
|
"float32": torch.float32,
|
|
}[dtype]
|
|
|
|
def random_dropout_input(
|
|
self, in_tensor: Optional[torch.Tensor] = None, dropout_rate: Optional[float] = None, key: Optional[str] = None
|
|
) -> Optional[torch.Tensor]:
|
|
if in_tensor is None:
|
|
return None
|
|
return super().random_dropout_input(in_tensor, dropout_rate, key)
|
|
|
|
def forward(
|
|
self,
|
|
image_tensor: Optional[torch.Tensor] = None,
|
|
video_tensor: Optional[torch.Tensor] = None,
|
|
media_latents: Optional[torch.Tensor] = None,
|
|
) -> Dict[str, torch.Tensor]:
|
|
b, _, latent_f, latent_h, latent_w = media_latents.shape
|
|
mask = torch.zeros(b, 4, latent_f, latent_h, latent_w).type_as(media_latents).to(self.dtype)
|
|
if image_tensor is not None: # image case
|
|
context_B_L_D = torch.zeros(b, self.num_token, self.model_dim).type_as(media_latents).to(self.dtype)
|
|
else:
|
|
first_frame_B_C_H_W = video_tensor[:, :, 0, :, :]
|
|
with torch.no_grad():
|
|
context_B_L_D = self.clip_model.visual(first_frame_B_C_H_W).to(self.dtype)
|
|
|
|
mask[:, :, :1] = 1.0
|
|
y = torch.concat([mask, media_latents.to(self.dtype)], dim=1)
|
|
|
|
return {"frame_cond_crossattn_emb_B_L_D": context_B_L_D, "y_B_C_T_H_W": y}
|
|
|
|
def details(self) -> str:
|
|
output_key = ["frame_cond_crossattn_emb_B_L_D", "y_B_C_T_H_W"]
|
|
return f"Input key: {self.input_key} \n\tOutput key: {output_key}"
|