Spaces:
Running
on
Zero
Running
on
Zero
File size: 6,059 Bytes
142a1ac |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 |
import torch
import torch.nn as nn
from einops import rearrange, repeat
from transformers import get_scheduler
from .modules.clip import clip_xlm_roberta_vit_h_14
from .wan_t2v import WanTextToVideo
class WanImageToVideo(WanTextToVideo):
"""
Main class for WanImageToVideo, inheriting from WanTextToVideo
"""
def __init__(self, cfg):
super().__init__(cfg)
self.cfg.model.in_dim = self.cfg.vae.z_dim * 2 + 4
def configure_model(self):
# Call parent's configure_model first
super().configure_model()
if self.cfg.model.tuned_ckpt_path is None:
self.model.hack_embedding_ckpt()
# Additionally initialize CLIP for image encoding
clip, clip_transform = clip_xlm_roberta_vit_h_14(
pretrained=False,
return_transforms=True,
return_tokenizer=False,
dtype=torch.float16 if self.is_inference else self.dtype,
device="cpu",
)
if self.cfg.clip.ckpt_path is not None:
clip.load_state_dict(
torch.load(
self.cfg.clip.ckpt_path, map_location="cpu", weights_only=True
)
)
if self.cfg.clip.compile:
clip = torch.compile(clip)
self.clip = clip
self.clip_normalize = clip_transform.transforms[-1]
def configure_optimizers(self):
optimizer = torch.optim.AdamW(
[
{"params": self.model.parameters(), "lr": self.cfg.lr},
{"params": self.vae.parameters(), "lr": 0},
{"params": self.clip.parameters(), "lr": 0},
],
weight_decay=self.cfg.weight_decay,
betas=self.cfg.betas,
)
# optimizer = torch.optim.AdamW(
# self.model.parameters(),
# lr=self.cfg.lr,
# weight_decay=self.cfg.weight_decay,
# betas=self.cfg.betas,
# )
lr_scheduler_config = {
"scheduler": get_scheduler(
optimizer=optimizer,
**self.cfg.lr_scheduler,
),
"interval": "step",
"frequency": 1,
}
return {
"optimizer": optimizer,
"lr_scheduler": lr_scheduler_config,
}
def clip_features(self, videos):
size = (self.clip.image_size,) * 2
videos = rearrange(videos, "b t c h w -> (b t) c h w")
videos = nn.functional.interpolate(
videos, size=size, mode="bicubic", align_corners=False
)
videos = self.clip_normalize(videos.mul_(0.5).add_(0.5))
return self.clip.visual(videos, use_31_block=True)
@torch.no_grad()
def prepare_embeds(self, batch):
batch = super().prepare_embeds(batch)
videos = batch["videos"]
images = videos[:, :1]
has_bbox = batch["has_bbox"] # [B, 2]
bbox_render = batch["bbox_render"] # [B, 2, H, W]
batch_size, t, _, h, w = videos.shape
lat_c, lat_t, lat_h, lat_w = self.lat_c, self.lat_t, self.lat_h, self.lat_w
clip_embeds = self.clip_features(images)
batch["clip_embeds"] = clip_embeds
mask = torch.zeros(
batch_size,
self.vae_stride[0],
lat_t,
lat_h,
lat_w,
device=self.device,
dtype=self.dtype,
)
# after the ckpt hack, we repurpose the 4 mask channels for bounding box conditioning
# second last channel is indicator of bounding box
mask[:, 2, 0] = has_bbox[..., 0, None, None]
mask[:, 2, -1] = has_bbox[..., -1, None, None]
# Interpolate bbox_render to match latent dimensions
bbox_render_resized = nn.functional.interpolate(
bbox_render,
size=(lat_h, lat_w),
mode="bicubic",
align_corners=False,
)
# last channel is renderred bbox
mask[:, 3, 0] = bbox_render_resized[:, 0]
mask[:, 3, -1] = bbox_render_resized[:, -1]
if self.diffusion_forcing.enabled:
image_embeds = torch.zeros(
batch_size,
4 + lat_c,
lat_t,
lat_h,
lat_w,
device=self.device,
dtype=self.dtype,
)
else:
padded_images = torch.zeros(batch_size, 3, t - 1, h, w, device=self.device)
padded_images = torch.cat(
[rearrange(images, "b 1 c h w -> b c 1 h w"), padded_images], dim=2
)
image_embeds = self.encode_video(
padded_images
) # b, lat_c, lat_t, lat_h, lat_w
image_embeds = torch.cat([mask, image_embeds], 1)
mask[:, :2, 0] = 1
batch["image_embeds"] = image_embeds
return batch
def visualize(self, video_pred, batch):
bbox_render = batch["bbox_render"] # b, 2, h, w for first and last frame
has_bbox = batch["has_bbox"] # b, 2 for first and last frame
video_gt = batch["videos"] # b, t, 3, h, w
alpha = 0.4
l = video_gt.shape[1] // 4
# Apply green bbox overlay with transparency to first frame if has_bbox for first frame
mask = has_bbox[:, 0].bool()
green = torch.zeros_like(video_gt[mask, :1])
green[:, :, 1] = 1.0
if mask.any():
bbox = bbox_render[:, None, 0:1][mask] * alpha # b', 1, 1, h, w
video_gt[mask, :l] = (1 - bbox) * video_gt[mask, :l] + bbox * green
# Apply green bbox overlay with transparency to last frame if has_bbox for last frame
mask = has_bbox[:, 1].bool()
green = torch.zeros_like(video_gt[mask, :1])
green[:, :, 1] = 1.0
if mask.any():
bbox = bbox_render[:, None, 1:2][mask] * alpha # b', 1, 1, h, w
video_gt[mask, -l:] = (1 - bbox) * video_gt[mask, -l:] + bbox * green
batch["videos"] = video_gt
return super().visualize(video_pred, batch)
|