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da7bf91 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | from __future__ import annotations
import torch
import torch.nn as nn
import einops
from graphwm.config_graph import GraphWMArgs
from graphwm.models.graph_encoder_pyg import GraphSpatialEncoder
from graphwm.models.graph_resampler import GraphResampler
from graphwm.models.temporal_graph_conditioner import TemporalGraphConditioner
from graphwm.original_ctrl_world import import_original_modules
class GraphConditioner(nn.Module):
"""Per-frame PyG encoder -> fixed-K graph tokens -> temporal transformer."""
def __init__(self, args: GraphWMArgs):
super().__init__()
self.spatial = GraphSpatialEncoder(
node_in_dim=args.graph_in_dim,
edge_in_dim=args.edge_in_dim,
hidden_dim=args.graph_hidden_dim,
num_layers=args.graph_num_layers,
dropout=args.graph_dropout,
backbone=args.graph_backbone,
num_heads=args.graph_num_heads,
)
self.resampler = GraphResampler(
hidden_dim=args.graph_hidden_dim,
num_tokens=args.graph_num_tokens,
num_heads=args.graph_num_heads,
dropout=args.graph_dropout,
)
self.temporal = TemporalGraphConditioner(
hidden_dim=args.graph_hidden_dim,
cond_dim=args.graph_cond_dim,
num_layers=args.graph_temporal_layers,
num_heads=args.graph_temporal_heads,
dropout=args.graph_dropout,
)
def forward(self, graph_seq):
per_frame_tokens = []
for graph_batch in graph_seq:
node_tokens = self.spatial(graph_batch)
frame_tokens = self.resampler(node_tokens, graph_batch.batch)
per_frame_tokens.append(frame_tokens)
frame_tokens = torch.stack(per_frame_tokens, dim=1)
return self.temporal(frame_tokens)
class CtrlWorldGraph(nn.Module):
"""Graph-conditioned wrapper around the original Ctrl-World backbone."""
def __init__(self, args: GraphWMArgs):
super().__init__()
self.args = args
original = import_original_modules(args.ctrl_world_root)
StableVideoDiffusionPipeline = original["StableVideoDiffusionPipeline"]
UNetSpatioTemporalConditionModel = original["UNetSpatioTemporalConditionModel"]
self.pipeline = StableVideoDiffusionPipeline.from_pretrained(args.svd_model_path)
unet = UNetSpatioTemporalConditionModel()
unet.load_state_dict(self.pipeline.unet.state_dict(), strict=False)
self.pipeline.unet = unet
self.unet = self.pipeline.unet
self.vae = self.pipeline.vae
self.image_encoder = self.pipeline.image_encoder
self.scheduler = self.pipeline.scheduler
self.vae.requires_grad_(False)
self.image_encoder.requires_grad_(False)
self.unet.requires_grad_(True)
self.unet.enable_gradient_checkpointing()
self.graph_conditioner = GraphConditioner(args)
def encode_graph_condition(self, batch) -> torch.Tensor:
return self.graph_conditioner(batch["graph_seq"])
@torch.no_grad()
def encode_rgb_to_latents(self, rgb: torch.Tensor) -> torch.Tensor:
"""Encode RGB clips [B, T, 3, H, W] in [0,1] into VAE latents."""
device = self.unet.device
rgb = rgb.to(device)
bsz, num_frames, channels, height, width = rgb.shape
flat_rgb = rgb.flatten(0, 1)
flat_rgb = flat_rgb * 2.0 - 1.0
needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
if needs_upcasting:
self.vae.to(dtype=torch.float32)
flat_rgb = flat_rgb.to(torch.float32)
else:
flat_rgb = flat_rgb.to(self.vae.dtype)
posterior = self.vae.encode(flat_rgb).latent_dist
flat_latents = posterior.sample() * self.vae.config.scaling_factor
if needs_upcasting:
self.vae.to(dtype=self.unet.dtype)
latents = flat_latents.reshape(bsz, num_frames, *flat_latents.shape[1:])
return latents.to(self.unet.dtype)
def forward(self, batch):
if "latent" in batch:
latents = batch["latent"]
elif "rgb" in batch:
latents = self.encode_rgb_to_latents(batch["rgb"])
else:
raise KeyError("Batch must contain either 'latent' or 'rgb'.")
device = self.unet.device
dtype = self.unet.dtype
P_mean = 0.7
P_std = 1.6
noise_aug_strength = 0.0
num_history = self.args.num_history
latents = latents.to(device)
current_img = latents[:, num_history:(num_history + 1)]
bsz, num_frames = latents.shape[:2]
current_img = current_img[:, 0]
sigma = torch.rand([bsz, 1, 1, 1], device=device) * 0.2
c_in = 1 / (sigma**2 + 1) ** 0.5
current_img = c_in * (current_img + torch.randn_like(current_img) * sigma)
condition_latent = einops.repeat(current_img, "b c h w -> b f c h w", f=num_frames)
if self.args.his_cond_zero:
condition_latent[:, :num_history] = 0.0
graph_hidden = self.encode_graph_condition(batch).to(device=device, dtype=dtype)
uncond_hidden_states = torch.zeros_like(graph_hidden)
cond_mask = (torch.rand(graph_hidden.shape[0], device=device) > 0.05).view(-1, 1, 1, 1)
graph_hidden = graph_hidden * cond_mask + uncond_hidden_states * (~cond_mask)
rnd_normal = torch.randn([bsz, 1, 1, 1, 1], device=device)
sigma = (rnd_normal * P_std + P_mean).exp()
c_skip = 1 / (sigma**2 + 1)
c_out = -sigma / (sigma**2 + 1) ** 0.5
c_in = 1 / (sigma**2 + 1) ** 0.5
c_noise = (sigma.log() / 4).reshape([bsz])
loss_weight = (sigma**2 + 1) / sigma**2
noisy_latents = latents + torch.randn_like(latents) * sigma
sigma_h = torch.randn([bsz, num_history, 1, 1, 1], device=device) * 0.3
history = latents[:, :num_history]
noisy_history = 1 / (sigma_h**2 + 1) ** 0.5 * (history + sigma_h * torch.randn_like(history))
input_latents = torch.cat([noisy_history, c_in * noisy_latents[:, num_history:]], dim=1)
input_latents = torch.cat([input_latents, condition_latent / self.vae.config.scaling_factor], dim=2)
added_time_ids = self.pipeline._get_add_time_ids(
self.args.fps,
self.args.motion_bucket_id,
noise_aug_strength,
graph_hidden.dtype,
bsz,
1,
False,
).to(device)
model_pred = self.unet(
input_latents,
c_noise,
encoder_hidden_states=graph_hidden,
added_time_ids=added_time_ids,
frame_level_cond=self.args.frame_level_cond,
).sample
predict_x0 = c_out * model_pred + c_skip * noisy_latents
loss = ((predict_x0[:, num_history:] - latents[:, num_history:]) ** 2 * loss_weight).mean()
return loss, torch.tensor(0.0, device=device, dtype=dtype)
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