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4cd55fa | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | from models.pipeline_stable_video_diffusion import StableVideoDiffusionPipeline
from models.unet_spatio_temporal_condition import UNetSpatioTemporalConditionModel
from models.pipeline_ctrl_world import CtrlWorldDiffusionPipeline
import torch
import torch.nn as nn
import json
import einops
import numpy as np
from huggingface_hub import snapshot_download
from transformers import AutoTokenizer, CLIPTextModelWithProjection
class Action_encoder2(nn.Module):
def __init__(self, action_dim, action_num, hidden_size, text_cond=True):
super().__init__()
self.action_dim = action_dim
self.action_num = action_num
self.hidden_size = hidden_size
self.text_cond = text_cond
input_dim = int(action_dim)
self.action_encode = nn.Sequential(
nn.Linear(input_dim, 1024),
nn.SiLU(),
nn.Linear(1024, 1024),
nn.SiLU(),
nn.Linear(1024, 1024)
)
# kaiming initialization
nn.init.kaiming_normal_(self.action_encode[0].weight, mode='fan_in', nonlinearity='relu')
nn.init.kaiming_normal_(self.action_encode[2].weight, mode='fan_in', nonlinearity='relu')
def forward(self, action, texts=None, text_tokinizer=None, text_encoder=None, frame_level_cond=True,):
# action: (B, action_num, action_dim)
B,T,D = action.shape
if not frame_level_cond:
action = einops.rearrange(action, 'b t d -> b 1 (t d)')
action = self.action_encode(action)
if texts is not None and self.text_cond:
# with 50% probability, add text condition
with torch.no_grad():
inputs = text_tokinizer(texts, padding='max_length', return_tensors="pt", truncation=True).to(text_encoder.device)
outputs = text_encoder(**inputs)
hidden_text = outputs.text_embeds # (B, 512)
hidden_text = einops.repeat(hidden_text, 'b c -> b 1 (n c)', n=2) # (B, 1, 1024)
action = action + hidden_text # (B, T, hidden_size)
return action # (B, 1, hidden_size) or (B, T, hidden_size) if frame_level_cond
class CrtlWorld(nn.Module):
def __init__(self, config: dict):
super(CrtlWorld, self).__init__()
self.config = config
# load from pretrained stable video diffusion
model_local_path = snapshot_download(
repo_id=config["svd_model_path"], # e.g. "stabilityai/stable-video-diffusion-img2vid"
repo_type="model"
)
# Load pipeline from downloaded path
self.pipeline = StableVideoDiffusionPipeline.from_pretrained(
model_local_path,
torch_dtype="auto"
)
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
# freeze vae, image_encoder, enable unet gradient ckpt
self.vae.requires_grad_(False)
self.image_encoder.requires_grad_(False)
self.unet.requires_grad_(True)
self.unet.enable_gradient_checkpointing()
# SVD is a img2video model, load a clip text encoder
model_local_path = snapshot_download(
repo_id=config["clip_model_path"], # e.g. "stabilityai/stable-video-diffusion-img2vid"
repo_type="model"
)
self.text_encoder = CLIPTextModelWithProjection.from_pretrained(
model_local_path,
torch_dtype="auto"
)
self.tokenizer = AutoTokenizer.from_pretrained(model_local_path, use_fast=False)
self.text_encoder.requires_grad_(False)
# initialize an action projector
self.action_encoder = Action_encoder2(action_dim=config["action_dim"], action_num=int(config["num_history"]+config["num_frames"]), hidden_size=1024, text_cond=config["text_cond"])
with open(f"{config["data_stat_path"]}", 'r') as f:
data_stat = json.load(f)
self.state_p01 = np.array(data_stat['state_01'])[None,:]
self.state_p99 = np.array(data_stat['state_99'])[None,:]
def normalize_bound(
self,
data: np.ndarray,
clip_min: float = -1,
clip_max: float = 1,
eps: float = 1e-8,
) -> np.ndarray:
ndata = 2 * (data - self.state_p01) / (self.state_p99 - self.state_p01 + eps) - 1
return np.clip(ndata, clip_min, clip_max)
def decode(self, latents: torch.Tensor):
bsz, frame_num = latents.shape[:2]
x = latents.flatten(0, 1)
decoded = []
chunk_size = self.config["decode_chunk_size"]
for i in range(0, x.shape[0], chunk_size):
chunk = x[i:i + chunk_size] / self.pipeline.vae.config.scaling_factor
decode_kwargs = {"num_frames": chunk.shape[0]}
out = self.pipeline.vae.decode(chunk, **decode_kwargs).sample
decoded.append(out)
videos = torch.cat(decoded, dim=0)
videos = videos.reshape(bsz, frame_num, *videos.shape[1:])
videos = ((videos / 2.0 + 0.5).clamp(0, 1))
videos = videos.detach().float().cpu()
def encode(self, img: torch.Tensor):
x = img.unsqueeze(0)
x = x * 2 - 1 # [0,1] → [-1,1]
vae = self.pipeline.vae
with torch.no_grad():
latent = vae.encode(x).latent_dist.sample()
latent = latent * vae.config.scaling_factor
return latent.detach()
def action_text_encode(self, action: torch.Tensor, text):
action_tensor = action.unsqueeze(0)
# ── Encode action (+ optional text) ───────────────────
with torch.no_grad():
if text is not None and self.config["text_cond"]:
text_token = self.action_encoder(action_tensor, [text], self.tokenizer, self.text_encoder)
else:
text_token = self.action_encoder(action_tensor)
return text_token.detach()
def get_latent_views(self, frames, current_latent, text_token):
his_cond = torch.cat(frames, dim=0).unsqueeze(0) # (1, num_history, 4, stacked_H, W)
# ── Run CtrlWorldDiffusionPipeline ────────────────────
with torch.no_grad():
_, latents = CtrlWorldDiffusionPipeline.__call__(
self.pipeline,
image=current_latent,
text=text_token,
width=self.config["width"],
height=int(self.config["height"] * 3), # 3 views stacked
num_frames=self.config["num_frames"],
history=his_cond,
num_inference_steps=self.config["num_inference_steps"],
decode_chunk_size=self.config["decode_chunk_size"],
max_guidance_scale=self.config["guidance_scale"],
fps=self.config["fps"],
motion_bucket_id=self.config["motion_bucket_id"],
mask=None,
output_type="latent",
return_dict=False,
frame_level_cond=True,
)
return latents |