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This commit is contained in:
AI Lab
2025-10-26 08:53:24 -07:00
committed by GitHub
parent e4460e4e36
commit 8e214e4fd0
3 changed files with 219 additions and 53 deletions
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FlashVSR/pipelines/flashvsr_full.py
+68 -17
View File
@@ -18,6 +18,14 @@ from ..models.wan_video_vae import WanVideoVAE, RMS_norm, CausalConv3d, Upsample
from ..schedulers.flow_match import FlowMatchScheduler
from .base import BasePipeline
# --- Safetensors support ---
st_load_file = None # Define the variable in global scope first
try:
from safetensors.torch import load_file as st_load_file
except ImportError:
# st_load_file remains None if import fails
print("Warning: 'safetensors' not installed. Safetensors (.safetensors) files cannot be loaded.")
# ---------------------------
# -----------------------------
# 基础工具：ADAIN 所需的统计量（保留以备需要；管线默认用 wavelet）
@@ -80,7 +88,7 @@ def _wavelet_reconstruct(content: torch.Tensor, style: torch.Tensor, levels: int
_, s_low = _wavelet_decompose(style, levels=levels)
return c_high + s_low
from ..models.utils import clean_vram
# -----------------------------
# 无状态颜色矫正模块（视频友好，默认 wavelet）
# -----------------------------
@@ -165,13 +173,12 @@ class FlashVSRFullPipeline(BasePipeline):
self.ColorCorrector = TorchColorCorrectorWavelet(levels=5)
print(r"""
███████╗██╗ █████╗ ███████╗██╗ ██╗██╗ ██╗███████╗█████╗
██╔════╝██║ ██╔══██╗██╔════╝██║ ██║██║ ██║██╔════╝██╔══██╗
█████╗ ██║ ███████║███████╗███████║╚██╗ ██╔╝███████╗███████║
██╔══╝ ██║ ██╔══██║╚════██║██╔══██║ ╚████╔╝ ╚════██║██╔═██║
██║ ███████╗██║ ██║███████║██║ ██║ ╚██╔╝ ███████║██║ ██║
╚═╝ ╚══════╝╚═╝ ╚═╝╚══════╝╚═╝ ╚═╝ ╚═╝ ╚══════╝╚═╝ ╚═╝
⚡FlashVSR
███████╗██╗ █████╗ ███████╗██╗ ██╗██╗ ██╗███████╗█████╗
██╔════╝██║ ██╔══██╗██╔════╝██║ ██║██║ ██║██╔════╝██╔══██╗
█████╗ ██║ ███████║███████╗███████║╚██╗ ██╔╝███████╗███████║
██╔══╝ ██║ ██╔══██║╚════██║██╔══██║ ╚████╔╝ ╚════██║██╔═██║
██║ ███████╗██║ ██║███████║██║ ██║ ╚██╔╝ ███████║██║ ██║
╚═╝ ╚══════╝╚═╝ ╚═╝╚══════╝╚═╝ ╚═╝ ╚═╝ ╚══════╝╚═╝ ╚═╝
""")
def enable_vram_management(self, num_persistent_param_in_dit=None):
@@ -250,20 +257,44 @@ class FlashVSRFullPipeline(BasePipeline):
def init_cross_kv(
self,
context_tensor: Optional[torch.Tensor] = None,
prompt_path = None
):
self.load_models_to_device(["dit"])
"""
使用固定 prompt 生成文本 context，并在 WanModel 中初始化所有 CrossAttention 的 KV 缓存。
必须在 __call__ 前显式调用一次。
"""
prompt_path = "../../examples/WanVSR/prompt_tensor/posi_prompt.pth"
#prompt_path = "../../examples/WanVSR/prompt_tensor/posi_prompt.pth"
if self.dit is None:
raise RuntimeError("请先通过 fetch_models / from_model_manager 初始化 self.dit")
if context_tensor is None:
if prompt_path is None:
raise ValueError("init_cross_kv: 需要提供 prompt_path 或 context_tensor 其一")
ctx = torch.load(prompt_path, map_location=self.device)
# --- Safetensors loading logic added here ---
prompt_path_lower = prompt_path.lower()
if prompt_path_lower.endswith(".safetensors"):
if st_load_file is None:
raise ImportError("The 'safetensors' library must be installed to load .safetensors files.")
# Load the tensor from safetensors
loaded_dict = st_load_file(prompt_path, device=self.device)
# Safetensors loads a dict. Assuming the context tensor is the only or primary key.
if len(loaded_dict) == 1:
ctx = list(loaded_dict.values())[0]
elif 'context' in loaded_dict: # Common key for text context
ctx = loaded_dict['context']
else:
raise ValueError(f"Safetensors file {prompt_path} does not contain an obvious single tensor ('context' key not found and multiple keys exist).")
else:
# Default behavior for .pth, .pt, etc.
ctx = torch.load(prompt_path, map_location=self.device)
# --------------------------------------------
# ctx = torch.load(prompt_path, map_location=self.device)
else:
ctx = context_tensor
@@ -326,6 +357,8 @@ class FlashVSRFullPipeline(BasePipeline):
kv_ratio=3.0,
local_range = 9,
color_fix = True,
unload_dit = False,
skip_vae = False,
):
# 只接受 cfg=1.0（与原代码一致）
assert cfg_scale == 1.0, "cfg_scale must be 1.0"
@@ -361,15 +394,17 @@ class FlashVSRFullPipeline(BasePipeline):
if hasattr(self.dit, "LQ_proj_in"):
self.dit.LQ_proj_in.clear_cache()
latents_total = []
self.vae.clear_cache()
if unload_dit and hasattr(self, 'dit') and self.dit is not None:
current_dit_device = next(iter(self.dit.parameters())).device
if str(current_dit_device) != str(self.device):
print(f"[FlashVSR] DiT is on {current_dit_device}, moving it to target device {self.device}...")
self.dit.to(self.device)
with torch.no_grad():
for cur_process_idx in tqdm(range(process_total_num)):
torch.cuda.synchronize()
dit_time_start = time.time()
for cur_process_idx in progress_bar_cmd(range(process_total_num)):
if cur_process_idx == 0:
pre_cache_k = [None] * len(self.dit.blocks)
pre_cache_v = [None] * len(self.dit.blocks)
@@ -402,7 +437,7 @@ class FlashVSRFullPipeline(BasePipeline):
for layer_idx in range(len(LQ_latents)):
LQ_latents[layer_idx] = torch.cat([LQ_latents[layer_idx], cur[layer_idx]], dim=1)
cur_latents = latents[:, :, 4+cur_process_idx*2:6+cur_process_idx*2, :, :]
# 推理（无 motion_controller / vace）
noise_pred_posi, pre_cache_k, pre_cache_v = model_fn_wan_video(
self.dit,
@@ -427,10 +462,26 @@ class FlashVSRFullPipeline(BasePipeline):
# 更新 latent
cur_latents = cur_latents - noise_pred_posi
latents_total.append(cur_latents)
if unload_dit and hasattr(self, 'dit') and not next(self.dit.parameters()).is_cpu:
try:
del pre_cache_k, pre_cache_v
except NameError:
pass
print("[FlashVSR] Offloading DiT to the CPU to free up VRAM...")
self.dit.to('cpu')
clean_vram()
latents = torch.cat(latents_total, dim=2)
del latents_total
clean_vram()
if skip_vae:
return latents
# Decode
print("[FlashVSR] Starting VAE decoding...")
frames = self.decode_video(latents, **tiler_kwargs)
# 颜色校正（wavelet）
FlashVSR/pipelines/flashvsr_tiny.py
+80 -15
View File
@@ -18,6 +18,14 @@ from ..models.wan_video_vae import WanVideoVAE, RMS_norm, CausalConv3d, Upsample
from ..schedulers.flow_match import FlowMatchScheduler
from .base import BasePipeline
# --- Safetensors support ---
st_load_file = None # Define the variable in global scope first
try:
from safetensors.torch import load_file as st_load_file
except ImportError:
# st_load_file remains None if import fails
print("Warning: 'safetensors' not installed. Safetensors (.safetensors) files cannot be loaded.")
# ---------------------------
# -----------------------------
# 基础工具：ADAIN 所需的统计量（保留以备需要；管线默认用 wavelet）
@@ -80,7 +88,7 @@ def _wavelet_reconstruct(content: torch.Tensor, style: torch.Tensor, levels: int
_, s_low = _wavelet_decompose(style, levels=levels)
return c_high + s_low
from ..models.utils import clean_vram
# -----------------------------
# 无状态颜色矫正模块（视频友好，默认 wavelet）
# -----------------------------
@@ -165,13 +173,12 @@ class FlashVSRTinyPipeline(BasePipeline):
self.ColorCorrector = TorchColorCorrectorWavelet(levels=5)
print(r"""
███████╗██╗ █████╗ ███████╗██╗ ██╗██╗ ██╗███████╗█████╗
██╔════╝██║ ██╔══██╗██╔════╝██║ ██║██║ ██║██╔════╝██╔══██╗
█████╗ ██║ ███████║███████╗███████║╚██╗ ██╔╝███████╗███████║
██╔══╝ ██║ ██╔══██║╚════██║██╔══██║ ╚████╔╝ ╚════██║██╔═██║
██║ ███████╗██║ ██║███████║██║ ██║ ╚██╔╝ ███████║██║ ██║
╚═╝ ╚══════╝╚═╝ ╚═╝╚══════╝╚═╝ ╚═╝ ╚═╝ ╚══════╝╚═╝ ╚═╝
⚡FlashVSR
███████╗██╗ █████╗ ███████╗██╗ ██╗██╗ ██╗███████╗█████╗
██╔════╝██║ ██╔══██╗██╔════╝██║ ██║██║ ██║██╔════╝██╔══██╗
█████╗ ██║ ███████║███████╗███████║╚██╗ ██╔╝███████╗███████║
██╔══╝ ██║ ██╔══██║╚════██║██╔══██║ ╚████╔╝ ╚════██║██╔═██║
██║ ███████╗██║ ██║███████║██║ ██║ ╚██╔╝ ███████║██║ ██║
╚═╝ ╚══════╝╚═╝ ╚═╝╚══════╝╚═╝ ╚═╝ ╚═╝ ╚══════╝╚═╝ ╚═╝
""")
def enable_vram_management(self, num_persistent_param_in_dit=None):
@@ -229,13 +236,14 @@ class FlashVSRTinyPipeline(BasePipeline):
def init_cross_kv(
self,
context_tensor: Optional[torch.Tensor] = None,
prompt_path = None,
):
self.load_models_to_device(["dit"])
"""
使用固定 prompt 生成文本 context，并在 WanModel 中初始化所有 CrossAttention 的 KV 缓存。
必须在 __call__ 前显式调用一次。
"""
prompt_path = "../../examples/WanVSR/prompt_tensor/posi_prompt.pth"
#prompt_path = "../../examples/WanVSR/prompt_tensor/posi_prompt.pth"
if self.dit is None:
raise RuntimeError("请先通过 fetch_models / from_model_manager 初始化 self.dit")
@@ -243,7 +251,30 @@ class FlashVSRTinyPipeline(BasePipeline):
if context_tensor is None:
if prompt_path is None:
raise ValueError("init_cross_kv: 需要提供 prompt_path 或 context_tensor 其一")
ctx = torch.load(prompt_path, map_location=self.device)
# --- Safetensors loading logic added here ---
prompt_path_lower = prompt_path.lower()
if prompt_path_lower.endswith(".safetensors"):
if st_load_file is None:
raise ImportError("The 'safetensors' library must be installed to load .safetensors files.")
# Load the tensor from safetensors
loaded_dict = st_load_file(prompt_path, device=self.device)
# Safetensors loads a dict. Assuming the context tensor is the only or primary key.
if len(loaded_dict) == 1:
ctx = list(loaded_dict.values())[0]
elif 'context' in loaded_dict: # Common key for text context
ctx = loaded_dict['context']
else:
raise ValueError(f"Safetensors file {prompt_path} does not contain an obvious single tensor ('context' key not found and multiple keys exist).")
else:
# Default behavior for .pth, .pt, etc.
ctx = torch.load(prompt_path, map_location=self.device)
# --------------------------------------------
# ctx = torch.load(prompt_path, map_location=self.device)
else:
ctx = context_tensor
@@ -274,9 +305,19 @@ class FlashVSRTinyPipeline(BasePipeline):
latents = self.vae.encode(input_video, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
return latents
def decode_video(self, latents, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)):
def _decode_video(self, latents, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)):
frames = self.vae.decode(latents, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
return frames
def decode_video(self, latents, cond=None, **kwargs):
frames = self.TCDecoder.decode_video(
latents.transpose(1, 2), # TCDecoder 需要 (B, F, C, H, W)
parallel=False,
show_progress_bar=False,
cond=cond
).transpose(1, 2).mul_(2).sub_(1) # 转回 (B, C, F, H, W) 格式，范围 -1 to 1
return frames
@torch.no_grad()
def __call__(
@@ -306,10 +347,12 @@ class FlashVSRTinyPipeline(BasePipeline):
kv_ratio=3.0,
local_range = 9,
color_fix = True,
unload_dit = False,
skip_vae = False,
):
# 只接受 cfg=1.0（与原代码一致）
assert cfg_scale == 1.0, "cfg_scale must be 1.0"
# 要求：必须先 init_cross_kv()
if self.prompt_emb_posi is None or 'context' not in self.prompt_emb_posi:
raise RuntimeError(
@@ -347,9 +390,15 @@ class FlashVSRTinyPipeline(BasePipeline):
self.TCDecoder.clean_mem()
LQ_pre_idx = 0
LQ_cur_idx = 0
if unload_dit and hasattr(self, 'dit') and self.dit is not None:
current_dit_device = next(iter(self.dit.parameters())).device
if str(current_dit_device) != str(self.device):
print(f"[FlashVSR] DiT is on {current_dit_device}, moving it to target device {self.device}...")
self.dit.to(self.device)
with torch.no_grad():
for cur_process_idx in tqdm(range(process_total_num)):
for cur_process_idx in progress_bar_cmd(range(process_total_num)):
if cur_process_idx == 0:
pre_cache_k = [None] * len(self.dit.blocks)
pre_cache_v = [None] * len(self.dit.blocks)
@@ -384,7 +433,7 @@ class FlashVSRTinyPipeline(BasePipeline):
LQ_latents[layer_idx] = torch.cat([LQ_latents[layer_idx], cur[layer_idx]], dim=1)
LQ_cur_idx = cur_process_idx*8+21+(inner_loop_num-2)*4
cur_latents = latents[:, :, 4+cur_process_idx*2:6+cur_process_idx*2, :, :]
# 推理（无 motion_controller / vace）
noise_pred_posi, pre_cache_k, pre_cache_v = model_fn_wan_video(
self.dit,
@@ -410,10 +459,26 @@ class FlashVSRTinyPipeline(BasePipeline):
cur_latents = cur_latents - noise_pred_posi
latents_total.append(cur_latents)
LQ_pre_idx = LQ_cur_idx
if unload_dit and hasattr(self, 'dit') and not next(self.dit.parameters()).is_cpu:
try:
del pre_cache_k, pre_cache_v
except NameError:
pass
print("[FlashVSR] Offloading DiT to the CPU to free up VRAM...")
self.dit.to('cpu')
clean_vram()
latents = torch.cat(latents_total, dim=2)
del latents_total
clean_vram()
if skip_vae:
return latents
# Decode
print("[FlashVSR] Starting VAE decoding...")
frames = self.TCDecoder.decode_video(latents.transpose(1, 2),parallel=False, show_progress_bar=False, cond=LQ_video[:,:,:LQ_cur_idx,:,:]).transpose(1, 2).mul_(2).sub_(1)
# 颜色校正（wavelet）
FlashVSR/pipelines/flashvsr_tiny_long.py
+71 -21
View File
@@ -18,6 +18,14 @@ from ..models.wan_video_vae import WanVideoVAE, RMS_norm, CausalConv3d, Upsample
from ..schedulers.flow_match import FlowMatchScheduler
from .base import BasePipeline
# --- Safetensors support ---
st_load_file = None # Define the variable in global scope first
try:
from safetensors.torch import load_file as st_load_file
except ImportError:
# st_load_file remains None if import fails
print("Warning: 'safetensors' not installed. Safetensors (.safetensors) files cannot be loaded.")
# ---------------------------
# -----------------------------
# 基础工具：ADAIN 所需的统计量（保留以备需要；管线默认用 wavelet）
@@ -146,7 +154,7 @@ class TorchColorCorrectorWavelet(nn.Module):
out = torch.cat(outs, dim=2)
return out
from ..models.utils import clean_vram
# -----------------------------
# 简化版 Pipeline（仅 dit + vae）
# -----------------------------
@@ -165,13 +173,12 @@ class FlashVSRTinyLongPipeline(BasePipeline):
self.ColorCorrector = TorchColorCorrectorWavelet(levels=5)
print(r"""
███████╗██╗ █████╗ ███████╗██╗ ██╗██╗ ██╗███████╗█████╗
██╔════╝██║ ██╔══██╗██╔════╝██║ ██║██║ ██║██╔════╝██╔══██╗
█████╗ ██║ ███████║███████╗███████║╚██╗ ██╔╝███████╗███████║
██╔══╝ ██║ ██╔══██║╚════██║██╔══██║ ╚████╔╝ ╚════██║██╔═██║
██║ ███████╗██║ ██║███████║██║ ██║ ╚██╔╝ ███████║██║ ██║
╚═╝ ╚══════╝╚═╝ ╚═╝╚══════╝╚═╝ ╚═╝ ╚═╝ ╚══════╝╚═╝ ╚═╝
⚡FlashVSR
███████╗██╗ █████╗ ███████╗██╗ ██╗██╗ ██╗███████╗█████╗
██╔════╝██║ ██╔══██╗██╔════╝██║ ██║██║ ██║██╔════╝██╔══██╗
█████╗ ██║ ███████║███████╗███████║╚██╗ ██╔╝███████╗███████║
██╔══╝ ██║ ██╔══██║╚════██║██╔══██║ ╚████╔╝ ╚════██║██╔═██║
██║ ███████╗██║ ██║███████║██║ ██║ ╚██╔╝ ███████║██║ ██║
╚═╝ ╚══════╝╚═╝ ╚═╝╚══════╝╚═╝ ╚═╝ ╚═╝ ╚══════╝╚═╝ ╚═╝
""")
def enable_vram_management(self, num_persistent_param_in_dit=None):
@@ -229,13 +236,14 @@ class FlashVSRTinyLongPipeline(BasePipeline):
def init_cross_kv(
self,
context_tensor: Optional[torch.Tensor] = None,
prompt_path = None,
):
self.load_models_to_device(["dit"])
"""
使用固定 prompt 生成文本 context，并在 WanModel 中初始化所有 CrossAttention 的 KV 缓存。
必须在 __call__ 前显式调用一次。
"""
prompt_path = "../../examples/WanVSR/prompt_tensor/posi_prompt.pth"
#prompt_path = "../../examples/WanVSR/prompt_tensor/posi_prompt.pth"
if self.dit is None:
raise RuntimeError("请先通过 fetch_models / from_model_manager 初始化 self.dit")
@@ -243,7 +251,30 @@ class FlashVSRTinyLongPipeline(BasePipeline):
if context_tensor is None:
if prompt_path is None:
raise ValueError("init_cross_kv: 需要提供 prompt_path 或 context_tensor 其一")
ctx = torch.load(prompt_path, map_location=self.device)
# --- Safetensors loading logic added here ---
prompt_path_lower = prompt_path.lower()
if prompt_path_lower.endswith(".safetensors"):
if st_load_file is None:
raise ImportError("The 'safetensors' library must be installed to load .safetensors files.")
# Load the tensor from safetensors
loaded_dict = st_load_file(prompt_path, device=self.device)
# Safetensors loads a dict. Assuming the context tensor is the only or primary key.
if len(loaded_dict) == 1:
ctx = list(loaded_dict.values())[0]
elif 'context' in loaded_dict: # Common key for text context
ctx = loaded_dict['context']
else:
raise ValueError(f"Safetensors file {prompt_path} does not contain an obvious single tensor ('context' key not found and multiple keys exist).")
else:
# Default behavior for .pth, .pt, etc.
ctx = torch.load(prompt_path, map_location=self.device)
# --------------------------------------------
# ctx = torch.load(prompt_path, map_location=self.device)
else:
ctx = context_tensor
@@ -274,9 +305,19 @@ class FlashVSRTinyLongPipeline(BasePipeline):
latents = self.vae.encode(input_video, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
return latents
def decode_video(self, latents, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)):
def _decode_video(self, latents, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)):
frames = self.vae.decode(latents, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
return frames
def decode_video(self, latents, cond=None, **kwargs):
frames = self.TCDecoder.decode_video(
latents.transpose(1, 2), # TCDecoder 需要 (B, F, C, H, W)
parallel=False,
show_progress_bar=False,
cond=cond
).transpose(1, 2).mul_(2).sub_(1) # 转回 (B, C, F, H, W) 格式，范围 -1 to 1
return frames
@torch.no_grad()
def __call__(
@@ -296,7 +337,7 @@ class FlashVSRTinyLongPipeline(BasePipeline):
tile_size=(60, 104),
tile_stride=(30, 52),
tea_cache_l1_thresh=None,
tea_cache_model_id="Wan2.1-T2V-14B",
tea_cache_model_id="Wan2.1-T2V-1.3B",
progress_bar_cmd=tqdm,
progress_bar_st=None,
LQ_video=None,
@@ -306,10 +347,12 @@ class FlashVSRTinyLongPipeline(BasePipeline):
kv_ratio=3.0,
local_range = 9,
color_fix = True,
unload_dit = False,
skip_vae = False,
):
# 只接受 cfg=1.0（与原代码一致）
assert cfg_scale == 1.0, "cfg_scale must be 1.0"
# 要求：必须先 init_cross_kv()
if self.prompt_emb_posi is None or 'context' not in self.prompt_emb_posi:
raise RuntimeError(
@@ -343,13 +386,13 @@ class FlashVSRTinyLongPipeline(BasePipeline):
if hasattr(self.dit, "LQ_proj_in"):
self.dit.LQ_proj_in.clear_cache()
self.TCDecoder.clean_mem()
frames_total = []
LQ_pre_idx = 0
LQ_cur_idx = 0
frames_total = []
self.TCDecoder.clean_mem()
with torch.no_grad():
for cur_process_idx in tqdm(range(process_total_num)):
for cur_process_idx in progress_bar_cmd(range(process_total_num)):
if cur_process_idx == 0:
pre_cache_k = [None] * len(self.dit.blocks)
pre_cache_v = [None] * len(self.dit.blocks)
@@ -384,7 +427,7 @@ class FlashVSRTinyLongPipeline(BasePipeline):
LQ_latents[layer_idx] = torch.cat([LQ_latents[layer_idx], cur[layer_idx]], dim=1)
LQ_cur_idx = cur_process_idx*8+21+(inner_loop_num-2)*4
cur_latents = latents[:, :, 4+cur_process_idx*2:6+cur_process_idx*2, :, :]
# 推理（无 motion_controller / vace）
noise_pred_posi, pre_cache_k, pre_cache_v = model_fn_wan_video(
self.dit,
@@ -408,9 +451,14 @@ class FlashVSRTinyLongPipeline(BasePipeline):
# 更新 latent
cur_latents = cur_latents - noise_pred_posi
# Decode
cur_LQ_frame = LQ_video[:,:,LQ_pre_idx:LQ_cur_idx,:,:].to(self.device)
cur_frames = self.TCDecoder.decode_video(cur_latents.transpose(1, 2),parallel=False, show_progress_bar=False, cond=LQ_video[:,:,LQ_pre_idx:LQ_cur_idx,:,:].to(self.device)).transpose(1, 2).mul_(2).sub_(1)
cur_frames = self.TCDecoder.decode_video(
cur_latents.transpose(1, 2),
parallel=False,
show_progress_bar=False,
cond=cur_LQ_frame).transpose(1, 2).mul_(2).sub_(1)
# 颜色校正（wavelet）
try:
@@ -424,12 +472,14 @@ class FlashVSRTinyLongPipeline(BasePipeline):
)
except:
pass
frames_total.append(cur_frames.to('cpu'))
LQ_pre_idx = LQ_cur_idx
del cur_frames, cur_latents, cur_LQ_frame
clean_vram()
frames = torch.cat(frames_total, dim=2)
return frames[0]