nodes/havvk-ComfyUI_AIIA
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fix FLOAT node error

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This commit is contained in:
l.y@live.cn
2025-06-15 15:14:12 +08:00
parent 0d200498bf
commit 5a22015e6c
1 changed files with 130 additions and 117 deletions
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aiia_float_nodes.py
+130 -117
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@@ -1,4 +1,4 @@
# --- START OF FILE aiia_float_nodes.py ---
# --- START OF FILE aiia_float_nodes.py (FIXED for MP3 Codec & Tensor Dim) ---
import torch
import os
@@ -20,16 +20,16 @@ from tqdm import tqdm # 导入 tqdm
def _patched_decode_for_in_memory_stack(
self_float_model, # FLOATModel 实例 (float_pipe.G)
s_r: torch.Tensor,
s_r_feats: list,
s_r: torch.Tensor,
s_r_feats: list,
r_d: torch.Tensor
) -> dict: # 返回 {'d_hat': cpu_stacked_tensor_tchw}
T_prime = r_d.shape[1]
B = r_d.shape[0] # 应该总是 1
comfy_pbar = ProgressBar(T_prime) # ComfyUI 进度条
console_pbar_desc = "[FLOAT In-Memory] Processing Frames"
# 尝试获取更具体的描述，如果 self_float_model 是可预期的类型
if hasattr(self_float_model, '__class__') and hasattr(self_float_model.__class__, '__name__'):
@@ -40,14 +40,14 @@ def _patched_decode_for_in_memory_stack(
with tqdm(total=T_prime, desc=console_pbar_desc, unit="frame") as console_pbar:
processed_frames_cpu_list = []
opt = self_float_model.opt
FRAMES_PER_GPU_CHUNK = getattr(opt, 'decode_gpu_chunk_size', 32)
opt = self_float_model.opt
FRAMES_PER_GPU_CHUNK = getattr(opt, 'decode_gpu_chunk_size', 32)
gpu_frame_buffer = []
for t_idx in range(T_prime):
current_motion_vector = r_d[:, t_idx]
s_r_plus_motion = s_r + current_motion_vector
current_motion_vector = r_d[:, t_idx]
s_r_plus_motion = s_r + current_motion_vector
img_t_gpu_raw, _ = self_float_model.motion_autoencoder.dec(s_r_plus_motion, alpha=None, feats=s_r_feats)
img_t_gpu_clamped = torch.clamp(img_t_gpu_raw, -1, 1) # 值域 [-1, 1]
@@ -58,11 +58,11 @@ def _patched_decode_for_in_memory_stack(
for frame_gpu in gpu_frame_buffer:
processed_frames_cpu_list.append(frame_gpu.cpu())
gpu_frame_buffer = [] # 清空 buffer
comfy_pbar.update(1)
console_pbar.update(1) # 更新 tqdm 进度条
del r_d, s_r, s_r_feats
del r_d, s_r, s_r_feats
if not processed_frames_cpu_list:
print(f"警告: [PatchedDecodeInMemory] 未生成任何帧。")
@@ -70,39 +70,39 @@ def _patched_decode_for_in_memory_stack(
return {'d_hat': torch.empty((0, img_c, img_h, img_w), device='cpu')}
try:
d_hat_stacked_cpu = torch.stack(processed_frames_cpu_list, dim=0) # (T, C, H, W)
return {'d_hat': d_hat_stacked_cpu}
except RuntimeError as e_cpu_stack:
return {'d_hat': d_hat_stacked_cpu}
except RuntimeError as e_cpu_stack:
print(f"错误: [PatchedDecodeInMemory] CPU堆叠错误: {e_cpu_stack}")
raise
def _patched_decode_and_save_to_disk(
self_float_model,
s_r: torch.Tensor,
s_r_feats: list,
self_float_model,
s_r: torch.Tensor,
s_r_feats: list,
r_d: torch.Tensor,
output_frames_dir: str,
output_frames_dir: str,
node_name_log_prefix: str # 这个可以用作 tqdm 的 desc
) -> dict:
T_prime = r_d.shape[1]
B = r_d.shape[0] # 应该总是 1 (由原始代码的 squeeze(0) 暗示)
comfy_pbar = ProgressBar(T_prime) # ComfyUI 进度条
# 使用传入的 node_name_log_prefix 作为基础描述，并添加操作说明
console_pbar_desc = f"{node_name_log_prefix} Saving Frames"
with tqdm(total=T_prime, desc=console_pbar_desc, unit="frame") as console_pbar:
opt = self_float_model.opt
FRAMES_PER_GPU_CHUNK_FOR_PROCESSING = getattr(opt, 'frames_per_gpu_chunk_for_processing', 16)
gpu_frame_buffer = []; saved_frame_count = 0
self_float_model._last_run_saved_frames = 0
for t_idx in range(T_prime):
current_motion_vector = r_d[:, t_idx]
s_r_plus_motion = s_r + current_motion_vector
current_motion_vector = r_d[:, t_idx]
s_r_plus_motion = s_r + current_motion_vector
img_t_gpu_raw, _ = self_float_model.motion_autoencoder.dec(s_r_plus_motion, alpha=None, feats=s_r_feats)
img_t_gpu_clamped = torch.clamp(img_t_gpu_raw, -1, 1)
gpu_frame_buffer.append(img_t_gpu_clamped.squeeze(0) if B == 1 else img_t_gpu_clamped[0])
@@ -111,39 +111,34 @@ def _patched_decode_and_save_to_disk(
(t_idx == T_prime - 1 and len(gpu_frame_buffer) > 0):
if gpu_frame_buffer: # 确保 buffer 不为空
current_gpu_chunk_to_process = torch.stack(gpu_frame_buffer, dim=0) if len(gpu_frame_buffer) > 1 else gpu_frame_buffer[0].unsqueeze(0)
gpu_frame_buffer = []
chunk_cpu_chw = current_gpu_chunk_to_process.cpu(); del current_gpu_chunk_to_process
gpu_frame_buffer = []
chunk_cpu_chw = current_gpu_chunk_to_process.cpu(); del current_gpu_chunk_to_process
chunk_cpu_hwc_float_0_1 = ((chunk_cpu_chw.permute(0, 2, 3, 1).clamp(-1,1) + 1.0) / 2.0)
for frame_idx_in_chunk in range(chunk_cpu_hwc_float_0_1.shape[0]):
frame_to_save_np = (chunk_cpu_hwc_float_0_1[frame_idx_in_chunk].numpy() * 255).astype(np.uint8)
filename = f"frame_{saved_frame_count:06d}.png"
filepath = os.path.join(output_frames_dir, filename)
try:
try:
Image.fromarray(frame_to_save_np).save(filepath)
saved_frame_count += 1
except Exception as e_save:
# 对于保存单个帧的错误，可以选择不打断 tqdm，只打印警告
# console_pbar.write(f"警告: 保存帧 {filepath} 失败: {e_save}")
# 或者如果希望错误更明显，可以打印到标准错误流
print(f"警告: [{node_name_log_prefix}] 保存帧 {filepath} 失败: {e_save}", file=sys.stderr if 'sys' in globals() else None) # pylint: disable=undefined-variable
del chunk_cpu_chw, chunk_cpu_hwc_float_0_1
except Exception as e_save:
import sys
print(f"警告: [{node_name_log_prefix}] 保存帧 {filepath} 失败: {e_save}", file=sys.stderr)
del chunk_cpu_chw, chunk_cpu_hwc_float_0_1
comfy_pbar.update(1)
console_pbar.update(1) # 更新 tqdm 进度条
del r_d, s_r, s_r_feats
# tqdm 会在 with 块结束时自动清理/关闭
# 如果有任何最终信息要在进度条下方打印，确保在 with 块之后
# （tqdm 默认会这样做，除非 leave=False 且没有其他输出）
print(f"信息: [{node_name_log_prefix}] 已处理并尝试保存 {saved_frame_count} 帧到 {output_frames_dir}")
self_float_model._last_run_saved_frames = saved_frame_count
img_c = getattr(opt, 'output_nc', 3); img_h = getattr(opt, 'input_size', 64); img_w = getattr(opt, 'input_size', 64)
batch_size_for_placeholder = B if B > 0 else 1 # 确保批次大小至少为1
d_hat_placeholder_btchw = torch.empty((batch_size_for_placeholder, 0, img_c, img_h, img_w), device='cpu')
d_hat_placeholder_btchw = torch.empty((batch_size_for_placeholder, 0, img_c, img_h, img_w), device='cpu')
return {'d_hat': d_hat_placeholder_btchw}
@@ -163,43 +158,58 @@ class AIIA_FloatProcess_InMemory:
print(f"错误: [{self.__class__.NODE_NAME}] {error_message_text}")
return (torch.zeros((1, 64, 64, 3), dtype=torch.float32),)
def floatprocess_in_memory(self, float_pipe, ref_image, ref_audio,
a_cfg_scale, r_cfg_scale, e_cfg_scale,
fps, emotion, crop_input_image, seed, nfe,
device_override: str = "default",
def floatprocess_in_memory(self, float_pipe, ref_image, ref_audio,
a_cfg_scale, r_cfg_scale, e_cfg_scale,
fps, emotion, crop_input_image, seed, nfe,
device_override: str = "default",
decode_gpu_chunk_size: int = 32):
node_name_log = f"[{self.__class__.NODE_NAME}]"
print(f"{node_name_log} 流程开始 (内存输出模式)。")
start_time_process = time.time()
_default_error_tuple = self._create_error_image("未知错误 (初始化或预处理失败)", log_message=False)
return_value = _default_error_tuple
return_value = _default_error_tuple
if float_pipe is None or not hasattr(float_pipe, 'opt') or not hasattr(float_pipe.G, 'decode_latent_into_image'):
return self._create_error_image("float_pipe 无效或不完整", log_message=True)
processing_device = torch.device(device_override) if device_override != "default" else torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"{node_name_log} 本次运行将在设备上: {processing_device}")
original_decode_method = None
original_opt_rank_backup = getattr(float_pipe.opt, 'rank', None)
original_opt_fps_backup = getattr(float_pipe.opt, 'fps', None)
original_opt_fps_backup = getattr(float_pipe.opt, 'fps', None)
original_opt_decode_chunk_backup = getattr(float_pipe.opt, 'decode_gpu_chunk_size', None)
with tempfile.TemporaryDirectory(prefix="aiia_fp_inmem_") as temp_run_dir:
try:
audio_waveform_to_save = ref_audio['waveform'].squeeze(0);
if audio_waveform_to_save.ndim > 1 and audio_waveform_to_save.shape[0] > 1: audio_waveform_to_save = audio_waveform_to_save[0]
audio_save_path = os.path.join(temp_run_dir, "temp_audio.wav"); torchaudio.save(audio_save_path, audio_waveform_to_save.cpu(), ref_audio["sample_rate"])
# --- START OF AUDIO FIX for In-Memory Node ---
waveform_2d = ref_audio['waveform'].squeeze(0)
if waveform_2d.shape[0] > 1:
audio_waveform_to_save = waveform_2d[0:1, :]
else:
audio_waveform_to_save = waveform_2d
audio_save_path = os.path.join(temp_run_dir, "temp_audio.wav")
torchaudio.save(
audio_save_path,
audio_waveform_to_save.cpu(),
ref_audio["sample_rate"],
encoding="PCM_S",
bits_per_sample=16
)
# --- END OF AUDIO FIX for In-Memory Node ---
ref_image_chw = ref_image[0].permute(2, 0, 1).cpu(); image_save_path = os.path.join(temp_run_dir, "temp_ref_image.png")
vutils.save_image(ref_image_chw, image_save_path, normalize=False)
if hasattr(float_pipe.opt, 'rank'):
float_pipe.opt.rank = processing_device.index if processing_device.type == 'cuda' and processing_device.index is not None else (0 if processing_device.type == 'cuda' else -1)
if hasattr(float_pipe.opt, 'fps'): float_pipe.opt.fps = float(fps)
float_pipe.opt.decode_gpu_chunk_size = decode_gpu_chunk_size
print(f"{node_name_log} opt 更新: rank={getattr(float_pipe.opt, 'rank', 'N/A')}, fps={getattr(float_pipe.opt, 'fps', 'N/A')}, decode_chunk={getattr(float_pipe.opt, 'decode_gpu_chunk_size', 'N/A')}")
model_current_device_before_move = next(float_pipe.G.parameters()).device
if model_current_device_before_move != processing_device: float_pipe.G.to(processing_device)
print(f"{node_name_log} 模型移至: {processing_device}")
@@ -207,17 +217,17 @@ class AIIA_FloatProcess_InMemory:
original_decode_method = float_pipe.G.decode_latent_into_image
float_pipe.G.decode_latent_into_image = types.MethodType(_patched_decode_for_in_memory_stack, float_pipe.G)
print(f"信息: {node_name_log} 已替换 decode_latent_into_image 为内存堆叠版本。")
print(f"{node_name_log} 开始运行推理...")
images_thwc_cpu_float01 = float_pipe.run_inference(
res_video_path=None, ref_path=image_save_path, audio_path=audio_save_path,
a_cfg_scale=a_cfg_scale, r_cfg_scale=r_cfg_scale, e_cfg_scale=e_cfg_scale,
emo=None if emotion == "none" else emotion,
no_crop=not crop_input_image, nfe=nfe, seed=seed, verbose=False
no_crop=not crop_input_image, nfe=nfe, seed=seed, verbose=False
)
if not isinstance(images_thwc_cpu_float01, torch.Tensor):
images_thwc_cpu_float01 = torch.from_numpy(images_thwc_cpu_float01.astype(np.float32))
print(f"信息: {node_name_log} 推理完成。输出图像序列形状: {images_thwc_cpu_float01.shape if images_thwc_cpu_float01 is not None else 'None'}")
return_value = (images_thwc_cpu_float01,)
except Exception as e_proc_inner:
@@ -226,11 +236,9 @@ class AIIA_FloatProcess_InMemory:
finally:
if original_decode_method and hasattr(float_pipe.G, 'decode_latent_into_image'):
current_decode_method = getattr(float_pipe.G, 'decode_latent_into_image', None)
# 检查当前绑定方法的 __func__ 是否是我们的 patch 函数
if hasattr(current_decode_method, '__func__') and current_decode_method.__func__ is _patched_decode_for_in_memory_stack:
if hasattr(current_decode_method, '__func__') and current_decode_method.__func__ is _patched_decode_for_in_memory_stack:
float_pipe.G.decode_latent_into_image = original_decode_method
print(f"信息: {node_name_log} (finally) 已恢复 float_pipe.G.decode_latent_into_image 为原始方法。")
# 添加一个 elif 以防万一我们的 patch 方式改变了或者上面没匹配上，但方法确实不是原始的了
elif current_decode_method is not original_decode_method:
print(f"警告: {node_name_log} (finally) decode_latent_into_image 不是预期中的 patch 方法，但也不是原始方法。仍尝试恢复为原始方法。")
float_pipe.G.decode_latent_into_image = original_decode_method
@@ -238,20 +246,20 @@ class AIIA_FloatProcess_InMemory:
if original_opt_rank_backup is not None: float_pipe.opt.rank = original_opt_rank_backup
if original_opt_fps_backup is not None: float_pipe.opt.fps = original_opt_fps_backup
if original_opt_decode_chunk_backup is not None : float_pipe.opt.decode_gpu_chunk_size = original_opt_decode_chunk_backup
elif hasattr(float_pipe.opt, 'decode_gpu_chunk_size'):
elif hasattr(float_pipe.opt, 'decode_gpu_chunk_size'):
try:
del float_pipe.opt.decode_gpu_chunk_size
except AttributeError: # 以防万一 opt 不是 OmegaConf 或普通 dict
pass
except AttributeError:
pass
current_g_device_after_proc = next(float_pipe.G.parameters()).device
if current_g_device_after_proc.type == 'cuda':
try:
try:
float_pipe.G.to(torch.device("cpu"))
torch.cuda.empty_cache()
except Exception as e_to_cpu:
except Exception as e_to_cpu:
print(f"{node_name_log} (finally) 模型移至CPU或清空缓存时出错: {e_to_cpu}")
end_time_process = time.time()
print(f"{node_name_log} 方法总执行耗时: {end_time_process - start_time_process:.2f} 秒。")
return return_value
@@ -261,38 +269,36 @@ class AIIA_FloatProcess_ToDisk:
NODE_NAME = "AIIA Float Process (To Disk)"
CATEGORY = "AIIA/FLOAT"
FUNCTION = "floatprocess_to_disk"
RETURN_TYPES = ("STRING", "INT")
RETURN_TYPES = ("STRING", "INT")
RETURN_NAMES = ("frames_output_directory", "saved_frame_count")
@classmethod
def INPUT_TYPES(cls):
# ... (INPUT_TYPES 不变) ...
base_inputs = AIIA_FloatProcess_InMemory.INPUT_TYPES()
base_inputs["optional"]["output_subdir_name"] = ("STRING", {"default": "float_frames_AIIA", "tooltip": "在ComfyUI输出目录下创建的子目录名"})
if "decode_gpu_chunk_size" not in base_inputs["optional"]:
base_inputs["optional"]["decode_gpu_chunk_size"] = ("INT", {"default": 32, "min":1, "max":128, "step":1, "tooltip":"GPU解码后一次处理并保存多少帧。影响显存和IO。"})
else:
base_inputs["optional"]["decode_gpu_chunk_size"][1]["default"] = 32
else:
base_inputs["optional"]["decode_gpu_chunk_size"][1]["default"] = 32
base_inputs["optional"]["decode_gpu_chunk_size"][1]["tooltip"] = "(To Disk) GPU解码后一次处理并保存多少帧。影响显存和IO。"
return base_inputs
def _create_error_string_count(self, error_message_text: str, log_message: bool = True) -> tuple:
# ... (不变) ...
if log_message:
print(f"错误: [{self.__class__.NODE_NAME}] {error_message_text}")
return (f"错误: {error_message_text}", 0)
def floatprocess_to_disk(self, float_pipe, ref_image, ref_audio,
a_cfg_scale, r_cfg_scale, e_cfg_scale,
fps, emotion, crop_input_image, seed, nfe,
def floatprocess_to_disk(self, float_pipe, ref_image, ref_audio,
a_cfg_scale, r_cfg_scale, e_cfg_scale,
fps, emotion, crop_input_image, seed, nfe,
device_override: str = "default",
output_subdir_name: str = "float_frames_AIIA",
decode_gpu_chunk_size: int = 16):
node_name_log = f"[{self.__class__.NODE_NAME}]"
print(f"{node_name_log} 流程开始 (输出到磁盘模式)。")
start_time_process = time.time()
_default_error_tuple = self._create_error_string_count("未知错误 (初始化或预处理失败)", log_message=False)
return_value = _default_error_tuple
@@ -306,7 +312,7 @@ class AIIA_FloatProcess_ToDisk:
_intermediate_wrapper_for_patch = None # 声明以便 finally 块可以引用
original_opt_rank_backup = getattr(float_pipe.opt, 'rank', None)
original_opt_fps_backup = getattr(float_pipe.opt, 'fps', None)
original_opt_fps_backup = getattr(float_pipe.opt, 'fps', None)
original_opt_frames_per_gpu_chunk_backup = getattr(float_pipe.opt, 'frames_per_gpu_chunk_for_processing', None)
output_node_main_dir = folder_paths.get_output_directory()
@@ -320,9 +326,23 @@ class AIIA_FloatProcess_ToDisk:
with tempfile.TemporaryDirectory(prefix="aiia_fp_disk_input_") as input_temp_dir:
try:
audio_waveform_to_save = ref_audio['waveform'].squeeze(0);
if audio_waveform_to_save.ndim > 1 and audio_waveform_to_save.shape[0] > 1: audio_waveform_to_save = audio_waveform_to_save[0]
audio_save_path = os.path.join(input_temp_dir, "temp_audio.wav"); torchaudio.save(audio_save_path, audio_waveform_to_save.cpu(), ref_audio["sample_rate"])
# --- START OF AUDIO FIX for To-Disk Node ---
waveform_2d = ref_audio['waveform'].squeeze(0)
if waveform_2d.shape[0] > 1:
audio_waveform_to_save = waveform_2d[0:1, :]
else:
audio_waveform_to_save = waveform_2d
audio_save_path = os.path.join(input_temp_dir, "temp_audio.wav")
torchaudio.save(
audio_save_path,
audio_waveform_to_save.cpu(),
ref_audio["sample_rate"],
encoding="PCM_S",
bits_per_sample=16
)
# --- END OF AUDIO FIX for To-Disk Node ---
ref_image_chw = ref_image[0].permute(2, 0, 1).cpu(); image_save_path = os.path.join(input_temp_dir, "temp_ref_image.png")
vutils.save_image(ref_image_chw, image_save_path, normalize=False)
@@ -331,44 +351,40 @@ class AIIA_FloatProcess_ToDisk:
if hasattr(float_pipe.opt, 'fps'): float_pipe.opt.fps = float(fps)
float_pipe.opt.frames_per_gpu_chunk_for_processing = decode_gpu_chunk_size
print(f"{node_name_log} opt 更新: rank={getattr(float_pipe.opt, 'rank', 'N/A')}, fps={getattr(float_pipe.opt, 'fps', 'N/A')}, frames_chunk_for_processing={getattr(float_pipe.opt, 'frames_per_gpu_chunk_for_processing', 'N/A')}")
model_current_device_before_move = next(float_pipe.G.parameters()).device
model_current_device_before_move = next(float_pipe.G.parameters()).device
if model_current_device_before_move != processing_device: float_pipe.G.to(processing_device)
print(f"{node_name_log} 模型移至: {processing_device}")
original_decode_method = float_pipe.G.decode_latent_into_image
# _intermediate_wrapper_for_patch_local 需要定义在这里，以便捕获 frames_output_directory_final 和 node_name_log
# 修改签名以明确接受关键字参数
def _intermediate_wrapper_for_patch_local(actual_self, *, s_r, s_r_feats, r_d):
# actual_self 在这里就是 float_pipe.G
# frames_output_directory_final 和 node_name_log 是从外部作用域捕获的 (闭包)
return _patched_decode_and_save_to_disk(
actual_self, # 传递 self (模型实例)
s_r=s_r, # 以关键字参数传递
s_r_feats=s_r_feats, # 以关键字参数传递
r_d=r_d, # 以关键字参数传递
output_frames_dir=frames_output_directory_final, # 我们自己的参数
node_name_log_prefix=node_name_log # 我们自己的参数
actual_self,
s_r=s_r,
s_r_feats=s_r_feats,
r_d=r_d,
output_frames_dir=frames_output_directory_final,
node_name_log_prefix=node_name_log
)
_intermediate_wrapper_for_patch = _intermediate_wrapper_for_patch_local
float_pipe.G.decode_latent_into_image = types.MethodType(_intermediate_wrapper_for_patch, float_pipe.G)
print(f"信息: {node_name_log} 已替换 float_pipe.G.decode_latent_into_image 为磁盘保存版本。")
print(f"{node_name_log} 开始运行推理 (帧将保存到磁盘)...")
_ = float_pipe.run_inference( # 这里会调用被 patch 的方法
_ = float_pipe.run_inference(
res_video_path=None, ref_path=image_save_path, audio_path=audio_save_path,
a_cfg_scale=a_cfg_scale, r_cfg_scale=r_cfg_scale, e_cfg_scale=e_cfg_scale,
emo=None if emotion == "none" else emotion,
no_crop=not crop_input_image, nfe=nfe, seed=seed, verbose=False
no_crop=not crop_input_image, nfe=nfe, seed=seed, verbose=False
)
actual_saved_frames = getattr(float_pipe.G, '_last_run_saved_frames', 0)
if hasattr(float_pipe.G, '_last_run_saved_frames'):
delattr(float_pipe.G, '_last_run_saved_frames')
if hasattr(float_pipe.G, '_last_run_saved_frames'):
delattr(float_pipe.G, '_last_run_saved_frames')
if actual_saved_frames > 0:
print(f"信息: {node_name_log} 推理完成。{actual_saved_frames} 帧已保存到 {frames_output_directory_final}")
return_value = (frames_output_directory_final, actual_saved_frames)
@@ -381,34 +397,33 @@ class AIIA_FloatProcess_ToDisk:
finally:
if original_decode_method and hasattr(float_pipe.G, 'decode_latent_into_image'):
current_decode_method = getattr(float_pipe.G, 'decode_latent_into_image', None)
# 检查 _intermediate_wrapper_for_patch 是否已被定义 (即 try 块中 patch 代码已执行)
if _intermediate_wrapper_for_patch is not None and \
hasattr(current_decode_method, '__func__') and \
current_decode_method.__func__ is _intermediate_wrapper_for_patch: # 比较函数对象本身
current_decode_method.__func__ is _intermediate_wrapper_for_patch:
float_pipe.G.decode_latent_into_image = original_decode_method
print(f"信息: {node_name_log} (finally) 已恢复 float_pipe.G.decode_latent_into_image 为原始方法。")
elif current_decode_method is not original_decode_method:
print(f"警告: {node_name_log} (finally) decode_latent_into_image 不是预期中的 patch 方法，但也不是原始方法。仍尝试恢复为原始方法。")
float_pipe.G.decode_latent_into_image = original_decode_method
if original_opt_rank_backup is not None: float_pipe.opt.rank = original_opt_rank_backup
if original_opt_fps_backup is not None: float_pipe.opt.fps = original_opt_fps_backup
if original_opt_frames_per_gpu_chunk_backup is not None :
if original_opt_frames_per_gpu_chunk_backup is not None :
float_pipe.opt.frames_per_gpu_chunk_for_processing = original_opt_frames_per_gpu_chunk_backup
elif hasattr(float_pipe.opt, 'frames_per_gpu_chunk_for_processing'):
elif hasattr(float_pipe.opt, 'frames_per_gpu_chunk_for_processing'):
try:
del float_pipe.opt.frames_per_gpu_chunk_for_processing
except AttributeError:
pass
current_g_device_after_proc = next(float_pipe.G.parameters()).device
if current_g_device_after_proc.type == 'cuda':
try:
try:
float_pipe.G.to(torch.device("cpu"))
torch.cuda.empty_cache()
except Exception as e_to_cpu:
except Exception as e_to_cpu:
print(f"{node_name_log} (finally) 模型移至CPU或清空缓存时出错: {e_to_cpu}")
end_time_process = time.time()
print(f"{node_name_log} 方法总执行耗时: {end_time_process - start_time_process:.2f} 秒。")
return return_value
@@ -422,6 +437,4 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"AIIA_FloatProcess_InMemory": "Float Process (AIIA In-Memory)",
"AIIA_FloatProcess_ToDisk": "Float Process (AIIA To-Disk for Long Audio)",
}
print(f"--- AIIA FLOAT Process Nodes (InMemory & ToDisk) Loaded ---")
# --- END OF FILE aiia_float_nodes.py ---
print(f"--- AIIA FLOAT Process Nodes (InMemory & ToDisk) Loaded ---")