Merge pull request PaddlePaddle#39 from westfish/remove_literal

lvdm delete redundant imports

ppdiffusers/examples/text_to_video_lvdm/lvdm/_functional_video.py

@@ -1,5 +1,4 @@
 import paddle
-import warnings
 
 
 def _is_tensor_video_clip(clip):

ppdiffusers/examples/text_to_video_lvdm/lvdm/_transforms_video.py

@@ -1,7 +1,6 @@
 import paddle
 import numbers
 import random
-import warnings
 from . import _functional_video as F
 
 

ppdiffusers/examples/text_to_video_lvdm/lvdm/lvdm_model.py

@@ -22,7 +22,6 @@
 import numpy as np
 import paddle
 import paddle.nn as nn
-import paddle.nn.functional as F
 
 from paddlenlp.transformers import AutoTokenizer, CLIPTextModel
 from paddlenlp.utils.log import logger

ppdiffusers/examples/text_to_video_lvdm/lvdm/lvdm_trainer.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import contextlib
-import os
 import sys
 
 import paddle
@@ -25,11 +24,8 @@
     INTEGRATION_TO_CALLBACK,
     VisualDLCallback,
     rewrite_logs, )
-from paddlenlp.trainer.utils.helper import (  # nested_truncate,
-    distributed_concat, nested_concat, nested_detach, nested_numpify,
-    nested_truncate, )
+from paddlenlp.trainer.utils.helper import nested_detach
 from paddlenlp.utils.log import logger
-from ppdiffusers.training_utils import unwrap_model
 
 from .frame_dataset import VideoFrameDataset
 from .webvid_dataset import WebVidDataset

ppdiffusers/examples/text_to_video_lvdm/scripts/lvdm_sample_short.py

@@ -1,6 +1,3 @@
-import numpy as np
-import os
-from PIL import Image
 import paddle
 from ppdiffusers import LVDMUncondPipeline
 

ppdiffusers/examples/text_to_video_lvdm/scripts/lvdm_sample_text2video.py

@@ -1,6 +1,3 @@
-import numpy as np
-import os
-from PIL import Image
 import paddle
 from ppdiffusers import LVDMTextToVideoPipeline
 

ppdiffusers/examples/text_to_video_lvdm/tools/convert_orig_lvdm_short_ckpt_to_ppdiffusers.py

@@ -21,8 +21,6 @@
 import paddle
 import torch
 
-from paddlenlp.utils.downloader import get_path_from_url
-
 try:
     from omegaconf import OmegaConf
 except ImportError:

ppdiffusers/examples/text_to_video_lvdm/tools/convert_orig_lvdm_text2video_ckpt_to_ppdiffusers.py

@@ -20,8 +20,6 @@
 import paddle
 import torch
 
-from paddlenlp.utils.downloader import get_path_from_url
-
 try:
     from omegaconf import OmegaConf
 except ImportError:

ppdiffusers/examples/text_to_video_lvdm/train_lvdm_text2video.py

@@ -19,7 +19,6 @@
 from lvdm import (
     LatentVideoDiffusion,
     LatentVideoDiffusionTrainer,
-    VideoFrameDataset,
     WebVidDataset, )
 from lvdm.lvdm_args_text2video import WebVidDatasetArguments, TrainerArguments, ModelArguments
 

ppdiffusers/ppdiffusers/models/lvdm_attention_temporal.py

@@ -9,7 +9,6 @@
     _ppxformers_available = False
 
 import math
-from typing import Optional, Any
 from einops import rearrange, repeat
 from .lvdm_util import GEGLU, exists, default, Normalize, zero_module, normalization, conv_nd
 from ..utils.initializer_utils import xavier_uniform_, constant_
@@ -189,9 +188,6 @@ def forward(self, x, context=None, mask=None):
 
 
 class MemoryEfficientCrossAttention(paddle.nn.Layer):
-    """https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
-    """
-
     def __init__(self,
                  query_dim,
                  context_dim=None,

ppdiffusers/ppdiffusers/models/lvdm_unet_3d.py

@@ -2,18 +2,15 @@
 from paddle.distributed.fleet.utils import recompute
 
 from abc import abstractmethod
-import math
 from einops import rearrange
-from functools import partial
-import numpy as np
 
 from .lvdm_util import conv_nd, linear, avg_pool_nd, zero_module, normalization, timestep_embedding, nonlinearity
 from .lvdm_attention_temporal import STAttentionBlock
 from .lvdm_attention_temporal import SpatialTemporalTransformer
 
 from dataclasses import dataclass
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput, apply_forward_hook
+from ..utils import BaseOutput
 from .modeling_utils import ModelMixin
 
 

ppdiffusers/ppdiffusers/models/lvdm_vae.py

@@ -1,11 +1,9 @@
-import nntplib
-import paddle
 from .lvdm_distributions import DiagonalGaussianDistribution
 from .lvdm_aemodules3d import SamePadConv3d, Encoder, Decoder
 
 from dataclasses import dataclass
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput, apply_forward_hook
+from ..utils import BaseOutput
 from .modeling_utils import ModelMixin
 from .vae import DecoderOutput
 

ppdiffusers/ppdiffusers/pipelines/lvdm/__init__.py

@@ -14,7 +14,7 @@
 # limitations under the License.
 
 from dataclasses import dataclass
-from typing import List, Optional, Union
+from typing import List, Union
 
 import numpy as np
 import paddle

ppdiffusers/ppdiffusers/pipelines/lvdm/pipeline_latent_video_diffusion_model_text2video.py

@@ -15,14 +15,12 @@
 import inspect
 from typing import Any, Callable, Dict, List, Optional, Union
 import os
-from tqdm import trange
 import numpy as np
 from einops import rearrange
 
 import paddle
-from packaging import version
 
-from paddlenlp.transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from paddlenlp.transformers import CLIPTextModel, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
 from ...models import LVDMAutoencoderKL, LVDMUNet3DModel

ppdiffusers/ppdiffusers/pipelines/lvdm/video_save.py

@@ -8,8 +8,8 @@
     import accimage
 except ImportError:
     accimage = None
-from PIL import Image, ImageEnhance, ImageOps
-from typing import Any, Dict, List, Literal, Optional, Sequence, Tuple, Union
+from PIL import Image
+from typing import Any, Dict, List, Optional, Tuple, Union
 import math
 try:
     import av
@@ -53,8 +53,6 @@ def write_video(filename: str,
         audio_codec (str): the name of the audio codec, i.e. "mp3", "aac", etc.
         audio_options (Dict): dictionary containing options to be passed into the PyAV audio stream
     """
-    # >>>    if not torch.jit.is_scripting() and not torch.jit.is_tracing():
-    #         _log_api_usage_once(write_video)
     _check_av_available()
     video_array = paddle.to_tensor(data=video_array).astype('uint8').numpy()
     if isinstance(fps, float):
@@ -131,8 +129,6 @@ def make_grid(tensor: Union[paddle.Tensor, List[paddle.Tensor]],
     Returns:
         grid (Tensor): the tensor containing grid of images.
     """
-    # >>>    if not torch.jit.is_scripting() and not torch.jit.is_tracing():
-    #         _log_api_usage_once(make_grid)
     if not paddle.is_tensor(x=tensor):
         if isinstance(tensor, list):
             for t in tensor:
@@ -162,9 +158,6 @@ def make_grid(tensor: Union[paddle.Tensor, List[paddle.Tensor]],
 
         def norm_ip(img, low, high):
             img.clip_(min=low, max=high)
-            #             """Class Method: *.sub_, not convert, please check whether it is torch.Tensor.*/Optimizer.*/nn.Module.*, and convert manually"""
-            #             """Class Method: *.div_, not convert, please check whether it is torch.Tensor.*/Optimizer.*/nn.Module.*, and convert manually"""
-            # >>>            img.sub_(low).div_(max(high - low, 1e-05))
             img = img.substract(low).divide(max(high - low, 1e-05))
 
         def norm_range(t, value_range):
@@ -241,40 +234,25 @@ def to_tensor(pic) -> paddle.Tensor:
     Returns:
         Tensor: Converted image.
     """
-    # >>>    if not torch.jit.is_scripting() and not torch.jit.is_tracing():
-    #         _log_api_usage_once(to_tensor)
-    #     if not (F_pil._is_pil_image(pic) or _is_numpy(pic)):
-    #         raise TypeError(f'pic should be PIL Image or ndarray. Got {type(pic)}')
-    #     if _is_numpy(pic) and not _is_numpy_image(pic):
-    #         raise ValueError(
-    #             f'pic should be 2/3 dimensional. Got {pic.ndim} dimensions.')
     default_float_dtype = paddle.get_default_dtype()
     if isinstance(pic, np.ndarray):
         if pic.ndim == 2:
             pic = pic[:, :, (None)]
         img = paddle.to_tensor(data=pic.transpose((2, 0, 1)))
-        # >>>        if isinstance(img, torch.ByteTensor):
         if img.dtype == paddle.uint8:
             return paddle.divide(
                 img.cast(default_float_dtype),
                 paddle.to_tensor(
                     255, dtype=paddle.float32))
         else:
             return img
-    # if accimage is not None and isinstance(pic, accimage.Image):
-    #     nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.
-    #         float32)
-    #     pic.copyto(nppic)
-    #     return paddle.to_tensor(data=nppic).cast(default_float_dtype)
     mode_to_nptype = {'I': np.int32, 'I;16': np.int16, 'F': np.float32}
     img = paddle.to_tensor(data=np.array(
         pic, mode_to_nptype.get(pic.mode, np.uint8), copy=True))
     if pic.mode == '1':
         img = 255 * img
-    # img = img.reshape([pic.size[1], pic.size[0], F_pil.get_image_num_channels(pic)])
     img = img.reshape([pic.size[1], pic.size[0], get_image_num_channels(pic)])
     img = img.transpose(perm=(2, 0, 1))
-    # >>>    if isinstance(img, torch.ByteTensor):
     if img.dtype == paddle.uint8:
         return paddle.divide(img.cast(default_float_dtype), 255)
     else:
@@ -341,15 +319,13 @@ def npz_to_video_grid(data_path,
     if nrow is None:
         nrow = int(np.ceil(np.sqrt(n)))
     if verbose:
-        # >>>        frame_grids = [torchvision.utils.make_grid(fs, nrow=nrow) for fs in tqdm(frame_grids, desc='Making grids')]
         frame_grids = [
             make_grid(
                 fs, nrow=nrow) for fs in tqdm(
                     frame_grids, desc='Making grids')
         ]
 
     else:
-        # >>>        frame_grids = [torchvision.utils.make_grid(fs, nrow=nrow) for fs in frame_grids]
         frame_grids = [make_grid(fs, nrow=nrow) for fs in frame_grids]
 
     if os.path.dirname(out_path) != '':
@@ -365,8 +341,6 @@ def npz_to_video_grid(data_path,
         dtype = (paddle.stack(x=frame_grids) * 255).dtype
     frame_grids = (paddle.stack(x=frame_grids) * 255).transpose(
         perm=[0, 2, 3, 1]).cast(dtype)
-    # >>>    torchvision.io.write_video(out_path, frame_grids, fps=fps, video_codec=
-    #         'h264', options={'crf': '10'})
     write_video(
         out_path,
         frame_grids,