chore: fix linting

.gitignore

@@ -24,6 +24,6 @@ SwissArmyTransformer/
 
 trainingdata
 temp
-
+.cursor
 
 *.outputs

.pre-commit-config.yaml

@@ -10,12 +10,12 @@ repos:
         pass_filenames: false
         language: system
         stages: [pre-commit]
-#      - id: linting
-#        name: linting
-#        entry: poetry run lint
-#        pass_filenames: false
-#        language: system
-#        stages: [commit]
+      - id: linting
+        name: linting
+        entry: poetry run lint
+        pass_filenames: false
+        language: system
+        stages: [commit]
 #      - id: type-checking
 #        name: type checking
 #        entry: poetry run type-check

pyproject.toml

@@ -125,6 +125,6 @@ module = [
 ]
 ignore_missing_imports = true
 
-[tool.ruff]
+[tool.ruff.lint]
 select = ["E", "F", "C90"]
-ignore = []
+ignore = ["E501", "C901"]

scripts/inference_flux_lora.py

@@ -3,6 +3,7 @@
 
 import torch
 from diffusers import FluxPipeline
+
 from videotuna.utils.inference_utils import load_prompts_from_txt
 
 
@@ -56,9 +57,16 @@ def inference(args):
     parser.add_argument(
         "--model_type", type=str, default="dev", choices=["dev", "schnell"]
     )
-    parser.add_argument("--prompt", type=str, default="A photo of a cat", help="Inference prompt, string or path to a .txt file")
+    parser.add_argument(
+        "--prompt",
+        type=str,
+        default="A photo of a cat",
+        help="Inference prompt, string or path to a .txt file",
+    )
     parser.add_argument("--out_path", type=str, default="./results/t2i/image.png")
-    parser.add_argument("--lora_path", type=str, default=None, help="Full path to lora weights")
+    parser.add_argument(
+        "--lora_path", type=str, default=None, help="Full path to lora weights"
+    )
     parser.add_argument("--width", type=int, default=1360)
     parser.add_argument("--height", type=int, default=768)
     parser.add_argument("--num_inference_steps", type=int, default=4)

scripts/train_flux_lora.py

@@ -23,6 +23,7 @@
 logger = logging.getLogger("SimpleTuner")
 logger.setLevel(environ.get("SIMPLETUNER_LOG_LEVEL", "INFO"))
 
+
 def add_timestamp_to_output_dir(output_dir):
     time_str = time.strftime("%Y%m%d%H%M%S")
     folder_name = output_dir.stem
@@ -33,6 +34,7 @@ def add_timestamp_to_output_dir(output_dir):
     output_dir = output_dir.parent / folder_name
     return str(output_dir)
 
+
 def config_process(config):
     # add timestamp to the output_dir
     output_dir = Path(config["--output_dir"])
@@ -42,6 +44,7 @@ def config_process(config):
         json.dump(config, f, indent=4)
     return config
 
+
 def load_yaml_config(config_path):
     with open(config_path) as f:
         config = yaml.safe_load(f)
@@ -58,6 +61,7 @@ def load_yaml_config(config_path):
 
     return config, data_config_json
 
+
 def load_json_config(config_path, data_config_path):
     # load config files
     with open(config_path) as f:
@@ -68,6 +72,7 @@ def load_json_config(config_path, data_config_path):
     config = config_process(config)
     return config, data_config
 
+
 def main(args):
     try:
         import multiprocessing

tests/datasets/test_dataset_from_csv.py

@@ -240,7 +240,8 @@ def test_video_dataset_from_csv_with_split(self):
         print(f"len(dataset): {len(val_dataset)}")
         self.assertLessEqual(len(val_dataset), 128)
         self.assertEqual(val_dataset[0]["video"].shape[2], 256)
-        # Check if the sum of the lengths of the training and validation datasets is equal to the total number of samples
+        # Check if the sum of the lengths of the training and validation datasets
+        # is equal to the total number of samples
         self.assertEqual(len(train_dataset) + len(val_dataset), 128)
 
 

videotuna/base/ddim.py

@@ -19,7 +19,7 @@ def __init__(self, model, schedule="linear", **kwargs):
         self.counter = 0
 
     def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
+        if isinstance(attr, torch.Tensor):
             if attr.device != torch.device("cuda"):
                 attr = attr.to(torch.device("cuda"))
         setattr(self, name, attr)
@@ -37,7 +37,9 @@ def make_schedule(
         assert (
             alphas_cumprod.shape[0] == self.ddpm_num_timesteps
         ), "alphas have to be defined for each timestep"
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+        def to_torch(x):
+            return x.clone().detach().to(torch.float32).to(self.model.device)
 
         self.register_buffer("betas", to_torch(self.model.diffusion_scheduler.betas))
         self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
@@ -133,22 +135,24 @@ def sample(
             if isinstance(conditioning, dict):
                 try:
                     cbs = conditioning[list(conditioning.keys())[0]].shape[0]
-                except:
+                except Exception:
                     try:
                         cbs = conditioning[list(conditioning.keys())[0]][0].shape[0]
-                    except:
+                    except Exception:
                         cbs = int(
                             conditioning[list(conditioning.keys())[0]][0]["y"].shape[0]
                         )
 
                 if cbs != batch_size:
                     print(
-                        f"Warning: Got {cbs} conditionings but batch-size is {batch_size}"
+                        f"Warning: Got {cbs} conditionings but "
+                        f"batch-size is {batch_size}"
                     )
             else:
                 if conditioning.shape[0] != batch_size:
                     print(
-                        f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}"
+                        f"Warning: Got {conditioning.shape[0]} conditionings but "
+                        f"batch-size is {batch_size}"
                     )
 
         self.make_schedule(

videotuna/base/ddim_multiplecond.py

@@ -1,5 +1,3 @@
-import copy
-
 import numpy as np
 import torch
 from tqdm import tqdm
@@ -21,7 +19,7 @@ def __init__(self, model, schedule="linear", **kwargs):
         self.counter = 0
 
     def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
+        if isinstance(attr, torch.Tensor):
             if attr.device != torch.device("cuda"):
                 attr = attr.to(torch.device("cuda"))
         setattr(self, name, attr)
@@ -39,7 +37,9 @@ def make_schedule(
         assert (
             alphas_cumprod.shape[0] == self.ddpm_num_timesteps
         ), "alphas have to be defined for each timestep"
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+        def to_torch(x):
+            return x.clone().detach().to(torch.float32).to(self.model.device)
 
         if self.model.use_scale:
             self.ddim_scale_arr = self.model.scale_arr[self.ddim_timesteps]
@@ -120,7 +120,8 @@ def sample(
         fs=None,
         timestep_spacing="uniform",  # uniform_trailing for starting from last timestep
         guidance_rescale=0.0,
-        # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+        # this has to come in the same format as the conditioning,
+        # e.g. as encoded tokens, ...
         **kwargs,
     ):
 
@@ -129,7 +130,7 @@ def sample(
             if isinstance(conditioning, dict):
                 try:
                     cbs = conditioning[list(conditioning.keys())[0]].shape[0]
-                except:
+                except Exception:
                     cbs = conditioning[list(conditioning.keys())[0]][0].shape[0]
 
                 if cbs != batch_size:

videotuna/base/ddpm3d.py

@@ -7,46 +7,37 @@
 """
 
 import logging
-import os
 import random
 from contextlib import contextmanager
 from functools import partial
 
 import numpy as np
-from einops import rearrange, repeat
-from tqdm import tqdm
-
-mainlogger = logging.getLogger("mainlogger")
-
 import peft
 import pytorch_lightning as pl
 import torch
-import torch.nn as nn
+from einops import rearrange, repeat
 from pytorch_lightning.utilities import rank_zero_only
 from torch.optim.lr_scheduler import CosineAnnealingLR, LambdaLR
 from torchvision.utils import make_grid
+from tqdm import tqdm
 
 from videotuna.base.ddim import DDIMSampler
-from videotuna.base.distributions import DiagonalGaussianDistribution, normal_kl
+from videotuna.base.distributions import DiagonalGaussianDistribution
 from videotuna.base.ema import LitEma
-from videotuna.base.utils_diffusion import make_beta_schedule, rescale_zero_terminal_snr
 
 # import rlhf utils
 from videotuna.lvdm.models.rlhf_utils.batch_ddim import batch_ddim_sampling
 from videotuna.lvdm.models.rlhf_utils.reward_fn import aesthetic_loss_fn
 from videotuna.lvdm.modules.encoders.ip_resampler import ImageProjModel, Resampler
-from videotuna.lvdm.modules.utils import (
-    default,
-    disabled_train,
-    exists,
-    extract_into_tensor,
-    noise_like,
-)
+from videotuna.lvdm.modules.utils import default, disabled_train, extract_into_tensor
 from videotuna.utils.common_utils import instantiate_from_config
 
 __conditioning_keys__ = {"concat": "c_concat", "crossattn": "c_crossattn", "adm": "y"}
 
 
+mainlogger = logging.getLogger("mainlogger")
+
+
 class DDPMFlow(pl.LightningModule):
     # classic DDPM with Gaussian diffusion, in image space
     def __init__(
@@ -430,7 +421,7 @@ def load_lora_from_ckpt(self, model, path):
                     f"Parameter {key} from lora_state_dict was not copied to the model."
                 )
                 # print(f"Parameter {key} from lora_state_dict was not copied to the model.")
-        print(f"All Parameters was copied successfully.")
+        print("All Parameters was copied successfully.")
 
     def inject_lora(self):
         """inject lora into the denoising module.
@@ -519,7 +510,7 @@ def __init__(
 
         try:
             self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
+        except Exception:
             self.num_downs = 0
         if not scale_by_std:
             self.scale_factor = scale_factor
@@ -1586,7 +1577,7 @@ def configure_optimizers(self):
 
         if self.cond_stage_trainable:
             params_cond_stage = [
-                p for p in self.cond_stage_model.parameters() if p.requires_grad == True
+                p for p in self.cond_stage_model.parameters() if p.requires_grad is True
             ]
             mainlogger.info(
                 f"@Training [{len(params_cond_stage)}] Paramters for Cond_stage_model."

videotuna/base/diffusion_schedulers.py

@@ -8,7 +8,6 @@
 from videotuna.base.utils_diffusion import make_beta_schedule, rescale_zero_terminal_snr
 from videotuna.lvdm.modules.utils import (
     default,
-    disabled_train,
     exists,
     extract_into_tensor,
     noise_like,

videotuna/base/ema.py

@@ -49,7 +49,7 @@ def forward(self, model):
                         one_minus_decay * (shadow_params[sname] - m_param[key])
                     )
                 else:
-                    assert not key in self.m_name2s_name
+                    assert key not in self.m_name2s_name
 
     def copy_to(self, model):
         m_param = dict(model.named_parameters())
@@ -58,7 +58,7 @@ def copy_to(self, model):
             if m_param[key].requires_grad:
                 m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
             else:
-                assert not key in self.m_name2s_name
+                assert key not in self.m_name2s_name
 
     def store(self, parameters):
         """

videotuna/base/iddpm3d.py

@@ -1,33 +1,23 @@
 import enum
 import logging
 import math
-import os
 import random
-from contextlib import contextmanager
 from functools import partial
 
 import numpy as np
-from einops import rearrange, repeat
-from omegaconf.listconfig import ListConfig
-from tqdm import tqdm
-
-mainlogger = logging.getLogger("mainlogger")
-
 import torch
-import torch.nn as nn
+from einops import rearrange
+from omegaconf.listconfig import ListConfig
 from pytorch_lightning.utilities import rank_zero_only
 from torch.optim.lr_scheduler import CosineAnnealingLR, LambdaLR
 from torchvision.utils import make_grid
+from tqdm import tqdm
 
 from videotuna.base.ddim import DDIMSampler
 from videotuna.base.ddpm3d import DDPMFlow
 from videotuna.base.diffusion_schedulers import DDPMScheduler
 from videotuna.base.distributions import DiagonalGaussianDistribution, normal_kl
-from videotuna.base.utils_diffusion import (
-    discretized_gaussian_log_likelihood,
-    make_beta_schedule,
-    rescale_zero_terminal_snr,
-)
+from videotuna.base.utils_diffusion import discretized_gaussian_log_likelihood
 from videotuna.lvdm.modules.utils import (
     default,
     disabled_train,
@@ -37,6 +27,8 @@
 )
 from videotuna.utils.common_utils import instantiate_from_config
 
+mainlogger = logging.getLogger("mainlogger")
+
 
 def mean_flat(tensor: torch.Tensor, mask=None) -> torch.Tensor:
     """
@@ -1039,7 +1031,7 @@ def __init__(
 
         try:
             self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
+        except Exception:
             self.num_downs = 0
         if not scale_by_std:
             self.scale_factor = scale_factor
@@ -1305,7 +1297,7 @@ def apply_model(self, x_noisy, t, cond, **kwargs):
                     key: [cond["c_crossattn"][0]["y"]],
                     "mask": [cond["c_crossattn"][0]["mask"]],
                 }
-            except:
+            except Exception:
                 cond = {key: [cond["y"]], "mask": [cond["mask"]]}  # support mask for T5
         else:
             if isinstance(cond, dict):

videotuna/base/utils_diffusion.py

@@ -2,7 +2,6 @@
 
 import numpy as np
 import torch
-import torch.nn.functional as F
 from einops import repeat