ruff fixes

Author: colinurbs

modules/MMAudio/mmaudio/data/eval/audiocaps.py

@@ -1,6 +1,5 @@
 import logging
 import os
-from collections import defaultdict
 from pathlib import Path
 from typing import Union
 

modules/MMAudio/mmaudio/data/extracted_audio.py

@@ -7,7 +7,6 @@
 from tensordict import TensorDict
 from torch.utils.data.dataset import Dataset
 
-from mmaudio.utils.dist_utils import local_rank
 
 log = logging.getLogger()
 

modules/MMAudio/mmaudio/data/extraction/wav_dataset.py

@@ -89,7 +89,7 @@ def __getitem__(self, idx: int) -> torch.Tensor:
                 audio_chunk = audio_chunk / abs_max * 0.95
 
             if self.reject_silent and abs_max < 1e-6:
-                log.warning(f'Rejecting silent audio')
+                log.warning('Rejecting silent audio')
                 return None
 
             audio_chunk = audio_chunk[start_sample:end_sample]

modules/MMAudio/mmaudio/ext/bigvgan_v2/bigvgan.py

@@ -397,13 +397,13 @@ def _from_pretrained(
         # instantiate BigVGAN using h
         if use_cuda_kernel:
             print(
-                f"[WARNING] You have specified use_cuda_kernel=True during BigVGAN.from_pretrained(). Only inference is supported (training is not implemented)!"
+                "[WARNING] You have specified use_cuda_kernel=True during BigVGAN.from_pretrained(). Only inference is supported (training is not implemented)!"
             )
             print(
-                f"[WARNING] You need nvcc and ninja installed in your system that matches your PyTorch build is using to build the kernel. If not, the model will fail to initialize or generate incorrect waveform!"
+                "[WARNING] You need nvcc and ninja installed in your system that matches your PyTorch build is using to build the kernel. If not, the model will fail to initialize or generate incorrect waveform!"
             )
             print(
-                f"[WARNING] For detail, see the official GitHub repository: https://github.com/NVIDIA/BigVGAN?tab=readme-ov-file#using-custom-cuda-kernel-for-synthesis"
+                "[WARNING] For detail, see the official GitHub repository: https://github.com/NVIDIA/BigVGAN?tab=readme-ov-file#using-custom-cuda-kernel-for-synthesis"
             )
         model = cls(h, use_cuda_kernel=use_cuda_kernel)
 
@@ -431,7 +431,7 @@ def _from_pretrained(
             model.load_state_dict(checkpoint_dict["generator"])
         except RuntimeError:
             print(
-                f"[INFO] the pretrained checkpoint does not contain weight norm. Loading the checkpoint after removing weight norm!"
+                "[INFO] the pretrained checkpoint does not contain weight norm. Loading the checkpoint after removing weight norm!"
             )
             model.remove_weight_norm()
             model.load_state_dict(checkpoint_dict["generator"])

modules/MMAudio/mmaudio/ext/synchformer/synchformer.py

@@ -1,4 +1,3 @@
-import logging
 from typing import Any, Mapping
 
 import torch

modules/MMAudio/mmaudio/ext/synchformer/utils.py

@@ -72,7 +72,7 @@ def check_if_file_exists_else_download(path, fname2link=FNAME2LINK, chunk_size=1
         link = fname2link.get(path.name, None)
         if link is None:
             raise ValueError(f'Cant find the checkpoint file: {path}.',
-                             f'Please download it manually and ensure the path exists.')
+                             'Please download it manually and ensure the path exists.')
         with requests.get(fname2link[path.name], stream=True) as r:
             total_size = int(r.headers.get('content-length', 0))
             with tqdm(total=total_size, unit='B', unit_scale=True) as pbar:

modules/MMAudio/mmaudio/ext/synchformer/vit_helper.py

@@ -11,7 +11,6 @@
 from einops import rearrange, repeat
 from timm.layers import to_2tuple
 from torch import einsum
-from torch.nn import functional as F
 
 default_cfgs = {
     'vit_1k':
@@ -360,7 +359,7 @@ def load_pretrained(model,
                 print(
                     f'Converted input conv {input_conv_name} pretrained weights from 3 to {in_chans} channel(s)'
                 )
-            except NotImplementedError as e:
+            except NotImplementedError:
                 del state_dict[weight_name]
                 strict = False
                 print(

modules/interface.py

@@ -116,7 +116,7 @@ def update_stats(*args):
                     f"""<div style="display: flex; align-items: center;"><h1 class='toolbar-title'>FP Studio</h1><p class='toolbar-version'>{APP_VERSION_DISPLAY}</p><p class='toolbar-patreon'><a href='https://patreon.com/Colinu' target='_blank'>{patreon_svg_inline}</a></p><p class='toolbar-discord'><a href='https://discord.com/invite/MtuM7gFJ3V' target='_blank'>{discord_svg_inline}</a></p></div>"""
                 )
             with gr.Column(scale=3, min_width=250, elem_id="toolbar-right-col"):
-                queue_stats_display = gr.HTML(
+                gr.HTML(
                     """
                     <div id='queue-stats' class='queue-stats'>
                       <div class='queue-stats-bar'>
@@ -128,8 +128,8 @@ def update_stats(*args):
                     </div>
                     """
                 )
-            with gr.Column(scale=9, min_width=250, elem_id="toolbar-right-col"):    
-                toolbar_stats_container = gr.HTML(
+            with gr.Column(scale=9, min_width=250, elem_id="toolbar-right-col"):
+                gr.HTML(
                     """
                     <div id="toolbar-stats-container" class="toolbar-stat-html">
                       <div class="stats-stack">
@@ -162,10 +162,10 @@ def update_stats(*args):
                   --qs-label: 'Q: 0 • R: 0 • C: 0';
                 }
                 </style>
-                """
-            , elem_id="queue-stats-vars-wrapper")
-            
-            
+                """,
+                elem_id="queue-stats-vars-wrapper",
+            )
+
             toolbar_stats_vars = gr.HTML(
                 """
                 <style id="toolbar-stats-vars">
@@ -178,8 +178,9 @@ def update_stats(*args):
                   --gpu-text: 'N/A';
                 }
                 </style>
-                """
-            , elem_id="toolbar-stats-vars-wrapper")
+                """,
+                elem_id="toolbar-stats-vars-wrapper",
+            )
 
         # --- Tabs ---
         with gr.Tabs(elem_id="main_tabs") as main_tabs_component:

modules/pipelines/metadata_utils.py

@@ -280,14 +280,20 @@ def create_metadata(job_params, job_id, settings, save_placeholder=False):
                         # Fallback: positional mapping when lengths match selected list
                         idx = selected_loras.index(lora_name)
                         weight = lora_values[idx]
-                elif isinstance(lora_values, list) and len(lora_values) == len(selected_loras):
+                elif isinstance(lora_values, list) and len(lora_values) == len(
+                    selected_loras
+                ):
                     # Fallback when no loaded_names but lengths match
                     idx = selected_loras.index(lora_name)
                     weight = lora_values[idx]
 
                 # Normalize weight types
                 if isinstance(weight, np.ndarray):
-                    weight_value = float(weight.item()) if weight.size == 1 else float(weight.mean())
+                    weight_value = (
+                        float(weight.item())
+                        if weight.size == 1
+                        else float(weight.mean())
+                    )
                 elif isinstance(weight, list):
                     weight_value = float(weight[0]) if len(weight) > 0 else 1.0
                 else: