linting

Signed-off-by: Vladimir Mandic <mandic00@live.com>

modules/lora/lora_extract.py

@@ -182,7 +182,7 @@ def make_lora(fn, maxrank, auto_rank, rank_ratio, modules, overwrite):
             progress.remove_task(task)
         t3 = time.time()
 
-        # TODO: Handle quant for Flux
+        # TODO: make lora for quantized flux
         # if 'te' in modules and getattr(shared.sd_model, 'transformer', None) is not None:
         #     for name, module in shared.sd_model.transformer.named_modules():
         #         if "norm" in name and "linear" not in name:

modules/model_flux.py

@@ -149,7 +149,7 @@ def load_quants(kwargs, repo_id, cache_dir):
         quant_args = model_quant.create_bnb_config(quant_args)
         quant_args = model_quant.create_ao_config(quant_args)
         if not quant_args:
-            return
+            return kwargs
         model_quant.load_bnb(f'Load model: type=FLUX quant={quant_args}')
         if 'Model' in shared.opts.bnb_quantization and 'transformer' not in kwargs:
             kwargs['transformer'] = diffusers.FluxTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", cache_dir=cache_dir, torch_dtype=devices.dtype, **quant_args)
@@ -208,7 +208,7 @@ def load_transformer(file_path): # triggered by opts.sd_unet change
         _transformer, _text_encoder_2 = load_flux_bnb(file_path, diffusers_load_config)
         if _transformer is not None:
             transformer = _transformer
-    elif 'nf4' in quant: # TODO right now this is not working for civitai published nf4 models
+    elif 'nf4' in quant: # TODO fix flux loader for civitai nf4 models
         from modules.model_flux_nf4 import load_flux_nf4
         _transformer, _text_encoder_2 = load_flux_nf4(file_path)
         if _transformer is not None:

modules/model_sana.py

@@ -13,7 +13,7 @@ def load_quants(kwargs, repo_id, cache_dir):
         quant_args = model_quant.create_ao_config(quant_args)
         load_args = kwargs.copy()
         if not quant_args:
-            return
+            return kwargs
         model_quant.load_bnb(f'Load model: type=SD3 quant={quant_args} args={load_args}')
         if 'Model' in shared.opts.bnb_quantization and 'transformer' not in kwargs:
             kwargs['transformer'] = diffusers.models.SanaTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", cache_dir=cache_dir, **load_args, **quant_args)

modules/model_sd3.py

@@ -55,7 +55,7 @@ def load_quants(kwargs, repo_id, cache_dir):
         quant_args = model_quant.create_bnb_config(quant_args)
         quant_args = model_quant.create_ao_config(quant_args)
         if not quant_args:
-            return
+            return kwargs
         model_quant.load_bnb(f'Load model: type=SD3 quant={quant_args}')
         if 'Model' in shared.opts.bnb_quantization and 'transformer' not in kwargs:
             kwargs['transformer'] = diffusers.SD3Transformer2DModel.from_pretrained(repo_id, subfolder="transformer", cache_dir=cache_dir, torch_dtype=devices.dtype, **quant_args)

modules/processing_class.py

@@ -170,7 +170,7 @@ class StableDiffusionProcessing:
         self.image_cfg_scale = image_cfg_scale
         self.scale_by = scale_by
         self.mask = mask
-        self.image_mask = mask # TODO duplciate mask params
+        self.image_mask = mask # TODO remove duplicate mask params
         self.latent_mask = latent_mask
         self.mask_blur = mask_blur
         self.inpainting_fill = inpainting_fill

modules/sd_models.py

@@ -1495,7 +1495,7 @@ def reload_model_weights(sd_model=None, info=None, reuse_dict=False, op='model',
             unload_model_weights(op=op)
             sd_model = None
     timer = Timer()
-    # TODO implement caching after diffusers implement state_dict loading
+    # TODO implement model in-memory caching
     state_dict = get_checkpoint_state_dict(checkpoint_info, timer) if not shared.native else None
     checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
     timer.record("config")

modules/sd_models_compile.py

@@ -515,7 +515,7 @@ def torchao_quantization(sd_model):
     if fn is None:
         shared.log.error(f"Quantization: type=TorchAO type={shared.opts.torchao_quantization_type} not supported")
         return sd_model
-    def torchao_model(model, op=None, sd_model=None):
+    def torchao_model(model, op=None, sd_model=None): # pylint: disable=unused-argument
         q.quantize_(model, fn(), device=devices.device)
         return model
 

modules/textual_inversion/textual_inversion.py

@@ -4,7 +4,7 @@ import time
 import torch
 import safetensors.torch
 from PIL import Image
-from modules import shared, devices, sd_models, errors
+from modules import shared, devices, errors
 from modules.textual_inversion.image_embedding import embedding_from_b64, extract_image_data_embed
 from modules.files_cache import directory_files, directory_mtime, extension_filter