linting

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

TODO.md

@@ -10,10 +10,8 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
 
 ## Future Candidates
 
-- SD35 IPAdapter: <https://github.com/huggingface/diffusers/pull/9987>
 - Flux IPAdapter: <https://github.com/huggingface/diffusers/pull/10261>
 - Flux NF4: <https://github.com/huggingface/diffusers/issues/9996>
-- LTX-Video: <https://github.com/huggingface/diffusers/pull/10021> <https://huggingface.co/Lightricks/LTX-Video> <https://huggingface.co/spaces/Lightricks/LTX-Video-Playground/tree/main>
 - GGUF: <https://github.com/huggingface/diffusers/pull/9964>
 
 ## Other
@@ -21,3 +19,18 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
 - IPAdapter negative: <https://github.com/huggingface/diffusers/discussions/7167>
 - Control API enhance scripts compatibility
 - PixelSmith: <https://github.com/Thanos-DB/Pixelsmith>
+
+## Code TODO
+
+- python 3.12.4 or higher cause a mess with pydantic
+- enable ROCm for windows when available
+- enable full VAE mode for resize-latent
+- remove duplicate mask params
+- fix flux loader for civitai nf4 models
+- implement model in-memory caching
+- hypertile vae breaks for diffusers when using non-standard sizes
+- forcing reloading entire model as loading transformers only leads to massive memory usage
+- lora-direct with bnb
+- make lora for quantized flux
+- control script process
+- monkey-patch for modernui missing tabs.select event

extensions-builtin/Lora/lora_extract.py

@@ -182,19 +182,6 @@ def make_lora(fn, maxrank, auto_rank, rank_ratio, modules, overwrite):
             progress.remove_task(task)
         t3 = time.time()
 
-        # TODO: Handle quant for 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:
-        #             continue
-        #         weights_backup = getattr(module, "network_weights_backup", None)
-        #         if weights_backup is None:
-        #             continue
-        #         module.svdhandler = SVDHandler()
-        #         module.svdhandler.network_name = "lora_transformer_" + name.replace(".", "_")
-        #         module.svdhandler.decompose(module.weight, weights_backup)
-        #         module.svdhandler.findrank(rank, rank_ratio)
-
         lora_state_dict = {}
         for sub in ['text_encoder', 'text_encoder_2', 'unet', 'transformer']:
             submodel = getattr(shared.sd_model, sub, None)

installer.py

@@ -552,7 +552,7 @@ def install_rocm_zluda():
     log.info(msg)
     torch_command = ''
     if sys.platform == "win32":
-        # TODO after ROCm for Windows is released
+        # TODO enable ROCm for windows when available
 
         if args.device_id is not None:
             if os.environ.get('HIP_VISIBLE_DEVICES', None) is not None:

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