lint fixes

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

modules/control/units/xs_pipe.py

@@ -39,16 +39,11 @@ from diffusers.utils import (
     scale_lora_layers,
     unscale_lora_layers,
 )
-from diffusers.utils.import_utils import is_invisible_watermark_available
 from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from modules.control.units.xs_model import ControlNetXSModel
 
 
-if is_invisible_watermark_available():
-    from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
-
-
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -110,7 +105,7 @@ class StableDiffusionXLControlNetXSPipeline(
         controlnet: ControlNetXSModel,
         scheduler: KarrasDiffusionSchedulers,
         force_zeros_for_empty_prompt: bool = True,
-        add_watermarker: Optional[bool] = None,
+        add_watermarker: Optional[bool] = None, # pylint: disable=unused-argument
     ):
         super().__init__()
 
@@ -145,12 +140,6 @@ class StableDiffusionXLControlNetXSPipeline(
         self.control_image_processor = VaeImageProcessor(
             vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
         )
-        add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
-
-        if add_watermarker:
-            self.watermark = StableDiffusionXLWatermarker()
-        else:
-            self.watermark = None
 
         self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
 
@@ -251,7 +240,7 @@ class StableDiffusionXLControlNetXSPipeline(
         # set lora scale so that monkey patched LoRA
         # function of text encoder can correctly access it
         if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
-            self._lora_scale = lora_scale
+            self._lora_scale = lora_scale # pylint: disable=attribute-defined-outside-init
 
             # dynamically adjust the LoRA scale
             if self.text_encoder is not None:
@@ -286,7 +275,7 @@ class StableDiffusionXLControlNetXSPipeline(
             # textual inversion: procecss multi-vector tokens if necessary
             prompt_embeds_list = []
             prompts = [prompt, prompt_2]
-            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): # pylint: disable=redefined-argument-from-local
                 if isinstance(self, TextualInversionLoaderMixin):
                     prompt = self.maybe_convert_prompt(prompt, tokenizer)
 
@@ -325,7 +314,7 @@ class StableDiffusionXLControlNetXSPipeline(
             prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
 
         # get unconditional embeddings for classifier free guidance
-        zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
+        zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt # pylint: disable=no-member
         if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
             negative_prompt_embeds = torch.zeros_like(prompt_embeds)
             negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
@@ -355,7 +344,7 @@ class StableDiffusionXLControlNetXSPipeline(
                 uncond_tokens = [negative_prompt, negative_prompt_2]
 
             negative_prompt_embeds_list = []
-            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
+            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders): # pylint: disable=redefined-argument-from-local
                 if isinstance(self, TextualInversionLoaderMixin):
                     negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
 
@@ -514,12 +503,12 @@ class StableDiffusionXLControlNetXSPipeline(
 
         # Check `image`
         is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
-            self.controlnet, torch._dynamo.eval_frame.OptimizedModule
+            self.controlnet, torch._dynamo.eval_frame.OptimizedModule # pylint: disable=protected-access
         )
         if (
             isinstance(self.controlnet, ControlNetXSModel)
             or (is_compiled
-            and isinstance(self.controlnet._orig_mod, ControlNetXSModel))
+            and isinstance(self.controlnet._orig_mod, ControlNetXSModel)) # pylint: disable=protected-access
         ):
             self.check_image(image, prompt, prompt_embeds)
         else:
@@ -529,7 +518,7 @@ class StableDiffusionXLControlNetXSPipeline(
         if (
             isinstance(self.controlnet, ControlNetXSModel)
             or (is_compiled
-            and isinstance(self.controlnet._orig_mod, ControlNetXSModel))
+            and isinstance(self.controlnet._orig_mod, ControlNetXSModel)) # pylint: disable=protected-access
         ):
             if not isinstance(controlnet_conditioning_scale, float):
                 raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
@@ -547,7 +536,7 @@ class StableDiffusionXLControlNetXSPipeline(
             raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
 
     # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
-    def check_image(self, image, prompt, prompt_embeds):
+    def check_image(self, image, prompt, prompt_embeds): # pylint: disable=unused-argument
         image_is_pil = isinstance(image, Image.Image)
         image_is_tensor = isinstance(image, torch.Tensor)
         image_is_np = isinstance(image, np.ndarray)
@@ -567,22 +556,6 @@ class StableDiffusionXLControlNetXSPipeline(
                 f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
             )
 
-        if image_is_pil:
-            image_batch_size = 1
-        else:
-            image_batch_size = len(image)
-
-        if prompt is not None and isinstance(prompt, str):
-            prompt_batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            prompt_batch_size = len(prompt)
-        elif prompt_embeds is not None:
-            prompt_batch_size = prompt_embeds.shape[0]
-
-        if image_batch_size != 1 and image_batch_size != prompt_batch_size:
-            raise ValueError(
-                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
-            )
 
     def prepare_image(
         self,
@@ -884,9 +857,6 @@ class StableDiffusionXLControlNetXSPipeline(
             batch_size = prompt_embeds.shape[0]
 
         device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
         do_classifier_free_guidance = guidance_scale > 1.0
 
         # 3. Encode input prompt
@@ -1290,7 +1260,7 @@ class StableDiffusionControlNetXSPipeline(
         # set lora scale so that monkey patched LoRA
         # function of text encoder can correctly access it
         if lora_scale is not None and isinstance(self, LoraLoaderMixin):
-            self._lora_scale = lora_scale
+            self._lora_scale = lora_scale # pylint: disable=attribute-defined-outside-init
 
             # dynamically adjust the LoRA scale
             if not USE_PEFT_BACKEND:
@@ -1517,12 +1487,12 @@ class StableDiffusionControlNetXSPipeline(
 
         # Check `image`
         is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
-            self.controlnet, torch._dynamo.eval_frame.OptimizedModule
+            self.controlnet, torch._dynamo.eval_frame.OptimizedModule # pylint: disable=protected-access
         )
         if (
             isinstance(self.controlnet, ControlNetXSModel)
             or (is_compiled
-            and isinstance(self.controlnet._orig_mod, ControlNetXSModel))
+            and isinstance(self.controlnet._orig_mod, ControlNetXSModel)) # pylint: disable=protected-access
         ):
             self.check_image(image, prompt, prompt_embeds)
         else:
@@ -1532,7 +1502,7 @@ class StableDiffusionControlNetXSPipeline(
         if (
             isinstance(self.controlnet, ControlNetXSModel)
             or (is_compiled
-            and isinstance(self.controlnet._orig_mod, ControlNetXSModel))
+            and isinstance(self.controlnet._orig_mod, ControlNetXSModel)) # pylint: disable=protected-access
         ):
             if not isinstance(controlnet_conditioning_scale, float):
                 raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
@@ -1549,7 +1519,7 @@ class StableDiffusionControlNetXSPipeline(
         if end > 1.0:
             raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
 
-    def check_image(self, image, prompt, prompt_embeds):
+    def check_image(self, image, prompt, prompt_embeds): # pylint: disable=unused-argument
         image_is_pil = isinstance(image, Image.Image)
         image_is_tensor = isinstance(image, torch.Tensor)
         image_is_np = isinstance(image, np.ndarray)
@@ -1569,22 +1539,6 @@ class StableDiffusionControlNetXSPipeline(
                 f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
             )
 
-        if image_is_pil:
-            image_batch_size = 1
-        else:
-            image_batch_size = len(image)
-
-        if prompt is not None and isinstance(prompt, str):
-            prompt_batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            prompt_batch_size = len(prompt)
-        elif prompt_embeds is not None:
-            prompt_batch_size = prompt_embeds.shape[0]
-
-        if image_batch_size != 1 and image_batch_size != prompt_batch_size:
-            raise ValueError(
-                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
-            )
 
     def prepare_image(
         self,
@@ -1791,9 +1745,6 @@ class StableDiffusionControlNetXSPipeline(
             batch_size = prompt_embeds.shape[0]
 
         device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
         do_classifier_free_guidance = guidance_scale > 1.0
 
         # 3. Encode input prompt
@@ -1811,9 +1762,6 @@ class StableDiffusionControlNetXSPipeline(
             lora_scale=text_encoder_lora_scale,
             clip_skip=clip_skip,
         )
-        # For classifier free guidance, we need to do two forward passes.
-        # Here we concatenate the unconditional and text embeddings into a single batch
-        # to avoid doing two forward passes
         if do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
 

modules/seedvr/src/common/diffusion/timesteps/base.py

@@ -1,11 +1,11 @@
 from abc import ABC, abstractmethod
-from typing import Sequence, Union
+from typing import Union
 import torch
 
 from ..types import SamplingDirection
 
 
-class Timesteps(ABC):
+class Timesteps(ABC): # noqa: B024
     """
     Timesteps base class.
     """

modules/seedvr/src/common/distributed/advanced.py

@@ -19,7 +19,7 @@ Advanced distributed functions for sequence parallel.
 from __future__ import annotations
 
 import logging
-from typing import Optional, List
+from typing import List
 import torch
 
 from .basic import _is_dist, get_global_rank, get_world_size
@@ -37,21 +37,21 @@ _MODEL_SHARD_INTRA_GROUP = None
 _SEQUENCE_PARALLEL_GLOBAL_RANKS = None
 
 
-def get_data_parallel_group() -> Optional[dist.ProcessGroup]:
+def get_data_parallel_group():
     """
     Get data parallel process group.
     """
     return _DATA_PARALLEL_GROUP
 
 
-def get_sequence_parallel_group() -> Optional[dist.ProcessGroup]:
+def get_sequence_parallel_group():
     """
     Get sequence parallel process group.
     """
     return _SEQUENCE_PARALLEL_GROUP
 
 
-def get_sequence_parallel_cpu_group() -> Optional[dist.ProcessGroup]:
+def get_sequence_parallel_cpu_group():
     """
     Get sequence parallel CPU process group.
     """
@@ -102,28 +102,28 @@ def get_sequence_parallel_world_size() -> int:
     return 1
 
 
-def get_model_shard_cpu_intra_group() -> Optional[dist.ProcessGroup]:
+def get_model_shard_cpu_intra_group():
     """
     Get the CPU intra process group of model sharding.
     """
     return _MODEL_SHARD_CPU_INTRA_GROUP
 
 
-def get_model_shard_cpu_inter_group() -> Optional[dist.ProcessGroup]:
+def get_model_shard_cpu_inter_group():
     """
     Get the CPU inter process group of model sharding.
     """
     return _MODEL_SHARD_CPU_INTER_GROUP
 
 
-def get_model_shard_intra_group() -> Optional[dist.ProcessGroup]:
+def get_model_shard_intra_group():
     """
     Get the GPU intra process group of model sharding.
     """
     return _MODEL_SHARD_INTRA_GROUP
 
 
-def get_model_shard_inter_group() -> Optional[dist.ProcessGroup]:
+def get_model_shard_inter_group():
     """
     Get the GPU inter process group of model sharding.
     """

modules/seedvr/src/optimization/memory_manager.py

@@ -56,14 +56,13 @@ def preinitialize_rope_cache(runner) -> None:
                 # Calculate with reduced dimensions to avoid OOM
                 with torch.no_grad():
                     # Detect RoPE module type
-                    module_type = type(rope_module).__name__
-
+                    module_type = type(rope_module).__name__ # noqa: B023
                     if module_type == 'NaRotaryEmbedding3d':
                         # NaRotaryEmbedding3d: only takes shape (vid_shape)
-                        return rope_module.get_freqs(vid_shape.cpu())
+                        return rope_module.get_freqs(vid_shape.cpu()) # noqa: B023
                     else:
                         # Standard RoPE: takes vid_shape and txt_shape
-                        return rope_module.get_freqs(vid_shape.cpu(), txt_shape.cpu())
+                        return rope_module.get_freqs(vid_shape.cpu(), txt_shape.cpu()) # noqa: B023
 
             # Store in cache
             temp_cache(cache_key, compute_freqs)

pyproject.toml

@@ -12,9 +12,6 @@ target-version = "py310"
 exclude = [
     "venv",
     ".git",
-    ".ruff_cache",
-    ".vscode",
-    "modules/seedvr/src",
 ]
 
 [tool.ruff.lint]
@@ -106,7 +103,6 @@ main.ignore-paths=[
     ".ruff_cache",
     ".vscode",
     ".*/node_modules/.*",
-    "modules/control/units/xs_pipe.py",
     "modules/control/proc",
     "modules/schedulers/scheduler_*.py",
     "modules/apg",