Ruff upgrade

.github/workflows/python-checks.yml

@@ -62,7 +62,7 @@ jobs:
 
       - name: install ruff
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
-        run: pip install ruff==0.6.0
+        run: pip install ruff==0.9.9
         shell: bash
 
       - name: ruff check

invokeai/app/invocations/baseinvocation.py

@@ -417,7 +417,7 @@ def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None
 
         ui_type = field.json_schema_extra.get("ui_type", None)
         if isinstance(ui_type, str) and ui_type.startswith("DEPRECATED_"):
-            logger.warn(f"\"UIType.{ui_type.split('_')[-1]}\" is deprecated, ignoring")
+            logger.warn(f'"UIType.{ui_type.split("_")[-1]}" is deprecated, ignoring')
             field.json_schema_extra.pop("ui_type")
     return None
 

invokeai/app/invocations/compel.py

@@ -513,7 +513,7 @@ def log_tokenization_for_text(
             usedTokens += 1
 
     if usedTokens > 0:
-        print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
+        print(f"\n>> [TOKENLOG] Tokens {display_label or ''} ({usedTokens}):")
         print(f"{tokenized}\x1b[0m")
 
     if discarded != "":

invokeai/app/invocations/segment_anything.py

@@ -185,9 +185,9 @@ def _filter_masks(
             # Find the largest mask.
             return [max(masks, key=lambda x: float(x.sum()))]
         elif self.mask_filter == "highest_box_score":
-            assert (
-                bounding_boxes is not None
-            ), "Bounding boxes must be provided to use the 'highest_box_score' mask filter."
+            assert bounding_boxes is not None, (
+                "Bounding boxes must be provided to use the 'highest_box_score' mask filter."
+            )
             assert len(masks) == len(bounding_boxes)
             # Find the index of the bounding box with the highest score.
             # Note that we fallback to -1.0 if the score is None. This is mainly to satisfy the type checker. In most

invokeai/app/services/config/config_default.py

@@ -476,9 +476,9 @@ def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
     try:
         # Meta is not included in the model fields, so we need to validate it separately
         config = InvokeAIAppConfig.model_validate(loaded_config_dict)
-        assert (
-            config.schema_version == CONFIG_SCHEMA_VERSION
-        ), f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config.schema_version}"
+        assert config.schema_version == CONFIG_SCHEMA_VERSION, (
+            f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config.schema_version}"
+        )
         return config
     except Exception as e:
         raise RuntimeError(f"Failed to load config file {config_path}: {e}") from e

invokeai/backend/image_util/pngwriter.py

@@ -91,10 +91,10 @@ def normalize_prompt(self):
 
         switches = []
         switches.append(f'"{opt.prompt}"')
-        switches.append(f"-s{opt.steps        or t2i.steps}")
-        switches.append(f"-W{opt.width        or t2i.width}")
-        switches.append(f"-H{opt.height       or t2i.height}")
-        switches.append(f"-C{opt.cfg_scale    or t2i.cfg_scale}")
+        switches.append(f"-s{opt.steps or t2i.steps}")
+        switches.append(f"-W{opt.width or t2i.width}")
+        switches.append(f"-H{opt.height or t2i.height}")
+        switches.append(f"-C{opt.cfg_scale or t2i.cfg_scale}")
         switches.append(f"-A{opt.sampler_name or t2i.sampler_name}")
         # to do: put model name into the t2i object
         #        switches.append(f'--model{t2i.model_name}')
@@ -109,7 +109,7 @@ def normalize_prompt(self):
         if opt.gfpgan_strength:
             switches.append(f"-G{opt.gfpgan_strength}")
         if opt.upscale:
-            switches.append(f'-U {" ".join([str(u) for u in opt.upscale])}')
+            switches.append(f"-U {' '.join([str(u) for u in opt.upscale])}")
         if opt.variation_amount > 0:
             switches.append(f"-v{opt.variation_amount}")
         if opt.with_variations:

invokeai/backend/model_manager/load/memory_snapshot.py

@@ -70,7 +70,7 @@ def get_pretty_snapshot_diff(snapshot_1: Optional[MemorySnapshot], snapshot_2: O
 
     def get_msg_line(prefix: str, val1: int, val2: int) -> str:
         diff = val2 - val1
-        return f"{prefix: <30} ({(diff/GB):+5.3f}): {(val1/GB):5.3f}GB -> {(val2/GB):5.3f}GB\n"
+        return f"{prefix: <30} ({(diff / GB):+5.3f}): {(val1 / GB):5.3f}GB -> {(val2 / GB):5.3f}GB\n"
 
     msg = ""
 

invokeai/backend/model_manager/load/model_cache/model_cache.py

@@ -192,7 +192,7 @@ def put(self, key: str, model: AnyModel) -> None:
         self._cached_models[key] = cache_record
         self._cache_stack.append(key)
         self._logger.debug(
-            f"Added model {key} (Type: {model.__class__.__name__}, Wrap mode: {wrapped_model.__class__.__name__}, Model size: {size/MB:.2f}MB)"
+            f"Added model {key} (Type: {model.__class__.__name__}, Wrap mode: {wrapped_model.__class__.__name__}, Model size: {size / MB:.2f}MB)"
         )
 
     @synchronized
@@ -303,7 +303,7 @@ def _load_locked_model(self, cache_entry: CacheRecord, working_mem_bytes: Option
         # 2. If the model can't fit fully into VRAM, then unload all other models and load as much of the model as
         #    possible.
         vram_bytes_freed = self._offload_unlocked_models(model_vram_needed, working_mem_bytes)
-        self._logger.debug(f"Unloaded models (if necessary): vram_bytes_freed={(vram_bytes_freed/MB):.2f}MB")
+        self._logger.debug(f"Unloaded models (if necessary): vram_bytes_freed={(vram_bytes_freed / MB):.2f}MB")
 
         # Check the updated vram_available after offloading.
         vram_available = self._get_vram_available(working_mem_bytes)
@@ -317,7 +317,7 @@ def _load_locked_model(self, cache_entry: CacheRecord, working_mem_bytes: Option
             vram_bytes_freed_from_own_model = self._move_model_to_ram(cache_entry, -vram_available)
             vram_available = self._get_vram_available(working_mem_bytes)
             self._logger.debug(
-                f"Unloaded {vram_bytes_freed_from_own_model/MB:.2f}MB from the model being locked ({cache_entry.key})."
+                f"Unloaded {vram_bytes_freed_from_own_model / MB:.2f}MB from the model being locked ({cache_entry.key})."
             )
 
         # Move as much of the model as possible into VRAM.
@@ -333,10 +333,12 @@ def _load_locked_model(self, cache_entry: CacheRecord, working_mem_bytes: Option
         self._logger.info(
             f"Loaded model '{cache_entry.key}' ({cache_entry.cached_model.model.__class__.__name__}) onto "
             f"{self._execution_device.type} device in {(time.time() - start_time):.2f}s. "
-            f"Total model size: {model_total_bytes/MB:.2f}MB, "
-            f"VRAM: {model_cur_vram_bytes/MB:.2f}MB ({loaded_percent:.1%})"
+            f"Total model size: {model_total_bytes / MB:.2f}MB, "
+            f"VRAM: {model_cur_vram_bytes / MB:.2f}MB ({loaded_percent:.1%})"
+        )
+        self._logger.debug(
+            f"Loaded model onto execution device: model_bytes_loaded={(model_bytes_loaded / MB):.2f}MB, "
         )
-        self._logger.debug(f"Loaded model onto execution device: model_bytes_loaded={(model_bytes_loaded/MB):.2f}MB, ")
         self._logger.debug(
             f"After loading: {self._get_vram_state_str(model_cur_vram_bytes, model_total_bytes, vram_available)}"
         )
@@ -495,10 +497,10 @@ def _get_vram_state_str(self, model_cur_vram_bytes: int, model_total_bytes: int,
         """Helper function for preparing a VRAM state log string."""
         model_cur_vram_bytes_percent = model_cur_vram_bytes / model_total_bytes if model_total_bytes > 0 else 0
         return (
-            f"model_total={model_total_bytes/MB:.0f} MB, "
-            + f"model_vram={model_cur_vram_bytes/MB:.0f} MB ({model_cur_vram_bytes_percent:.1%} %), "
+            f"model_total={model_total_bytes / MB:.0f} MB, "
+            + f"model_vram={model_cur_vram_bytes / MB:.0f} MB ({model_cur_vram_bytes_percent:.1%} %), "
             # + f"vram_total={int(self._max_vram_cache_size * GB)/MB:.0f} MB, "
-            + f"vram_available={(vram_available/MB):.0f} MB, "
+            + f"vram_available={(vram_available / MB):.0f} MB, "
         )
 
     def _offload_unlocked_models(self, vram_bytes_required: int, working_mem_bytes: Optional[int] = None) -> int:
@@ -509,7 +511,7 @@ def _offload_unlocked_models(self, vram_bytes_required: int, working_mem_bytes:
             int: The number of bytes freed based on believed model sizes. The actual change in VRAM may be different.
         """
         self._logger.debug(
-            f"Offloading unlocked models with goal of making room for {vram_bytes_required/MB:.2f}MB of VRAM."
+            f"Offloading unlocked models with goal of making room for {vram_bytes_required / MB:.2f}MB of VRAM."
         )
         vram_bytes_freed = 0
         # TODO(ryand): Give more thought to the offloading policy used here.
@@ -527,7 +529,7 @@ def _offload_unlocked_models(self, vram_bytes_required: int, working_mem_bytes:
             cache_entry_bytes_freed = self._move_model_to_ram(cache_entry, vram_bytes_to_free)
             if cache_entry_bytes_freed > 0:
                 self._logger.debug(
-                    f"Unloaded {cache_entry.key} from VRAM to free {(cache_entry_bytes_freed/MB):.0f} MB."
+                    f"Unloaded {cache_entry.key} from VRAM to free {(cache_entry_bytes_freed / MB):.0f} MB."
                 )
             vram_bytes_freed += cache_entry_bytes_freed
 
@@ -609,7 +611,7 @@ def make_room(self, bytes_needed: int) -> None:
         external references to the model, there's nothing that the cache can do about it, and those models will not be
         garbage-collected.
         """
-        self._logger.debug(f"Making room for {bytes_needed/MB:.2f}MB of RAM.")
+        self._logger.debug(f"Making room for {bytes_needed / MB:.2f}MB of RAM.")
         self._log_cache_state(title="Before dropping models:")
 
         ram_bytes_available = self._get_ram_available()
@@ -625,7 +627,7 @@ def make_room(self, bytes_needed: int) -> None:
             if not cache_entry.is_locked:
                 ram_bytes_freed += cache_entry.cached_model.total_bytes()
                 self._logger.debug(
-                    f"Dropping {model_key} from RAM cache to free {(cache_entry.cached_model.total_bytes()/MB):.2f}MB."
+                    f"Dropping {model_key} from RAM cache to free {(cache_entry.cached_model.total_bytes() / MB):.2f}MB."
                 )
                 self._delete_cache_entry(cache_entry)
                 del cache_entry
@@ -650,7 +652,7 @@ def make_room(self, bytes_needed: int) -> None:
             gc.collect()
 
         TorchDevice.empty_cache()
-        self._logger.debug(f"Dropped {models_cleared} models to free {ram_bytes_freed/MB:.2f}MB of RAM.")
+        self._logger.debug(f"Dropped {models_cleared} models to free {ram_bytes_freed / MB:.2f}MB of RAM.")
         self._log_cache_state(title="After dropping models:")
 
     def _delete_cache_entry(self, cache_entry: CacheRecord) -> None:

invokeai/backend/model_manager/merge.py

@@ -115,19 +115,19 @@ def merge_diffusion_models_and_save(
         base_models: Set[BaseModelType] = set()
         variant = None if self._installer.app_config.precision == "float32" else "fp16"
 
-        assert (
-            len(model_keys) <= 2 or interp == MergeInterpolationMethod.AddDifference
-        ), "When merging three models, only the 'add_difference' merge method is supported"
+        assert len(model_keys) <= 2 or interp == MergeInterpolationMethod.AddDifference, (
+            "When merging three models, only the 'add_difference' merge method is supported"
+        )
 
         for key in model_keys:
             info = store.get_model(key)
             model_names.append(info.name)
-            assert isinstance(
-                info, MainDiffusersConfig
-            ), f"{info.name} ({info.key}) is not a diffusers model. It must be optimized before merging"
-            assert info.variant == ModelVariantType(
-                "normal"
-            ), f"{info.name} ({info.key}) is a {info.variant} model, which cannot currently be merged"
+            assert isinstance(info, MainDiffusersConfig), (
+                f"{info.name} ({info.key}) is not a diffusers model. It must be optimized before merging"
+            )
+            assert info.variant == ModelVariantType("normal"), (
+                f"{info.name} ({info.key}) is a {info.variant} model, which cannot currently be merged"
+            )
 
             # tally base models used
             base_models.add(info.base)

invokeai/backend/model_manager/util/libc_util.py

@@ -37,19 +37,21 @@ class Struct_mallinfo2(ctypes.Structure):
 
     def __str__(self) -> str:
         s = ""
-        s += f"{'arena': <10}= {(self.arena/2**30):15.5f}   # Non-mmapped space allocated (GB) (uordblks + fordblks)\n"
+        s += (
+            f"{'arena': <10}= {(self.arena / 2**30):15.5f}   # Non-mmapped space allocated (GB) (uordblks + fordblks)\n"
+        )
         s += f"{'ordblks': <10}= {(self.ordblks): >15}   # Number of free chunks\n"
         s += f"{'smblks': <10}= {(self.smblks): >15}   # Number of free fastbin blocks \n"
         s += f"{'hblks': <10}= {(self.hblks): >15}   # Number of mmapped regions \n"
-        s += f"{'hblkhd': <10}= {(self.hblkhd/2**30):15.5f}   # Space allocated in mmapped regions (GB)\n"
+        s += f"{'hblkhd': <10}= {(self.hblkhd / 2**30):15.5f}   # Space allocated in mmapped regions (GB)\n"
         s += f"{'usmblks': <10}= {(self.usmblks): >15}   # Unused\n"
-        s += f"{'fsmblks': <10}= {(self.fsmblks/2**30):15.5f}   # Space in freed fastbin blocks (GB)\n"
+        s += f"{'fsmblks': <10}= {(self.fsmblks / 2**30):15.5f}   # Space in freed fastbin blocks (GB)\n"
         s += (
-            f"{'uordblks': <10}= {(self.uordblks/2**30):15.5f}   # Space used by in-use allocations (non-mmapped)"
+            f"{'uordblks': <10}= {(self.uordblks / 2**30):15.5f}   # Space used by in-use allocations (non-mmapped)"
             " (GB)\n"
         )
-        s += f"{'fordblks': <10}= {(self.fordblks/2**30):15.5f}   # Space in free blocks (non-mmapped) (GB)\n"
-        s += f"{'keepcost': <10}= {(self.keepcost/2**30):15.5f}   # Top-most, releasable space (GB)\n"
+        s += f"{'fordblks': <10}= {(self.fordblks / 2**30):15.5f}   # Space in free blocks (non-mmapped) (GB)\n"
+        s += f"{'keepcost': <10}= {(self.keepcost / 2**30):15.5f}   # Top-most, releasable space (GB)\n"
         return s
 
 

invokeai/backend/patches/lora_conversions/sdxl_lora_conversion_utils.py

@@ -73,36 +73,36 @@ def _make_sdxl_unet_conversion_map() -> List[Tuple[str, str]]:
         for j in range(2):
             # loop over resnets/attentions for downblocks
             hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
-            sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
+            sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0."
             unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
 
             if i < 3:
                 # no attention layers in down_blocks.3
                 hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
-                sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
+                sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1."
                 unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
 
         for j in range(3):
             # loop over resnets/attentions for upblocks
             hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
-            sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
+            sd_up_res_prefix = f"output_blocks.{3 * i + j}.0."
             unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
 
             # if i > 0: commentout for sdxl
             # no attention layers in up_blocks.0
             hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
-            sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
+            sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1."
             unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
 
         if i < 3:
             # no downsample in down_blocks.3
             hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
-            sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
+            sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op."
             unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
 
             # no upsample in up_blocks.3
             hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
-            sd_upsample_prefix = f"output_blocks.{3*i + 2}.{2}."  # change for sdxl
+            sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{2}."  # change for sdxl
             unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
 
     hf_mid_atn_prefix = "mid_block.attentions.0."
@@ -111,7 +111,7 @@ def _make_sdxl_unet_conversion_map() -> List[Tuple[str, str]]:
 
     for j in range(2):
         hf_mid_res_prefix = f"mid_block.resnets.{j}."
-        sd_mid_res_prefix = f"middle_block.{2*j}."
+        sd_mid_res_prefix = f"middle_block.{2 * j}."
         unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
 
     unet_conversion_map_resnet = [
@@ -133,13 +133,13 @@ def _make_sdxl_unet_conversion_map() -> List[Tuple[str, str]]:
             unet_conversion_map.append((sd, hf))
 
     for j in range(2):
-        hf_time_embed_prefix = f"time_embedding.linear_{j+1}."
-        sd_time_embed_prefix = f"time_embed.{j*2}."
+        hf_time_embed_prefix = f"time_embedding.linear_{j + 1}."
+        sd_time_embed_prefix = f"time_embed.{j * 2}."
         unet_conversion_map.append((sd_time_embed_prefix, hf_time_embed_prefix))
 
     for j in range(2):
-        hf_label_embed_prefix = f"add_embedding.linear_{j+1}."
-        sd_label_embed_prefix = f"label_emb.0.{j*2}."
+        hf_label_embed_prefix = f"add_embedding.linear_{j + 1}."
+        sd_label_embed_prefix = f"label_emb.0.{j * 2}."
         unet_conversion_map.append((sd_label_embed_prefix, hf_label_embed_prefix))
 
     unet_conversion_map.append(("input_blocks.0.0.", "conv_in."))

pyproject.toml

@@ -115,8 +115,8 @@ dependencies = [
 ]
 "dev" = ["jurigged", "pudb", "snakeviz", "gprof2dot"]
 "test" = [
-  "ruff>=0.3.3",
-  "ruff-lsp>=0.0.53",
+  "ruff~=0.9.9",
+  "ruff-lsp~=0.0.62",
   "mypy",
   "pre-commit",
   "pytest>6.0.0",

tests/app/services/download/test_download_queue.py

@@ -212,12 +212,12 @@ def event_handler(job: DownloadJob | MultiFileDownloadJob, excp: Optional[Except
     assert job.bytes > 0, "expected download bytes to be positive"
     assert job.bytes == job.total_bytes, "expected download bytes to equal total bytes"
     assert job.download_path == tmp_path / "sdxl-turbo"
-    assert Path(
-        tmp_path, "sdxl-turbo/model_index.json"
-    ).exists(), f"expected {tmp_path}/sdxl-turbo/model_inded.json to exist"
-    assert Path(
-        tmp_path, "sdxl-turbo/text_encoder/config.json"
-    ).exists(), f"expected {tmp_path}/sdxl-turbo/text_encoder/config.json to exist"
+    assert Path(tmp_path, "sdxl-turbo/model_index.json").exists(), (
+        f"expected {tmp_path}/sdxl-turbo/model_inded.json to exist"
+    )
+    assert Path(tmp_path, "sdxl-turbo/text_encoder/config.json").exists(), (
+        f"expected {tmp_path}/sdxl-turbo/text_encoder/config.json to exist"
+    )
 
     assert events == {DownloadJobStatus.RUNNING, DownloadJobStatus.COMPLETED}
     queue.stop()