Ruff formatting

Author: Billy

invokeai/backend/model_manager/config.py

@@ -37,7 +37,7 @@
 from invokeai.backend.model_hash.hash_validator import validate_hash
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS
 from invokeai.backend.model_manager.model_on_disk import ModelOnDisk
-from invokeai.backend.model_manager.omi import stable_diffusion_xl_1_lora, flux_dev_1_lora
+from invokeai.backend.model_manager.omi import flux_dev_1_lora, stable_diffusion_xl_1_lora
 from invokeai.backend.model_manager.taxonomy import (
     AnyVariant,
     BaseModelType,

invokeai/backend/model_manager/omi/__init__.py

@@ -1,6 +1,7 @@
+from invokeai.backend.model_manager.omi.omi import convert_from_omi
 from invokeai.backend.model_manager.omi.vendor.model_spec.architecture import (
+    flux_dev_1_lora,
     stable_diffusion_xl_1_lora,
-    flux_dev_1_lora
 )
 
-from invokeai.backend.model_manager.omi.omi import convert_from_omi
+__all__ = ["flux_dev_1_lora", "stable_diffusion_xl_1_lora", "convert_from_omi"]

invokeai/backend/model_manager/omi/omi.py

@@ -1,10 +1,13 @@
+from invokeai.backend.model_manager.model_on_disk import StateDict
 from invokeai.backend.model_manager.omi.vendor.convert.lora import (
-    convert_lora_util as lora_util,
     convert_flux_lora as omi_flux,
+)
+from invokeai.backend.model_manager.omi.vendor.convert.lora import (
+    convert_lora_util as lora_util,
+)
+from invokeai.backend.model_manager.omi.vendor.convert.lora import (
     convert_sdxl_lora as omi_sdxl,
 )
-
-from invokeai.backend.model_manager.model_on_disk import StateDict
 from invokeai.backend.model_manager.taxonomy import BaseModelType
 
 

invokeai/backend/model_manager/omi/vendor/convert/lora/convert_clip.py

@@ -1,4 +1,7 @@
-from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_lora_util import LoraConversionKeySet, map_prefix_range
+from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_lora_util import (
+    LoraConversionKeySet,
+    map_prefix_range,
+)
 
 
 def map_clip(key_prefix: LoraConversionKeySet) -> list[LoraConversionKeySet]:

invokeai/backend/model_manager/omi/vendor/convert/lora/convert_flux_lora.py

@@ -1,5 +1,8 @@
 from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_clip import map_clip
-from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_lora_util import LoraConversionKeySet, map_prefix_range
+from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_lora_util import (
+    LoraConversionKeySet,
+    map_prefix_range,
+)
 from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_t5 import map_t5
 
 
@@ -45,10 +48,16 @@ def __map_transformer(key_prefix: LoraConversionKeySet) -> list[LoraConversionKe
     keys = []
 
     keys += [LoraConversionKeySet("txt_in", "context_embedder", parent=key_prefix)]
-    keys += [LoraConversionKeySet("final_layer.adaLN_modulation.1", "norm_out.linear", parent=key_prefix, swap_chunks=True)]
+    keys += [
+        LoraConversionKeySet("final_layer.adaLN_modulation.1", "norm_out.linear", parent=key_prefix, swap_chunks=True)
+    ]
     keys += [LoraConversionKeySet("final_layer.linear", "proj_out", parent=key_prefix)]
-    keys += [LoraConversionKeySet("guidance_in.in_layer", "time_text_embed.guidance_embedder.linear_1", parent=key_prefix)]
-    keys += [LoraConversionKeySet("guidance_in.out_layer", "time_text_embed.guidance_embedder.linear_2", parent=key_prefix)]
+    keys += [
+        LoraConversionKeySet("guidance_in.in_layer", "time_text_embed.guidance_embedder.linear_1", parent=key_prefix)
+    ]
+    keys += [
+        LoraConversionKeySet("guidance_in.out_layer", "time_text_embed.guidance_embedder.linear_2", parent=key_prefix)
+    ]
     keys += [LoraConversionKeySet("vector_in.in_layer", "time_text_embed.text_embedder.linear_1", parent=key_prefix)]
     keys += [LoraConversionKeySet("vector_in.out_layer", "time_text_embed.text_embedder.linear_2", parent=key_prefix)]
     keys += [LoraConversionKeySet("time_in.in_layer", "time_text_embed.timestep_embedder.linear_1", parent=key_prefix)]

invokeai/backend/model_manager/omi/vendor/convert/lora/convert_lora_util.py

@@ -1,20 +1,19 @@
 import torch
 from torch import Tensor
-
 from typing_extensions import Self
 
 
 class LoraConversionKeySet:
     def __init__(
-            self,
-            omi_prefix: str,
-            diffusers_prefix: str,
-            legacy_diffusers_prefix: str | None = None,
-            parent: Self | None = None,
-            swap_chunks: bool = False,
-            filter_is_last: bool | None = None,
-            next_omi_prefix: str | None = None,
-            next_diffusers_prefix: str | None = None,
+        self,
+        omi_prefix: str,
+        diffusers_prefix: str,
+        legacy_diffusers_prefix: str | None = None,
+        parent: Self | None = None,
+        swap_chunks: bool = False,
+        filter_is_last: bool | None = None,
+        next_omi_prefix: str | None = None,
+        next_diffusers_prefix: str | None = None,
     ):
         if parent is not None:
             self.omi_prefix = combine(parent.omi_prefix, omi_prefix)
@@ -24,9 +23,11 @@ def __init__(
             self.diffusers_prefix = diffusers_prefix
 
         if legacy_diffusers_prefix is None:
-            self.legacy_diffusers_prefix = self.diffusers_prefix.replace('.', '_')
+            self.legacy_diffusers_prefix = self.diffusers_prefix.replace(".", "_")
         elif parent is not None:
-            self.legacy_diffusers_prefix = combine(parent.legacy_diffusers_prefix, legacy_diffusers_prefix).replace('.', '_')
+            self.legacy_diffusers_prefix = combine(parent.legacy_diffusers_prefix, legacy_diffusers_prefix).replace(
+                ".", "_"
+            )
         else:
             self.legacy_diffusers_prefix = legacy_diffusers_prefix
 
@@ -42,11 +43,13 @@ def __init__(
         elif next_omi_prefix is not None and parent is not None:
             self.next_omi_prefix = combine(parent.omi_prefix, next_omi_prefix)
             self.next_diffusers_prefix = combine(parent.diffusers_prefix, next_diffusers_prefix)
-            self.next_legacy_diffusers_prefix = combine(parent.legacy_diffusers_prefix, next_diffusers_prefix).replace('.', '_')
+            self.next_legacy_diffusers_prefix = combine(parent.legacy_diffusers_prefix, next_diffusers_prefix).replace(
+                ".", "_"
+            )
         elif next_omi_prefix is not None and parent is None:
             self.next_omi_prefix = next_omi_prefix
             self.next_diffusers_prefix = next_diffusers_prefix
-            self.next_legacy_diffusers_prefix = next_diffusers_prefix.replace('.', '_')
+            self.next_legacy_diffusers_prefix = next_diffusers_prefix.replace(".", "_")
         else:
             self.next_omi_prefix = None
             self.next_diffusers_prefix = None
@@ -61,19 +64,19 @@ def __get_diffusers(self, in_prefix: str, key: str) -> str:
     def __get_legacy_diffusers(self, in_prefix: str, key: str) -> str:
         key = self.legacy_diffusers_prefix + key.removeprefix(in_prefix)
 
-        suffix = key[key.rfind('.'):]
-        if suffix not in ['.alpha', '.dora_scale']:  # some keys only have a single . in the suffix
-            suffix = key[key.removesuffix(suffix).rfind('.'):]
+        suffix = key[key.rfind(".") :]
+        if suffix not in [".alpha", ".dora_scale"]:  # some keys only have a single . in the suffix
+            suffix = key[key.removesuffix(suffix).rfind(".") :]
         key = key.removesuffix(suffix)
 
-        return key.replace('.', '_') + suffix
+        return key.replace(".", "_") + suffix
 
     def get_key(self, in_prefix: str, key: str, target: str) -> str:
-        if target == 'omi':
+        if target == "omi":
             return self.__get_omi(in_prefix, key)
-        elif target == 'diffusers':
+        elif target == "diffusers":
             return self.__get_diffusers(in_prefix, key)
-        elif target == 'legacy_diffusers':
+        elif target == "legacy_diffusers":
             return self.__get_legacy_diffusers(in_prefix, key)
         return key
 
@@ -82,8 +85,8 @@ def __str__(self) -> str:
 
 
 def combine(left: str, right: str) -> str:
-    left = left.rstrip('.')
-    right = right.lstrip('.')
+    left = left.rstrip(".")
+    right = right.lstrip(".")
     if left == "" or left is None:
         return right
     elif right == "" or right is None:
@@ -93,25 +96,28 @@ def combine(left: str, right: str) -> str:
 
 
 def map_prefix_range(
-        omi_prefix: str,
-        diffusers_prefix: str,
-        parent: LoraConversionKeySet,
+    omi_prefix: str,
+    diffusers_prefix: str,
+    parent: LoraConversionKeySet,
 ) -> list[LoraConversionKeySet]:
     # 100 should be a safe upper bound. increase if it's not enough in the future
-    return [LoraConversionKeySet(
-        omi_prefix=f"{omi_prefix}.{i}",
-        diffusers_prefix=f"{diffusers_prefix}.{i}",
-        parent=parent,
-        next_omi_prefix=f"{omi_prefix}.{i + 1}",
-        next_diffusers_prefix=f"{diffusers_prefix}.{i + 1}",
-    ) for i in range(100)]
+    return [
+        LoraConversionKeySet(
+            omi_prefix=f"{omi_prefix}.{i}",
+            diffusers_prefix=f"{diffusers_prefix}.{i}",
+            parent=parent,
+            next_omi_prefix=f"{omi_prefix}.{i + 1}",
+            next_diffusers_prefix=f"{diffusers_prefix}.{i + 1}",
+        )
+        for i in range(100)
+    ]
 
 
 def __convert(
-        state_dict: dict[str, Tensor],
-        key_sets: list[LoraConversionKeySet],
-        source: str,
-        target: str,
+    state_dict: dict[str, Tensor],
+    key_sets: list[LoraConversionKeySet],
+    source: str,
+    target: str,
 ) -> dict[str, Tensor]:
     out_states = {}
 
@@ -121,25 +127,25 @@ def __convert(
     # TODO: maybe replace with a non O(n^2) algorithm
     for key, tensor in state_dict.items():
         for key_set in key_sets:
-            in_prefix = ''
+            in_prefix = ""
 
-            if source == 'omi':
+            if source == "omi":
                 in_prefix = key_set.omi_prefix
-            elif source == 'diffusers':
+            elif source == "diffusers":
                 in_prefix = key_set.diffusers_prefix
-            elif source == 'legacy_diffusers':
+            elif source == "legacy_diffusers":
                 in_prefix = key_set.legacy_diffusers_prefix
 
             if not key.startswith(in_prefix):
                 continue
 
             if key_set.filter_is_last is not None:
                 next_prefix = None
-                if source == 'omi':
+                if source == "omi":
                     next_prefix = key_set.next_omi_prefix
-                elif source == 'diffusers':
+                elif source == "diffusers":
                     next_prefix = key_set.next_diffusers_prefix
-                elif source == 'legacy_diffusers':
+                elif source == "legacy_diffusers":
                     next_prefix = key_set.next_legacy_diffusers_prefix
 
                 is_last = not any(k.startswith(next_prefix) for k in state_dict)
@@ -148,8 +154,8 @@ def __convert(
 
             name = key_set.get_key(in_prefix, key, target)
 
-            can_swap_chunks = target == 'omi' or source == 'omi'
-            if key_set.swap_chunks and name.endswith('.lora_up.weight') and can_swap_chunks:
+            can_swap_chunks = target == "omi" or source == "omi"
+            if key_set.swap_chunks and name.endswith(".lora_up.weight") and can_swap_chunks:
                 chunk_0, chunk_1 = tensor.chunk(2, dim=0)
                 tensor = torch.cat([chunk_1, chunk_0], dim=0)
 
@@ -161,8 +167,8 @@ def __convert(
 
 
 def __detect_source(
-        state_dict: dict[str, Tensor],
-        key_sets: list[LoraConversionKeySet],
+    state_dict: dict[str, Tensor],
+    key_sets: list[LoraConversionKeySet],
 ) -> str:
     omi_count = 0
     diffusers_count = 0
@@ -178,34 +184,34 @@ def __detect_source(
                 legacy_diffusers_count += 1
 
     if omi_count > diffusers_count and omi_count > legacy_diffusers_count:
-        return 'omi'
+        return "omi"
     if diffusers_count > omi_count and diffusers_count > legacy_diffusers_count:
-        return 'diffusers'
+        return "diffusers"
     if legacy_diffusers_count > omi_count and legacy_diffusers_count > diffusers_count:
-        return 'legacy_diffusers'
+        return "legacy_diffusers"
 
-    return ''
+    return ""
 
 
 def convert_to_omi(
-        state_dict: dict[str, Tensor],
-        key_sets: list[LoraConversionKeySet],
+    state_dict: dict[str, Tensor],
+    key_sets: list[LoraConversionKeySet],
 ) -> dict[str, Tensor]:
     source = __detect_source(state_dict, key_sets)
-    return __convert(state_dict, key_sets, source, 'omi')
+    return __convert(state_dict, key_sets, source, "omi")
 
 
 def convert_to_diffusers(
-        state_dict: dict[str, Tensor],
-        key_sets: list[LoraConversionKeySet],
+    state_dict: dict[str, Tensor],
+    key_sets: list[LoraConversionKeySet],
 ) -> dict[str, Tensor]:
     source = __detect_source(state_dict, key_sets)
-    return __convert(state_dict, key_sets, source, 'diffusers')
+    return __convert(state_dict, key_sets, source, "diffusers")
 
 
 def convert_to_legacy_diffusers(
-        state_dict: dict[str, Tensor],
-        key_sets: list[LoraConversionKeySet],
+    state_dict: dict[str, Tensor],
+    key_sets: list[LoraConversionKeySet],
 ) -> dict[str, Tensor]:
     source = __detect_source(state_dict, key_sets)
-    return __convert(state_dict, key_sets, source, 'legacy_diffusers')
+    return __convert(state_dict, key_sets, source, "legacy_diffusers")

invokeai/backend/model_manager/omi/vendor/convert/lora/convert_sdxl_lora.py

@@ -1,5 +1,8 @@
 from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_clip import map_clip
-from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_lora_util import LoraConversionKeySet, map_prefix_range
+from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_lora_util import (
+    LoraConversionKeySet,
+    map_prefix_range,
+)
 
 
 def __map_unet_resnet_block(key_prefix: LoraConversionKeySet) -> list[LoraConversionKeySet]:
@@ -115,7 +118,7 @@ def convert_sdxl_lora_key_sets() -> list[LoraConversionKeySet]:
     keys = []
 
     keys += [LoraConversionKeySet("bundle_emb", "bundle_emb")]
-    keys += __map_unet(LoraConversionKeySet( "unet", "lora_unet"))
+    keys += __map_unet(LoraConversionKeySet("unet", "lora_unet"))
     keys += map_clip(LoraConversionKeySet("clip_l", "lora_te1"))
     keys += map_clip(LoraConversionKeySet("clip_g", "lora_te2"))
 

invokeai/backend/model_manager/omi/vendor/convert/lora/convert_t5.py

@@ -1,4 +1,7 @@
-from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_lora_util import LoraConversionKeySet, map_prefix_range
+from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_lora_util import (
+    LoraConversionKeySet,
+    map_prefix_range,
+)
 
 
 def map_t5(key_prefix: LoraConversionKeySet) -> list[LoraConversionKeySet]: