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make quality
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16 files changed

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-75
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‎examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -744,9 +744,9 @@ def initialize_new_tokens(self, inserting_toks: List[str]):
744744
.to(dtype=self.dtype)
745745
* std_token_embedding
746746
)
747-
self.embeddings_settings[
748-
f"original_embeddings_{idx}"
749-
] = text_encoder.text_model.embeddings.token_embedding.weight.data.clone()
747+
self.embeddings_settings[f"original_embeddings_{idx}"] = (
748+
text_encoder.text_model.embeddings.token_embedding.weight.data.clone()
749+
)
750750
self.embeddings_settings[f"std_token_embedding_{idx}"] = std_token_embedding
751751

752752
inu = torch.ones((len(tokenizer),), dtype=torch.bool)

‎examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -776,9 +776,9 @@ def initialize_new_tokens(self, inserting_toks: List[str]):
776776
.to(dtype=self.dtype)
777777
* std_token_embedding
778778
)
779-
self.embeddings_settings[
780-
f"original_embeddings_{idx}"
781-
] = text_encoder.text_model.embeddings.token_embedding.weight.data.clone()
779+
self.embeddings_settings[f"original_embeddings_{idx}"] = (
780+
text_encoder.text_model.embeddings.token_embedding.weight.data.clone()
781+
)
782782
self.embeddings_settings[f"std_token_embedding_{idx}"] = std_token_embedding
783783

784784
inu = torch.ones((len(tokenizer),), dtype=torch.bool)

‎examples/research_projects/multi_token_textual_inversion/textual_inversion.py‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -830,9 +830,9 @@ def main():
830830
# Let's make sure we don't update any embedding weights besides the newly added token
831831
index_no_updates = get_mask(tokenizer, accelerator)
832832
with torch.no_grad():
833-
accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
834-
index_no_updates
835-
] = orig_embeds_params[index_no_updates]
833+
accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = (
834+
orig_embeds_params[index_no_updates]
835+
)
836836

837837
# Checks if the accelerator has performed an optimization step behind the scenes
838838
if accelerator.sync_gradients:

‎examples/research_projects/onnxruntime/textual_inversion/textual_inversion.py‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -886,9 +886,9 @@ def main():
886886
index_no_updates[min(placeholder_token_ids) : max(placeholder_token_ids) + 1] = False
887887

888888
with torch.no_grad():
889-
accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
890-
index_no_updates
891-
] = orig_embeds_params[index_no_updates]
889+
accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = (
890+
orig_embeds_params[index_no_updates]
891+
)
892892

893893
# Checks if the accelerator has performed an optimization step behind the scenes
894894
if accelerator.sync_gradients:

‎examples/textual_inversion/textual_inversion.py‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -910,9 +910,9 @@ def main():
910910
index_no_updates[min(placeholder_token_ids) : max(placeholder_token_ids) + 1] = False
911911

912912
with torch.no_grad():
913-
accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
914-
index_no_updates
915-
] = orig_embeds_params[index_no_updates]
913+
accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = (
914+
orig_embeds_params[index_no_updates]
915+
)
916916

917917
# Checks if the accelerator has performed an optimization step behind the scenes
918918
if accelerator.sync_gradients:

‎examples/textual_inversion/textual_inversion_sdxl.py‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -940,9 +940,9 @@ def main():
940940
index_no_updates[min(placeholder_token_ids) : max(placeholder_token_ids) + 1] = False
941941

942942
with torch.no_grad():
943-
accelerator.unwrap_model(text_encoder_1).get_input_embeddings().weight[
944-
index_no_updates
945-
] = orig_embeds_params[index_no_updates]
943+
accelerator.unwrap_model(text_encoder_1).get_input_embeddings().weight[index_no_updates] = (
944+
orig_embeds_params[index_no_updates]
945+
)
946946

947947
# Checks if the accelerator has performed an optimization step behind the scenes
948948
if accelerator.sync_gradients:

‎scripts/convert_svd_to_diffusers.py‎

Lines changed: 6 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -381,9 +381,9 @@ def convert_ldm_unet_checkpoint(
381381

382382
# TODO resnet time_mixer.mix_factor
383383
if f"input_blocks.{i}.0.time_mixer.mix_factor" in unet_state_dict:
384-
new_checkpoint[
385-
f"down_blocks.{block_id}.resnets.{layer_in_block_id}.time_mixer.mix_factor"
386-
] = unet_state_dict[f"input_blocks.{i}.0.time_mixer.mix_factor"]
384+
new_checkpoint[f"down_blocks.{block_id}.resnets.{layer_in_block_id}.time_mixer.mix_factor"] = (
385+
unet_state_dict[f"input_blocks.{i}.0.time_mixer.mix_factor"]
386+
)
387387

388388
if len(attentions):
389389
paths = renew_attention_paths(attentions)
@@ -478,9 +478,9 @@ def convert_ldm_unet_checkpoint(
478478
)
479479

480480
if f"output_blocks.{i}.0.time_mixer.mix_factor" in unet_state_dict:
481-
new_checkpoint[
482-
f"up_blocks.{block_id}.resnets.{layer_in_block_id}.time_mixer.mix_factor"
483-
] = unet_state_dict[f"output_blocks.{i}.0.time_mixer.mix_factor"]
481+
new_checkpoint[f"up_blocks.{block_id}.resnets.{layer_in_block_id}.time_mixer.mix_factor"] = (
482+
unet_state_dict[f"output_blocks.{i}.0.time_mixer.mix_factor"]
483+
)
484484

485485
output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
486486
if ["conv.bias", "conv.weight"] in output_block_list.values():

‎src/diffusers/loaders/ip_adapter.py‎

Lines changed: 7 additions & 24 deletions
Original file line numberDiff line numberDiff line change
@@ -231,8 +231,8 @@ def load_ip_adapter(
231231

232232
def set_ip_adapter_scale(self, scale_configs: Union[float, Dict, List[Union[float, Dict]]], default_scale=0.0):
233233
"""
234-
Set IP-Adapter scales per-transformer block. Input `scale_configs` could be a single config or a list of configs
235-
for granular control over each IP-Adapter behavior. A config can be a float or a dictionary.
234+
Set IP-Adapter scales per-transformer block. Input `scale_configs` could be a single config or a list of
235+
configs for granular control over each IP-Adapter behavior. A config can be a float or a dictionary.
236236
237237
Example:
238238
@@ -243,36 +243,19 @@ def set_ip_adapter_scale(self, scale_configs: Union[float, Dict, List[Union[floa
243243
244244
# To use style block only
245245
scale_configs = {
246-
"up": {
247-
"block_0": [0.0, 1.0, 0.0]
248-
},
246+
"up": {"block_0": [0.0, 1.0, 0.0]},
249247
}
250248
pipeline.set_ip_adapter_scale(scale_configs)
251249
252250
# To use style+layout blocks
253251
scale_configs = {
254-
"down": {
255-
"block_2": [0.0, 1.0]
256-
},
257-
"up": {
258-
"block_0": [0.0, 1.0, 0.0]
259-
},
252+
"down": {"block_2": [0.0, 1.0]},
253+
"up": {"block_0": [0.0, 1.0, 0.0]},
260254
}
261255
pipeline.set_ip_adapter_scale(scale_configs)
262256
263257
# To use style and layout from 2 reference images
264-
scale_configs = [
265-
{
266-
"down": {
267-
"block_2": [0.0, 1.0]
268-
}
269-
},
270-
{
271-
"up": {
272-
"block_0": [0.0, 1.0, 0.0]
273-
}
274-
}
275-
]
258+
scale_configs = [{"down": {"block_2": [0.0, 1.0]}}, {"up": {"block_0": [0.0, 1.0, 0.0]}}]
276259
pipeline.set_ip_adapter_scale(scale_configs)
277260
```
278261
"""
@@ -288,7 +271,7 @@ def set_ip_adapter_scale(self, scale_configs: Union[float, Dict, List[Union[floa
288271
f"Cannot assign {len(scale_configs)} scale_configs to "
289272
f"{len(attn_processor.scale)} IP-Adapter."
290273
)
291-
elif len(scale_configs)==1:
274+
elif len(scale_configs) == 1:
292275
scale_configs = scale_configs * len(attn_processor.scale)
293276
for i, scale_config in enumerate(scale_configs):
294277
if isinstance(scale_config, dict):

‎src/diffusers/loaders/lora.py‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -1288,9 +1288,9 @@ def set_lora_device(self, adapter_names: List[str], device: Union[torch.device,
12881288
text_encoder_module.lora_A[adapter_name].to(device)
12891289
text_encoder_module.lora_B[adapter_name].to(device)
12901290
# this is a param, not a module, so device placement is not in-place -> re-assign
1291-
text_encoder_module.lora_magnitude_vector[
1292-
adapter_name
1293-
] = text_encoder_module.lora_magnitude_vector[adapter_name].to(device)
1291+
text_encoder_module.lora_magnitude_vector[adapter_name] = (
1292+
text_encoder_module.lora_magnitude_vector[adapter_name].to(device)
1293+
)
12941294

12951295

12961296
class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):

‎src/diffusers/loaders/lora_conversion_utils.py‎

Lines changed: 9 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -209,9 +209,9 @@ def _convert_kohya_lora_to_diffusers(state_dict, unet_name="unet", text_encoder_
209209

210210
if is_unet_dora_lora:
211211
dora_scale_key_to_replace = "_lora.down." if "_lora.down." in diffusers_name else ".lora.down."
212-
unet_state_dict[
213-
diffusers_name.replace(dora_scale_key_to_replace, ".lora_magnitude_vector.")
214-
] = state_dict.pop(key.replace("lora_down.weight", "dora_scale"))
212+
unet_state_dict[diffusers_name.replace(dora_scale_key_to_replace, ".lora_magnitude_vector.")] = (
213+
state_dict.pop(key.replace("lora_down.weight", "dora_scale"))
214+
)
215215

216216
elif lora_name.startswith(("lora_te_", "lora_te1_", "lora_te2_")):
217217
if lora_name.startswith(("lora_te_", "lora_te1_")):
@@ -249,13 +249,13 @@ def _convert_kohya_lora_to_diffusers(state_dict, unet_name="unet", text_encoder_
249249
"_lora.down." if "_lora.down." in diffusers_name else ".lora_linear_layer."
250250
)
251251
if lora_name.startswith(("lora_te_", "lora_te1_")):
252-
te_state_dict[
253-
diffusers_name.replace(dora_scale_key_to_replace_te, ".lora_magnitude_vector.")
254-
] = state_dict.pop(key.replace("lora_down.weight", "dora_scale"))
252+
te_state_dict[diffusers_name.replace(dora_scale_key_to_replace_te, ".lora_magnitude_vector.")] = (
253+
state_dict.pop(key.replace("lora_down.weight", "dora_scale"))
254+
)
255255
elif lora_name.startswith("lora_te2_"):
256-
te2_state_dict[
257-
diffusers_name.replace(dora_scale_key_to_replace_te, ".lora_magnitude_vector.")
258-
] = state_dict.pop(key.replace("lora_down.weight", "dora_scale"))
256+
te2_state_dict[diffusers_name.replace(dora_scale_key_to_replace_te, ".lora_magnitude_vector.")] = (
257+
state_dict.pop(key.replace("lora_down.weight", "dora_scale"))
258+
)
259259

260260
# Rename the alphas so that they can be mapped appropriately.
261261
if lora_name_alpha in state_dict:

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