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Fix typos in strings and comments (#11407)

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This commit is contained in:
co63oc
2025-04-25 02:53:47 +08:00
committed by GitHub
parent e8312e7ca9
commit f00a995753
43 changed files with 76 additions and 76 deletions
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2
examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py
View File

@@ -2181,7 +2181,7 @@ def main(args):
# Predict the noise residual
model_pred = transformer(
hidden_states=packed_noisy_model_input,
# YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
# YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
timestep=timesteps / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,

2
examples/community/README.md
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@@ -5381,7 +5381,7 @@ pipe = DiffusionPipeline.from_pretrained(
# Here we need use pipeline internal unet model
pipe.unet = pipe.unet_model.from_pretrained(model_id, subfolder="unet", variant="fp16", use_safetensors=True)
# Load aditional layers to the model
# Load additional layers to the model
pipe.unet.load_additional_layers(weight_path="proc_data/faithdiff/FaithDiff.bin", dtype=dtype)
# Enable vae tiling

6
examples/community/dps_pipeline.py
View File

@@ -312,9 +312,9 @@ if __name__ == "__main__":
# These are the coordinates of the output image
out_coordinates = np.arange(1, out_length + 1)
# since both scale-factor and output size can be provided simulatneously, perserving the center of the image requires shifting
# the output coordinates. the deviation is because out_length doesn't necesary equal in_length*scale.
# to keep the center we need to subtract half of this deivation so that we get equal margins for boths sides and center is preserved.
# since both scale-factor and output size can be provided simultaneously, preserving the center of the image requires shifting
# the output coordinates. the deviation is because out_length doesn't necessary equal in_length*scale.
# to keep the center we need to subtract half of this deviation so that we get equal margins for both sides and center is preserved.
shifted_out_coordinates = out_coordinates - (out_length - in_length * scale) / 2
# These are the matching positions of the output-coordinates on the input image coordinates.

8
examples/community/fresco_v2v.py
View File

@@ -351,7 +351,7 @@ def my_forward(
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
added_cond_kwargs: (`dict`, *optional*):
A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
are passed along to the UNet blocks.
Returns:
@@ -864,9 +864,9 @@ def get_flow_and_interframe_paras(flow_model, imgs):
class AttentionControl:
"""
Control FRESCO-based attention
* enable/diable spatial-guided attention
* enable/diable temporal-guided attention
* enable/diable cross-frame attention
* enable/disable spatial-guided attention
* enable/disable temporal-guided attention
* enable/disable cross-frame attention
* collect intermediate attention feature (for spatial-guided attention)
"""

2
examples/community/hd_painter.py
View File

@@ -34,7 +34,7 @@ class RASGAttnProcessor:
temb: Optional[torch.Tensor] = None,
scale: float = 1.0,
) -> torch.Tensor:
# Same as the default AttnProcessor up untill the part where similarity matrix gets saved
# Same as the default AttnProcessor up until the part where similarity matrix gets saved
downscale_factor = self.mask_resoltuion // hidden_states.shape[1]
residual = hidden_states

2
examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py
View File

@@ -889,7 +889,7 @@ def main(args):
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
split_batches=True, # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
split_batches=True, # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be divided by the number of processes assuming batches are multiplied by the number of processes
)
# Make one log on every process with the configuration for debugging.

2
examples/consistency_distillation/train_lcm_distill_lora_sdxl.py
View File

@@ -721,7 +721,7 @@ def main(args):
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
split_batches=True, # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
split_batches=True, # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be divided by the number of processes assuming batches are multiplied by the number of processes
)
# Make one log on every process with the configuration for debugging.

2
examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py
View File

@@ -884,7 +884,7 @@ def main(args):
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
split_batches=True, # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
split_batches=True, # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be divided by the number of processes assuming batches are multiplied by the number of processes
)
# Make one log on every process with the configuration for debugging.

2
examples/consistency_distillation/train_lcm_distill_sd_wds.py
View File

@@ -854,7 +854,7 @@ def main(args):
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
split_batches=True, # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
split_batches=True, # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be divided by the number of processes assuming batches are multiplied by the number of processes
)
# Make one log on every process with the configuration for debugging.

2
examples/consistency_distillation/train_lcm_distill_sdxl_wds.py
View File

@@ -894,7 +894,7 @@ def main(args):
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
split_batches=True, # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
split_batches=True, # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be divided by the number of processes assuming batches are multiplied by the number of processes
)
# Make one log on every process with the configuration for debugging.

2
examples/dreambooth/train_dreambooth_flux.py
View File

@@ -1634,7 +1634,7 @@ def main(args):
# Predict the noise residual
model_pred = transformer(
hidden_states=packed_noisy_model_input,
# YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
# YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
timestep=timesteps / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,

2
examples/dreambooth/train_dreambooth_lora_flux.py
View File

@@ -1749,7 +1749,7 @@ def main(args):
# Predict the noise residual
model_pred = transformer(
hidden_states=packed_noisy_model_input,
# YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
# YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
timestep=timesteps / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,

2
examples/research_projects/flux_lora_quantization/train_dreambooth_lora_flux_miniature.py
View File

@@ -1088,7 +1088,7 @@ def main(args):
text_ids = batch["text_ids"].to(device=accelerator.device, dtype=weight_dtype)
model_pred = transformer(
hidden_states=packed_noisy_model_input,
# YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
# YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
timestep=timesteps / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,