diff --git a/docs/source/en/api/pipelines/wan.md b/docs/source/en/api/pipelines/wan.md index 12afe4d2c436..dbf3b973d79c 100644 --- a/docs/source/en/api/pipelines/wan.md +++ b/docs/source/en/api/pipelines/wan.md @@ -24,7 +24,7 @@ ## Generating Videos with Wan 2.1 -We will first need to install some addtional dependencies. +We will first need to install some additional dependencies. ```shell pip install -u ftfy imageio-ffmpeg imageio diff --git a/docs/source/en/training/cogvideox.md b/docs/source/en/training/cogvideox.md index 657e58bfd5eb..c2b0f9ea1b50 100644 --- a/docs/source/en/training/cogvideox.md +++ b/docs/source/en/training/cogvideox.md @@ -216,7 +216,7 @@ Setting the `` is not necessary. From some limited experimentation, we > - The original repository uses a `lora_alpha` of `1`. We found this not suitable in many runs, possibly due to difference in modeling backends and training settings. Our recommendation is to set to the `lora_alpha` to either `rank` or `rank // 2`. > - If you're training on data whose captions generate bad results with the original model, a `rank` of 64 and above is good and also the recommendation by the team behind CogVideoX. If the generations are already moderately good on your training captions, a `rank` of 16/32 should work. We found that setting the rank too low, say `4`, is not ideal and doesn't produce promising results. > - The authors of CogVideoX recommend 4000 training steps and 100 training videos overall to achieve the best result. While that might yield the best results, we found from our limited experimentation that 2000 steps and 25 videos could also be sufficient. -> - When using the Prodigy opitimizer for training, one can follow the recommendations from [this](https://huggingface.co/blog/sdxl_lora_advanced_script) blog. Prodigy tends to overfit quickly. From my very limited testing, I found a learning rate of `0.5` to be suitable in addition to `--prodigy_use_bias_correction`, `prodigy_safeguard_warmup` and `--prodigy_decouple`. +> - When using the Prodigy optimizer for training, one can follow the recommendations from [this](https://huggingface.co/blog/sdxl_lora_advanced_script) blog. Prodigy tends to overfit quickly. From my very limited testing, I found a learning rate of `0.5` to be suitable in addition to `--prodigy_use_bias_correction`, `prodigy_safeguard_warmup` and `--prodigy_decouple`. > - The recommended learning rate by the CogVideoX authors and from our experimentation with Adam/AdamW is between `1e-3` and `1e-4` for a dataset of 25+ videos. > > Note that our testing is not exhaustive due to limited time for exploration. Our recommendation would be to play around with the different knobs and dials to find the best settings for your data. diff --git a/docs/source/en/training/dreambooth.md b/docs/source/en/training/dreambooth.md index 932d73ce8fb9..cfc23fe246f8 100644 --- a/docs/source/en/training/dreambooth.md +++ b/docs/source/en/training/dreambooth.md @@ -589,7 +589,7 @@ For stage 2 of DeepFloyd IF with DreamBooth, pay attention to these parameters: * `--learning_rate=5e-6`, use a lower learning rate with a smaller effective batch size * `--resolution=256`, the expected resolution for the upscaler -* `--train_batch_size=2` and `--gradient_accumulation_steps=6`, to effectively train on images wiht faces requires larger batch sizes +* `--train_batch_size=2` and `--gradient_accumulation_steps=6`, to effectively train on images with faces requires larger batch sizes ```bash export MODEL_NAME="DeepFloyd/IF-II-L-v1.0" diff --git a/docs/source/en/training/t2i_adapters.md b/docs/source/en/training/t2i_adapters.md index eef401ce8fb3..24819cdfb0ed 100644 --- a/docs/source/en/training/t2i_adapters.md +++ b/docs/source/en/training/t2i_adapters.md @@ -89,7 +89,7 @@ Many of the basic and important parameters are described in the [Text-to-image]( As with the script parameters, a walkthrough of the training script is provided in the [Text-to-image](text2image#training-script) training guide. Instead, this guide takes a look at the T2I-Adapter relevant parts of the script. -The training script begins by preparing the dataset. This incudes [tokenizing](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L674) the prompt and [applying transforms](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L714) to the images and conditioning images. +The training script begins by preparing the dataset. This includes [tokenizing](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L674) the prompt and [applying transforms](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L714) to the images and conditioning images. ```py conditioning_image_transforms = transforms.Compose( diff --git a/examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py b/examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py index b756fba7d659..bdb9f99f31fb 100644 --- a/examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py +++ b/examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py @@ -2181,7 +2181,7 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32): # 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, diff --git a/examples/community/README.md b/examples/community/README.md index 7f58e325b7ae..ee823873c7b0 100644 --- a/examples/community/README.md +++ b/examples/community/README.md @@ -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 diff --git a/examples/community/dps_pipeline.py b/examples/community/dps_pipeline.py index a0bf3e0ad33d..7b349f6693e7 100755 --- a/examples/community/dps_pipeline.py +++ b/examples/community/dps_pipeline.py @@ -312,9 +312,9 @@ def contributions(self, in_length, out_length, scale, kernel, kernel_width, anti # 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. diff --git a/examples/community/fresco_v2v.py b/examples/community/fresco_v2v.py index d6c2683f1d86..052130cd93f6 100644 --- a/examples/community/fresco_v2v.py +++ b/examples/community/fresco_v2v.py @@ -351,7 +351,7 @@ def 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) """ diff --git a/examples/community/hd_painter.py b/examples/community/hd_painter.py index 9d7b95b62c6e..9711b40b117e 100644 --- a/examples/community/hd_painter.py +++ b/examples/community/hd_painter.py @@ -34,7 +34,7 @@ def __call__( 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 diff --git a/examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py b/examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py index 8611ee60358a..3414640f55cf 100644 --- a/examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py +++ b/examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py @@ -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. diff --git a/examples/consistency_distillation/train_lcm_distill_lora_sdxl.py b/examples/consistency_distillation/train_lcm_distill_lora_sdxl.py index 94a315941519..cb8c425bcbec 100644 --- a/examples/consistency_distillation/train_lcm_distill_lora_sdxl.py +++ b/examples/consistency_distillation/train_lcm_distill_lora_sdxl.py @@ -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. diff --git a/examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py b/examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py index d3cf6879ea54..d636c145ffd9 100644 --- a/examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py +++ b/examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py @@ -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. diff --git a/examples/consistency_distillation/train_lcm_distill_sd_wds.py b/examples/consistency_distillation/train_lcm_distill_sd_wds.py index 59e2aa8a6e39..50a3d4ebd190 100644 --- a/examples/consistency_distillation/train_lcm_distill_sd_wds.py +++ b/examples/consistency_distillation/train_lcm_distill_sd_wds.py @@ -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. diff --git a/examples/consistency_distillation/train_lcm_distill_sdxl_wds.py b/examples/consistency_distillation/train_lcm_distill_sdxl_wds.py index 3435675dc3b4..a719db9a895d 100644 --- a/examples/consistency_distillation/train_lcm_distill_sdxl_wds.py +++ b/examples/consistency_distillation/train_lcm_distill_sdxl_wds.py @@ -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. diff --git a/examples/dreambooth/train_dreambooth_flux.py b/examples/dreambooth/train_dreambooth_flux.py index 6fe24634b5e3..02b83bb6b175 100644 --- a/examples/dreambooth/train_dreambooth_flux.py +++ b/examples/dreambooth/train_dreambooth_flux.py @@ -1634,7 +1634,7 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32): # 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, diff --git a/examples/dreambooth/train_dreambooth_lora_flux.py b/examples/dreambooth/train_dreambooth_lora_flux.py index 9de4973b6f64..193c5affe600 100644 --- a/examples/dreambooth/train_dreambooth_lora_flux.py +++ b/examples/dreambooth/train_dreambooth_lora_flux.py @@ -1749,7 +1749,7 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32): # 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, diff --git a/examples/research_projects/flux_lora_quantization/train_dreambooth_lora_flux_miniature.py b/examples/research_projects/flux_lora_quantization/train_dreambooth_lora_flux_miniature.py index cc535bbaaa85..ca6166405968 100644 --- a/examples/research_projects/flux_lora_quantization/train_dreambooth_lora_flux_miniature.py +++ b/examples/research_projects/flux_lora_quantization/train_dreambooth_lora_flux_miniature.py @@ -1088,7 +1088,7 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32): 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, diff --git a/src/diffusers/models/downsampling.py b/src/diffusers/models/downsampling.py index 3ac8953e3dcc..1e7366359ff3 100644 --- a/src/diffusers/models/downsampling.py +++ b/src/diffusers/models/downsampling.py @@ -286,7 +286,7 @@ def forward(self, inputs: torch.Tensor) -> torch.Tensor: class CogVideoXDownsample3D(nn.Module): - # Todo: Wait for paper relase. + # Todo: Wait for paper release. r""" A 3D Downsampling layer using in [CogVideoX]() by Tsinghua University & ZhipuAI diff --git a/src/diffusers/models/upsampling.py b/src/diffusers/models/upsampling.py index af04ae4b93cf..f2f07a5824b8 100644 --- a/src/diffusers/models/upsampling.py +++ b/src/diffusers/models/upsampling.py @@ -358,7 +358,7 @@ def forward(self, inputs: torch.Tensor) -> torch.Tensor: class CogVideoXUpsample3D(nn.Module): r""" - A 3D Upsample layer using in CogVideoX by Tsinghua University & ZhipuAI # Todo: Wait for paper relase. + A 3D Upsample layer using in CogVideoX by Tsinghua University & ZhipuAI # Todo: Wait for paper release. Args: in_channels (`int`): diff --git a/src/diffusers/pipelines/allegro/pipeline_allegro.py b/src/diffusers/pipelines/allegro/pipeline_allegro.py index cb36a7a672de..1fc1c0901ab3 100644 --- a/src/diffusers/pipelines/allegro/pipeline_allegro.py +++ b/src/diffusers/pipelines/allegro/pipeline_allegro.py @@ -514,7 +514,7 @@ def _clean_caption(self, caption): # & caption = re.sub(r"&", "", caption) - # ip adresses: + # ip addresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: diff --git a/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py b/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py index 150978de6e5e..4c39381231c5 100644 --- a/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py +++ b/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py @@ -484,7 +484,7 @@ def _clean_caption(self, caption): # & caption = re.sub(r"&", "", caption) - # ip adresses: + # ip addresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: diff --git a/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py b/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py index a92d7be6a11c..80f94aa5c7a2 100644 --- a/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py +++ b/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py @@ -528,7 +528,7 @@ def _clean_caption(self, caption): # & caption = re.sub(r"&", "", caption) - # ip adresses: + # ip addresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: diff --git a/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py b/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py index b23ea39bb292..160849cd4e8d 100644 --- a/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py +++ b/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py @@ -281,7 +281,7 @@ def _clean_caption(self, caption): # & caption = re.sub(r"&", "", caption) - # ip adresses: + # ip addresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: diff --git a/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py b/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py index 030821b789aa..36559c2e1763 100644 --- a/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py +++ b/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py @@ -568,7 +568,7 @@ def _clean_caption(self, caption): # & caption = re.sub(r"&", "", caption) - # ip adresses: + # ip addresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: diff --git a/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py b/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py index bdad9c29b18f..3c71bd96f14e 100644 --- a/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py +++ b/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py @@ -283,7 +283,7 @@ def _clean_caption(self, caption): # & caption = re.sub(r"&", "", caption) - # ip adresses: + # ip addresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: diff --git a/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py b/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py index 012c4ca6d448..849c68319056 100644 --- a/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py +++ b/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py @@ -239,7 +239,7 @@ def _clean_caption(self, caption): # & caption = re.sub(r"&", "", caption) - # ip adresses: + # ip addresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py index 06db871daf62..7225f2f234be 100644 --- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py +++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py @@ -574,7 +574,7 @@ def edit_model( idxs_replace.append(76) idxs_replaces.append(idxs_replace) - # prepare batch: for each pair of setences, old context and new values + # prepare batch: for each pair of sentences, old context and new values contexts, valuess = [], [] for old_emb, new_emb, idxs_replace in zip(old_embs, new_embs, idxs_replaces): context = old_emb.detach() diff --git a/src/diffusers/pipelines/latte/pipeline_latte.py b/src/diffusers/pipelines/latte/pipeline_latte.py index e9a95e8be45c..977e648d85a3 100644 --- a/src/diffusers/pipelines/latte/pipeline_latte.py +++ b/src/diffusers/pipelines/latte/pipeline_latte.py @@ -501,7 +501,7 @@ def _clean_caption(self, caption): # & caption = re.sub(r"&", "", caption) - # ip adresses: + # ip addresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 816213f105cb..22ff926afaff 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -534,7 +534,7 @@ def _clean_caption(self, caption): # & caption = re.sub(r"&", "", caption) - # ip adresses: + # ip addresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: diff --git a/src/diffusers/pipelines/pag/pipeline_pag_pixart_sigma.py b/src/diffusers/pipelines/pag/pipeline_pag_pixart_sigma.py index affda7e18add..71ee5879d01e 100644 --- a/src/diffusers/pipelines/pag/pipeline_pag_pixart_sigma.py +++ b/src/diffusers/pipelines/pag/pipeline_pag_pixart_sigma.py @@ -488,7 +488,7 @@ def _clean_caption(self, caption): # & caption = re.sub(r"&", "", caption) - # ip adresses: + # ip addresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sana.py b/src/diffusers/pipelines/pag/pipeline_pag_sana.py index 030ab6db7391..a233f70136af 100644 --- a/src/diffusers/pipelines/pag/pipeline_pag_sana.py +++ b/src/diffusers/pipelines/pag/pipeline_pag_sana.py @@ -524,7 +524,7 @@ def _clean_caption(self, caption): # & caption = re.sub(r"&", "", caption) - # ip adresses: + # ip addresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: diff --git a/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py b/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py index 988e049dd684..79e007fea319 100644 --- a/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py +++ b/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py @@ -598,7 +598,7 @@ def _clean_caption(self, caption): # & caption = re.sub(r"&", "", caption) - # ip adresses: + # ip addresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: diff --git a/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py b/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py index 4b4b85e63ed5..14f4bdcce87f 100644 --- a/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py +++ b/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py @@ -525,7 +525,7 @@ def _clean_caption(self, caption): # & caption = re.sub(r"&", "", caption) - # ip adresses: + # ip addresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: diff --git a/src/diffusers/pipelines/sana/pipeline_sana.py b/src/diffusers/pipelines/sana/pipeline_sana.py index 80e0d9bb933f..34b84a89e60d 100644 --- a/src/diffusers/pipelines/sana/pipeline_sana.py +++ b/src/diffusers/pipelines/sana/pipeline_sana.py @@ -600,7 +600,7 @@ def _clean_caption(self, caption): # & caption = re.sub(r"&", "", caption) - # ip adresses: + # ip addresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: diff --git a/src/diffusers/pipelines/sana/pipeline_sana_controlnet.py b/src/diffusers/pipelines/sana/pipeline_sana_controlnet.py index 21547d7d4974..a7c7d027fbda 100644 --- a/src/diffusers/pipelines/sana/pipeline_sana_controlnet.py +++ b/src/diffusers/pipelines/sana/pipeline_sana_controlnet.py @@ -615,7 +615,7 @@ def _clean_caption(self, caption): # & caption = re.sub(r"&", "", caption) - # ip adresses: + # ip addresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: diff --git a/src/diffusers/pipelines/sana/pipeline_sana_sprint.py b/src/diffusers/pipelines/sana/pipeline_sana_sprint.py index 30cc8d5f32d0..03b306b539c0 100644 --- a/src/diffusers/pipelines/sana/pipeline_sana_sprint.py +++ b/src/diffusers/pipelines/sana/pipeline_sana_sprint.py @@ -491,7 +491,7 @@ def _clean_caption(self, caption): # & caption = re.sub(r"&", "", caption) - # ip adresses: + # ip addresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: diff --git a/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py b/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py index 86ef01784057..ac9b8ce19cd2 100644 --- a/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py +++ b/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py @@ -175,7 +175,7 @@ class StableDiffusionGLIGENTextImagePipeline(DiffusionPipeline, StableDiffusionM tokenizer ([`~transformers.CLIPTokenizer`]): A `CLIPTokenizer` to tokenize text. processor ([`~transformers.CLIPProcessor`]): - A `CLIPProcessor` to procces reference image. + A `CLIPProcessor` to process reference image. image_encoder ([`~transformers.CLIPVisionModelWithProjection`]): Frozen image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)). image_project ([`CLIPImageProjection`]): diff --git a/src/diffusers/schedulers/scheduling_deis_multistep.py b/src/diffusers/schedulers/scheduling_deis_multistep.py index 6a653f183bba..af9a7f79e305 100644 --- a/src/diffusers/schedulers/scheduling_deis_multistep.py +++ b/src/diffusers/schedulers/scheduling_deis_multistep.py @@ -486,7 +486,7 @@ def convert_model_output( if len(args) > 1: sample = args[1] else: - raise ValueError("missing `sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep is not None: deprecate( "timesteps", @@ -549,7 +549,7 @@ def deis_first_order_update( if len(args) > 2: sample = args[2] else: - raise ValueError(" missing `sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep is not None: deprecate( "timesteps", @@ -603,7 +603,7 @@ def multistep_deis_second_order_update( if len(args) > 2: sample = args[2] else: - raise ValueError(" missing `sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep_list is not None: deprecate( "timestep_list", @@ -673,7 +673,7 @@ def multistep_deis_third_order_update( if len(args) > 2: sample = args[2] else: - raise ValueError(" missing`sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep_list is not None: deprecate( "timestep_list", diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py b/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py index ed60dd4eaee1..4c59d060cf50 100644 --- a/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py +++ b/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py @@ -646,7 +646,7 @@ def convert_model_output( if len(args) > 1: sample = args[1] else: - raise ValueError("missing `sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep is not None: deprecate( "timesteps", @@ -741,7 +741,7 @@ def dpm_solver_first_order_update( if len(args) > 2: sample = args[2] else: - raise ValueError(" missing `sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep is not None: deprecate( "timesteps", @@ -810,7 +810,7 @@ def multistep_dpm_solver_second_order_update( if len(args) > 2: sample = args[2] else: - raise ValueError(" missing `sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep_list is not None: deprecate( "timestep_list", @@ -934,7 +934,7 @@ def multistep_dpm_solver_third_order_update( if len(args) > 2: sample = args[2] else: - raise ValueError(" missing`sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep_list is not None: deprecate( "timestep_list", diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py b/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py index 971817f7b777..011294c6f23d 100644 --- a/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py +++ b/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py @@ -513,7 +513,7 @@ def convert_model_output( if len(args) > 1: sample = args[1] else: - raise ValueError("missing `sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep is not None: deprecate( "timesteps", @@ -609,7 +609,7 @@ def dpm_solver_first_order_update( if len(args) > 2: sample = args[2] else: - raise ValueError(" missing `sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep is not None: deprecate( "timesteps", @@ -679,7 +679,7 @@ def multistep_dpm_solver_second_order_update( if len(args) > 2: sample = args[2] else: - raise ValueError(" missing `sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep_list is not None: deprecate( "timestep_list", @@ -804,7 +804,7 @@ def multistep_dpm_solver_third_order_update( if len(args) > 2: sample = args[2] else: - raise ValueError(" missing`sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep_list is not None: deprecate( "timestep_list", diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py b/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py index bf68d6c99bd6..daae50627d87 100644 --- a/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py +++ b/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py @@ -584,7 +584,7 @@ def convert_model_output( if len(args) > 1: sample = args[1] else: - raise ValueError("missing `sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep is not None: deprecate( "timesteps", @@ -681,7 +681,7 @@ def dpm_solver_first_order_update( if len(args) > 2: sample = args[2] else: - raise ValueError(" missing `sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep is not None: deprecate( "timesteps", @@ -746,7 +746,7 @@ def singlestep_dpm_solver_second_order_update( if len(args) > 2: sample = args[2] else: - raise ValueError(" missing `sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep_list is not None: deprecate( "timestep_list", @@ -858,7 +858,7 @@ def singlestep_dpm_solver_third_order_update( if len(args) > 2: sample = args[2] else: - raise ValueError(" missing`sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep_list is not None: deprecate( "timestep_list", @@ -981,12 +981,12 @@ def singlestep_dpm_solver_update( if len(args) > 2: sample = args[2] else: - raise ValueError(" missing`sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if order is None: if len(args) > 3: order = args[3] else: - raise ValueError(" missing `order` as a required keyward argument") + raise ValueError("missing `order` as a required keyword argument") if timestep_list is not None: deprecate( "timestep_list", diff --git a/src/diffusers/schedulers/scheduling_sasolver.py b/src/diffusers/schedulers/scheduling_sasolver.py index d45c93880bc5..c741955699c4 100644 --- a/src/diffusers/schedulers/scheduling_sasolver.py +++ b/src/diffusers/schedulers/scheduling_sasolver.py @@ -522,7 +522,7 @@ def convert_model_output( if len(args) > 1: sample = args[1] else: - raise ValueError("missing `sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep is not None: deprecate( "timesteps", @@ -812,22 +812,22 @@ def stochastic_adams_bashforth_update( if len(args) > 1: sample = args[1] else: - raise ValueError(" missing `sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if noise is None: if len(args) > 2: noise = args[2] else: - raise ValueError(" missing `noise` as a required keyward argument") + raise ValueError("missing `noise` as a required keyword argument") if order is None: if len(args) > 3: order = args[3] else: - raise ValueError(" missing `order` as a required keyward argument") + raise ValueError("missing `order` as a required keyword argument") if tau is None: if len(args) > 4: tau = args[4] else: - raise ValueError(" missing `tau` as a required keyward argument") + raise ValueError("missing `tau` as a required keyword argument") if prev_timestep is not None: deprecate( "prev_timestep", @@ -943,27 +943,27 @@ def stochastic_adams_moulton_update( if len(args) > 1: last_sample = args[1] else: - raise ValueError(" missing`last_sample` as a required keyward argument") + raise ValueError("missing `last_sample` as a required keyword argument") if last_noise is None: if len(args) > 2: last_noise = args[2] else: - raise ValueError(" missing`last_noise` as a required keyward argument") + raise ValueError("missing `last_noise` as a required keyword argument") if this_sample is None: if len(args) > 3: this_sample = args[3] else: - raise ValueError(" missing`this_sample` as a required keyward argument") + raise ValueError("missing `this_sample` as a required keyword argument") if order is None: if len(args) > 4: order = args[4] else: - raise ValueError(" missing`order` as a required keyward argument") + raise ValueError("missing `order` as a required keyword argument") if tau is None: if len(args) > 5: tau = args[5] else: - raise ValueError(" missing`tau` as a required keyward argument") + raise ValueError("missing `tau` as a required keyword argument") if this_timestep is not None: deprecate( "this_timestep", diff --git a/src/diffusers/schedulers/scheduling_unipc_multistep.py b/src/diffusers/schedulers/scheduling_unipc_multistep.py index 01500426305c..d7f795dff8fc 100644 --- a/src/diffusers/schedulers/scheduling_unipc_multistep.py +++ b/src/diffusers/schedulers/scheduling_unipc_multistep.py @@ -596,7 +596,7 @@ def convert_model_output( if len(args) > 1: sample = args[1] else: - raise ValueError("missing `sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if timestep is not None: deprecate( "timesteps", @@ -672,12 +672,12 @@ def multistep_uni_p_bh_update( if len(args) > 1: sample = args[1] else: - raise ValueError(" missing `sample` as a required keyward argument") + raise ValueError("missing `sample` as a required keyword argument") if order is None: if len(args) > 2: order = args[2] else: - raise ValueError(" missing `order` as a required keyward argument") + raise ValueError("missing `order` as a required keyword argument") if prev_timestep is not None: deprecate( "prev_timestep", @@ -804,17 +804,17 @@ def multistep_uni_c_bh_update( if len(args) > 1: last_sample = args[1] else: - raise ValueError(" missing`last_sample` as a required keyward argument") + raise ValueError("missing `last_sample` as a required keyword argument") if this_sample is None: if len(args) > 2: this_sample = args[2] else: - raise ValueError(" missing`this_sample` as a required keyward argument") + raise ValueError("missing `this_sample` as a required keyword argument") if order is None: if len(args) > 3: order = args[3] else: - raise ValueError(" missing`order` as a required keyward argument") + raise ValueError("missing `order` as a required keyword argument") if this_timestep is not None: deprecate( "this_timestep",