From db627652b15cb3a76a5a0b79e5b95dc68f02b117 Mon Sep 17 00:00:00 2001 From: sayakpaul Date: Mon, 12 Jan 2026 13:57:55 +0530 Subject: [PATCH] up --- LICENSE | 2 +- .../en/using-diffusers/custom_pipeline_overview.md | 2 +- docs/source/en/using-diffusers/other-formats.md | 2 +- .../train_dreambooth_lora_flux_advanced.py | 2 +- .../train_dreambooth_lora_sd15_advanced.py | 2 +- .../train_dreambooth_lora_sdxl_advanced.py | 2 +- examples/research_projects/sdxl_flax/README.md | 2 +- .../research_projects/sdxl_flax/sdxl_single_aot.py | 2 +- .../modular_pipelines/modular_pipeline.py | 2 +- .../pipelines/audioldm2/modeling_audioldm2.py | 14 ++++++-------- .../versatile_diffusion/modeling_text_unet.py | 4 ++-- src/diffusers/training_utils.py | 6 +++--- .../hunyuan_video/test_hunyuan_image2video.py | 4 ++-- .../test_hunyuan_skyreels_image2video.py | 4 ++-- .../pipelines/hunyuan_video/test_hunyuan_video.py | 4 ++-- .../hunyuan_video/test_hunyuan_video_framepack.py | 4 ++-- tests/pipelines/pag/test_pag_sana.py | 4 ++-- tests/pipelines/sana/test_sana.py | 4 ++-- tests/pipelines/sana/test_sana_controlnet.py | 4 ++-- tests/pipelines/sana/test_sana_sprint.py | 4 ++-- tests/pipelines/sana/test_sana_sprint_img2img.py | 4 ++-- tests/pipelines/sana_video/test_sana_video.py | 4 ++-- tests/pipelines/sana_video/test_sana_video_i2v.py | 4 ++-- 23 files changed, 42 insertions(+), 44 deletions(-) diff --git a/LICENSE b/LICENSE index 261eeb9e9f..038e32f644 100644 --- a/LICENSE +++ b/LICENSE @@ -144,7 +144,7 @@ agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or - implied, including, without limitation, any warranties or conditions + implied, including, without limitation, Any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any diff --git a/docs/source/en/using-diffusers/custom_pipeline_overview.md b/docs/source/en/using-diffusers/custom_pipeline_overview.md index b087e57056..012fb911f6 100644 --- a/docs/source/en/using-diffusers/custom_pipeline_overview.md +++ b/docs/source/en/using-diffusers/custom_pipeline_overview.md @@ -121,7 +121,7 @@ from showone_unet_3d_condition import ShowOneUNet3DConditionModel unet = ShowOneUNet3DConditionModel.from_pretrained(pipeline_id, subfolder="unet") ``` -3. Load the custom pipeline code (already implemented in [pipeline_t2v_base_pixel.py](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/pipeline_t2v_base_pixel.py)). This script contains a custom `TextToVideoIFPipeline` class for generating videos from text. Like the custom UNet, any code required for `TextToVideIFPipeline` should be placed in `pipeline_t2v_base_pixel.py`. +3. Load the custom pipeline code (already implemented in [pipeline_t2v_base_pixel.py](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/pipeline_t2v_base_pixel.py)). This script contains a custom `TextToVideoIFPipeline` class for generating videos from text. Like the custom UNet, Any code required for `TextToVideIFPipeline` should be placed in `pipeline_t2v_base_pixel.py`. Initialize `TextToVideoIFPipeline` with `ShowOneUNet3DConditionModel`. diff --git a/docs/source/en/using-diffusers/other-formats.md b/docs/source/en/using-diffusers/other-formats.md index b6e333ed77..8f4c9077d1 100644 --- a/docs/source/en/using-diffusers/other-formats.md +++ b/docs/source/en/using-diffusers/other-formats.md @@ -103,7 +103,7 @@ model = UNet2DConditionModel.from_single_file(ckpt_path, upcast_attention=True) ### Local files -The [`~loaders.FromSingleFileMixin.from_single_file`] method attempts to configure a pipeline or model by inferring the model type from the keys in the checkpoint file. For example, any single file checkpoint based on the Stable Diffusion XL base model is configured from [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0). +The [`~loaders.FromSingleFileMixin.from_single_file`] method attempts to configure a pipeline or model by inferring the model type from the keys in the checkpoint file. For example, Any single file checkpoint based on the Stable Diffusion XL base model is configured from [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0). If you're working with local files, download the config files with the [`~huggingface_hub.snapshot_download`] method and the model checkpoint with [`~huggingface_hub.hf_hub_download`]. These files are downloaded to your [cache directory](https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache), but you can download them to a specific directory with the `local_dir` argument. 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 05f2b1ee17..6cdf10e6d6 100644 --- a/examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py +++ b/examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py @@ -945,7 +945,7 @@ class TokenEmbeddingsHandler: new_token_embeddings = embeds.weight.data[train_ids] # New tokens for each text encoder are saved under "clip_l" (for text_encoder 0), - # Note: When loading with diffusers, any name can work - simply specify in inference + # Note: When loading with diffusers, Any name can work - simply specify in inference tensors[idx_to_text_encoder_name[idx]] = new_token_embeddings # tensors[f"text_encoders_{idx}"] = new_token_embeddings diff --git a/examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py b/examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py index 8fba00afc3..78addb1fe0 100644 --- a/examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py +++ b/examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py @@ -801,7 +801,7 @@ class TokenEmbeddingsHandler: # New tokens for each text encoder are saved under "clip_l" (for text_encoder 0), "clip_g" (for # text_encoder 1) to keep compatible with the ecosystem. - # Note: When loading with diffusers, any name can work - simply specify in inference + # Note: When loading with diffusers, Any name can work - simply specify in inference tensors[idx_to_text_encoder_name[idx]] = new_token_embeddings # tensors[f"text_encoders_{idx}"] = new_token_embeddings diff --git a/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py b/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py index 8fb749d328..64d4882a85 100644 --- a/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py +++ b/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py @@ -966,7 +966,7 @@ class TokenEmbeddingsHandler: # New tokens for each text encoder are saved under "clip_l" (for text_encoder 0), "clip_g" (for # text_encoder 1) to keep compatible with the ecosystem. - # Note: When loading with diffusers, any name can work - simply specify in inference + # Note: When loading with diffusers, Any name can work - simply specify in inference tensors[idx_to_text_encoder_name[idx]] = new_token_embeddings # tensors[f"text_encoders_{idx}"] = new_token_embeddings diff --git a/examples/research_projects/sdxl_flax/README.md b/examples/research_projects/sdxl_flax/README.md index dfbe90e63b..e2c02d7bb6 100644 --- a/examples/research_projects/sdxl_flax/README.md +++ b/examples/research_projects/sdxl_flax/README.md @@ -231,7 +231,7 @@ images = generate(prompt, neg_prompt) print(f"First inference in {time.time() - start}") ``` -From this point forward, any calls to generate should result in a faster inference +From this point forward, Any calls to generate should result in a faster inference time and it won't change. ```python diff --git a/examples/research_projects/sdxl_flax/sdxl_single_aot.py b/examples/research_projects/sdxl_flax/sdxl_single_aot.py index 08bd13902a..a0f6b243b5 100644 --- a/examples/research_projects/sdxl_flax/sdxl_single_aot.py +++ b/examples/research_projects/sdxl_flax/sdxl_single_aot.py @@ -131,7 +131,7 @@ neg_prompt = "cartoon, illustration, animation. face. male, female" images = generate(prompt, neg_prompt) print(f"First inference in {time.time() - start}") -# 9. From this point forward, any calls to generate should result in a faster inference +# 9. From this point forward, Any calls to generate should result in a faster inference # time and it won't change. start = time.time() prompt = "photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang" diff --git a/src/diffusers/modular_pipelines/modular_pipeline.py b/src/diffusers/modular_pipelines/modular_pipeline.py index 112c93e112..53c1dd94e4 100644 --- a/src/diffusers/modular_pipelines/modular_pipeline.py +++ b/src/diffusers/modular_pipelines/modular_pipeline.py @@ -1616,7 +1616,7 @@ class ModularPipeline(ConfigMixin, PushToHubMixin): params[input_param.name] = input_param.default return params - def get_default_blocks_name(self, config_dict: Optional[dict[str, any]]) -> Optional[str]: + def get_default_blocks_name(self, config_dict: Optional[dict[str, Any]]) -> Optional[str]: return self.default_blocks_name @classmethod diff --git a/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py b/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py index 3f7ca56bb1..09aa0ad170 100644 --- a/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py +++ b/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py @@ -13,6 +13,7 @@ # limitations under the License. from dataclasses import dataclass +from typing import Any import torch import torch.nn as nn @@ -27,10 +28,7 @@ from ...models.attention_processor import ( AttnAddedKVProcessor, AttnProcessor, ) -from ...models.embeddings import ( - TimestepEmbedding, - Timesteps, -) +from ...models.embeddings import TimestepEmbedding, Timesteps from ...models.modeling_utils import ModelMixin from ...models.resnet import Downsample2D, ResnetBlock2D, Upsample2D from ...models.transformers.transformer_2d import Transformer2DModel @@ -619,7 +617,7 @@ class AudioLDM2UNet2DConditionModel(ModelMixin, AttentionMixin, ConfigMixin, UNe class_labels: torch.Tensor | None = None, timestep_cond: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, - cross_attention_kwargs: dict[str, any] | None = None, + cross_attention_kwargs: dict[str, Any] | None = None, encoder_attention_mask: torch.Tensor | None = None, return_dict: bool = True, encoder_hidden_states_1: torch.Tensor | None = None, @@ -1029,7 +1027,7 @@ class CrossAttnDownBlock2D(nn.Module): temb: torch.Tensor | None = None, encoder_hidden_states: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, - cross_attention_kwargs: dict[str, any] | None = None, + cross_attention_kwargs: dict[str, Any] | None = None, encoder_attention_mask: torch.Tensor | None = None, encoder_hidden_states_1: torch.Tensor | None = None, encoder_attention_mask_1: torch.Tensor | None = None, @@ -1191,7 +1189,7 @@ class UNetMidBlock2DCrossAttn(nn.Module): temb: torch.Tensor | None = None, encoder_hidden_states: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, - cross_attention_kwargs: dict[str, any] | None = None, + cross_attention_kwargs: dict[str, Any] | None = None, encoder_attention_mask: torch.Tensor | None = None, encoder_hidden_states_1: torch.Tensor | None = None, encoder_attention_mask_1: torch.Tensor | None = None, @@ -1341,7 +1339,7 @@ class CrossAttnUpBlock2D(nn.Module): res_hidden_states_tuple: tuple[torch.Tensor, ...], temb: torch.Tensor | None = None, encoder_hidden_states: torch.Tensor | None = None, - cross_attention_kwargs: dict[str, any] | None = None, + cross_attention_kwargs: dict[str, Any] | None = None, upsample_size: int | None = None, attention_mask: torch.Tensor | None = None, encoder_attention_mask: torch.Tensor | None = None, diff --git a/src/diffusers/pipelines/deprecated/versatile_diffusion/modeling_text_unet.py b/src/diffusers/pipelines/deprecated/versatile_diffusion/modeling_text_unet.py index 1c00510ee1..6e8ca68311 100644 --- a/src/diffusers/pipelines/deprecated/versatile_diffusion/modeling_text_unet.py +++ b/src/diffusers/pipelines/deprecated/versatile_diffusion/modeling_text_unet.py @@ -1042,7 +1042,7 @@ class UNetFlatConditionModel(ModelMixin, ConfigMixin): class_labels: torch.Tensor | None = None, timestep_cond: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, - cross_attention_kwargs: dict[str, any] | None = None, + cross_attention_kwargs: dict[str, Any] | None = None, added_cond_kwargs: dict[str, torch.Tensor] | None = None, down_block_additional_residuals: tuple[torch.Tensor] | None = None, mid_block_additional_residual: torch.Tensor | None = None, @@ -1698,7 +1698,7 @@ class CrossAttnDownBlockFlat(nn.Module): temb: torch.Tensor | None = None, encoder_hidden_states: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, - cross_attention_kwargs: dict[str, any] | None = None, + cross_attention_kwargs: dict[str, Any] | None = None, encoder_attention_mask: torch.Tensor | None = None, additional_residuals: torch.Tensor | None = None, ) -> tuple[torch.Tensor, tuple[torch.Tensor, ...]]: diff --git a/src/diffusers/training_utils.py b/src/diffusers/training_utils.py index a36455a43f..3c8042d692 100644 --- a/src/diffusers/training_utils.py +++ b/src/diffusers/training_utils.py @@ -436,7 +436,7 @@ def wrap_with_fsdp( offload: bool = True, use_orig_params: bool = True, limit_all_gathers: bool = True, - fsdp_kwargs: dict[str, any] | None = None, + fsdp_kwargs: dict[str, Any] | None = None, transformer_layer_cls: set[type[torch.nn.Module]] | None = None, ) -> FSDP: """ @@ -496,8 +496,8 @@ class EMAModel: inv_gamma: float | int = 1.0, power: float | int = 2 / 3, foreach: bool = False, - model_cls: any | None = None, - model_config: dict[str, any] | None = None, + model_cls: Any | None = None, + model_config: dict[str, Any] | None = None, **kwargs, ): """ diff --git a/tests/pipelines/hunyuan_video/test_hunyuan_image2video.py b/tests/pipelines/hunyuan_video/test_hunyuan_image2video.py index 27b5bde310..3c6c50791a 100644 --- a/tests/pipelines/hunyuan_video/test_hunyuan_image2video.py +++ b/tests/pipelines/hunyuan_video/test_hunyuan_image2video.py @@ -371,13 +371,13 @@ class HunyuanVideoImageToVideoPipelineFastTests( # TODO(aryan): Create a dummy gemma model with smol vocab size @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_consistent(self): pass @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_single_identical(self): pass diff --git a/tests/pipelines/hunyuan_video/test_hunyuan_skyreels_image2video.py b/tests/pipelines/hunyuan_video/test_hunyuan_skyreels_image2video.py index 7ebe797feb..0ac15d4c40 100644 --- a/tests/pipelines/hunyuan_video/test_hunyuan_skyreels_image2video.py +++ b/tests/pipelines/hunyuan_video/test_hunyuan_skyreels_image2video.py @@ -333,13 +333,13 @@ class HunyuanSkyreelsImageToVideoPipelineFastTests( # TODO(aryan): Create a dummy gemma model with smol vocab size @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_consistent(self): pass @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_single_identical(self): pass diff --git a/tests/pipelines/hunyuan_video/test_hunyuan_video.py b/tests/pipelines/hunyuan_video/test_hunyuan_video.py index 57a6daebad..719bb9e67f 100644 --- a/tests/pipelines/hunyuan_video/test_hunyuan_video.py +++ b/tests/pipelines/hunyuan_video/test_hunyuan_video.py @@ -346,13 +346,13 @@ class HunyuanVideoPipelineFastTests( # TODO(aryan): Create a dummy gemma model with smol vocab size @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_consistent(self): pass @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_single_identical(self): pass diff --git a/tests/pipelines/hunyuan_video/test_hunyuan_video_framepack.py b/tests/pipelines/hunyuan_video/test_hunyuan_video_framepack.py index 51c258b15c..f7f8908b6f 100644 --- a/tests/pipelines/hunyuan_video/test_hunyuan_video_framepack.py +++ b/tests/pipelines/hunyuan_video/test_hunyuan_video_framepack.py @@ -392,13 +392,13 @@ class HunyuanVideoFramepackPipelineFastTests( # TODO(aryan): Create a dummy gemma model with smol vocab size @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_consistent(self): pass @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_single_identical(self): pass diff --git a/tests/pipelines/pag/test_pag_sana.py b/tests/pipelines/pag/test_pag_sana.py index 5408595c72..366267dc09 100644 --- a/tests/pipelines/pag/test_pag_sana.py +++ b/tests/pipelines/pag/test_pag_sana.py @@ -325,13 +325,13 @@ class SanaPAGPipelineFastTests(PipelineTesterMixin, unittest.TestCase): # TODO(aryan): Create a dummy gemma model with smol vocab size @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_consistent(self): pass @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_single_identical(self): pass diff --git a/tests/pipelines/sana/test_sana.py b/tests/pipelines/sana/test_sana.py index f23303c966..fc533dd7bf 100644 --- a/tests/pipelines/sana/test_sana.py +++ b/tests/pipelines/sana/test_sana.py @@ -290,13 +290,13 @@ class SanaPipelineFastTests(PipelineTesterMixin, unittest.TestCase): # TODO(aryan): Create a dummy gemma model with smol vocab size @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_consistent(self): pass @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_single_identical(self): pass diff --git a/tests/pipelines/sana/test_sana_controlnet.py b/tests/pipelines/sana/test_sana_controlnet.py index df14d935ed..0f432a998d 100644 --- a/tests/pipelines/sana/test_sana_controlnet.py +++ b/tests/pipelines/sana/test_sana_controlnet.py @@ -309,13 +309,13 @@ class SanaControlNetPipelineFastTests(PipelineTesterMixin, unittest.TestCase): # TODO(aryan): Create a dummy gemma model with smol vocab size @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_consistent(self): pass @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_single_identical(self): pass diff --git a/tests/pipelines/sana/test_sana_sprint.py b/tests/pipelines/sana/test_sana_sprint.py index 0d45205ea8..9f88d0f344 100644 --- a/tests/pipelines/sana/test_sana_sprint.py +++ b/tests/pipelines/sana/test_sana_sprint.py @@ -283,13 +283,13 @@ class SanaSprintPipelineFastTests(PipelineTesterMixin, unittest.TestCase): # TODO(aryan): Create a dummy gemma model with smol vocab size @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_consistent(self): pass @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_single_identical(self): pass diff --git a/tests/pipelines/sana/test_sana_sprint_img2img.py b/tests/pipelines/sana/test_sana_sprint_img2img.py index 5de5c7f446..312e2b2b80 100644 --- a/tests/pipelines/sana/test_sana_sprint_img2img.py +++ b/tests/pipelines/sana/test_sana_sprint_img2img.py @@ -295,13 +295,13 @@ class SanaSprintImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase) # TODO(aryan): Create a dummy gemma model with smol vocab size @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_consistent(self): pass @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_single_identical(self): pass diff --git a/tests/pipelines/sana_video/test_sana_video.py b/tests/pipelines/sana_video/test_sana_video.py index 9f360a942a..dd897a848b 100644 --- a/tests/pipelines/sana_video/test_sana_video.py +++ b/tests/pipelines/sana_video/test_sana_video.py @@ -185,13 +185,13 @@ class SanaVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase): # TODO(aryan): Create a dummy gemma model with smol vocab size @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_consistent(self): pass @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_single_identical(self): pass diff --git a/tests/pipelines/sana_video/test_sana_video_i2v.py b/tests/pipelines/sana_video/test_sana_video_i2v.py index 36a646ca52..2232968eb2 100644 --- a/tests/pipelines/sana_video/test_sana_video_i2v.py +++ b/tests/pipelines/sana_video/test_sana_video_i2v.py @@ -196,13 +196,13 @@ class SanaImageToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase): # TODO(aryan): Create a dummy gemma model with smol vocab size @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_consistent(self): pass @unittest.skip( - "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." + "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_single_identical(self): pass