[Modular] Consolidate load_default_components into load_components (#12217)

* update

* Apply style fixes

* update

* update

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

Author: DN6

docs/source/en/modular_diffusers/components_manager.md

@@ -51,10 +51,10 @@ t2i_pipeline = t2i_blocks.init_pipeline(modular_repo_id, components_manager=comp
 </hfoption>
 </hfoptions>
 
-Components are only loaded and registered when using [`~ModularPipeline.load_components`] or [`~ModularPipeline.load_default_components`]. The example below uses [`~ModularPipeline.load_default_components`] to create a second pipeline that reuses all the components from the first one, and assigns it to a different collection
+Components are only loaded and registered when using [`~ModularPipeline.load_components`] or [`~ModularPipeline.load_components`]. The example below uses [`~ModularPipeline.load_components`] to create a second pipeline that reuses all the components from the first one, and assigns it to a different collection
 
 ```py
-pipe.load_default_components()
+pipe.load_components()
 pipe2 = ModularPipeline.from_pretrained("YiYiXu/modular-demo-auto", components_manager=comp, collection="test2")
 ```
 
@@ -187,4 +187,4 @@ comp.enable_auto_cpu_offload(device="cuda")
 
 All models begin on the CPU and [`ComponentsManager`] moves them to the appropriate device right before they're needed, and moves other models back to the CPU when GPU memory is low.
 
-You can set your own rules for which models to offload first.
+You can set your own rules for which models to offload first.

docs/source/en/modular_diffusers/guiders.md

@@ -75,13 +75,13 @@ Guiders that are already saved on the Hub with a `modular_model_index.json` file
 }
 ```
 
-The guider is only created after calling [`~ModularPipeline.load_default_components`] based on the loading specification in `modular_model_index.json`.
+The guider is only created after calling [`~ModularPipeline.load_components`] based on the loading specification in `modular_model_index.json`.
 
 ```py
 t2i_pipeline = t2i_blocks.init_pipeline("YiYiXu/modular-doc-guider")
 # not created during init
 assert t2i_pipeline.guider is None
-t2i_pipeline.load_default_components()
+t2i_pipeline.load_components()
 # loaded as PAG guider
 t2i_pipeline.guider
 ```
@@ -172,4 +172,4 @@ t2i_pipeline.push_to_hub("YiYiXu/modular-doc-guider")
 ```
 
 </hfoption>
-</hfoptions>
+</hfoptions>

docs/source/en/modular_diffusers/modular_pipeline.md

@@ -29,7 +29,7 @@ blocks = SequentialPipelineBlocks.from_blocks_dict(TEXT2IMAGE_BLOCKS)
 modular_repo_id = "YiYiXu/modular-loader-t2i-0704"
 pipeline = blocks.init_pipeline(modular_repo_id)
 
-pipeline.load_default_components(torch_dtype=torch.float16)
+pipeline.load_components(torch_dtype=torch.float16)
 pipeline.to("cuda")
 
 image = pipeline(prompt="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", output="images")[0]
@@ -49,7 +49,7 @@ blocks = SequentialPipelineBlocks.from_blocks_dict(IMAGE2IMAGE_BLOCKS)
 modular_repo_id = "YiYiXu/modular-loader-t2i-0704"
 pipeline = blocks.init_pipeline(modular_repo_id)
 
-pipeline.load_default_components(torch_dtype=torch.float16)
+pipeline.load_components(torch_dtype=torch.float16)
 pipeline.to("cuda")
 
 url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
@@ -73,7 +73,7 @@ blocks = SequentialPipelineBlocks.from_blocks_dict(INPAINT_BLOCKS)
 modular_repo_id = "YiYiXu/modular-loader-t2i-0704"
 pipeline = blocks.init_pipeline(modular_repo_id)
 
-pipeline.load_default_components(torch_dtype=torch.float16)
+pipeline.load_components(torch_dtype=torch.float16)
 pipeline.to("cuda")
 
 img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
@@ -176,15 +176,15 @@ diffdiff_pipeline = ModularPipeline.from_pretrained(modular_repo_id, trust_remot
 
 ## Loading components
 
-A [`ModularPipeline`] doesn't automatically instantiate with components. It only loads the configuration and component specifications. You can load all components with [`~ModularPipeline.load_default_components`] or only load specific components with [`~ModularPipeline.load_components`].
+A [`ModularPipeline`] doesn't automatically instantiate with components. It only loads the configuration and component specifications. You can load all components with [`~ModularPipeline.load_components`] or only load specific components with [`~ModularPipeline.load_components`].
 
 <hfoptions id="load">
-<hfoption id="load_default_components">
+<hfoption id="load_components">
 
 ```py
 import torch
 
-t2i_pipeline.load_default_components(torch_dtype=torch.float16)
+t2i_pipeline.load_components(torch_dtype=torch.float16)
 t2i_pipeline.to("cuda")
 ```
 
@@ -355,4 +355,4 @@ The [config.json](https://huggingface.co/YiYiXu/modular-diffdiff-0704/blob/main/
     "ModularPipelineBlocks": "block.DiffDiffBlocks"
   }
 }
-```
+```

docs/source/en/modular_diffusers/quickstart.md

@@ -173,9 +173,9 @@ print(dd_blocks)
 
 ## ModularPipeline
 
-Convert the [`SequentialPipelineBlocks`] into a [`ModularPipeline`] with the [`ModularPipeline.init_pipeline`] method. This initializes the expected components to load from a `modular_model_index.json` file. Explicitly load the components by calling [`ModularPipeline.load_default_components`].
+Convert the [`SequentialPipelineBlocks`] into a [`ModularPipeline`] with the [`ModularPipeline.init_pipeline`] method. This initializes the expected components to load from a `modular_model_index.json` file. Explicitly load the components by calling [`ModularPipeline.load_components`].
 
-It is a good idea to initialize the [`ComponentManager`] with the pipeline to help manage the different components. Once you call [`~ModularPipeline.load_default_components`], the components are registered to the [`ComponentManager`] and can be shared between workflows. The example below uses the `collection` argument to assign the components a `"diffdiff"` label for better organization.
+It is a good idea to initialize the [`ComponentManager`] with the pipeline to help manage the different components. Once you call [`~ModularPipeline.load_components`], the components are registered to the [`ComponentManager`] and can be shared between workflows. The example below uses the `collection` argument to assign the components a `"diffdiff"` label for better organization.
 
 ```py
 from diffusers.modular_pipelines import ComponentsManager
@@ -209,11 +209,11 @@ Use the [`sub_blocks.insert`] method to insert it into the [`ModularPipeline`].
 dd_blocks.sub_blocks.insert("ip_adapter", ip_adapter_block, 0)
 ```
 
-Call [`~ModularPipeline.init_pipeline`] to initialize a [`ModularPipeline`] and use [`~ModularPipeline.load_default_components`] to load the model components. Load and set the IP-Adapter to run the pipeline.
+Call [`~ModularPipeline.init_pipeline`] to initialize a [`ModularPipeline`] and use [`~ModularPipeline.load_components`] to load the model components. Load and set the IP-Adapter to run the pipeline.
 
 ```py
 dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff")
-dd_pipeline.load_default_components(torch_dtype=torch.float16)
+dd_pipeline.load_components(torch_dtype=torch.float16)
 dd_pipeline.loader.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
 dd_pipeline.loader.set_ip_adapter_scale(0.6)
 dd_pipeline = dd_pipeline.to(device)
@@ -260,14 +260,14 @@ class SDXLDiffDiffControlNetDenoiseStep(StableDiffusionXLDenoiseLoopWrapper):
 controlnet_denoise_block = SDXLDiffDiffControlNetDenoiseStep()
 ```
 
-Insert the `controlnet_input` block and replace the `denoise` block with the new `controlnet_denoise_block`. Initialize a [`ModularPipeline`] and [`~ModularPipeline.load_default_components`] into it.
+Insert the `controlnet_input` block and replace the `denoise` block with the new `controlnet_denoise_block`. Initialize a [`ModularPipeline`] and [`~ModularPipeline.load_components`] into it.
 
 ```py
 dd_blocks.sub_blocks.insert("controlnet_input", control_input_block, 7)
 dd_blocks.sub_blocks["denoise"] = controlnet_denoise_block
 
 dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff")
-dd_pipeline.load_default_components(torch_dtype=torch.float16)
+dd_pipeline.load_components(torch_dtype=torch.float16)
 dd_pipeline = dd_pipeline.to(device)
 
 control_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/diffdiff_tomato_canny.jpeg")
@@ -320,7 +320,7 @@ Call [`SequentialPipelineBlocks.from_blocks_dict`] to create a [`SequentialPipel
 ```py
 dd_auto_blocks = SequentialPipelineBlocks.from_blocks_dict(DIFFDIFF_AUTO_BLOCKS)
 dd_pipeline = dd_auto_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff")
-dd_pipeline.load_default_components(torch_dtype=torch.float16)
+dd_pipeline.load_components(torch_dtype=torch.float16)
 ```
 
 ## Share
@@ -340,5 +340,5 @@ from diffusers.modular_pipelines import ModularPipeline, ComponentsManager
 components = ComponentsManager()
 
 diffdiff_pipeline = ModularPipeline.from_pretrained("YiYiXu/modular-diffdiff-0704", trust_remote_code=True, components_manager=components, collection="diffdiff")
-diffdiff_pipeline.load_default_components(torch_dtype=torch.float16)
-```
+diffdiff_pipeline.load_components(torch_dtype=torch.float16)
+```

docs/source/zh/modular_diffusers/components_manager.md

@@ -48,10 +48,10 @@ t2i_pipeline = t2i_blocks.init_pipeline(modular_repo_id, components_manager=comp
 </hfoption>
 </hfoptions>
 
-组件仅在调用 [`~ModularPipeline.load_components`] 或 [`~ModularPipeline.load_default_components`] 时加载和注册。以下示例使用 [`~ModularPipeline.load_default_components`] 创建第二个管道，重用第一个管道的所有组件，并将其分配到不同的集合。
+组件仅在调用 [`~ModularPipeline.load_components`] 或 [`~ModularPipeline.load_components`] 时加载和注册。以下示例使用 [`~ModularPipeline.load_components`] 创建第二个管道，重用第一个管道的所有组件，并将其分配到不同的集合。
 
 ```py
-pipe.load_default_components()
+pipe.load_components()
 pipe2 = ModularPipeline.from_pretrained("YiYiXu/modular-demo-auto", components_manager=comp, collection="test2")
 ```
 
@@ -185,4 +185,4 @@ comp.enable_auto_cpu_offload(device="cuda")
 
 所有模型开始时都在 CPU 上，[`ComponentsManager`] 在需要它们之前将它们移动到适当的设备，并在 GPU 内存不足时将其他模型移回 CPU。
 
-您可以设置自己的规则来决定哪些模型要卸载。
+您可以设置自己的规则来决定哪些模型要卸载。

docs/source/zh/modular_diffusers/guiders.md

@@ -73,13 +73,13 @@ ComponentSpec(name='guider', type_hint=<class 'diffusers.guiders.perturbed_atten
 }
 ```
 
-引导器只有在调用 [`~ModularPipeline.load_default_components`] 之后才会创建，基于 `modular_model_index.json` 中的加载规范。
+引导器只有在调用 [`~ModularPipeline.load_components`] 之后才会创建，基于 `modular_model_index.json` 中的加载规范。
 
 ```py
 t2i_pipeline = t2i_blocks.init_pipeline("YiYiXu/modular-doc-guider")
 # 在初始化时未创建
 assert t2i_pipeline.guider is None
-t2i_pipeline.load_default_components()
+t2i_pipeline.load_components()
 # 加载为 PAG 引导器
 t2i_pipeline.guider
 ```
@@ -170,4 +170,4 @@ t2i_pipeline.push_to_hub("YiYiXu/modular-doc-guider")
 ```
 
 </hfoption>
-</hfoptions>
+</hfoptions>

docs/source/zh/modular_diffusers/modular_pipeline.md

@@ -28,7 +28,7 @@ blocks = SequentialPipelineBlocks.from_blocks_dict(TEXT2IMAGE_BLOCKS)
 modular_repo_id = "YiYiXu/modular-loader-t2i-0704"
 pipeline = blocks.init_pipeline(modular_repo_id)
 
-pipeline.load_default_components(torch_dtype=torch.float16)
+pipeline.load_components(torch_dtype=torch.float16)
 pipeline.to("cuda")
 
 image = pipeline(prompt="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", output="images")[0]
@@ -48,7 +48,7 @@ blocks = SequentialPipelineBlocks.from_blocks_dict(IMAGE2IMAGE_BLOCKS)
 modular_repo_id = "YiYiXu/modular-loader-t2i-0704"
 pipeline = blocks.init_pipeline(modular_repo_id)
 
-pipeline.load_default_components(torch_dtype=torch.float16)
+pipeline.load_components(torch_dtype=torch.float16)
 pipeline.to("cuda")
 
 url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
@@ -72,7 +72,7 @@ blocks = SequentialPipelineBlocks.from_blocks_dict(INPAINT_BLOCKS)
 modular_repo_id = "YiYiXu/modular-loader-t2i-0704"
 pipeline = blocks.init_pipeline(modular_repo_id)
 
-pipeline.load_default_components(torch_dtype=torch.float16)
+pipeline.load_components(torch_dtype=torch.float16)
 pipeline.to("cuda")
 
 img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
@@ -176,15 +176,15 @@ diffdiff_pipeline = ModularPipeline.from_pretrained(modular_repo_id, trust_remot
 
 ## 加载组件
 
-一个[`ModularPipeline`]不会自动实例化组件。它只加载配置和组件规范。您可以使用[`~ModularPipeline.load_default_components`]加载所有组件，或仅使用[`~ModularPipeline.load_components`]加载特定组件。
+一个[`ModularPipeline`]不会自动实例化组件。它只加载配置和组件规范。您可以使用[`~ModularPipeline.load_components`]加载所有组件，或仅使用[`~ModularPipeline.load_components`]加载特定组件。
 
 <hfoptions id="load">
-<hfoption id="load_default_components">
+<hfoption id="load_components">
 
 ```py
 import torch
 
-t2i_pipeline.load_default_components(torch_dtype=torch.float16)
+t2i_pipeline.load_components(torch_dtype=torch.float16)
 t2i_pipeline.to("cuda")
 ```
 

docs/source/zh/modular_diffusers/quickstart.md

@@ -175,7 +175,7 @@ print(dd_blocks)
 将 [`SequentialPipelineBlocks`] 转换为 [`ModularPipeline`]，使用 [`ModularPipeline.init_pipeline`] 方法。这会初始化从 `modular_model_index.json` 文件加载的预期组件。通过调用 [`ModularPipeline.load_defau
 lt_components`]。
 
-初始化[`ComponentManager`]时传入pipeline是一个好主意，以帮助管理不同的组件。一旦调用[`~ModularPipeline.load_default_components`]，组件就会被注册到[`ComponentManager`]中，并且可以在工作流之间共享。下面的例子使用`collection`参数为组件分配了一个`"diffdiff"`标签，以便更好地组织。
+初始化[`ComponentManager`]时传入pipeline是一个好主意，以帮助管理不同的组件。一旦调用[`~ModularPipeline.load_components`]，组件就会被注册到[`ComponentManager`]中，并且可以在工作流之间共享。下面的例子使用`collection`参数为组件分配了一个`"diffdiff"`标签，以便更好地组织。
 
 ```py
 from diffusers.modular_pipelines import ComponentsManager
@@ -209,11 +209,11 @@ ip_adapter_block = StableDiffusionXLAutoIPAdapterStep()
 dd_blocks.sub_blocks.insert("ip_adapter", ip_adapter_block, 0)
 ```
 
-调用[`~ModularPipeline.init_pipeline`]来初始化一个[`ModularPipeline`]，并使用[`~ModularPipeline.load_default_components`]加载模型组件。加载并设置IP-Adapter以运行pipeline。
+调用[`~ModularPipeline.init_pipeline`]来初始化一个[`ModularPipeline`]，并使用[`~ModularPipeline.load_components`]加载模型组件。加载并设置IP-Adapter以运行pipeline。
 
 ```py
 dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff")
-dd_pipeline.load_default_components(torch_dtype=torch.float16)
+dd_pipeline.load_components(torch_dtype=torch.float16)
 dd_pipeline.loader.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
 dd_pipeline.loader.set_ip_adapter_scale(0.6)
 dd_pipeline = dd_pipeline.to(device)
@@ -261,14 +261,14 @@ class SDXLDiffDiffControlNetDenoiseStep(StableDiffusionXLDenoiseLoopWrapper):
 controlnet_denoise_block = SDXLDiffDiffControlNetDenoiseStep()
 ```
 
-插入 `controlnet_input` 块并用新的 `controlnet_denoise_block` 替换 `denoise` 块。初始化一个 [`ModularPipeline`] 并将 [`~ModularPipeline.load_default_components`] 加载到其中。
+插入 `controlnet_input` 块并用新的 `controlnet_denoise_block` 替换 `denoise` 块。初始化一个 [`ModularPipeline`] 并将 [`~ModularPipeline.load_components`] 加载到其中。
 
 ```py
 dd_blocks.sub_blocks.insert("controlnet_input", control_input_block, 7)
 dd_blocks.sub_blocks["denoise"] = controlnet_denoise_block
 
 dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff")
-dd_pipeline.load_default_components(torch_dtype=torch.float16)
+dd_pipeline.load_components(torch_dtype=torch.float16)
 dd_pipeline = dd_pipeline.to(device)
 
 control_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/diffdiff_tomato_canny.jpeg")
@@ -322,7 +322,7 @@ DIFFDIFF_AUTO_BLOCKS.insert("controlnet_input",StableDiffusionXLControlNetAutoIn
 ```py
 dd_auto_blocks = SequentialPipelineBlocks.from_blocks_dict(DIFFDIFF_AUTO_BLOCKS)
 dd_pipeline = dd_auto_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff")
-dd_pipeline.load_default_components(torch_dtype=torch.float16)
+dd_pipeline.load_components(torch_dtype=torch.float16)
 ```
 
 ## 分享
@@ -342,5 +342,5 @@ from diffusers.modular_pipelines import ModularPipeline, ComponentsManager
 components = ComponentsManager()
 
 diffdiff_pipeline = ModularPipeline.from_pretrained("YiYiXu/modular-diffdiff-0704", trust_remote_code=True, components_manager=components, collection="diffdiff")
-diffdiff_pipeline.load_default_components(torch_dtype=torch.float16)
+diffdiff_pipeline.load_components(torch_dtype=torch.float16)
 ```