add experimental marks to all modular docs

Author: yiyixuxu

docs/source/en/modular_diffusers/components_manager.md

@@ -12,6 +12,12 @@ specific language governing permissions and limitations under the License.
 
 # Components Manager
 
+<Tip warning={true}>
+
+🧪 **Experimental Feature**: This is an experimental feature we are actively developing. The API may be subject to breaking changes.
+
+</Tip>
+
 The Components Manager is a central model registry and management system in diffusers. It lets you add models then reuse them across multiple pipelines and workflows. It tracks all models in one place with useful metadata such as model size, device placement and loaded adapters (LoRA, IP-Adapter). It has mechanisms in place to prevent duplicate model instances, enables memory-efficient sharing. Most significantly, it offers offloading that works across pipelines — unlike regular DiffusionPipeline offloading which is limited to one pipeline with predefined sequences, the Components Manager automatically manages your device memory across all your models and workflows. 
 
 

docs/source/en/modular_diffusers/end_to_end_guide.md

@@ -12,6 +12,12 @@ specific language governing permissions and limitations under the License.
 
 # End-to-End Developer Guide: Building with Modular Diffusers
 
+<Tip warning={true}>
+
+🧪 **Experimental Feature**: Modular Diffusers is an experimental feature we are actively developing. The API may be subject to breaking changes.
+
+</Tip>
+
 
 In this tutorial we will walk through the process of adding a new pipeline to the modular framework using differential diffusion as our example. We'll cover the complete workflow from implementation to deployment: implementing the new pipeline, ensuring compatibility with existing tools, sharing the code on Hugging Face Hub, and deploying it as a UI node. 
 
@@ -164,7 +170,7 @@ We will use this example script:
 >>> prompt = "a green pear"
 >>> negative_prompt = "blurry"
 >>> 
->>> image = dd_pipeline.run(
+>>> image = dd_pipeline(
 ...     prompt=prompt,
 ...     negative_prompt=negative_prompt,
 ...     num_inference_steps=25,

docs/source/en/modular_diffusers/getting_started.md

@@ -12,6 +12,12 @@ specific language governing permissions and limitations under the License.
 
 # Getting Started with Modular Diffusers: A Comprehensive Overview
 
+<Tip warning={true}>
+
+🧪 **Experimental Feature**: Modular Diffusers is an experimental feature we are actively developing. The API may be subject to breaking changes.
+
+</Tip>
+
 With Modular Diffusers, we introduce a unified pipeline system that simplifies how you work with diffusion models. Instead of creating separate pipelines for each task, Modular Diffusers lets you:
 
 **Write Only What's New**: You won't need to write an entire pipeline from scratch every time you have a new use case. You can create pipeline blocks just for your new workflow's unique aspects and reuse existing blocks for existing functionalities. 

docs/source/en/modular_diffusers/write_own_pipeline_block.md

@@ -12,6 +12,12 @@ specific language governing permissions and limitations under the License.
 
 # Writing Your Own Pipeline Blocks
 
+<Tip warning={true}>
+
+🧪 **Experimental Feature**: Modular Diffusers is an experimental feature we are actively developing. The API may be subject to breaking changes.
+
+</Tip>
+
 In Modular Diffusers, you build your workflow using `ModularPipelineBlocks`. We support 4 different types of blocks: `PipelineBlock`, `SequentialPipelineBlocks`, `LoopSequentialPipelineBlocks`, and `AutoPipelineBlocks`. Among them, `PipelineBlock` is the most fundamental building block of the whole system - it's like a brick in a Lego system. These blocks are designed to easily connect with each other, allowing for modular construction of creative and potentially very complex workflows.
 
 In this tutorial, we will focus on how to write a basic `PipelineBlock` and how it interacts with other components in the system. We will also cover how to connect them together using the multi-blocks: `SequentialPipelineBlocks`, `LoopSequentialPipelineBlocks`, and `AutoPipelineBlocks`.