Update README.md

README.md

@@ -9,7 +9,7 @@ pinned: false
 
 # diffusers-modular
 
-Creative workflows built with **Modular Diffusers** — the demos, and the checkpoints they run on.
+Creative generative workflows built with **Modular Diffusers** 🧨 for state-of-the-art open source models.
 
 ## What Modular Diffusers is
 
@@ -17,14 +17,8 @@ Diffusion pipelines are usually monolithic: one class per task, and a new task m
 Diffusers breaks that into **composable blocks** — encode, prepare, denoise, decode — that you assemble into a
 workflow, share components between them through a `ComponentsManager` so one loaded model serves several tasks,
 and swap or subclass a single block instead of forking a pipeline. It is how a workflow that would be a graph of
-custom nodes elsewhere becomes a few lines of Python.
+custom nodes becomes a few lines of Python.
 
 - 📚 [Documentation](https://huggingface.co/docs/diffusers/main/en/modular_diffusers/overview) · [Quickstart](https://huggingface.co/docs/diffusers/main/en/modular_diffusers/quickstart)
 - 📝 [Introducing Modular Diffusers](https://huggingface.co/blog/modular-diffusers)
 - 🧪 [Modular Diffusers on GitHub](https://github.com/huggingface/diffusers)
-
-## In this org
-
-- [**MiniMax-H3-Pruned-Ref-Delta-Fused-r1024**](https://huggingface.co/diffusers-modular/MiniMax-H3-Pruned-Ref-Delta-Fused-r1024)
-  — one MiniMax-H3 transformer that serves both reference conditioning *and* first/last-frame conditioning, where
-  the model normally needs two separate 37.5 GB partitions.