Apply Gemini review: docs copyedits

a/an agreement, missing colon + comma splice, duplicate "needed",
GB's -> GBs, worfklows -> workflows, missing periods, 16GB -> 16 GB.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

Author: mohnjiles

docs/getting-started/overview.md

@@ -55,7 +55,7 @@ Stability Matrix itself is distributed as a portable, self-contained desktop app
 - **Operating system and architecture**: Use one of the supported desktop targets listed above: Windows x64, Linux x64, or Apple Silicon macOS.
 - **GPU**: A dedicated GPU is strongly recommended for image and video generation. NVIDIA CUDA is the broadest and most mature path in current Stability Matrix workflows, with 900-series cards as a practical minimum and 2000-series or newer recommended for better compatibility and speed. AMD ROCm, AMD ZLUDA, Intel Arc (IPEX), and Apple Silicon (MPS) are also supported depending on platform.
 - **VRAM**: These figures are rough community rules of thumb, not values enforced by the app, and they'll keep shifting as new models arrive. As of mid-2026, roughly 4 GB of VRAM is a practical minimum for older and lighter image-generation setups (Stable Diffusion 1.5), but roughly 12+ GB is a better minimal target for most current basic models and workflows (e.g. SDXL, Z-Image Turbo). Large modern models such as unquantized FLUX variants, and many video-generation workflows, can push that much higher. Lower-VRAM video variants may work in roughly the 6-8 GB range, while larger video models can require roughly 16+ GB. As a general recommendation, a 16 GB VRAM GPU is a comfortable target for most commonly used workflows today.
-- **System RAM**: 16GB recommended minimum. Requirements vary by backend and model size, but more system RAM becomes important when workloads spill out of VRAM. 32+ GB of RAM can help avoid hard out-of-memory crashes on constrained VRAM setups, even though performance will still slow down when offloading occurs. On memory-constrained systems, it also helps to make sure the page file on Windows or the swap file or swap partition on Linux is configured with enough space to act as a last-resort buffer when both VRAM and system RAM are exhausted.
+- **System RAM**: 16 GB recommended minimum. Requirements vary by backend and model size, but more system RAM becomes important when workloads spill out of VRAM. 32+ GB of RAM can help avoid hard out-of-memory crashes on constrained VRAM setups, even though performance will still slow down when offloading occurs. On memory-constrained systems, it also helps to make sure the page file on Windows or the swap file or swap partition on Linux is configured with enough space to act as a last-resort buffer when both VRAM and system RAM are exhausted.
 - **Storage**: Plan for significant disk usage in the data directory. A single package install is typically in the 2-10 GB range, checkpoint models are often 2-20 GB or more each, and LoRAs or other adapters commonly range from hundreds of megabytes to around 1 GB each. An SSD is recommended for packages and active workflows, while slower bulk storage (HDD) can still be reasonable for large model libraries at the cost of initial model loading speed.
 - **CPU-only fallback**: CPU-only operation is possible, but it is mainly useful for testing or very light use. For real generation workloads, it is much slower than any supported GPU backend.
 

docs/package-manager/installing-packages.md

@@ -1,6 +1,6 @@
 # Installing Packages
 
-This page walks through installing an WebUI package in Stability Matrix using the **Add Package** screen.
+This page walks through installing a WebUI package in Stability Matrix using the **Add Package** screen.
 
 [`Section Overview`](overview.md) | [`Home`](../README.md)
 
@@ -25,7 +25,7 @@ Packages are displayed as a scrollable list of cards organized into three tabs a
 - **Training**: Model fine-tuning and training tools such as Kohya's GUI and OneTrainer.
 - **Legacy**: Older packages that are maintained for existing users but not recommended for new installations. May be stale and no longer receiving updates.
 
-Each package card shows the package name, author, a short description, and a row of **hardware compatibility badges** indicating which PyTorch backends the package supports from the following types CUDA (NVIDIA), ROCm (AMD-native), DirectML, macOS (MPS), ZLUDA (AMD), IPEX (Intel), or CPU. Note that the absence of a particular hardware badge does not necessarily mean the package is incompatible, some packages may still be usable with manual configuration or community-provided workarounds. Within each tab, beginner-friendly packages appear first, followed by advanced tools in alphabetical order.
+Each package card shows the package name, author, a short description, and a row of **hardware compatibility badges** indicating which PyTorch backends the package supports from the following types: CUDA (NVIDIA), ROCm (AMD-native), DirectML, macOS (MPS), ZLUDA (AMD), IPEX (Intel), or CPU. Note that the absence of a particular hardware badge does not necessarily mean the package is incompatible; some packages may still be usable with manual configuration or community-provided workarounds. Within each tab, beginner-friendly packages appear first, followed by advanced tools in alphabetical order.
 
 Use the tabs to switch between package types, or type in the search bar to filter the list by name in real time. Incompatible packages are hidden by default: enable *Show All Packages* to see packages that do not officially support your current hardware (e.g., CUDA-only packages on an AMD system).
 
@@ -134,7 +134,7 @@ The progress dialog shows a real-time log of each step. If any step fails, the d
 | First install (no cached wheels) | 5–15 minutes |
 | Slow connection or CPU-only install | 10–25 minutes |
 
-> **Note:** PyTorch wheels are large and the multiple needed WHL files needed can accumulate to several GB's or more in total download size depending on backend used. The first installation on a fresh system downloads these wheels. Subsequent installs reuse cached wheels, making them significantly faster.
+> **Note:** PyTorch wheels are large and the multiple WHL files needed can accumulate to several GBs or more in total download size depending on the backend used. The first installation on a fresh system downloads these wheels. Subsequent installs reuse cached wheels, making them significantly faster.
 
 ## One-Click Install
 
@@ -148,7 +148,7 @@ For new users, Stability Matrix offers a streamlined **one-click install** exper
    - The **latest release version** (or latest commit, for packages without releases).
    - The **recommended PyTorch backend** detected from your hardware.
    - The **recommended shared folder method** (symlinks for most packages).
-   - The **package's recommended default Python version** 
+   - The **package's recommended default Python version**.
 
 3. **Installation**: clicking Install runs the same step pipeline described in [Installation Progress](#installation-progress). A progress bar shows the current status, and status text updates as each step completes.
 

docs/package-manager/supported-packages.md

@@ -30,7 +30,7 @@ Inference packages are used for generating images and video. They provide their
 | **Stable Diffusion Web UI (DirectML)** | A fork of the AUTOMATIC1111 WebUI with DirectML support for running on Windows without CUDA. |
 | **FramePack** | An advanced next-frame-prediction neural network for progressively generating video content. |
 | **FramePack Studio** | A full-featured video generation application built on top of the FramePack architecture. |
-| **Wan2GP** | A highly optimized Gradio UI for AI video creation using WAN-based models, with performance-focused features and worfklows aimed at making modern and newer video-generation models more practical on lower-VRAM systems. |
+| **Wan2GP** | A highly optimized Gradio UI for AI video creation using WAN-based models, with performance-focused features and workflows aimed at making modern and newer video-generation models more practical on lower-VRAM systems. |
 
 ## Training Packages
 

docs/tips/terminology.md

@@ -494,7 +494,7 @@ Common modern families and ecosystems include:
 - **SDXL 1.0**: the major open Stable Diffusion XL base family, still foundational for a huge amount of community work
 - **Pony**: a large SDXL-derived ecosystem known for stylized, character-heavy, and expressive prompt behavior
 - **Illustrious / illustrative SDXL families**: SDXL derivatives centered on polished illustration and anime-adjacent output
-- **NoobAI**: a newer, growing anime and illustration ecosystem derived from Illustrious. Many Illustrious LoRAs still work well with it, though the broader community content base is still larger around Illustrious. Workflows may use either v-prediction or EPS depending on the specific release and setup
+- **NoobAI**: a newer, growing anime and illustration ecosystem derived from Illustrious. Many Illustrious LoRAs still work well with it, though the broader community content base is still larger around Illustrious. Workflows may use either v-prediction or EPS depending on the specific release and setup.
 - **Anima**: a 2B anime and illustration-focused base model family made by CircleStone Labs in collaboration with Comfy Org, built for stylized character art, illustration-heavy workflows, and strong anime-oriented visual behavior
 - **FLUX Kontext**: FLUX-family releases focused on instruction-following, contextual edits, and image-aware generation behavior
 - **FLUX Klein**: smaller FLUX.2-oriented variants designed to be lighter and faster than the heavier full-dev style releases