feat: update README and pyproject.toml for enhanced WanVideoWrapper integration and version bump to 1.8.0

Author: pollockjj

README.md

@@ -1,10 +1,20 @@
-# ComfyUI-MultiGPU: Tools to free up your primary GPU’s VRAM by using your CPU or additional GPUs[^1]
+# ComfyUI-MultiGPU: Tools to free up your primary GPU’s VRAM by using your CPU or additional GPUs, now with tighter integration into kijai's WanVideoWrapper[^1]
 <p align="center">
   <img src="https://raw.githubusercontent.com/pollockjj/ComfyUI-MultiGPU/main/assets/distorch_average.png" width="600">
   <br>
   <em>Free almost all of your GPU for what matters: Maximum latent space processing</em>
 </p>
 
+### WanVideoWrapper Integration
+
+ComfyUI-MultiGPU now includes a custom, tightly integrated implementation for WanVideoWrapper, providing true multi-GPU support for advanced video generation workflows. Unlike the standard override nodes, these are bespoke wrappers that offer a more stable and feature-rich experience.
+
+**Key Capabilities:**
+
+*   **Load Models Anywhere:** Load different WanVideo models onto separate GPUs within the same workflow (e.g., Model 1 on `cuda:0`, Model 2 on `cuda:1`).
+*   **Swap Blocks Anywhere:** Utilize any GPU as a target for offloading transformer blocks via the `WanVideoBlockSwapMultiGPU` node (e.g., swap to `cuda:2`).
+*   **Reliable Device Tracking:** The device context is correctly managed even in complex dual-model workflows, ensuring the right model runs on the right GPU.
+
 ## The Core of ComfyUI-MultiGPU:
 [^1]: This **enhances memory management,** not parallel processing. Workflow steps still execute sequentially, but with components (in full or in part) loaded across your specified devices. *Performance gains* come from avoiding repeated model loading/unloading when VRAM is constrained. *Capability gains* come from offloading as much of the model (VAE/CLIP/UNet) off of your main **compute** device as possible—allowing you to maximize latent space for actual computation.
 
@@ -125,9 +135,13 @@ Currently supported nodes (automatically detected if available):
   - HyVideoVAELoaderMultiGPU
   - DownloadAndLoadHyVideoTextEncoderMultiGPU
 - WanVideoWrapper (requires [ComfyUI-WanVideoWrapper](https://github.com/kijai/ComfyUI-WanVideoWrapper)):
-  - WanVideoModelLoader
-  - WanVideoVAELoader
-  - LoadWanVideoT5TextEncoder
+  - `WanVideoModelLoaderMultiGPU` & `WanVideoModelLoaderMultiGPU_2`
+  - `WanVideoVAELoaderMultiGPU`
+  - `LoadWanVideoT5TextEncoderMultiGPU`
+  - `LoadWanVideoClipTextEncoderMultiGPU`
+  - `WanVideoTextEncodeMultiGPU`
+  - `WanVideoBlockSwapMultiGPU`
+  - `WanVideoSamplerMultiGPU`
 - **Native to ComfyUI-MultiGPU**
   - DeviceSelectorMultiGPU - Allows user to link loaders together to use the same selected device
   - HunyuanVideoEmbeddingsAdapter - Allows Kijai's excellent IP2V CLIP for HunyuanVideo to be used with Comfy Core sampler.
@@ -136,7 +150,12 @@ All MultiGPU nodes available for your install can be found in the "multigpu" cat
 
 ## Example workflows
 
-All workflows have been tested on a 2x 3090 linux setup, a 4070 win 11 setup, and a 3090/1070ti linux setup.
+All workflows have been tested on a 2x 3090 + 1060ti linux setup, a 4070 win 11 setup, and a 3090/1070ti linux setup.
+
+### Split WAN2.2 High and Low models on different GPUs while Block Swapping to a third GPU ###
+
+- [examples/wanvideo2_2_I2V_A14B_example_WIP_MultiGPU.json](https://github.com/pollockjj/ComfyUI-MultiGPU/blob/main/examples/wanvideo2_2_I2V_A14B_example_WIP_MultiGPU.json)
+  This workflow extends Kijai's I2V example for WanVideo 2.2, demonstrating a dual-model setup. The HIGH model is loaded on `cuda:0`, the LOW model on `cuda:1`, with block-swapping offloaded to `cuda:2`.
 
 ### Split GGUF-quantized UNet and CLIP models across multiple devices using DisTorch
 

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-multigpu"
-description = "This custom_node for ComfyUI adds one-click 'Virtual VRAM' for any GGUF UNet and CLIP loader, managing the offload of layers to DRAM or VRAM to maximize the latent space of your card. Also includes nodes for directly loading entire components (UNet, CLIP, VAE) onto the device you choose. Includes 16 examples covering common use cases. Includes support for kijai's ComfyUI-WanVideoWrapper and ComfyUI-HunyuanVideoWrapper, among other popular loaders."
-version = "1.7.3"
+description = "Adds full multi-GPU support for WanVideoWrapper, enabling model loading and block-swapping on any device. Provides a suite of custom nodes to manage multiple GPUs for ComfyUI, including advanced GGUF offloading with DisTorch and device overrides for core nodes."
+version = "1.8.0"
 license = {file = "LICENSE"}
 
 [project.urls]