Add Wan2.2 example

Author: r4victor

examples/models/wan22/README.md

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+# Wan2.2
+
+[Wan2.2](https://github.com/Wan-Video/Wan2.2) is an open-source SOTA foundational video model. This example shows how to run the T2V-A14B model variant via `dstack` for text-to-video generation.
+
+??? info "Prerequisites"
+    Once `dstack` is [installed](https://dstack.ai/docs/installation), clone the repo with examples.
+
+    <div class="termy">
+ 
+    ```shell
+    $ git clone https://github.com/dstackai/dstack
+    $ cd dstack
+    ```
+ 
+    </div>
+
+Apply the [configuration](https://github.com/dstackai/dstack/blob/master/examples/models/wan22/dev-env.dstack.yml) to provision a GPU instance and run a dev environment with all the Wan2.2 dependencies installed:
+
+<div class="termy">
+
+```shell
+$ dstack apply -f examples/models/wan22/dev-env.dstack.yml
+Provisioning...
+---> 100%
+```
+
+</div>
+
+Then you can attach to the dev environment and generate videos:
+
+<div class="termy">
+
+```shell
+$ torchrun --nproc_per_node=8 generate.py --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+
+[2025-08-26 05:41:54,911] INFO: Input prompt: Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.
+[2025-08-26 05:41:54,912] INFO: Creating WanT2V pipeline.
+[2025-08-26 05:42:50,296] INFO: loading ./Wan2.2-T2V-A14B/models_t5_umt5-xxl-enc-bf16.pth
+```
+
+</div>

examples/models/wan22/dev-env.dstack.yml

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+type: dev-environment
+name: wan22
+ide: vscode
+nvcc: true
+repos:
+  - https://github.com/Wan-Video/Wan2.2.git
+init:
+  - |
+    pip install torch
+    pip install -r requirements.txt
+    pip install "huggingface_hub[cli]"
+    huggingface-cli download Wan-AI/Wan2.2-T2V-A14B --local-dir ./Wan2.2-T2V-A14B
+resources: 
+  gpu:
+    name: H100
+    count: 8 # Set to 1.. if 8 GPUs not available trading inferece speed
+  disk: 200GB