Add Wan2.2 example

docs/examples.md

@@ -230,6 +230,22 @@ hide:
     </a>
 </div>
 
+## Models
+
+<div class="tx-landing__highlights_grid">
+    <a href="/examples/models/wan22"
+       class="feature-cell sky">
+        <h3>
+            Wan2.2
+        </h3>
+
+        <p>
+            Use Wan2.2 to generate videos from text
+        </p>
+    </a>
+</div>
+
+
 <!-- ## Misc
 
 <div class="tx-landing__highlights_grid">

docs/overrides/main.html

@@ -122,6 +122,7 @@
                 <a href="/examples#distributed-training" class="tx-footer__section-link">Distributed training</a>
                 <a href="/examples#clusters" class="tx-footer__section-link">Clusters</a>
                 <a href="/examples#inference" class="tx-footer__section-link">Inference</a>
+                <a href="/examples#models" class="tx-footer__section-link">Models</a>
               </div>
 
               <div class="tx-footer__section">

examples/models/wan22/.dstack.yml

@@ -0,0 +1,63 @@
+type: task
+name: wan22
+
+repos:
+  # Clones it to `/workflow` (the default working directory)
+  - https://github.com/Wan-Video/Wan2.2.git
+
+python: 3.12
+nvcc: true
+
+env:
+  - PROMPT="Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+  # Required for storing cache on a volume
+  - UV_LINK_MODE=copy
+commands:
+  # Install flash-attn
+  - |
+    uv pip install torch
+    uv pip install flash-attn --no-build-isolation 
+  # Install dependencies
+  - |
+    uv pip install . decord librosa
+    uv pip install "huggingface_hub[cli]"
+    hf download Wan-AI/Wan2.2-T2V-A14B --local-dir /root/.cache/Wan2.2-T2V-A14B
+  # Generate video
+  - |
+    if [ ${DSTACK_GPUS_NUM} -gt 1 ]; then
+      torchrun \
+        --nproc_per_node=${DSTACK_GPUS_NUM} \
+        generate.py \
+        --task t2v-A14B \
+        --size 1280*720 \
+        --ckpt_dir /root/.cache/Wan2.2-T2V-A14B \
+        --dit_fsdp --t5_fsdp --ulysses_size ${DSTACK_GPUS_NUM} \
+        --save_file ${DSTACK_RUN_NAME}.mp4 \
+        --prompt "${PROMPT}"
+    else
+      python generate.py \
+        --task t2v-A14B \
+        --size 1280*720 \
+        --ckpt_dir /root/.cache/Wan2.2-T2V-A14B \
+        --offload_model True \
+        --convert_model_dtype \
+        --save_file ${DSTACK_RUN_NAME}.mp4 \
+        --prompt "${PROMPT}"
+    fi
+  # Upload video
+  - curl https://bashupload.com/ -T ./${DSTACK_RUN_NAME}.mp4
+
+resources: 
+  gpu:
+    name: [H100, H200]
+    count: 1..8
+  disk: 300GB
+
+# Change to on-demand for disabling spot
+spot_policy: auto
+
+volumes:
+  # Cache pip packages and HF models
+  - instance_path: /root/dstack-cache
+    path: /root/.cache/
+    optional: true

examples/models/wan22/README.md

@@ -0,0 +1,142 @@
+# Wan2.2
+
+[Wan2.2 :material-arrow-top-right-thin:{ .external }](https://github.com/Wan-Video/Wan2.2){:target="_blank"} 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>
+
+## Define a configuration
+
+Below is a task configuration that generates a video using Wan2.2, uploads it, and provides the download link.
+
+<div editor-title="examples/models/wan22/.dstack.yml"> 
+
+```yaml
+type: task
+name: wan22
+
+repos:
+  # Clones it to `/workflow` (the default working directory)
+  - https://github.com/Wan-Video/Wan2.2.git
+
+python: 3.12
+nvcc: true
+
+env:
+  - PROMPT="Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+  # Required for storing cache on a volume
+  - UV_LINK_MODE=copy
+commands:
+  # Install flash-attn
+  - |
+    uv pip install torch
+    uv pip install flash-attn --no-build-isolation 
+  # Install dependencies
+  - |
+    uv pip install . decord librosa
+    uv pip install "huggingface_hub[cli]"
+    hf download Wan-AI/Wan2.2-T2V-A14B --local-dir /root/.cache/Wan2.2-T2V-A14B
+  # Generate video
+  - |
+    if [ ${DSTACK_GPUS_NUM} -gt 1 ]; then
+      torchrun \
+        --nproc_per_node=${DSTACK_GPUS_NUM} \
+        generate.py \
+        --task t2v-A14B \
+        --size 1280*720 \
+        --ckpt_dir /root/.cache/Wan2.2-T2V-A14B \
+        --dit_fsdp --t5_fsdp --ulysses_size ${DSTACK_GPUS_NUM} \
+        --save_file ${DSTACK_RUN_NAME}.mp4 \
+        --prompt "${PROMPT}"
+    else
+      python generate.py \
+        --task t2v-A14B \
+        --size 1280*720 \
+        --ckpt_dir /root/.cache/Wan2.2-T2V-A14B \
+        --offload_model True \
+        --convert_model_dtype \
+        --save_file ${DSTACK_RUN_NAME}.mp4 \
+        --prompt "${PROMPT}"
+    fi
+  # Upload video
+  - curl https://bashupload.com/ -T ./${DSTACK_RUN_NAME}.mp4
+
+resources: 
+  gpu:
+    name: [H100, H200]
+    count: 1..8
+  disk: 300GB
+
+# Change to on-demand for disabling spot
+spot_policy: auto
+
+volumes:
+  # Cache pip packages and HF models
+  - instance_path: /root/dstack-cache
+    path: /root/.cache/
+    optional: true
+```
+
+</div>
+
+You can customize the 
+
+## Run the configuration
+
+Once the configuration is ready, run `dstack apply -f <configuration file>`, and `dstack` will automatically provision the
+cloud resources and run the configuration.
+
+<div class="termy">
+
+```shell
+$ dstack apply -f examples/models/wan22/.dstack.yml
+
+ #  BACKEND              RESOURCES                                        INSTANCE TYPE   PRICE
+ 1  datacrunch (FIN-01)  cpu=30 mem=120GB disk=200GB H100:80GB:1 (spot)   1H100.80S.30V   $0.99
+ 2  datacrunch (FIN-01)  cpu=30 mem=120GB disk=200GB H100:80GB:1 (spot)   1H100.80S.30V   $0.99
+ 3  datacrunch (FIN-02)  cpu=44 mem=182GB disk=200GB H200:141GB:1 (spot)  1H200.141S.44V  $0.99
+
+---> 100%
+
+Uploaded 1 file, 8 375 523 bytes
+
+wget https://bashupload.com/fIo7l/wan22.mp4
+```
+
+</div>
+
+If you want you can override the default GPU, spot policy, and even the prompt via the CLI.
+
+<div class="termy">
+
+```shell
+$ PROMPT=...
+$ dstack apply -f examples/models/wan22/.dstack.yml --spot --gpu H100,H200:8
+
+ #  BACKEND              RESOURCES                                          INSTANCE TYPE    PRICE
+ 1  aws (us-east-2)      cpu=192 mem=2048GB disk=300GB H100:80GB:8 (spot)   p5.48xlarge      $6.963
+ 2  datacrunch (FIN-02)  cpu=176 mem=1480GB disk=300GB H100:80GB:8 (spot)   8H100.80S.176V   $7.93
+ 3  datacrunch (ICE-01)  cpu=176 mem=1450GB disk=300GB H200:141GB:8 (spot)  8H200.141S.176V  $7.96
+
+---> 100%
+
+Uploaded 1 file, 8 375 523 bytes
+
+wget https://bashupload.com/fIo7l/wan22.mp4
+```
+
+</div>
+
+## Source code
+
+The source-code of this example can be found in
+[`examples/models/wan22` :material-arrow-top-right-thin:{ .external }](https://github.com/dstackai/dstack/blob/master/examples/models/wan22){:target="_blank"}.

mkdocs.yml

@@ -292,6 +292,8 @@ nav:
         - TPU: examples/accelerators/tpu/index.md
         - Intel Gaudi: examples/accelerators/intel/index.md
         - Tenstorrent: examples/accelerators/tenstorrent/index.md
+    - Models:
+        - Wan2.2: examples/models/wan22/index.md
   - Changelog: blog/changelog.md
   - Case studies: blog/case-studies.md
   - Benchmarks: blog/benchmarks.md