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app.py

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+import torch
+import spaces
+
+import gradio as gr
+from diffusers import ModularPipelineBlocks
+from diffusers.utils import export_to_video, load_image
+from diffusers.modular_pipelines import WanModularPipeline
+
+class MatrixGameWanModularPipeline(WanModularPipeline):
+    """
+    A ModularPipeline for MatrixGameWan.
+
+    <Tip warning={true}>
+
+        This is an experimental feature and is likely to change in the future.
+
+    </Tip>
+    """
+
+    @property
+    def default_sample_height(self):
+        return 44
+
+    @property
+    def default_sample_width(self):
+        return 80
+
+
+blocks = ModularPipelineBlocks.from_pretrained("diffusers/matrix-game-2-modular", trust_remote_code=True)
+image_to_action_block = ModularPipelineBlocks.from_pretrained("dn6/matrix-game-image-to-action", trust_remote_code=True)
+
+blocks.sub_blocks.insert("image_to_action", image_to_action_block, 0)
+
+pipe = MatrixGameWanModularPipeline(blocks, "diffusers-internal-dev/matrix-game-2-modular")
+pipe.load_components(trust_remote_code=True, device_map="cuda", torch_dtype={"default": torch.bfloat16, "vae": torch.float32})
+
+@spaces.GPU(300)
+def predict(image, prompt):
+    output = pipe(image=image, prompt=prompt, num_frames=141)
+    return export_to_video(output.values['videos'][0], "output.mp4")
+
+
+examples = []
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 1024px;
+}
+#logo-title {
+    text-align: center;
+}
+#logo-title img {
+    width: 400px;
+}
+#edit_text{margin-top: -62px !important}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        with gr.Row():
+            with gr.Column():
+                input_images = gr.Gallery(label="Input Images",
+                                          show_label=False,
+                                          type="pil",
+                                          interactive=True)
+
+            with gr.Column():
+                result = gr.Gallery(label="Result", show_label=False, type="pil")
+
+        with gr.Row():
+            prompt = gr.Text(
+                    label="Prompt",
+                    show_label=False,
+                    placeholder="describe the edit instruction",
+                    container=False,
+            )
+            run_button = gr.Button("Run!", variant="primary")
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=predict,
+        inputs=[
+            input_images,
+            prompt,
+        ],
+        outputs=[result],  # Added use_output_btn to outputs
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+accelerate==1.10.1
+einops==0.8.1
+flash-attn @ https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.6cxx11abiTRUE-cp310-cp310-linux_x86_64.whl
+hf-transfer==0.1.9
+hf-xet==1.1.8
+huggingface-hub==0.34.4
+imageio==2.37.0
+imageio-ffmpeg==0.6.0
+safetensors==0.6.2
+sentencepiece==0.2.1
+torch==2.7.0
+torchao==0.12.0
+torchvision==0.22.0
+transformers==4.55.4