BruceLoveDecimal
5cd925631c
Signed-off-by: BruceLoveDecimal <liuqihao970610@gmail.com> Signed-off-by: liuqihao <liuqihao970610@gmail.com>
42 lines
1.6 KiB
YAML
42 lines
1.6 KiB
YAML
# LingBot-World 2.0 stepwise deploy: single diffusion stage for WS /v1/realtime/video.
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#
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# One AsyncOmni request is one rollout: prepare_encode runs once, then every
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# four DMD steps stream one AR block. Step execution keeps AR-Diffusion paged KV
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# on ARDiffusionModelRunner, so this stage must stay at max_num_seqs=1.
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#
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# The cache geometry is fixed at load time, so ar_diffusion_height/width must
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# match the height/width a client asks for.
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pipeline: lingbot_world
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async_chunk: false
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distributed_executor_backend: mp
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dtype: bfloat16
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stages:
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- stage_id: 0
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devices: "0"
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max_num_seqs: 1
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enforce_eager: true
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model_class_name: LingBotWorldCausalDMDPipeline
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# LingBot runs on the AR-Diffusion engine (engine-level paged KV cache).
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engine_backend: vllm_omni.experimental.ar_diffusion.engine.ARDiffusionEngine
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# One request, many streamed chunks. streaming_output implies step_execution,
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# but both are stated here so the topology is readable on its own.
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step_execution: true
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streaming_output: true
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# num_inference_steps is for the step scheduler, not the pipeline: it sizes a
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# request's step budget before the pipeline is reached, so a served request
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# does not have to carry it. The pipeline itself pins the causal-fast
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# checkpoint's four-step DMD schedule and rejects any other count.
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default_sampling_params:
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num_inference_steps: 4
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output_type: pil
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extra_args:
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flow_shift: 5.0
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model_config:
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ar_diffusion_height: 480
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ar_diffusion_width: 832
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ar_diffusion_kv_config:
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gpu_memory_fraction: 0.6
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warmup_cudagraph: true
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