Davids048andClaude Opus 5.5
0b80dd52db
These inference parameters could be set only through argparse flags or the untyped pipeline.experimental dict. Each one gains a typed GeneratorConfig field whose flat name is its FastVideoArgs or PipelineConfig field, so YAML, dotted CLI overrides, and from_pretrained keywords all reach it: - engine.attention: backend, vsa_sparsity, vsa_tile_size, moba_config_path - engine.precision: dit, vae, vae_decode, image_encoder, text_encoders - engine.compile.regional (inference_torch_compile) - pipeline: vae_sp, flow_shift, embedded_cfg_scale, dmd_denoising_steps - pipeline.components.lora_target_modules Every field defaults to None, which keeps the runtime or model default; a None field is not passed to FastVideoArgs. pipeline.experimental still accepts the flat names and still wins over the typed fields. The parity inventory lists the typed paths, and the docs show the typed forms. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
366 lines
11 KiB
Python
366 lines
11 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import json
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import yaml
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from fastvideo.api import (
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config_to_dict,
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ContinuationState,
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GenerationRequest,
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GeneratorConfig,
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GpuPoolConfig,
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load_run_config,
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load_serve_config,
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parse_config,
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PlannedStage,
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PromptEnhancerConfig,
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PromptSafetyConfig,
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RunConfig,
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ServeConfig,
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StreamingConfig,
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WarmupConfig,
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)
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def test_parse_config_builds_nested_typed_config() -> None:
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raw = {
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"generator": {
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"model_path": "/models/ltx2",
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"pipeline": {
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"workload_type": "t2v",
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"preset": "ltx2_two_stage",
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},
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},
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"request": {
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"prompt": ["a fox", "a wolf"],
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"sampling": {
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"num_frames": 121,
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"height": 1024,
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"width": 1536,
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"guidance_scale": 1.5,
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},
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"state": {
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"kind": "ltx2_continuation",
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"payload": {
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"segment_index": 1
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},
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},
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"plan": {
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"stages": [{
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"name": "base",
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"kind": "sample",
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}]
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},
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},
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}
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config = parse_config(RunConfig, raw)
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assert config.generator.pipeline.preset == "ltx2_two_stage"
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assert config.request.prompt == ["a fox", "a wolf"]
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assert config.request.state == ContinuationState(
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kind="ltx2_continuation",
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payload={"segment_index": 1},
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)
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assert config.request.plan is not None
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assert config.request.plan.stages == [PlannedStage(name="base", kind="sample")]
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def test_parse_config_accepts_existing_typed_instance() -> None:
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typed = RunConfig(
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generator=GeneratorConfig(model_path="/models/base"),
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request=GenerationRequest(prompt="hello"),
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)
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assert parse_config(RunConfig, typed) is typed
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def test_load_run_config_supports_yaml_roundtrip(tmp_path) -> None:
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raw = {
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"generator": {
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"model_path": "/models/wan"
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},
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"request": {
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"prompt": "hello",
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"sampling": {
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"num_frames": 16
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},
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},
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}
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path = tmp_path / "run.yaml"
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path.write_text(yaml.safe_dump(raw), encoding="utf-8")
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loaded = load_run_config(path)
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assert config_to_dict(loaded) == {
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"generator": {
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"model_path": "/models/wan",
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"revision": None,
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"trust_remote_code": False,
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"engine": {
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"num_gpus": 1,
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"execution_backend": "mp",
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"parallelism": {
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"tp_size": -1,
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"sp_size": -1,
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"hsdp_replicate_dim": 1,
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"hsdp_shard_dim": -1,
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"dist_timeout": None,
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},
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"offload": {
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"dit": True,
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"dit_layerwise": True,
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"text_encoder": True,
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"image_encoder": True,
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"vae": True,
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"pin_cpu_memory": True,
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"lazy_module_load": None,
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},
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"compile": {
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"enabled": False,
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"backend": None,
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"fullgraph": None,
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"mode": None,
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"dynamic": None,
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"extras": {},
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"text_encoder_enabled": None,
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"vae_enabled": None,
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"audio_vae_enabled": None,
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"regional": None,
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"dit_kwargs": {},
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"text_encoder_kwargs": {},
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"vae_kwargs": {},
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"audio_vae_kwargs": {},
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},
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"attention": {
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"backend": None,
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"vsa_sparsity": None,
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"vsa_tile_size": None,
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"moba_config_path": None,
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},
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"precision": {
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"dit": None,
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"vae": None,
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"vae_decode": None,
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"image_encoder": None,
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"text_encoders": None,
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},
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"enable_stage_verification": True,
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"use_fsdp_inference": False,
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"disable_autocast": False,
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"quantization": None,
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},
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"pipeline": {
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"workload_type": None,
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"preset": None,
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"preset_version": None,
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"components": {
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"config_root": None,
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"pipeline_config_path": None,
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"text_encoder_weights": None,
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"transformer_weights": None,
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"transformer_2_weights": None,
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"vae_weights": None,
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"upsampler_weights": None,
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"lora_path": None,
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"lora_nickname": "default",
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"lora_strength": 1.0,
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"lora_target_modules": None,
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"override_pipeline_cls_name": None,
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"override_transformer_cls_name": None,
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},
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"vae_tiling": None,
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"vae_sp": None,
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"flow_shift": None,
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"embedded_cfg_scale": None,
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"dmd_denoising_steps": None,
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"preset_overrides": {},
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"experimental": {},
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},
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},
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"request": {
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"prompt": "hello",
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"negative_prompt": None,
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"inputs": {
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"prompt_path": None,
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"image_path": None,
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"video_path": None,
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"pil_image": None,
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"last_image": None,
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"references": None,
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"latents": None,
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"audio_latents": None,
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"pose": None,
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"mouse_cond": None,
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"keyboard_cond": None,
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"grid_sizes": None,
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"c2ws_plucker_emb": None,
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"action_path": None,
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"refine_from": None,
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"stage1_video": None,
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},
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"sampling": {
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"num_videos_per_prompt": 1,
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"seed": 1024,
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"max_sequence_length": None,
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"num_frames": 16,
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"height": 720,
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"width": 1280,
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"height_sr": 1072,
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"width_sr": 1920,
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"fps": 24,
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"num_inference_steps": 50,
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"num_inference_steps_sr": 50,
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"guidance_scale": 1.0,
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"batch_cfg": False,
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"guidance_scale_2": None,
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"cfg_normalization": False,
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"cfg_truncation": 1.0,
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"guidance_rescale": 0.0,
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"true_cfg_scale": None,
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"use_embedded_guidance": None,
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"boundary_ratio": None,
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"sigmas": None,
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},
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"runtime": {
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"enable_teacache": False,
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"return_trajectory_latents": False,
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"return_trajectory_decoded": False,
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},
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"output": {
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"output_path": "outputs/",
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"output_video_name": None,
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"save_video": True,
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"return_frames": True,
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"return_state": False,
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},
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"stage_overrides": {},
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"state": None,
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"plan": None,
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"extensions": {},
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},
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}
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def test_load_serve_config_supports_json_roundtrip(tmp_path) -> None:
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raw = {
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"generator": {
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"model_path": "/models/server"
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},
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"server": {
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"port": 9000
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},
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"default_request": {
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"prompt": "serve default"
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},
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}
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path = tmp_path / "serve.json"
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path.write_text(json.dumps(raw), encoding="utf-8")
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loaded = load_serve_config(path)
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assert isinstance(loaded, ServeConfig)
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assert loaded.server.port == 9000
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assert loaded.default_request.prompt == "serve default"
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def test_serve_config_streaming_defaults_to_none() -> None:
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raw = {"generator": {"model_path": "/models/server"}}
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loaded = parse_config(ServeConfig, raw)
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assert loaded.streaming is None
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def test_serve_config_parses_streaming_block() -> None:
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raw = {
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"generator": {
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"model_path": "/models/server"
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},
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"streaming": {
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"session_timeout_seconds": 120,
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"generation_segment_cap": 4,
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"stream_mode": "legacy_jpeg",
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"warmup": {
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"enabled": False,
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"prompt": "warmup prompt",
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"timeout_seconds": 600,
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},
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"pool": {
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"num_workers": 2,
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"enable_audio_reencode": False,
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"conditioning_num_frames": 5,
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"conditioning_end_offset": 1,
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},
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"prompt": {
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"provider": "groq",
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"model": "llama-3-70b",
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"timeout_ms": 10000,
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"system_prompt_dir": "/opt/prompts",
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},
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"safety": {
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"enabled": True,
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"classifier_path": "/opt/safety.pt",
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},
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},
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}
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loaded = parse_config(ServeConfig, raw)
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assert isinstance(loaded.streaming, StreamingConfig)
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assert loaded.streaming.session_timeout_seconds == 120
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assert loaded.streaming.generation_segment_cap == 4
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assert loaded.streaming.stream_mode == "legacy_jpeg"
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assert loaded.streaming.warmup == WarmupConfig(enabled=False, prompt="warmup prompt", timeout_seconds=600)
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assert loaded.streaming.pool == GpuPoolConfig(
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num_workers=2,
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enable_audio_reencode=False,
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conditioning_num_frames=5,
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conditioning_end_offset=1,
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)
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assert loaded.streaming.prompt == PromptEnhancerConfig(
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provider="groq",
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model="llama-3-70b",
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timeout_ms=10000,
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system_prompt_dir="/opt/prompts",
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)
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assert loaded.streaming.safety == PromptSafetyConfig(enabled=True, classifier_path="/opt/safety.pt")
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def test_serve_config_streaming_round_trip_through_config_to_dict() -> None:
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raw = {
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"generator": {
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"model_path": "/models/server"
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},
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"streaming": {
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"session_timeout_seconds": 600
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},
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}
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loaded = parse_config(ServeConfig, raw)
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dumped = config_to_dict(loaded)
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assert dumped["streaming"]["session_timeout_seconds"] == 600
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assert dumped["streaming"]["warmup"]["enabled"] is True
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assert dumped["streaming"]["prompt"]["enabled"] is False
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assert dumped["streaming"]["prompt"]["provider"] == "cerebras"
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assert dumped["streaming"]["safety"]["enabled"] is False
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def test_load_serve_config_with_streaming_from_yaml(tmp_path) -> None:
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raw = {
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"generator": {
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"model_path": "/models/server"
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},
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"streaming": {
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"stream_mode": "av_fmp4",
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"pool": {
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"num_workers": 4
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},
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},
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}
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path = tmp_path / "serve.yaml"
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path.write_text(yaml.safe_dump(raw), encoding="utf-8")
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loaded = load_serve_config(path)
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assert loaded.streaming is not None
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assert loaded.streaming.stream_mode == "av_fmp4"
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assert loaded.streaming.pool.num_workers == 4
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