50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Request model for creating a job."""
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from pydantic import BaseModel
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class CreateJobRequest(BaseModel):
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model_id: str
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prompt: str
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workload_type: str = "t2v"
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job_type: str = "inference"
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image_path: str = ""
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data_path: str = ""
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max_train_steps: int = 1000
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train_batch_size: int = 1
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learning_rate: float = 5e-5
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num_latent_t: int = 20
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validation_dataset_file: str = ""
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lora_rank: int = 32
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ltx2_first_frame_conditioning_p: float | None = None
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negative_prompt: str = ""
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num_inference_steps: int = 50
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num_frames: int = 81
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height: int = 480
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width: int = 832
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guidance_scale: float = 5.0
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guidance_rescale: float = 0.0
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fps: int = 24
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seed: int = 1024
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num_gpus: int = 1
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dit_cpu_offload: bool = False
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text_encoder_cpu_offload: bool = False
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vae_cpu_offload: bool = False
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image_encoder_cpu_offload: bool = False
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use_fsdp_inference: bool = False
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enable_torch_compile: bool = False
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vsa_sparsity: float = 0.0
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tp_size: int = -1
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sp_size: int = -1
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# DMD (Distribution Matching Distillation) options
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dmd_use_vsa: bool = False
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dmd_vsa_sparsity: float = 0.8
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dmd_denoising_steps: str = "1000,757,522"
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min_timestep_ratio: float = 0.02
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max_timestep_ratio: float = 0.98
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real_score_guidance_scale: float = 3.5
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generator_update_interval: int = 5
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real_score_model_path: str = ""
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fake_score_model_path: str = ""
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