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https://github.com/storytold/FineTrainers-Conditioning.git
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CrossProduct
35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
from diffusers import LTXPipeline
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from .lora import (
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collate_fn_t2v,
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forward_pass,
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initialize_pipeline,
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load_condition_models,
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load_diffusion_models,
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load_conditioned_diffusion_models, # use this for conditioning
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load_latent_models,
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post_latent_preparation,
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prepare_conditions,
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prepare_latents,
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validation,
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conditional_validation,
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collate_fn_t2v_cond,
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conditioned_forward_pass # use this for conditioning
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)
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# TODO(aryan): refactor into model specs for better re-use
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LTX_VIDEO_T2V_FULL_FINETUNE_CONFIG = {
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"pipeline_cls": LTXPipeline,
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"load_condition_models": load_condition_models,
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"load_latent_models": load_latent_models,
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"load_diffusion_models": load_conditioned_diffusion_models,
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"initialize_pipeline": initialize_pipeline,
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"prepare_conditions": prepare_conditions,
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"prepare_latents": prepare_latents,
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"post_latent_preparation": post_latent_preparation,
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"collate_fn": collate_fn_t2v_cond,
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"forward_pass": conditioned_forward_pass,
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"validation": conditional_validation,
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}
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