Closed
Description
Describe the bug
@classmethod
def from_pretrained(cls, path, model_cls, foreach=False) -> "EMAModel":
_, ema_kwargs = model_cls.load_config(path, return_unused_kwargs=True)
model = model_cls.from_pretrained(path)
ema_model = cls(model.parameters(), model_cls=model_cls, model_config=model.config, foreach=foreach)
ema_model.load_state_dict(ema_kwargs)
return ema_modelThis is the from_pretrained method of EMAModel class.
first line ", ema_kwargs = model_cls.load_config(path, return_unused_kwargs=True)" will always return a empty dict "ema_kwargs"
I think this line should be ", ema_kwargs = model_cls.from_config(path, return_unused_kwargs=True)" this will return the ema_kwargs correctly.
Reproduction
can use official example have ema.
https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/train_unconditional.py
Logs
kwargs=model_cls.load_config(path, return_unused_kwargs=True)
print(kwargs)
({'_class_name': 'DiTTransformer2DModel', '_diffusers_version': '0.30.2', 'activation_fn': 'gelu-approximate', 'attention_bias': True, 'attention_head_dim': 72, 'decay': 0.9999, 'dropout': 0.0, 'in_channels': 4, 'inv_gamma': 1.0, 'min_decay': 0.0, 'norm_elementwise_affine': False, 'norm_eps': 1e-05, 'norm_num_groups': 32, 'norm_type': 'ada_norm_zero', 'num_attention_heads': 16, 'num_embeds_ada_norm': 1000, 'num_layers': 28, 'optimization_step': 280000, 'out_channels': 4, ...}, {})
as you can see second kwargs always empty. ema related config ('optimization_step': 280000,'inv_gamma': 1.0, 'min_decay': 0.0,'decay': 0.9999) is in first kwarys.
However if use
kwargs=model_cls.from_config(path, return_unused_kwargs=True)
print(kwargs)
(<DiTTransformer2DModel>, {'decay': 0.9999, 'inv_gamma': 1.0, 'min_decay': 0.0, 'optimization_step': 280000, 'power': 0.75, 'update_after_step': 0, 'use_ema_warmup': True, '_class_name': 'DiTTransformer2DModel', '_diffusers_version': '0.30.2'})
second kwargs is ema config! that is correct.System Info
diffusers 0.30.2