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Merge branch 'dev' into hunyuanvideo

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This commit is contained in:
Zhongjie Duan
2024-12-18 11:21:33 +08:00
committed by GitHub
parent 263166768e 7a45b7efa7
commit 42a717054a
13 changed files with 833 additions and 19 deletions
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13
examples/train/flux/train_flux_lora.py
View File

@@ -10,7 +10,7 @@ class LightningModel(LightningModelForT2ILoRA):
self,
torch_dtype=torch.float16, pretrained_weights=[], preset_lora_path=None,
learning_rate=1e-4, use_gradient_checkpointing=True,
lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="kaiming",
lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="kaiming", pretrained_lora_path=None,
state_dict_converter=None, quantize = None
):
super().__init__(learning_rate=learning_rate, use_gradient_checkpointing=use_gradient_checkpointing, state_dict_converter=state_dict_converter)
@@ -34,7 +34,15 @@ class LightningModel(LightningModelForT2ILoRA):
self.pipe.scheduler.set_timesteps(1000, training=True)
self.freeze_parameters()
self.add_lora_to_model(self.pipe.denoising_model(), lora_rank=lora_rank, lora_alpha=lora_alpha, lora_target_modules=lora_target_modules, init_lora_weights=init_lora_weights)
self.add_lora_to_model(
self.pipe.denoising_model(),
lora_rank=lora_rank,
lora_alpha=lora_alpha,
lora_target_modules=lora_target_modules,
init_lora_weights=init_lora_weights,
pretrained_lora_path=pretrained_lora_path,
state_dict_converter=FluxLoRAConverter.align_to_diffsynth_format
)
def parse_args():
@@ -109,6 +117,7 @@ if __name__ == '__main__':
lora_alpha=args.lora_alpha,
lora_target_modules=args.lora_target_modules,
init_lora_weights=args.init_lora_weights,
pretrained_lora_path=args.pretrained_lora_path,
state_dict_converter=FluxLoRAConverter.align_to_opensource_format if args.align_to_opensource_format else None,
quantize={"float8_e4m3fn": torch.float8_e4m3fn}.get(args.quantize, None),
)

12
examples/train/hunyuan_dit/train_hunyuan_dit_lora.py
View File

@@ -9,7 +9,7 @@ class LightningModel(LightningModelForT2ILoRA):
self,
torch_dtype=torch.float16, pretrained_weights=[],
learning_rate=1e-4, use_gradient_checkpointing=True,
lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian",
lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian", pretrained_lora_path=None,
):
super().__init__(learning_rate=learning_rate, use_gradient_checkpointing=use_gradient_checkpointing)
# Load models
@@ -19,7 +19,14 @@ class LightningModel(LightningModelForT2ILoRA):
self.pipe.scheduler.set_timesteps(1000)
self.freeze_parameters()
self.add_lora_to_model(self.pipe.denoising_model(), lora_rank=lora_rank, lora_alpha=lora_alpha, lora_target_modules=lora_target_modules, init_lora_weights=init_lora_weights)
self.add_lora_to_model(
self.pipe.denoising_model(),
lora_rank=lora_rank,
lora_alpha=lora_alpha,
lora_target_modules=lora_target_modules,
init_lora_weights=init_lora_weights,
pretrained_lora_path=pretrained_lora_path,
)
def parse_args():
@@ -57,6 +64,7 @@ if __name__ == '__main__':
lora_rank=args.lora_rank,
lora_alpha=args.lora_alpha,
init_lora_weights=args.init_lora_weights,
pretrained_lora_path=args.pretrained_lora_path,
lora_target_modules=args.lora_target_modules
)
launch_training_task(model, args)

12
examples/train/kolors/train_kolors_lora.py
View File

@@ -9,7 +9,7 @@ class LightningModel(LightningModelForT2ILoRA):
self,
torch_dtype=torch.float16, pretrained_weights=[],
learning_rate=1e-4, use_gradient_checkpointing=True,
lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian",
lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian", pretrained_lora_path=None,
):
super().__init__(learning_rate=learning_rate, use_gradient_checkpointing=use_gradient_checkpointing)
# Load models
@@ -22,7 +22,14 @@ class LightningModel(LightningModelForT2ILoRA):
self.pipe.vae_encoder.to(torch_dtype)
self.freeze_parameters()
self.add_lora_to_model(self.pipe.denoising_model(), lora_rank=lora_rank, lora_alpha=lora_alpha, lora_target_modules=lora_target_modules, init_lora_weights=init_lora_weights)
self.add_lora_to_model(
self.pipe.denoising_model(),
lora_rank=lora_rank,
lora_alpha=lora_alpha,
lora_target_modules=lora_target_modules,
init_lora_weights=init_lora_weights,
pretrained_lora_path=pretrained_lora_path,
)
def parse_args():
@@ -73,6 +80,7 @@ if __name__ == '__main__':
lora_rank=args.lora_rank,
lora_alpha=args.lora_alpha,
init_lora_weights=args.init_lora_weights,
pretrained_lora_path=args.pretrained_lora_path,
lora_target_modules=args.lora_target_modules
)
launch_training_task(model, args)

12
examples/train/stable_diffusion/train_sd_lora.py
View File

@@ -9,7 +9,7 @@ class LightningModel(LightningModelForT2ILoRA):
self,
torch_dtype=torch.float16, pretrained_weights=[],
learning_rate=1e-4, use_gradient_checkpointing=True,
lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian",
lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian", pretrained_lora_path=None,
):
super().__init__(learning_rate=learning_rate, use_gradient_checkpointing=use_gradient_checkpointing)
# Load models
@@ -19,7 +19,14 @@ class LightningModel(LightningModelForT2ILoRA):
self.pipe.scheduler.set_timesteps(1000)
self.freeze_parameters()
self.add_lora_to_model(self.pipe.denoising_model(), lora_rank=lora_rank, lora_alpha=lora_alpha, lora_target_modules=lora_target_modules, init_lora_weights=init_lora_weights)
self.add_lora_to_model(
self.pipe.denoising_model(),
lora_rank=lora_rank,
lora_alpha=lora_alpha,
lora_target_modules=lora_target_modules,
init_lora_weights=init_lora_weights,
pretrained_lora_path=pretrained_lora_path,
)
def parse_args():
@@ -52,6 +59,7 @@ if __name__ == '__main__':
lora_rank=args.lora_rank,
lora_alpha=args.lora_alpha,
init_lora_weights=args.init_lora_weights,
pretrained_lora_path=args.pretrained_lora_path,
lora_target_modules=args.lora_target_modules
)
launch_training_task(model, args)

12
examples/train/stable_diffusion_3/train_sd3_lora.py
View File

@@ -9,7 +9,7 @@ class LightningModel(LightningModelForT2ILoRA):
self,
torch_dtype=torch.float16, pretrained_weights=[], preset_lora_path=None,
learning_rate=1e-4, use_gradient_checkpointing=True,
lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian",
lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian", pretrained_lora_path=None,
):
super().__init__(learning_rate=learning_rate, use_gradient_checkpointing=use_gradient_checkpointing)
# Load models
@@ -24,7 +24,14 @@ class LightningModel(LightningModelForT2ILoRA):
model_manager.load_lora(path)
self.freeze_parameters()
self.add_lora_to_model(self.pipe.denoising_model(), lora_rank=lora_rank, lora_alpha=lora_alpha, lora_target_modules=lora_target_modules, init_lora_weights=init_lora_weights)
self.add_lora_to_model(
self.pipe.denoising_model(),
lora_rank=lora_rank,
lora_alpha=lora_alpha,
lora_target_modules=lora_target_modules,
init_lora_weights=init_lora_weights,
pretrained_lora_path=pretrained_lora_path,
)
def parse_args():
@@ -70,6 +77,7 @@ if __name__ == '__main__':
lora_rank=args.lora_rank,
lora_alpha=args.lora_alpha,
init_lora_weights=args.init_lora_weights,
pretrained_lora_path=args.pretrained_lora_path,
lora_target_modules=args.lora_target_modules
)
launch_training_task(model, args)

12
examples/train/stable_diffusion_xl/train_sdxl_lora.py
View File

@@ -9,7 +9,7 @@ class LightningModel(LightningModelForT2ILoRA):
self,
torch_dtype=torch.float16, pretrained_weights=[],
learning_rate=1e-4, use_gradient_checkpointing=True,
lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian",
lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian", pretrained_lora_path=None,
):
super().__init__(learning_rate=learning_rate, use_gradient_checkpointing=use_gradient_checkpointing)
# Load models
@@ -19,7 +19,14 @@ class LightningModel(LightningModelForT2ILoRA):
self.pipe.scheduler.set_timesteps(1000)
self.freeze_parameters()
self.add_lora_to_model(self.pipe.denoising_model(), lora_rank=lora_rank, lora_alpha=lora_alpha, lora_target_modules=lora_target_modules, init_lora_weights=init_lora_weights)
self.add_lora_to_model(
self.pipe.denoising_model(),
lora_rank=lora_rank,
lora_alpha=lora_alpha,
lora_target_modules=lora_target_modules,
init_lora_weights=init_lora_weights,
pretrained_lora_path=pretrained_lora_path,
)
def parse_args():
@@ -52,6 +59,7 @@ if __name__ == '__main__':
lora_rank=args.lora_rank,
lora_alpha=args.lora_alpha,
init_lora_weights=args.init_lora_weights,
pretrained_lora_path=args.pretrained_lora_path,
lora_target_modules=args.lora_target_modules
)
launch_training_task(model, args)