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refine training

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Artiprocher
2025-12-01 15:08:45 +08:00
parent 9048d2e9d4
commit 62c94a9927
5 changed files with 10 additions and 8 deletions
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2
diffsynth/diffusion/flow_match.py
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@@ -89,7 +89,7 @@ class FlowMatchScheduler():
return float(mu)
@staticmethod
def set_timesteps_flux2(num_inference_steps=100, denoising_strength=1.0, dynamic_shift_len=None):
def set_timesteps_flux2(num_inference_steps=100, denoising_strength=1.0, dynamic_shift_len=1024//16*1024//16):
sigma_min = 1 / num_inference_steps
sigma_max = 1.0
num_train_timesteps = 1000

2
diffsynth/diffusion/training_module.py
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@@ -29,6 +29,8 @@ class DiffusionTrainingModule(torch.nn.Module):
def add_lora_to_model(self, model, target_modules, lora_rank, lora_alpha=None, upcast_dtype=None):
if lora_alpha is None:
lora_alpha = lora_rank
if isinstance(target_modules, list) and len(target_modules) == 1:
target_modules = target_modules[0]
lora_config = LoraConfig(r=lora_rank, lora_alpha=lora_alpha, target_modules=target_modules)
model = inject_adapter_in_model(lora_config, model)
if upcast_dtype is not None: