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add shared.opts.lora_without_backup_weight option to reduce ram usage

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Won-Kyu Park 2024-09-07 12:31:00 +09:00
parent 537d9dd71c
commit 2060886450
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GPG Key ID: 53AA79C8C9535D15
2 changed files with 12 additions and 1 deletions
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12
extensions-builtin/Lora/networks.py
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@ -377,6 +377,8 @@ def store_weights_backup(weight):
if weight is None: if weight is None:
return None return None
if shared.opts.lora_without_backup_weight:
return True
return weight.to(devices.cpu, copy=True) return weight.to(devices.cpu, copy=True)
@ -395,6 +397,9 @@ def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Li
if weights_backup is None and bias_backup is None: if weights_backup is None and bias_backup is None:
return return
if weights_backup is True or weights_backup == (True, True): # fake backup
return
if weights_backup is not None: if weights_backup is not None:
if isinstance(self, torch.nn.MultiheadAttention): if isinstance(self, torch.nn.MultiheadAttention):
restore_weights_backup(self, 'in_proj_weight', weights_backup[0]) restore_weights_backup(self, 'in_proj_weight', weights_backup[0])
@ -539,7 +544,12 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation") logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation")
extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1 extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
self.network_current_names = wanted_names
if weights_backup is True or weights_backup == (True, True): # fake backup
self.network_weights_backup = None
self.network_bias_backup = None
else:
self.network_current_names = wanted_names
def network_forward(org_module, input, original_forward): def network_forward(org_module, input, original_forward):

1
modules/shared_options.py
View File

@ -242,6 +242,7 @@ options_templates.update(options_section(('optimizations', "Optimizations", "sd"
"batch_cond_uncond": OptionInfo(True, "Batch cond/uncond").info("do both conditional and unconditional denoising in one batch; uses a bit more VRAM during sampling, but improves speed; previously this was controlled by --always-batch-cond-uncond commandline argument"), "batch_cond_uncond": OptionInfo(True, "Batch cond/uncond").info("do both conditional and unconditional denoising in one batch; uses a bit more VRAM during sampling, but improves speed; previously this was controlled by --always-batch-cond-uncond commandline argument"),
"fp8_storage": OptionInfo("Disable", "FP8 weight", gr.Radio, {"choices": ["Disable", "Enable for SDXL", "Enable"]}).info("Use FP8 to store Linear/Conv layers' weight. Require pytorch>=2.1.0."), "fp8_storage": OptionInfo("Disable", "FP8 weight", gr.Radio, {"choices": ["Disable", "Enable for SDXL", "Enable"]}).info("Use FP8 to store Linear/Conv layers' weight. Require pytorch>=2.1.0."),
"cache_fp16_weight": OptionInfo(False, "Cache FP16 weight for LoRA").info("Cache fp16 weight when enabling FP8, will increase the quality of LoRA. Use more system ram."), "cache_fp16_weight": OptionInfo(False, "Cache FP16 weight for LoRA").info("Cache fp16 weight when enabling FP8, will increase the quality of LoRA. Use more system ram."),
"lora_without_backup_weight": OptionInfo(False, "LoRA without backup weights").info("LoRA without backup weights to save RAM."),
})) }))
options_templates.update(options_section(('compatibility', "Compatibility", "sd"), { options_templates.update(options_section(('compatibility', "Compatibility", "sd"), {