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Co-authored-by: Aarni Koskela <akx@iki.fi>
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AUTOMATIC1111 2023-05-19 09:17:36 +03:00 committed by GitHub
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commit 1e5afd4fa9
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1 changed files with 28 additions and 38 deletions
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66
modules/sd_hijack_optimizations.py
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@ -19,10 +19,10 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
class SdOptimization: class SdOptimization:
def __init__(self, name, label=None, cmd_opt=None): name: str = None
self.name = name label: str | None = None
self.label = label cmd_opt: str | None = None
self.cmd_opt = cmd_opt priority: int = 0
def title(self): def title(self):
if self.label is None: if self.label is None:
@ -33,9 +33,6 @@ class SdOptimization:
def is_available(self): def is_available(self):
return True return True
def priority(self):
return 0
def apply(self): def apply(self):
pass pass
@ -45,41 +42,37 @@ class SdOptimization:
class SdOptimizationXformers(SdOptimization): class SdOptimizationXformers(SdOptimization):
def __init__(self): name = "xformers"
super().__init__("xformers", cmd_opt="xformers") cmd_opt = "xformers"
priority = 100
def is_available(self): def is_available(self):
return shared.cmd_opts.force_enable_xformers or (shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)) return shared.cmd_opts.force_enable_xformers or (shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0))
def priority(self):
return 100
def apply(self): def apply(self):
ldm.modules.attention.CrossAttention.forward = xformers_attention_forward ldm.modules.attention.CrossAttention.forward = xformers_attention_forward
ldm.modules.diffusionmodules.model.AttnBlock.forward = xformers_attnblock_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = xformers_attnblock_forward
class SdOptimizationSdpNoMem(SdOptimization): class SdOptimizationSdpNoMem(SdOptimization):
def __init__(self, name="sdp-no-mem", label="scaled dot product without memory efficient attention", cmd_opt="opt_sdp_no_mem_attention"): name = "sdp-no-mem"
super().__init__(name, label, cmd_opt) label = "scaled dot product without memory efficient attention"
cmd_opt = "opt_sdp_no_mem_attention"
priority = 90
def is_available(self): def is_available(self):
return hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention) return hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention)
def priority(self):
return 90
def apply(self): def apply(self):
ldm.modules.attention.CrossAttention.forward = scaled_dot_product_no_mem_attention_forward ldm.modules.attention.CrossAttention.forward = scaled_dot_product_no_mem_attention_forward
ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_no_mem_attnblock_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_no_mem_attnblock_forward
class SdOptimizationSdp(SdOptimizationSdpNoMem): class SdOptimizationSdp(SdOptimizationSdpNoMem):
def __init__(self): name = "sdp"
super().__init__("sdp", "scaled dot product", cmd_opt="opt_sdp_attention") label = "scaled dot product"
cmd_opt = "opt_sdp_attention"
def priority(self): priority = 80
return 80
def apply(self): def apply(self):
ldm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward ldm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward
@ -87,11 +80,9 @@ class SdOptimizationSdp(SdOptimizationSdpNoMem):
class SdOptimizationSubQuad(SdOptimization): class SdOptimizationSubQuad(SdOptimization):
def __init__(self): name = "sub-quadratic"
super().__init__("sub-quadratic", cmd_opt="opt_sub_quad_attention") cmd_opt = "opt_sub_quad_attention"
priority = 10
def priority(self):
return 10
def apply(self): def apply(self):
ldm.modules.attention.CrossAttention.forward = sub_quad_attention_forward ldm.modules.attention.CrossAttention.forward = sub_quad_attention_forward
@ -99,20 +90,21 @@ class SdOptimizationSubQuad(SdOptimization):
class SdOptimizationV1(SdOptimization): class SdOptimizationV1(SdOptimization):
def __init__(self): name = "V1"
super().__init__("V1", "original v1", cmd_opt="opt_split_attention_v1") label = "original v1"
cmd_opt = "opt_split_attention_v1"
priority = 10
def priority(self):
return 10
def apply(self): def apply(self):
ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1 ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
class SdOptimizationInvokeAI(SdOptimization): class SdOptimizationInvokeAI(SdOptimization):
def __init__(self): name = "InvokeAI"
super().__init__("InvokeAI", cmd_opt="opt_split_attention_invokeai") cmd_opt = "opt_split_attention_invokeai"
@property
def priority(self): def priority(self):
return 1000 if not torch.cuda.is_available() else 10 return 1000 if not torch.cuda.is_available() else 10
@ -121,11 +113,9 @@ class SdOptimizationInvokeAI(SdOptimization):
class SdOptimizationDoggettx(SdOptimization): class SdOptimizationDoggettx(SdOptimization):
def __init__(self): name = "Doggettx"
super().__init__("Doggettx", cmd_opt="opt_split_attention") cmd_opt = "opt_split_attention"
priority = 20
def priority(self):
return 20
def apply(self): def apply(self):
ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward