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class SdOptimization:

    def __init__(self, name, label=None, cmd_opt=None):

        self.name = name

        self.label = label

        self.cmd_opt = cmd_opt

    name: str = None

    label: str | None = None

    cmd_opt: str | None = None

    priority: int = 0

    def title(self):

        if self.label is None:

    def is_available(self):

        return True

    def priority(self):

        return 0

    def apply(self):

        pass

class SdOptimizationXformers(SdOptimization):

    def __init__(self):

        super().__init__("xformers", cmd_opt="xformers")

    name = "xformers"

    cmd_opt = "xformers"

    priority = 100

    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))

    def priority(self):

        return 100

    def apply(self):

        ldm.modules.attention.CrossAttention.forward = xformers_attention_forward

        ldm.modules.diffusionmodules.model.AttnBlock.forward = xformers_attnblock_forward

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"):

        super().__init__(name, label, cmd_opt)

    name = "sdp-no-mem"

    label = "scaled dot product without memory efficient attention"

    cmd_opt = "opt_sdp_no_mem_attention"

    priority = 90

    def is_available(self):

        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):

        ldm.modules.attention.CrossAttention.forward = scaled_dot_product_no_mem_attention_forward

        ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_no_mem_attnblock_forward

class SdOptimizationSdp(SdOptimizationSdpNoMem):

    def __init__(self):

        super().__init__("sdp", "scaled dot product", cmd_opt="opt_sdp_attention")

    def priority(self):

        return 80

    name = "sdp"

    label = "scaled dot product"

    cmd_opt = "opt_sdp_attention"

    priority = 80

    def apply(self):

        ldm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward

class SdOptimizationSubQuad(SdOptimization):

    def __init__(self):

        super().__init__("sub-quadratic", cmd_opt="opt_sub_quad_attention")

    def priority(self):

        return 10

    name = "sub-quadratic"

    cmd_opt = "opt_sub_quad_attention"

    priority = 10

    def apply(self):

        ldm.modules.attention.CrossAttention.forward = sub_quad_attention_forward

class SdOptimizationV1(SdOptimization):

    def __init__(self):

        super().__init__("V1", "original v1", cmd_opt="opt_split_attention_v1")

    name = "V1"

    label = "original v1"

    cmd_opt = "opt_split_attention_v1"

    priority = 10

    def priority(self):

        return 10

    def apply(self):

        ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1

class SdOptimizationInvokeAI(SdOptimization):

    def __init__(self):

        super().__init__("InvokeAI", cmd_opt="opt_split_attention_invokeai")

    name = "InvokeAI"

    cmd_opt = "opt_split_attention_invokeai"

    @property

    def priority(self):

        return 1000 if not torch.cuda.is_available() else 10

class SdOptimizationDoggettx(SdOptimization):

    def __init__(self):

        super().__init__("Doggettx", cmd_opt="opt_split_attention")

    def priority(self):

        return 20

    name = "Doggettx"

    cmd_opt = "opt_split_attention"

    priority = 20

    def apply(self):

        ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward