revert default cross attention optimization to Doggettx

parser.add_argument("--opt-split-attention-v1", action='store_true', help="prefer older version of split attention optimization for automatic choice of optimization")

parser.add_argument("--opt-split-attention-v1", action='store_true', help="prefer older version of split attention optimization for automatic choice of optimization")

parser.add_argument("--opt-sdp-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization for automatic choice of optimization; requires PyTorch 2.*")

parser.add_argument("--opt-sdp-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization for automatic choice of optimization; requires PyTorch 2.*")

parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization without memory efficient attention for automatic choice of optimization, makes image generation deterministic; requires PyTorch 2.*")

parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization without memory efficient attention for automatic choice of optimization, makes image generation deterministic; requires PyTorch 2.*")

parser.add_argument("--disable-opt-split-attention", action='store_true', help="does not do anything")

parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")

parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")

parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")

parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)

parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)

parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")

parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")

    if selection == "None":

    if selection == "None":

        matching_optimizer = None

        matching_optimizer = None

    elif selection == "Automatic" and shared.cmd_opts.disable_opt_split_attention:

        matching_optimizer = None

    elif matching_optimizer is None:

    elif matching_optimizer is None:

        matching_optimizer = optimizers[0]

        matching_optimizer = optimizers[0]

    name = "sdp-no-mem"

    name = "sdp-no-mem"

    label = "scaled dot product without memory efficient attention"

    label = "scaled dot product without memory efficient attention"

    cmd_opt = "opt_sdp_no_mem_attention"

    cmd_opt = "opt_sdp_no_mem_attention"

    priority = 90

    priority = 80

    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)

    name = "sdp"

    name = "sdp"

    label = "scaled dot product"

    label = "scaled dot product"

    cmd_opt = "opt_sdp_attention"

    cmd_opt = "opt_sdp_attention"

    priority = 80

    priority = 70

    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

class SdOptimizationDoggettx(SdOptimization):

class SdOptimizationDoggettx(SdOptimization):

    name = "Doggettx"

    name = "Doggettx"

    cmd_opt = "opt_split_attention"

    cmd_opt = "opt_split_attention"

    priority = 20

    priority = 90

    def apply(self):

    def apply(self):

        ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward

        ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward