def all_possible_dict_opts(unet, value):
            """
            Generate every possible combination for how a lora weight dict can be.
            E.g. 2, {"unet: {"down": 2}}, {"unet: {"down": [2,2,2]}}, {"unet: {"mid": 2, "up": [2,2,2]}}, ...
            """

            down_blocks_with_tf = [i for i, d in enumerate(unet.down_blocks) if hasattr(d, "attentions")]
            up_blocks_with_tf = [i for i, u in enumerate(unet.up_blocks) if hasattr(u, "attentions")]

            layers_per_block = unet.config.layers_per_block

            text_encoder_opts = [None, value]
            text_encoder_2_opts = [None, value]
            mid_opts = [None, value]
            down_opts = [None] + updown_options(down_blocks_with_tf, layers_per_block, value)
            up_opts = [None] + updown_options(up_blocks_with_tf, layers_per_block + 1, value)

            opts = []

            for t1, t2, d, m, u in product(text_encoder_opts, text_encoder_2_opts, down_opts, mid_opts, up_opts):
                if all(o is None for o in (t1, t2, d, m, u)):
                    continue
                opt = {}
                if t1 is not None:
                    opt["text_encoder"] = t1
                if t2 is not None:
                    opt["text_encoder_2"] = t2
                if all(o is None for o in (d, m, u)):
                    # no unet scaling
                    continue
                opt["unet"] = {}
                if d is not None:
                    opt["unet"]["down"] = d
                if m is not None:
                    opt["unet"]["mid"] = m
                if u is not None:
                    opt["unet"]["up"] = u
                opts.append(opt)

            return opts

            Generate every possible combination for how a lora weight dict can be.
            E.g. 2, {"unet: {"down": 2}}, {"unet: {"down": [2,2,2]}}, {"unet: {"mid": 2, "up": [2,2,2]}}, ...
            
tests/lora/utils.py