automatic/modules/lora/extra_networks_lora.py

import re
import numpy as np
import modules.lora.networks as networks
from modules import extra_networks, shared


# from https://github.com/cheald/sd-webui-loractl/blob/master/loractl/lib/utils.py
def get_stepwise(param, step, steps):
    def sorted_positions(raw_steps):
        steps = [[float(s.strip()) for s in re.split("[@~]", x)]
                 for x in re.split("[,;]", str(raw_steps))]
        if len(steps[0]) == 1: # If we just got a single number, just return it
            return steps[0][0]
        steps = [[s[0], s[1] if len(s) == 2 else 1] for s in steps] # Add implicit 1s to any steps which don't have a weight
        steps.sort(key=lambda k: k[1]) # Sort by index
        steps = [list(v) for v in zip(*steps)]
        return steps

    def calculate_weight(m, step, max_steps, step_offset=2):
        if isinstance(m, list):
            if m[1][-1] <= 1.0:
                step = step / (max_steps - step_offset) if max_steps > 0 else 1.0
            v = np.interp(step, m[1], m[0])
            return v
        else:
            return m

    stepwise = calculate_weight(sorted_positions(param), step, steps)
    return stepwise


def prompt(p):
    if shared.opts.lora_apply_tags == 0:
        return
    all_tags = []
    for loaded in networks.loaded_networks:
        page = [en for en in shared.extra_networks if en.name == 'lora'][0]
        item = page.create_item(loaded.name)
        tags = (item or {}).get("tags", {})
        loaded.tags = list(tags)
        if len(loaded.tags) == 0:
            loaded.tags.append(loaded.name)
        if shared.opts.lora_apply_tags > 0:
            loaded.tags = loaded.tags[:shared.opts.lora_apply_tags]
        all_tags.extend(loaded.tags)
    if len(all_tags) > 0:
        all_tags = list(set(all_tags))
        all_tags = [t for t in all_tags if t not in p.prompt]
        shared.log.debug(f"Load network: type=LoRA tags={all_tags} max={shared.opts.lora_apply_tags} apply")
        all_tags = ', '.join(all_tags)
        p.extra_generation_params["LoRA tags"] = all_tags
        if '_tags_' in p.prompt:
            p.prompt = p.prompt.replace('_tags_', all_tags)
        else:
            p.prompt = f"{p.prompt}, {all_tags}"
        if p.all_prompts is not None:
            for i in range(len(p.all_prompts)):
                if '_tags_' in p.all_prompts[i]:
                    p.all_prompts[i] = p.all_prompts[i].replace('_tags_', all_tags)
                else:
                    p.all_prompts[i] = f"{p.all_prompts[i]}, {all_tags}"


def infotext(p):
    names = [i.name for i in networks.loaded_networks]
    if len(names) > 0:
        p.extra_generation_params["LoRA networks"] = ", ".join(names)
    if shared.opts.lora_add_hashes_to_infotext:
        network_hashes = []
        for item in networks.loaded_networks:
            if not item.network_on_disk.shorthash:
                continue
            network_hashes.append(item.network_on_disk.shorthash)
        if len(network_hashes) > 0:
            p.extra_generation_params["LoRA hashes"] = ", ".join(network_hashes)


def parse(p, params_list, step=0):
    names = []
    te_multipliers = []
    unet_multipliers = []
    dyn_dims = []
    for params in params_list:
        assert params.items
        names.append(params.positional[0])
        te_multiplier = params.named.get("te", params.positional[1] if len(params.positional) > 1 else shared.opts.extra_networks_default_multiplier)
        if isinstance(te_multiplier, str) and "@" in te_multiplier:
            te_multiplier = get_stepwise(te_multiplier, step, p.steps)
        else:
            te_multiplier = float(te_multiplier)
        unet_multiplier = [params.positional[2] if len(params.positional) > 2 else te_multiplier] * 3
        unet_multiplier = [params.named.get("unet", unet_multiplier[0])] * 3
        unet_multiplier[0] = params.named.get("in", unet_multiplier[0])
        unet_multiplier[1] = params.named.get("mid", unet_multiplier[1])
        unet_multiplier[2] = params.named.get("out", unet_multiplier[2])
        for i in range(len(unet_multiplier)):
            if isinstance(unet_multiplier[i], str) and "@" in unet_multiplier[i]:
                unet_multiplier[i] = get_stepwise(unet_multiplier[i], step, p.steps)
            else:
                unet_multiplier[i] = float(unet_multiplier[i])
        dyn_dim = int(params.positional[3]) if len(params.positional) > 3 else None
        dyn_dim = int(params.named["dyn"]) if "dyn" in params.named else dyn_dim
        te_multipliers.append(te_multiplier)
        unet_multipliers.append(unet_multiplier)
        dyn_dims.append(dyn_dim)
    return names, te_multipliers, unet_multipliers, dyn_dims


class ExtraNetworkLora(extra_networks.ExtraNetwork):

    def __init__(self):
        super().__init__('lora')
        self.active = False
        self.model = None
        self.errors = {}

    def activate(self, p, params_list, step=0, include=[], exclude=[]):
        self.errors.clear()
        if self.active:
            if self.model != shared.opts.sd_model_checkpoint: # reset if model changed
                self.active = False
        if len(params_list) > 0 and not self.active: # activate patches once
            # shared.log.debug(f'Activate network: type=LoRA model="{shared.opts.sd_model_checkpoint}"')
            self.active = True
            self.model = shared.opts.sd_model_checkpoint
        if 'text_encoder' in include:
            networks.timer.clear(complete=True)
        names, te_multipliers, unet_multipliers, dyn_dims = parse(p, params_list, step)
        networks.network_load(names, te_multipliers, unet_multipliers, dyn_dims) # load
        networks.network_activate(include, exclude)
        if len(networks.loaded_networks) > 0 and len(networks.applied_layers) > 0 and step == 0:
            infotext(p)
            prompt(p)
            shared.log.info(f'Load network: type=LoRA apply={[n.name for n in networks.loaded_networks]} mode={"fuse" if shared.opts.lora_fuse_diffusers else "backup"} te={te_multipliers} unet={unet_multipliers} time={networks.timer.summary}')

    def deactivate(self, p):
        if shared.native and len(networks.diffuser_loaded) > 0:
            if hasattr(shared.sd_model, "unload_lora_weights") and hasattr(shared.sd_model, "text_encoder"):
                if not (shared.compiled_model_state is not None and shared.compiled_model_state.is_compiled is True):
                    try:
                        if shared.opts.lora_fuse_diffusers:
                            shared.sd_model.unfuse_lora()
                        shared.sd_model.unload_lora_weights() # fails for non-CLIP models
                    except Exception:
                        pass
        networks.network_deactivate()
        if self.active and networks.debug:
            shared.log.debug(f"Network end: type=LoRA time={networks.timer.summary}")
        if self.errors:
            for k, v in self.errors.items():
                shared.log.error(f'LoRA: name="{k}" errors={v}')
            self.errors.clear()