mirror of https://github.com/vladmandic/automatic
152 lines
7.2 KiB
Python
152 lines
7.2 KiB
Python
import re
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import time
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import numpy as np
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import networks
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import lora_patches
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from modules import extra_networks, shared
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# from https://github.com/cheald/sd-webui-loractl/blob/master/loractl/lib/utils.py
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def get_stepwise(param, step, steps):
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def sorted_positions(raw_steps):
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steps = [[float(s.strip()) for s in re.split("[@~]", x)]
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for x in re.split("[,;]", str(raw_steps))]
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if len(steps[0]) == 1: # If we just got a single number, just return it
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return steps[0][0]
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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
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steps.sort(key=lambda k: k[1]) # Sort by index
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steps = [list(v) for v in zip(*steps)]
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return steps
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def calculate_weight(m, step, max_steps, step_offset=2):
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if isinstance(m, list):
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if m[1][-1] <= 1.0:
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step = (step) / (max_steps - step_offset) if max_steps > 0 else 1.0
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v = np.interp(step, m[1], m[0])
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return v
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else:
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return m
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return calculate_weight(sorted_positions(param), step, steps)
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class ExtraNetworkLora(extra_networks.ExtraNetwork):
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def __init__(self):
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super().__init__('lora')
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self.active = False
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self.model = None
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self.errors = {}
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networks.originals = lora_patches.LoraPatches()
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def prompt(self, p):
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if shared.opts.lora_apply_tags == 0:
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return
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all_tags = []
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for loaded in networks.loaded_networks:
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page = [en for en in shared.extra_networks if en.name == 'lora'][0]
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item = page.create_item(loaded.name)
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tags = (item or {}).get("tags", {})
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loaded.tags = list(tags)
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if len(loaded.tags) == 0:
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loaded.tags.append(loaded.name)
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if shared.opts.lora_apply_tags > 0:
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loaded.tags = loaded.tags[:shared.opts.lora_apply_tags]
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all_tags.extend(loaded.tags)
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if len(all_tags) > 0:
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shared.log.debug(f"Load network: type=LoRA tags={all_tags} max={shared.opts.lora_apply_tags} apply")
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all_tags = ', '.join(all_tags)
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p.extra_generation_params["LoRA tags"] = all_tags
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if '_tags_' in p.prompt:
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p.prompt = p.prompt.replace('_tags_', all_tags)
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else:
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p.prompt = f"{p.prompt}, {all_tags}"
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if p.all_prompts is not None:
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for i in range(len(p.all_prompts)):
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if '_tags_' in p.all_prompts[i]:
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p.all_prompts[i] = p.all_prompts[i].replace('_tags_', all_tags)
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else:
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p.all_prompts[i] = f"{p.all_prompts[i]}, {all_tags}"
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def infotext(self, p):
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names = [i.name for i in networks.loaded_networks]
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if len(names) > 0:
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p.extra_generation_params["LoRA networks"] = ", ".join(names)
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if shared.opts.lora_add_hashes_to_infotext:
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network_hashes = []
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for item in networks.loaded_networks:
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if not item.network_on_disk.shorthash:
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continue
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network_hashes.append(item.network_on_disk.shorthash)
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if len(network_hashes) > 0:
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p.extra_generation_params["LoRA hashes"] = ", ".join(network_hashes)
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def parse(self, p, params_list, step=0):
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names = []
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te_multipliers = []
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unet_multipliers = []
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dyn_dims = []
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for params in params_list:
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assert params.items
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names.append(params.positional[0])
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te_multiplier = params.named.get("te", params.positional[1] if len(params.positional) > 1 else shared.opts.extra_networks_default_multiplier)
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if isinstance(te_multiplier, str) and "@" in te_multiplier:
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te_multiplier = get_stepwise(te_multiplier, step, p.steps)
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else:
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te_multiplier = float(te_multiplier)
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unet_multiplier = [params.positional[2] if len(params.positional) > 2 else te_multiplier] * 3
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unet_multiplier = [params.named.get("unet", unet_multiplier[0])] * 3
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unet_multiplier[0] = params.named.get("in", unet_multiplier[0])
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unet_multiplier[1] = params.named.get("mid", unet_multiplier[1])
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unet_multiplier[2] = params.named.get("out", unet_multiplier[2])
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for i in range(len(unet_multiplier)):
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if isinstance(unet_multiplier[i], str) and "@" in unet_multiplier[i]:
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unet_multiplier[i] = get_stepwise(unet_multiplier[i], step, p.steps)
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else:
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unet_multiplier[i] = float(unet_multiplier[i])
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dyn_dim = int(params.positional[3]) if len(params.positional) > 3 else None
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dyn_dim = int(params.named["dyn"]) if "dyn" in params.named else dyn_dim
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te_multipliers.append(te_multiplier)
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unet_multipliers.append(unet_multiplier)
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dyn_dims.append(dyn_dim)
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return names, te_multipliers, unet_multipliers, dyn_dims
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def activate(self, p, params_list, step=0):
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t0 = time.time()
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self.errors.clear()
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if self.active:
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if self.model != shared.opts.sd_model_checkpoint: # reset if model changed
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self.active = False
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if len(params_list) > 0 and not self.active: # activate patches once
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shared.log.debug(f'Activate network: type=LoRA model="{shared.opts.sd_model_checkpoint}"')
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networks.originals.apply() # apply patches
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self.active = True
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self.model = shared.opts.sd_model_checkpoint
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names, te_multipliers, unet_multipliers, dyn_dims = self.parse(p, params_list, step)
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networks.load_networks(names, te_multipliers, unet_multipliers, dyn_dims)
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t1 = time.time()
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if len(networks.loaded_networks) > 0 and step == 0:
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self.infotext(p)
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self.prompt(p)
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shared.log.info(f'Load network: type=LoRA apply={[n.name for n in networks.loaded_networks]} te={te_multipliers} unet={unet_multipliers} dims={dyn_dims} load={t1-t0:.2f}')
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def deactivate(self, p):
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t0 = time.time()
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if shared.native and len(networks.diffuser_loaded) > 0:
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if hasattr(shared.sd_model, "unload_lora_weights") and hasattr(shared.sd_model, "text_encoder"):
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if not (shared.compiled_model_state is not None and shared.compiled_model_state.is_compiled is True):
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try:
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if shared.opts.lora_fuse_diffusers:
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shared.sd_model.unfuse_lora()
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shared.sd_model.unload_lora_weights() # fails for non-CLIP models
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except Exception:
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pass
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t1 = time.time()
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networks.timer['restore'] += t1 - t0
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if self.active and networks.debug:
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shared.log.debug(f"Network end: type=LoRA load={networks.timer['load']:.2f} apply={networks.timer['apply']:.2f} restore={networks.timer['restore']:.2f}")
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if self.errors:
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p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items()))
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for k, v in self.errors.items():
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shared.log.error(f'LoRA: name="{k}" errors={v}')
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self.errors.clear()
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