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Add experimental Nunchaku Qwen LoRA support (#873)
This commit is contained in:
Will Miao
@@ -1,22 +1,127 @@
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import logging
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import re
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import comfy.utils # type: ignore
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import comfy.sd # type: ignore
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import comfy.sd # type: ignore
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import comfy.utils # type: ignore
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from ..utils.utils import get_lora_info_absolute
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from .utils import FlexibleOptionalInputType, any_type, extract_lora_name, get_loras_list, nunchaku_load_lora
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from .nunchaku_qwen import nunchaku_load_qwen_loras
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from .utils import (
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FlexibleOptionalInputType,
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any_type,
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detect_nunchaku_model_kind,
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extract_lora_name,
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get_loras_list,
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nunchaku_load_lora,
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)
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logger = logging.getLogger(__name__)
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def _collect_stack_entries(lora_stack):
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entries = []
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if not lora_stack:
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return entries
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for lora_path, model_strength, clip_strength in lora_stack:
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lora_name = extract_lora_name(lora_path)
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absolute_lora_path, trigger_words = get_lora_info_absolute(lora_name)
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entries.append({
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"name": lora_name,
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"absolute_path": absolute_lora_path,
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"input_path": lora_path,
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"model_strength": float(model_strength),
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"clip_strength": float(clip_strength),
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"trigger_words": trigger_words,
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})
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return entries
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def _collect_widget_entries(kwargs):
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entries = []
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for lora in get_loras_list(kwargs):
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if not lora.get("active", False):
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continue
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lora_name = lora["name"]
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model_strength = float(lora["strength"])
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clip_strength = float(lora.get("clipStrength", model_strength))
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lora_path, trigger_words = get_lora_info_absolute(lora_name)
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entries.append({
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"name": lora_name,
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"absolute_path": lora_path,
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"input_path": lora_path,
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"model_strength": model_strength,
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"clip_strength": clip_strength,
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"trigger_words": trigger_words,
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})
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return entries
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def _format_loaded_loras(loaded_loras):
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formatted_loras = []
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for item in loaded_loras:
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if item["include_clip_strength"]:
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formatted_loras.append(
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f"<lora:{item['name']}:{item['model_strength']}:{item['clip_strength']}>"
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)
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else:
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formatted_loras.append(f"<lora:{item['name']}:{item['model_strength']}>")
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return " ".join(formatted_loras)
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def _apply_entries(model, clip, lora_entries, nunchaku_model_kind):
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loaded_loras = []
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all_trigger_words = []
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if nunchaku_model_kind == "qwen_image":
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qwen_lora_configs = []
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for entry in lora_entries:
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qwen_lora_configs.append((entry["absolute_path"], entry["model_strength"]))
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loaded_loras.append({
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"name": entry["name"],
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"model_strength": entry["model_strength"],
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"clip_strength": entry["model_strength"],
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"include_clip_strength": False,
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})
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all_trigger_words.extend(entry["trigger_words"])
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if qwen_lora_configs:
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model = nunchaku_load_qwen_loras(model, qwen_lora_configs)
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return model, clip, loaded_loras, all_trigger_words
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for entry in lora_entries:
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if nunchaku_model_kind == "flux":
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model = nunchaku_load_lora(model, entry["input_path"], entry["model_strength"])
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else:
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lora = comfy.utils.load_torch_file(entry["absolute_path"], safe_load=True)
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model, clip = comfy.sd.load_lora_for_models(
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model,
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clip,
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lora,
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entry["model_strength"],
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entry["clip_strength"],
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)
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include_clip_strength = nunchaku_model_kind is None and abs(entry["model_strength"] - entry["clip_strength"]) > 0.001
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loaded_loras.append({
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"name": entry["name"],
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"model_strength": entry["model_strength"],
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"clip_strength": entry["clip_strength"],
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"include_clip_strength": include_clip_strength,
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})
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all_trigger_words.extend(entry["trigger_words"])
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return model, clip, loaded_loras, all_trigger_words
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class LoraLoaderLM:
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NAME = "Lora Loader (LoraManager)"
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CATEGORY = "Lora Manager/loaders"
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model": ("MODEL",),
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# "clip": ("CLIP",),
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"text": ("AUTOCOMPLETE_TEXT_LORAS", {
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"placeholder": "Search LoRAs to add...",
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"tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
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@@ -28,114 +133,30 @@ class LoraLoaderLM:
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RETURN_TYPES = ("MODEL", "CLIP", "STRING", "STRING")
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RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
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FUNCTION = "load_loras"
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def load_loras(self, model, text, **kwargs):
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"""Loads multiple LoRAs based on the kwargs input and lora_stack."""
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loaded_loras = []
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all_trigger_words = []
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clip = kwargs.get('clip', None)
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lora_stack = kwargs.get('lora_stack', None)
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# Check if model is a Nunchaku Flux model - simplified approach
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is_nunchaku_model = False
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try:
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model_wrapper = model.model.diffusion_model
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# Check if model is a Nunchaku Flux model using only class name
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if model_wrapper.__class__.__name__ == "ComfyFluxWrapper":
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is_nunchaku_model = True
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logger.info("Detected Nunchaku Flux model")
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except (AttributeError, TypeError):
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# Not a model with the expected structure
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pass
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# First process lora_stack if available
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if lora_stack:
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for lora_path, model_strength, clip_strength in lora_stack:
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# Extract lora name and convert to absolute path
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# lora_stack stores relative paths, but load_torch_file needs absolute paths
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lora_name = extract_lora_name(lora_path)
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absolute_lora_path, trigger_words = get_lora_info_absolute(lora_name)
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# Apply the LoRA using the appropriate loader
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if is_nunchaku_model:
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# Use our custom function for Flux models
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model = nunchaku_load_lora(model, lora_path, model_strength)
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# clip remains unchanged for Nunchaku models
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else:
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# Use lower-level API to load LoRA directly without folder_paths validation
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lora = comfy.utils.load_torch_file(absolute_lora_path, safe_load=True)
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model, clip = comfy.sd.load_lora_for_models(model, clip, lora, model_strength, clip_strength)
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all_trigger_words.extend(trigger_words)
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# Add clip strength to output if different from model strength (except for Nunchaku models)
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if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
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loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
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else:
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loaded_loras.append(f"{lora_name}: {model_strength}")
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# Then process loras from kwargs with support for both old and new formats
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loras_list = get_loras_list(kwargs)
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for lora in loras_list:
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if not lora.get('active', False):
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continue
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lora_name = lora['name']
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model_strength = float(lora['strength'])
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# Get clip strength - use model strength as default if not specified
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clip_strength = float(lora.get('clipStrength', model_strength))
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# Get lora path and trigger words
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lora_path, trigger_words = get_lora_info_absolute(lora_name)
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# Apply the LoRA using the appropriate loader
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if is_nunchaku_model:
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# For Nunchaku models, use our custom function
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model = nunchaku_load_lora(model, lora_path, model_strength)
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# clip remains unchanged
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else:
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# Use lower-level API to load LoRA directly without folder_paths validation
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lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
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model, clip = comfy.sd.load_lora_for_models(model, clip, lora, model_strength, clip_strength)
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# Include clip strength in output if different from model strength and not a Nunchaku model
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if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
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loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
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else:
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loaded_loras.append(f"{lora_name}: {model_strength}")
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# Add trigger words to collection
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all_trigger_words.extend(trigger_words)
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# use ',, ' to separate trigger words for group mode
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trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
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# Format loaded_loras with support for both formats
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formatted_loras = []
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for item in loaded_loras:
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parts = item.split(":")
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lora_name = parts[0]
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strength_parts = parts[1].strip().split(",")
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if len(strength_parts) > 1:
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# Different model and clip strengths
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model_str = strength_parts[0].strip()
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clip_str = strength_parts[1].strip()
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formatted_loras.append(f"<lora:{lora_name}:{model_str}:{clip_str}>")
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else:
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# Same strength for both
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model_str = strength_parts[0].strip()
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formatted_loras.append(f"<lora:{lora_name}:{model_str}>")
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formatted_loras_text = " ".join(formatted_loras)
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del text
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clip = kwargs.get("clip", None)
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lora_entries = _collect_stack_entries(kwargs.get("lora_stack", None))
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lora_entries.extend(_collect_widget_entries(kwargs))
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nunchaku_model_kind = detect_nunchaku_model_kind(model)
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if nunchaku_model_kind == "flux":
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logger.info("Detected Nunchaku Flux model")
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elif nunchaku_model_kind == "qwen_image":
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logger.info("Detected Nunchaku Qwen-Image model")
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model, clip, loaded_loras, all_trigger_words = _apply_entries(model, clip, lora_entries, nunchaku_model_kind)
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trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
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formatted_loras_text = _format_loaded_loras(loaded_loras)
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return (model, clip, trigger_words_text, formatted_loras_text)
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class LoraTextLoaderLM:
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NAME = "LoRA Text Loader (LoraManager)"
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CATEGORY = "Lora Manager/loaders"
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@classmethod
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def INPUT_TYPES(cls):
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return {
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@@ -143,131 +164,55 @@ class LoraTextLoaderLM:
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"model": ("MODEL",),
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"lora_syntax": ("STRING", {
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"forceInput": True,
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"tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation"
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"tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
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}),
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},
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"optional": {
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"clip": ("CLIP",),
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"lora_stack": ("LORA_STACK",),
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}
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},
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}
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RETURN_TYPES = ("MODEL", "CLIP", "STRING", "STRING")
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RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
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FUNCTION = "load_loras_from_text"
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def parse_lora_syntax(self, text):
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"""Parse LoRA syntax from text input."""
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# Pattern to match <lora:name:strength> or <lora:name:model_strength:clip_strength>
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pattern = r'<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>'
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pattern = r"<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>"
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matches = re.findall(pattern, text, re.IGNORECASE)
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loras = []
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for match in matches:
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lora_name = match[0]
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model_strength = float(match[1])
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clip_strength = float(match[2]) if match[2] else model_strength
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loras.append({
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'name': lora_name,
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'model_strength': model_strength,
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'clip_strength': clip_strength
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"name": match[0],
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"model_strength": model_strength,
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"clip_strength": float(match[2]) if match[2] else model_strength,
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})
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return loras
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def load_loras_from_text(self, model, lora_syntax, clip=None, lora_stack=None):
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"""Load LoRAs based on text syntax input."""
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loaded_loras = []
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all_trigger_words = []
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# Check if model is a Nunchaku Flux model - simplified approach
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is_nunchaku_model = False
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try:
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model_wrapper = model.model.diffusion_model
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# Check if model is a Nunchaku Flux model using only class name
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if model_wrapper.__class__.__name__ == "ComfyFluxWrapper":
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is_nunchaku_model = True
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logger.info("Detected Nunchaku Flux model")
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except (AttributeError, TypeError):
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# Not a model with the expected structure
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pass
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# First process lora_stack if available
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if lora_stack:
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for lora_path, model_strength, clip_strength in lora_stack:
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# Extract lora name and convert to absolute path
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# lora_stack stores relative paths, but load_torch_file needs absolute paths
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lora_name = extract_lora_name(lora_path)
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absolute_lora_path, trigger_words = get_lora_info_absolute(lora_name)
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# Apply the LoRA using the appropriate loader
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if is_nunchaku_model:
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# Use our custom function for Flux models
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model = nunchaku_load_lora(model, lora_path, model_strength)
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# clip remains unchanged for Nunchaku models
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else:
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# Use lower-level API to load LoRA directly without folder_paths validation
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lora = comfy.utils.load_torch_file(absolute_lora_path, safe_load=True)
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model, clip = comfy.sd.load_lora_for_models(model, clip, lora, model_strength, clip_strength)
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all_trigger_words.extend(trigger_words)
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# Add clip strength to output if different from model strength (except for Nunchaku models)
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if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
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loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
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else:
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loaded_loras.append(f"{lora_name}: {model_strength}")
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# Parse and process LoRAs from text syntax
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parsed_loras = self.parse_lora_syntax(lora_syntax)
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for lora in parsed_loras:
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lora_name = lora['name']
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model_strength = lora['model_strength']
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clip_strength = lora['clip_strength']
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# Get lora path and trigger words
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lora_path, trigger_words = get_lora_info_absolute(lora_name)
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# Apply the LoRA using the appropriate loader
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if is_nunchaku_model:
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# For Nunchaku models, use our custom function
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model = nunchaku_load_lora(model, lora_path, model_strength)
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# clip remains unchanged
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else:
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# Use lower-level API to load LoRA directly without folder_paths validation
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lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
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model, clip = comfy.sd.load_lora_for_models(model, clip, lora, model_strength, clip_strength)
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# Include clip strength in output if different from model strength and not a Nunchaku model
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if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
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loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
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else:
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loaded_loras.append(f"{lora_name}: {model_strength}")
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# Add trigger words to collection
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all_trigger_words.extend(trigger_words)
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# use ',, ' to separate trigger words for group mode
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lora_entries = _collect_stack_entries(lora_stack)
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for lora in self.parse_lora_syntax(lora_syntax):
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lora_path, trigger_words = get_lora_info_absolute(lora["name"])
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lora_entries.append({
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"name": lora["name"],
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"absolute_path": lora_path,
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"input_path": lora_path,
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"model_strength": lora["model_strength"],
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"clip_strength": lora["clip_strength"],
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"trigger_words": trigger_words,
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})
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nunchaku_model_kind = detect_nunchaku_model_kind(model)
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if nunchaku_model_kind == "flux":
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logger.info("Detected Nunchaku Flux model")
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elif nunchaku_model_kind == "qwen_image":
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logger.info("Detected Nunchaku Qwen-Image model")
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model, clip, loaded_loras, all_trigger_words = _apply_entries(model, clip, lora_entries, nunchaku_model_kind)
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trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
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# Format loaded_loras with support for both formats
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formatted_loras = []
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for item in loaded_loras:
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parts = item.split(":")
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lora_name = parts[0].strip()
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strength_parts = parts[1].strip().split(",")
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if len(strength_parts) > 1:
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# Different model and clip strengths
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model_str = strength_parts[0].strip()
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clip_str = strength_parts[1].strip()
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formatted_loras.append(f"<lora:{lora_name}:{model_str}:{clip_str}>")
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else:
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# Same strength for both
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model_str = strength_parts[0].strip()
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formatted_loras.append(f"<lora:{lora_name}:{model_str}>")
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formatted_loras_text = " ".join(formatted_loras)
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return (model, clip, trigger_words_text, formatted_loras_text)
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formatted_loras_text = _format_loaded_loras(loaded_loras)
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return (model, clip, trigger_words_text, formatted_loras_text)
|
||||
|
||||
Reference in New Issue
Block a user