mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-21 21:22:11 -03:00
354 lines
13 KiB
Python
354 lines
13 KiB
Python
import os
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from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES
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class NodeMetadataExtractor:
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"""Base class for node-specific metadata extraction"""
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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"""Extract metadata from node inputs/outputs"""
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pass
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@staticmethod
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def update(node_id, outputs, metadata):
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"""Update metadata with node outputs after execution"""
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pass
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class GenericNodeExtractor(NodeMetadataExtractor):
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"""Default extractor for nodes without specific handling"""
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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pass
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class CheckpointLoaderExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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if not inputs or "ckpt_name" not in inputs:
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return
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model_name = inputs.get("ckpt_name")
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if model_name:
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metadata[MODELS][node_id] = {
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"name": model_name,
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"type": "checkpoint",
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"node_id": node_id
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}
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class CLIPTextEncodeExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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if not inputs or "text" not in inputs:
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return
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text = inputs.get("text", "")
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metadata[PROMPTS][node_id] = {
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"text": text,
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"node_id": node_id
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}
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class SamplerExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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if not inputs:
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return
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sampling_params = {}
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for key in ["seed", "steps", "cfg", "sampler_name", "scheduler", "denoise"]:
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if key in inputs:
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sampling_params[key] = inputs[key]
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metadata[SAMPLING][node_id] = {
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"parameters": sampling_params,
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"node_id": node_id
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}
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# Extract latent image dimensions if available
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if "latent_image" in inputs and inputs["latent_image"] is not None:
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latent = inputs["latent_image"]
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if isinstance(latent, dict) and "samples" in latent:
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# Extract dimensions from latent tensor
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samples = latent["samples"]
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if hasattr(samples, "shape") and len(samples.shape) >= 3:
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# Correct shape interpretation: [batch_size, channels, height/8, width/8]
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# Multiply by 8 to get actual pixel dimensions
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height = int(samples.shape[2] * 8)
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width = int(samples.shape[3] * 8)
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if SIZE not in metadata:
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metadata[SIZE] = {}
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metadata[SIZE][node_id] = {
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"width": width,
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"height": height,
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"node_id": node_id
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}
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class KSamplerAdvancedExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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if not inputs:
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return
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sampling_params = {}
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for key in ["noise_seed", "steps", "cfg", "sampler_name", "scheduler", "add_noise"]:
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if key in inputs:
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sampling_params[key] = inputs[key]
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metadata[SAMPLING][node_id] = {
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"parameters": sampling_params,
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"node_id": node_id
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}
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# Extract latent image dimensions if available
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if "latent_image" in inputs and inputs["latent_image"] is not None:
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latent = inputs["latent_image"]
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if isinstance(latent, dict) and "samples" in latent:
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# Extract dimensions from latent tensor
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samples = latent["samples"]
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if hasattr(samples, "shape") and len(samples.shape) >= 3:
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# Correct shape interpretation: [batch_size, channels, height/8, width/8]
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# Multiply by 8 to get actual pixel dimensions
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height = int(samples.shape[2] * 8)
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width = int(samples.shape[3] * 8)
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if SIZE not in metadata:
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metadata[SIZE] = {}
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metadata[SIZE][node_id] = {
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"width": width,
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"height": height,
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"node_id": node_id
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}
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class LoraLoaderExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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if not inputs or "lora_name" not in inputs:
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return
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lora_name = inputs.get("lora_name")
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# Extract base filename without extension from path
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lora_name = os.path.splitext(os.path.basename(lora_name))[0]
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strength_model = round(float(inputs.get("strength_model", 1.0)), 2)
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# Use the standardized format with lora_list
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metadata[LORAS][node_id] = {
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"lora_list": [
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{
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"name": lora_name,
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"strength": strength_model
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}
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],
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"node_id": node_id
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}
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class ImageSizeExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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if not inputs:
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return
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width = inputs.get("width", 512)
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height = inputs.get("height", 512)
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if SIZE not in metadata:
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metadata[SIZE] = {}
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metadata[SIZE][node_id] = {
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"width": width,
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"height": height,
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"node_id": node_id
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}
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class LoraLoaderManagerExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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if not inputs:
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return
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active_loras = []
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# Process lora_stack if available
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if "lora_stack" in inputs:
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lora_stack = inputs.get("lora_stack", [])
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for lora_path, model_strength, clip_strength in lora_stack:
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# Extract lora name from path (following the format in lora_loader.py)
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lora_name = os.path.splitext(os.path.basename(lora_path))[0]
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active_loras.append({
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"name": lora_name,
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"strength": model_strength
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})
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# Process loras from inputs
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if "loras" in inputs:
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loras_data = inputs.get("loras", [])
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# Handle new format: {'loras': {'__value__': [...]}}
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if isinstance(loras_data, dict) and '__value__' in loras_data:
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loras_list = loras_data['__value__']
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# Handle old format: {'loras': [...]}
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elif isinstance(loras_data, list):
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loras_list = loras_data
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else:
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loras_list = []
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# Filter for active loras
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for lora in loras_list:
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if isinstance(lora, dict) and lora.get("active", True) and not lora.get("_isDummy", False):
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active_loras.append({
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"name": lora.get("name", ""),
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"strength": float(lora.get("strength", 1.0))
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})
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if active_loras:
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metadata[LORAS][node_id] = {
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"lora_list": active_loras,
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"node_id": node_id
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}
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class FluxGuidanceExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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if not inputs or "guidance" not in inputs:
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return
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guidance_value = inputs.get("guidance")
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# Store the guidance value in SAMPLING category
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if node_id not in metadata[SAMPLING]:
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metadata[SAMPLING][node_id] = {"parameters": {}, "node_id": node_id}
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metadata[SAMPLING][node_id]["parameters"]["guidance"] = guidance_value
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class UNETLoaderExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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if not inputs or "unet_name" not in inputs:
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return
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model_name = inputs.get("unet_name")
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if model_name:
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metadata[MODELS][node_id] = {
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"name": model_name,
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"type": "checkpoint",
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"node_id": node_id
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}
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class VAEDecodeExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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pass
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@staticmethod
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def update(node_id, outputs, metadata):
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# Ensure IMAGES category exists
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if IMAGES not in metadata:
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metadata[IMAGES] = {}
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# Save image data under node ID index to be captured by caching mechanism
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metadata[IMAGES][node_id] = {
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"node_id": node_id,
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"image": outputs
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}
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# Only set first_decode if it hasn't been recorded yet
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if "first_decode" not in metadata[IMAGES]:
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metadata[IMAGES]["first_decode"] = metadata[IMAGES][node_id]
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class KSamplerSelectExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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if not inputs or "sampler_name" not in inputs:
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return
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sampling_params = {}
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if "sampler_name" in inputs:
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sampling_params["sampler_name"] = inputs["sampler_name"]
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metadata[SAMPLING][node_id] = {
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"parameters": sampling_params,
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"node_id": node_id
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}
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class BasicSchedulerExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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if not inputs:
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return
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sampling_params = {}
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for key in ["scheduler", "steps", "denoise"]:
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if key in inputs:
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sampling_params[key] = inputs[key]
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metadata[SAMPLING][node_id] = {
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"parameters": sampling_params,
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"node_id": node_id
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}
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class SamplerCustomAdvancedExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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if not inputs:
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return
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sampling_params = {}
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# Handle noise.seed as seed
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if "noise" in inputs and inputs["noise"] is not None and hasattr(inputs["noise"], "seed"):
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noise = inputs["noise"]
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sampling_params["seed"] = noise.seed
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metadata[SAMPLING][node_id] = {
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"parameters": sampling_params,
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"node_id": node_id
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}
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# Extract latent image dimensions if available
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if "latent_image" in inputs and inputs["latent_image"] is not None:
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latent = inputs["latent_image"]
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if isinstance(latent, dict) and "samples" in latent:
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# Extract dimensions from latent tensor
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samples = latent["samples"]
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if hasattr(samples, "shape") and len(samples.shape) >= 3:
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# Correct shape interpretation: [batch_size, channels, height/8, width/8]
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# Multiply by 8 to get actual pixel dimensions
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height = int(samples.shape[2] * 8)
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width = int(samples.shape[3] * 8)
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if SIZE not in metadata:
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metadata[SIZE] = {}
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metadata[SIZE][node_id] = {
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"width": width,
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"height": height,
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"node_id": node_id
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}
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# Registry of node-specific extractors
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NODE_EXTRACTORS = {
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# Sampling
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"KSampler": SamplerExtractor,
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"KSamplerAdvanced": KSamplerAdvancedExtractor,
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"SamplerCustomAdvanced": SamplerCustomAdvancedExtractor, # Updated to use dedicated extractor
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# Sampling Selectors
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"KSamplerSelect": KSamplerSelectExtractor, # Add KSamplerSelect
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"BasicScheduler": BasicSchedulerExtractor, # Add BasicScheduler
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# Loaders
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"CheckpointLoaderSimple": CheckpointLoaderExtractor,
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"UNETLoader": UNETLoaderExtractor, # Updated to use dedicated extractor
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"LoraLoader": LoraLoaderExtractor,
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"LoraManagerLoader": LoraLoaderManagerExtractor,
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# Conditioning
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"CLIPTextEncode": CLIPTextEncodeExtractor,
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# Latent
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"EmptyLatentImage": ImageSizeExtractor,
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# Flux
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"FluxGuidance": FluxGuidanceExtractor, # Add FluxGuidance
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# Image
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"VAEDecode": VAEDecodeExtractor, # Added VAEDecode extractor
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# Add other nodes as needed
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}
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