Marco
793402f93f
This commit introduces several new performance-oriented custom nodes and utilities: - Added LTXV Multi Concat and LTXV Multi Concat (beta) for faster frame injection using latent inpainting logic. - Refactored LTXV index resolution logic into ltxv_utils.py. - Updated ImageListSampler to anchor edge frames properly and support output index normalization scaling via target_frames. - Added metadata-based prompt extraction image loaders (LoadImageAndExtractPrompt, FolderImageAndExtractPrompt, and FolderImageMetadataByName). - Included animation tracking and camera switcher nodes.
114 lines
4.1 KiB
Python
114 lines
4.1 KiB
Python
import json
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import torch
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import numpy as np
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from PIL import Image, ImageOps
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class PromptExtractor:
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@staticmethod
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def extract_from_image(img):
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"""
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Extracts the prompt string from PIL Image metadata.
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Returns a string.
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"""
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metadata = img.info
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if not metadata:
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return ""
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# 1. ComfyUI Standard
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if 'prompt' in metadata:
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try:
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prompt_json = json.loads(metadata['prompt'])
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return PromptExtractor.extract_from_json(prompt_json)
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except Exception as e:
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print(f"[AnotherUtils] Error parsing prompt JSON: {e}")
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# 2. A1111 / WebUI Standard
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if 'parameters' in metadata:
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return metadata['parameters'].split("\n")[0]
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return ""
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@staticmethod
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def extract_from_json(prompt_json):
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"""
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Heuristic to find the 'main' positive prompt in a ComfyUI graph.
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"""
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candidates = []
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# Find all sampler-like nodes
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sampler_types = ['Sampler', 'Guider', 'SamplerCustom', 'KSampler', 'Flux2Sampler']
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sampler_nodes = {id: node for id, node in prompt_json.items()
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if any(st in node.get('class_type', '') for st in sampler_types)}
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for sid, snode in sampler_nodes.items():
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# Follow 'positive' link
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inputs = snode.get('inputs', {})
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pos = inputs.get('positive') or inputs.get('cond') or inputs.get('conditioning')
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if isinstance(pos, list):
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text = PromptExtractor._trace_back(prompt_json, str(pos[0]))
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if text:
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# Give higher score if it's connected to a sampler
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candidates.append((text, 10))
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# Fallback: Look for all CLIPTextEncode nodes
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for id, node in prompt_json.items():
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ctype = node.get('class_type', '')
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if 'CLIPTextEncode' in ctype:
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inputs = node.get('inputs', {})
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# Handle SDXL (text_g, text_l)
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text = inputs.get('text') or inputs.get('text_g') or inputs.get('text_l')
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if text and isinstance(text, str):
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score = 5
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title = node.get('_meta', {}).get('title', '').lower()
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if 'positive' in title:
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score += 5
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candidates.append((text, score))
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if not candidates:
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# Last ditch: look for any string longer than 20 chars in inputs
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for id, node in prompt_json.items():
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for val in node.get('inputs', {}).values():
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if isinstance(val, str) and len(val) > 20:
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candidates.append((val, 1))
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if candidates:
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# Sort by score (desc), then by length (desc)
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candidates.sort(key=lambda x: (x[1], len(x[0])), reverse=True)
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return candidates[0][0]
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return "Prompt not found"
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@staticmethod
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def _trace_back(prompt_json, node_id, visited=None):
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if visited is None:
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visited = set()
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if node_id in visited or node_id not in prompt_json:
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return None
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visited.add(node_id)
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node = prompt_json[node_id]
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inputs = node.get('inputs', {})
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# Direct text hit
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text = inputs.get('text') or inputs.get('text_g') or inputs.get('text_l')
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if text and isinstance(text, str) and len(text.strip()) > 0:
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return text
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# Recursive trace for conditioning links
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for key in ['conditioning', 'cond', 'text', 'string', 'samples']:
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val = inputs.get(key)
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if isinstance(val, list):
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result = PromptExtractor._trace_back(prompt_json, str(val[0]), visited)
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if result:
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return result
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return None
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@staticmethod
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def preprocess_image(img):
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"""Shared image to tensor logic"""
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img = ImageOps.exif_transpose(img)
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image = img.convert("RGB")
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image = np.array(image).astype(np.float32) / 255.0
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return torch.from_numpy(image)[None,]
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