refactor: merge metadata.py into meta_parser.py
This commit is contained in:
Manuel Schmid
parent
b438f7b43f
commit
f745d40687
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@ -45,7 +45,7 @@ def worker():
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get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate
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from modules.upscaler import perform_upscale
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from modules.flags import Performance, lora_count
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from modules.metadata import get_metadata_parser, MetadataScheme
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from modules.meta_parser import get_metadata_parser, MetadataScheme
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try:
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async_gradio_app = shared.gradio_root
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@ -804,7 +804,7 @@ def worker():
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metadata_parser = None
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if save_metadata_to_images:
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metadata_parser = modules.metadata.get_metadata_parser(metadata_scheme)
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metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
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metadata_parser.set_data(task['positive'], task['negative'], steps, base_model_name, refiner_model_name, loras)
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for li, (n, w) in enumerate(loras):
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@ -1,9 +1,23 @@
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import json
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import os
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import re
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from abc import ABC, abstractmethod
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from pathlib import Path
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import gradio as gr
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from PIL import Image
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import modules.config
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from modules.flags import lora_count, Steps
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import modules.config
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from modules.flags import MetadataScheme, Performance, Steps
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from modules.flags import lora_count
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from modules.util import quote, unquote, extract_styles_from_prompt, is_json, calculate_sha256
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re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
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re_param = re.compile(re_param_code)
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re_imagesize = re.compile(r"^(\d+)x(\d+)$")
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hash_cache = {}
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def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
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@ -160,3 +174,302 @@ def get_lora(key: str, fallback: str | None, source_dict: dict, results: list):
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except:
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results.append('None')
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results.append(1)
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def get_sha256(filepath):
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global hash_cache
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if filepath not in hash_cache:
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hash_cache[filepath] = calculate_sha256(filepath)
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return hash_cache[filepath]
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class MetadataParser(ABC):
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def __init__(self):
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self.full_prompt: str = ''
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self.full_negative_prompt: str = ''
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self.steps: int = 30
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self.base_model_name: str = ''
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self.base_model_hash: str = ''
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self.refiner_model_name: str = ''
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self.refiner_model_hash: str = ''
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self.loras: list = []
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@abstractmethod
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def get_scheme(self) -> MetadataScheme:
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raise NotImplementedError
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@abstractmethod
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def parse_json(self, metadata: dict) -> dict:
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raise NotImplementedError
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@abstractmethod
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def parse_string(self, metadata: dict) -> str:
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raise NotImplementedError
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def set_data(self, full_prompt, full_negative_prompt, steps, base_model_name, refiner_model_name, loras):
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self.full_prompt = full_prompt
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self.full_negative_prompt = full_negative_prompt
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self.steps = steps
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self.base_model_name = Path(base_model_name).stem
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base_model_path = os.path.join(modules.config.path_checkpoints, base_model_name)
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self.base_model_hash = get_sha256(base_model_path)
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if refiner_model_name not in ['', 'None']:
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self.refiner_model_name = Path(refiner_model_name).stem
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refiner_model_path = os.path.join(modules.config.path_checkpoints, refiner_model_name)
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self.refiner_model_hash = get_sha256(refiner_model_path)
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self.loras = []
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for (lora_name, lora_weight) in loras:
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if lora_name != 'None':
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lora_path = os.path.join(modules.config.path_loras, lora_name)
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lora_hash = get_sha256(lora_path)
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self.loras.append((Path(lora_name).stem, lora_weight, lora_hash))
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class A1111MetadataParser(MetadataParser):
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def get_scheme(self) -> MetadataScheme:
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return MetadataScheme.A1111
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fooocus_to_a1111 = {
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'negative_prompt': 'Negative prompt',
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'styles': 'Styles',
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'performance': 'Performance',
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'steps': 'Steps',
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'sampler': 'Sampler',
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'guidance_scale': 'CFG scale',
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'seed': 'Seed',
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'resolution': 'Size',
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'sharpness': 'Sharpness',
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'adm_guidance': 'ADM Guidance',
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'refiner_swap_method': 'Refiner Swap Method',
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'adaptive_cfg': 'Adaptive CFG',
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'overwrite_switch': 'Overwrite Switch',
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'freeu': 'FreeU',
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'base_model': 'Model',
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'base_model_hash': 'Model hash',
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'refiner_model': 'Refiner',
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'refiner_model_hash': 'Refiner hash',
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'lora_hashes': 'Lora hashes',
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'lora_weights': 'Lora weights',
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'created_by': 'User',
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'version': 'Version'
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}
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def parse_json(self, metadata: str) -> dict:
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prompt = ''
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negative_prompt = ''
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done_with_prompt = False
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*lines, lastline = metadata.strip().split("\n")
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if len(re_param.findall(lastline)) < 3:
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lines.append(lastline)
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lastline = ''
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for line in lines:
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line = line.strip()
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if line.startswith(f"{self.fooocus_to_a1111['negative_prompt']}:"):
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done_with_prompt = True
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line = line[len(f"{self.fooocus_to_a1111['negative_prompt']}:"):].strip()
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if done_with_prompt:
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negative_prompt += ('' if negative_prompt == '' else "\n") + line
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else:
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prompt += ('' if prompt == '' else "\n") + line
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found_styles, prompt, negative_prompt = extract_styles_from_prompt(prompt, negative_prompt)
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data = {
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'prompt': prompt,
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'negative_prompt': negative_prompt,
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'styles': str(found_styles)
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}
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for k, v in re_param.findall(lastline):
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try:
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if v[0] == '"' and v[-1] == '"':
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v = unquote(v)
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m = re_imagesize.match(v)
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if m is not None:
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data[f'resolution'] = str((m.group(1), m.group(2)))
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else:
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data[list(self.fooocus_to_a1111.keys())[list(self.fooocus_to_a1111.values()).index(k)]] = v
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except Exception:
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print(f"Error parsing \"{k}: {v}\"")
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# try to load performance based on steps, fallback for direct A1111 imports
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if 'steps' in data and 'performance' not in data:
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try:
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data['performance'] = Performance[Steps(int(data['steps'])).name].value
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except Exception:
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pass
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if 'base_model' in data:
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for filename in modules.config.model_filenames:
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path = Path(filename)
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if data['base_model'] == path.stem:
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data['base_model'] = filename
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break
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if 'lora_hashes' in data:
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lora_filenames = modules.config.lora_filenames.copy()
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lora_filenames.remove(modules.config.downloading_sdxl_lcm_lora())
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for li, lora in enumerate(data['lora_hashes'].split(', ')):
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lora_name, lora_hash, lora_weight = lora.split(': ')
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for filename in lora_filenames:
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path = Path(filename)
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if lora_name == path.stem:
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data[f'lora_combined_{li + 1}'] = f'{filename} : {lora_weight}'
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break
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return data
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def parse_string(self, metadata: dict) -> str:
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data = {k: v for _, k, v in metadata}
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width, height = eval(data['resolution'])
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generation_params = {
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self.fooocus_to_a1111['performance']: data['performance'],
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self.fooocus_to_a1111['steps']: self.steps,
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self.fooocus_to_a1111['sampler']: data['sampler'],
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self.fooocus_to_a1111['seed']: data['seed'],
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self.fooocus_to_a1111['resolution']: f'{width}x{height}',
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self.fooocus_to_a1111['guidance_scale']: data['guidance_scale'],
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self.fooocus_to_a1111['sharpness']: data['sharpness'],
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self.fooocus_to_a1111['adm_guidance']: data['adm_guidance'],
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self.fooocus_to_a1111['base_model']: Path(data['base_model']).stem,
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self.fooocus_to_a1111['base_model_hash']: self.base_model_hash,
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}
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# TODO evaluate if this should always be added
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if self.refiner_model_name not in ['', 'None']:
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generation_params |= {
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self.fooocus_to_a1111['refiner_model']: self.refiner_model_name,
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self.fooocus_to_a1111['refiner_model_hash']: self.refiner_model_hash
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}
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for key in ['adaptive_cfg', 'overwrite_switch', 'refiner_swap_method', 'freeu']:
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if key in data:
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generation_params[self.fooocus_to_a1111[key]] = data[key]
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lora_hashes = []
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for index, (lora_name, lora_weight, lora_hash) in enumerate(self.loras):
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# workaround for Fooocus not knowing LoRA name in LoRA metadata
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lora_hashes.append(f'{lora_name}: {lora_hash}: {lora_weight}')
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lora_hashes_string = ', '.join(lora_hashes)
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generation_params |= {
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self.fooocus_to_a1111['lora_hashes']: lora_hashes_string,
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self.fooocus_to_a1111['version']: data['version']
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}
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if modules.config.metadata_created_by != '':
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generation_params[self.fooocus_to_a1111['created_by']] = modules.config.metadata_created_by
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generation_params_text = ", ".join(
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[k if k == v else f'{k}: {quote(v)}' for k, v in dict(sorted(generation_params.items())).items() if
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v is not None])
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positive_prompt_resolved = ', '.join(self.full_prompt)
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negative_prompt_resolved = ', '.join(self.full_negative_prompt)
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negative_prompt_text = f"\nNegative prompt: {negative_prompt_resolved}" if negative_prompt_resolved else ""
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return f"{positive_prompt_resolved}{negative_prompt_text}\n{generation_params_text}".strip()
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class FooocusMetadataParser(MetadataParser):
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def get_scheme(self) -> MetadataScheme:
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return MetadataScheme.FOOOCUS
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def parse_json(self, metadata: dict) -> dict:
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model_filenames = modules.config.model_filenames.copy()
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lora_filenames = modules.config.lora_filenames.copy()
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lora_filenames.remove(modules.config.downloading_sdxl_lcm_lora())
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for key, value in metadata.items():
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if value in ['', 'None']:
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continue
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if key in ['base_model', 'refiner_model']:
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metadata[key] = self.replace_value_with_filename(key, value, model_filenames)
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elif key.startswith('lora_combined_'):
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metadata[key] = self.replace_value_with_filename(key, value, lora_filenames)
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else:
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continue
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return metadata
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def parse_string(self, metadata: list) -> str:
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for li, (label, key, value) in enumerate(metadata):
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# remove model folder paths from metadata
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if key.startswith('lora_combined_'):
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name, weight = value.split(' : ')
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name = Path(name).stem
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value = f'{name} : {weight}'
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metadata[li] = (label, key, value)
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res = {k: v for _, k, v in metadata}
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res['full_prompt'] = self.full_prompt
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res['full_negative_prompt'] = self.full_negative_prompt
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res['steps'] = self.steps
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res['base_model'] = self.base_model_name
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res['base_model_hash'] = self.base_model_hash
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# TODO evaluate if this should always be added
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if self.refiner_model_name not in ['', 'None']:
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res['refiner_model'] = self.refiner_model_name
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res['refiner_model_hash'] = self.refiner_model_hash
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res['loras'] = self.loras
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if modules.config.metadata_created_by != '':
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res['created_by'] = modules.config.metadata_created_by
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return json.dumps(dict(sorted(res.items())))
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@staticmethod
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def replace_value_with_filename(key, value, filenames):
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for filename in filenames:
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path = Path(filename)
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if key.startswith('lora_combined_'):
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name, weight = value.split(' : ')
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if name == path.stem:
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return f'{filename} : {weight}'
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elif value == path.stem:
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return filename
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def get_metadata_parser(metadata_scheme: MetadataScheme) -> MetadataParser:
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match metadata_scheme:
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case MetadataScheme.FOOOCUS:
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return FooocusMetadataParser()
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case MetadataScheme.A1111:
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return A1111MetadataParser()
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case _:
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raise NotImplementedError
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def read_info_from_image(filepath) -> tuple[str | None, dict, MetadataScheme | None]:
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with Image.open(filepath) as image:
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items = (image.info or {}).copy()
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parameters = items.pop('parameters', None)
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if parameters is not None and is_json(parameters):
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parameters = json.loads(parameters)
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try:
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metadata_scheme = MetadataScheme(items.pop('fooocus_scheme', None))
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except Exception:
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metadata_scheme = None
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# broad fallback
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if metadata_scheme is None and isinstance(parameters, dict):
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metadata_scheme = MetadataScheme.FOOOCUS
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if metadata_scheme is None and isinstance(parameters, str):
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metadata_scheme = MetadataScheme.A1111
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return parameters, items, metadata_scheme
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@ -1,315 +0,0 @@
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import json
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import os
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import re
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from abc import ABC, abstractmethod
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from pathlib import Path
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from PIL import Image
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import modules.config
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from modules.flags import MetadataScheme, Performance, Steps
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from modules.util import quote, unquote, extract_styles_from_prompt, is_json, calculate_sha256
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re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
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re_param = re.compile(re_param_code)
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re_imagesize = re.compile(r"^(\d+)x(\d+)$")
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hash_cache = {}
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def get_sha256(filepath):
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global hash_cache
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if filepath not in hash_cache:
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hash_cache[filepath] = calculate_sha256(filepath)
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return hash_cache[filepath]
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class MetadataParser(ABC):
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def __init__(self):
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self.full_prompt: str = ''
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self.full_negative_prompt: str = ''
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self.steps: int = 30
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self.base_model_name: str = ''
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self.base_model_hash: str = ''
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self.refiner_model_name: str = ''
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self.refiner_model_hash: str = ''
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self.loras: list = []
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@abstractmethod
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def get_scheme(self) -> MetadataScheme:
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raise NotImplementedError
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@abstractmethod
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def parse_json(self, metadata: dict) -> dict:
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raise NotImplementedError
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@abstractmethod
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def parse_string(self, metadata: dict) -> str:
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raise NotImplementedError
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def set_data(self, full_prompt, full_negative_prompt, steps, base_model_name, refiner_model_name, loras):
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self.full_prompt = full_prompt
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self.full_negative_prompt = full_negative_prompt
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self.steps = steps
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self.base_model_name = Path(base_model_name).stem
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base_model_path = os.path.join(modules.config.path_checkpoints, base_model_name)
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self.base_model_hash = get_sha256(base_model_path)
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if refiner_model_name not in ['', 'None']:
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self.refiner_model_name = Path(refiner_model_name).stem
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refiner_model_path = os.path.join(modules.config.path_checkpoints, refiner_model_name)
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self.refiner_model_hash = get_sha256(refiner_model_path)
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self.loras = []
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for (lora_name, lora_weight) in loras:
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if lora_name != 'None':
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lora_path = os.path.join(modules.config.path_loras, lora_name)
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lora_hash = get_sha256(lora_path)
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self.loras.append((Path(lora_name).stem, lora_weight, lora_hash))
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class A1111MetadataParser(MetadataParser):
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def get_scheme(self) -> MetadataScheme:
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return MetadataScheme.A1111
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fooocus_to_a1111 = {
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'negative_prompt': 'Negative prompt',
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'styles': 'Styles',
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'performance': 'Performance',
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'steps': 'Steps',
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'sampler': 'Sampler',
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'guidance_scale': 'CFG scale',
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'seed': 'Seed',
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'resolution': 'Size',
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'sharpness': 'Sharpness',
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'adm_guidance': 'ADM Guidance',
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'refiner_swap_method': 'Refiner Swap Method',
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'adaptive_cfg': 'Adaptive CFG',
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'overwrite_switch': 'Overwrite Switch',
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'freeu': 'FreeU',
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'base_model': 'Model',
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'base_model_hash': 'Model hash',
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'refiner_model': 'Refiner',
|
||||
'refiner_model_hash': 'Refiner hash',
|
||||
'lora_hashes': 'Lora hashes',
|
||||
'lora_weights': 'Lora weights',
|
||||
'created_by': 'User',
|
||||
'version': 'Version'
|
||||
}
|
||||
|
||||
def parse_json(self, metadata: str) -> dict:
|
||||
prompt = ''
|
||||
negative_prompt = ''
|
||||
|
||||
done_with_prompt = False
|
||||
|
||||
*lines, lastline = metadata.strip().split("\n")
|
||||
if len(re_param.findall(lastline)) < 3:
|
||||
lines.append(lastline)
|
||||
lastline = ''
|
||||
|
||||
for line in lines:
|
||||
line = line.strip()
|
||||
if line.startswith(f"{self.fooocus_to_a1111['negative_prompt']}:"):
|
||||
done_with_prompt = True
|
||||
line = line[len(f"{self.fooocus_to_a1111['negative_prompt']}:"):].strip()
|
||||
if done_with_prompt:
|
||||
negative_prompt += ('' if negative_prompt == '' else "\n") + line
|
||||
else:
|
||||
prompt += ('' if prompt == '' else "\n") + line
|
||||
|
||||
found_styles, prompt, negative_prompt = extract_styles_from_prompt(prompt, negative_prompt)
|
||||
|
||||
data = {
|
||||
'prompt': prompt,
|
||||
'negative_prompt': negative_prompt,
|
||||
'styles': str(found_styles)
|
||||
}
|
||||
|
||||
for k, v in re_param.findall(lastline):
|
||||
try:
|
||||
if v[0] == '"' and v[-1] == '"':
|
||||
v = unquote(v)
|
||||
|
||||
m = re_imagesize.match(v)
|
||||
if m is not None:
|
||||
data[f'resolution'] = str((m.group(1), m.group(2)))
|
||||
else:
|
||||
data[list(self.fooocus_to_a1111.keys())[list(self.fooocus_to_a1111.values()).index(k)]] = v
|
||||
except Exception:
|
||||
print(f"Error parsing \"{k}: {v}\"")
|
||||
|
||||
# try to load performance based on steps, fallback for direct A1111 imports
|
||||
if 'steps' in data and 'performance' not in data:
|
||||
try:
|
||||
data['performance'] = Performance[Steps(int(data['steps'])).name].value
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if 'base_model' in data:
|
||||
for filename in modules.config.model_filenames:
|
||||
path = Path(filename)
|
||||
if data['base_model'] == path.stem:
|
||||
data['base_model'] = filename
|
||||
break
|
||||
|
||||
if 'lora_hashes' in data:
|
||||
lora_filenames = modules.config.lora_filenames.copy()
|
||||
lora_filenames.remove(modules.config.downloading_sdxl_lcm_lora())
|
||||
for li, lora in enumerate(data['lora_hashes'].split(', ')):
|
||||
lora_name, lora_hash, lora_weight = lora.split(': ')
|
||||
for filename in lora_filenames:
|
||||
path = Path(filename)
|
||||
if lora_name == path.stem:
|
||||
data[f'lora_combined_{li + 1}'] = f'{filename} : {lora_weight}'
|
||||
break
|
||||
|
||||
return data
|
||||
|
||||
def parse_string(self, metadata: dict) -> str:
|
||||
data = {k: v for _, k, v in metadata}
|
||||
|
||||
width, height = eval(data['resolution'])
|
||||
|
||||
generation_params = {
|
||||
self.fooocus_to_a1111['performance']: data['performance'],
|
||||
self.fooocus_to_a1111['steps']: self.steps,
|
||||
self.fooocus_to_a1111['sampler']: data['sampler'],
|
||||
self.fooocus_to_a1111['seed']: data['seed'],
|
||||
self.fooocus_to_a1111['resolution']: f'{width}x{height}',
|
||||
self.fooocus_to_a1111['guidance_scale']: data['guidance_scale'],
|
||||
self.fooocus_to_a1111['sharpness']: data['sharpness'],
|
||||
self.fooocus_to_a1111['adm_guidance']: data['adm_guidance'],
|
||||
self.fooocus_to_a1111['base_model']: Path(data['base_model']).stem,
|
||||
self.fooocus_to_a1111['base_model_hash']: self.base_model_hash,
|
||||
}
|
||||
|
||||
# TODO evaluate if this should always be added
|
||||
if self.refiner_model_name not in ['', 'None']:
|
||||
generation_params |= {
|
||||
self.fooocus_to_a1111['refiner_model']: self.refiner_model_name,
|
||||
self.fooocus_to_a1111['refiner_model_hash']: self.refiner_model_hash
|
||||
}
|
||||
|
||||
for key in ['adaptive_cfg', 'overwrite_switch', 'refiner_swap_method', 'freeu']:
|
||||
if key in data:
|
||||
generation_params[self.fooocus_to_a1111[key]] = data[key]
|
||||
|
||||
lora_hashes = []
|
||||
for index, (lora_name, lora_weight, lora_hash) in enumerate(self.loras):
|
||||
# workaround for Fooocus not knowing LoRA name in LoRA metadata
|
||||
lora_hashes.append(f'{lora_name}: {lora_hash}: {lora_weight}')
|
||||
lora_hashes_string = ', '.join(lora_hashes)
|
||||
|
||||
generation_params |= {
|
||||
self.fooocus_to_a1111['lora_hashes']: lora_hashes_string,
|
||||
self.fooocus_to_a1111['version']: data['version']
|
||||
}
|
||||
|
||||
if modules.config.metadata_created_by != '':
|
||||
generation_params[self.fooocus_to_a1111['created_by']] = modules.config.metadata_created_by
|
||||
|
||||
generation_params_text = ", ".join(
|
||||
[k if k == v else f'{k}: {quote(v)}' for k, v in dict(sorted(generation_params.items())).items() if v is not None])
|
||||
positive_prompt_resolved = ', '.join(self.full_prompt)
|
||||
negative_prompt_resolved = ', '.join(self.full_negative_prompt)
|
||||
negative_prompt_text = f"\nNegative prompt: {negative_prompt_resolved}" if negative_prompt_resolved else ""
|
||||
return f"{positive_prompt_resolved}{negative_prompt_text}\n{generation_params_text}".strip()
|
||||
|
||||
|
||||
class FooocusMetadataParser(MetadataParser):
|
||||
def get_scheme(self) -> MetadataScheme:
|
||||
return MetadataScheme.FOOOCUS
|
||||
|
||||
def parse_json(self, metadata: dict) -> dict:
|
||||
model_filenames = modules.config.model_filenames.copy()
|
||||
lora_filenames = modules.config.lora_filenames.copy()
|
||||
lora_filenames.remove(modules.config.downloading_sdxl_lcm_lora())
|
||||
|
||||
for key, value in metadata.items():
|
||||
if value in ['', 'None']:
|
||||
continue
|
||||
if key in ['base_model', 'refiner_model']:
|
||||
metadata[key] = self.replace_value_with_filename(key, value, model_filenames)
|
||||
elif key.startswith('lora_combined_'):
|
||||
metadata[key] = self.replace_value_with_filename(key, value, lora_filenames)
|
||||
else:
|
||||
continue
|
||||
|
||||
return metadata
|
||||
|
||||
def parse_string(self, metadata: list) -> str:
|
||||
for li, (label, key, value) in enumerate(metadata):
|
||||
# remove model folder paths from metadata
|
||||
if key.startswith('lora_combined_'):
|
||||
name, weight = value.split(' : ')
|
||||
name = Path(name).stem
|
||||
value = f'{name} : {weight}'
|
||||
metadata[li] = (label, key, value)
|
||||
|
||||
res = {k: v for _, k, v in metadata}
|
||||
|
||||
res['full_prompt'] = self.full_prompt
|
||||
res['full_negative_prompt'] = self.full_negative_prompt
|
||||
res['steps'] = self.steps
|
||||
res['base_model'] = self.base_model_name
|
||||
res['base_model_hash'] = self.base_model_hash
|
||||
|
||||
# TODO evaluate if this should always be added
|
||||
if self.refiner_model_name not in ['', 'None']:
|
||||
res['refiner_model'] = self.refiner_model_name
|
||||
res['refiner_model_hash'] = self.refiner_model_hash
|
||||
|
||||
res['loras'] = self.loras
|
||||
|
||||
if modules.config.metadata_created_by != '':
|
||||
res['created_by'] = modules.config.metadata_created_by
|
||||
|
||||
return json.dumps(dict(sorted(res.items())))
|
||||
|
||||
@staticmethod
|
||||
def replace_value_with_filename(key, value, filenames):
|
||||
for filename in filenames:
|
||||
path = Path(filename)
|
||||
if key.startswith('lora_combined_'):
|
||||
name, weight = value.split(' : ')
|
||||
if name == path.stem:
|
||||
return f'{filename} : {weight}'
|
||||
elif value == path.stem:
|
||||
return filename
|
||||
|
||||
|
||||
def get_metadata_parser(metadata_scheme: MetadataScheme) -> MetadataParser:
|
||||
match metadata_scheme:
|
||||
case MetadataScheme.FOOOCUS:
|
||||
return FooocusMetadataParser()
|
||||
case MetadataScheme.A1111:
|
||||
return A1111MetadataParser()
|
||||
case _:
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
def read_info_from_image(filepath) -> tuple[str | None, dict, MetadataScheme | None]:
|
||||
with Image.open(filepath) as image:
|
||||
items = (image.info or {}).copy()
|
||||
|
||||
parameters = items.pop('parameters', None)
|
||||
if parameters is not None and is_json(parameters):
|
||||
parameters = json.loads(parameters)
|
||||
|
||||
try:
|
||||
metadata_scheme = MetadataScheme(items.pop('fooocus_scheme', None))
|
||||
except Exception:
|
||||
metadata_scheme = None
|
||||
|
||||
# broad fallback
|
||||
if metadata_scheme is None and isinstance(parameters, dict):
|
||||
metadata_scheme = modules.metadata.MetadataScheme.FOOOCUS
|
||||
|
||||
if metadata_scheme is None and isinstance(parameters, str):
|
||||
metadata_scheme = modules.metadata.MetadataScheme.A1111
|
||||
|
||||
return parameters, items, metadata_scheme
|
||||
|
|
@ -7,7 +7,7 @@ import urllib.parse
|
|||
from PIL import Image
|
||||
from PIL.PngImagePlugin import PngInfo
|
||||
from modules.util import generate_temp_filename
|
||||
from modules.metadata import MetadataParser
|
||||
from modules.meta_parser import MetadataParser
|
||||
|
||||
|
||||
log_cache = {}
|
||||
|
|
|
|||
7
webui.py
7
webui.py
|
|
@ -14,7 +14,6 @@ import modules.gradio_hijack as grh
|
|||
import modules.advanced_parameters as advanced_parameters
|
||||
import modules.style_sorter as style_sorter
|
||||
import modules.meta_parser
|
||||
import modules.metadata
|
||||
import args_manager
|
||||
import copy
|
||||
|
||||
|
|
@ -217,7 +216,7 @@ with shared.gradio_root:
|
|||
metadata_import_button = gr.Button(value='Apply Metadata')
|
||||
|
||||
def trigger_metadata_preview(filepath):
|
||||
parameters, items, metadata_scheme = modules.metadata.read_info_from_image(filepath)
|
||||
parameters, items, metadata_scheme = modules.meta_parser.read_info_from_image(filepath)
|
||||
|
||||
results = {}
|
||||
if parameters is not None:
|
||||
|
|
@ -597,12 +596,12 @@ with shared.gradio_root:
|
|||
load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=False)
|
||||
|
||||
def trigger_metadata_import(filepath, state_is_generating):
|
||||
parameters, items, metadata_scheme = modules.metadata.read_info_from_image(filepath)
|
||||
parameters, items, metadata_scheme = modules.meta_parser.read_info_from_image(filepath)
|
||||
if parameters is None:
|
||||
print('Could not find metadata in the image!')
|
||||
pass
|
||||
|
||||
metadata_parser = modules.metadata.get_metadata_parser(metadata_scheme)
|
||||
metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
|
||||
parsed_parameters = metadata_parser.parse_json(parameters)
|
||||
return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue