Foreign-Mirrors/Fooocus
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refactor: add step before parsing to set data in parser

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- add constructor for MetadataSchema class
- remove showable and copyable from log output
- add functional hash cache (model hashing takes about 5 seconds, only required once per model, using hash lazy loading)
This commit is contained in:
Manuel Schmid
2024-02-02 01:25:47 +01:00
parent 796cf3c78d
commit e55870124b
3 changed files with 126 additions and 101 deletions
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modules/metadata.py
+86 -41
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@@ -1,11 +1,12 @@
import json
import os
import re
from abc import ABC, abstractmethod
from pathlib import Path
from PIL import Image
import modules.config
from modules.flags import MetadataScheme, Performance, Steps, lora_count_with_lcm
from modules.flags import MetadataScheme, Performance, Steps
from modules.util import quote, unquote, extract_styles_from_prompt, is_json, calculate_sha256
re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
@@ -25,6 +26,16 @@ def get_sha256(filepath):
class MetadataParser(ABC):
def __init__(self):
self.full_prompt: str = ''
self.full_negative_prompt: str = ''
self.steps: int = 30
self.base_model_name: str = ''
self.base_model_hash: str = ''
self.refiner_model_name: str = ''
self.refiner_model_hash: str = ''
self.loras: list = []
@abstractmethod
def get_scheme(self) -> MetadataScheme:
raise NotImplementedError
@@ -37,6 +48,27 @@ class MetadataParser(ABC):
def parse_string(self, metadata: dict) -> str:
raise NotImplementedError
def set_data(self, full_prompt, full_negative_prompt, steps, base_model_name, refiner_model_name, loras):
self.full_prompt = full_prompt
self.full_negative_prompt = full_negative_prompt
self.steps = steps
self.base_model_name = Path(base_model_name).stem
base_model_path = os.path.join(modules.config.path_checkpoints, base_model_name)
self.base_model_hash = get_sha256(base_model_path)
if refiner_model_name not in ['', 'None']:
self.refiner_model_name = Path(refiner_model_name).stem
refiner_model_path = os.path.join(modules.config.path_checkpoints, refiner_model_name)
self.refiner_model_hash = get_sha256(refiner_model_path)
self.loras = []
for (lora_name, lora_weight) in loras:
if lora_name != 'None':
lora_path = os.path.join(modules.config.path_loras, lora_name)
lora_hash = get_sha256(lora_path)
self.loras.append((Path(lora_name).stem, lora_weight, lora_hash))
class A1111MetadataParser(MetadataParser):
def get_scheme(self) -> MetadataScheme:
@@ -63,6 +95,7 @@ class A1111MetadataParser(MetadataParser):
'refiner_model_hash': 'Refiner hash',
'lora_hashes': 'Lora hashes',
'lora_weights': 'Lora weights',
'created_by': 'User',
'version': 'Version'
}
@@ -127,65 +160,64 @@ class A1111MetadataParser(MetadataParser):
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(', ')):
name, _, weight = lora.split(': ')
lora_name, lora_hash, lora_weight = lora.split(': ')
for filename in lora_filenames:
path = Path(filename)
if name == path.stem:
data[f'lora_combined_{li + 1}'] = f'{filename} : {weight}'
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}
data = {k: v for _, k, v in metadata}
width, heigth = eval(data['resolution'])
lora_hashes = []
for index in range(lora_count_with_lcm):
key = f'lora_name_{index + 1}'
if key in data:
lora_name = Path(data[f'lora_name_{index + 1}']).stem
lora_weight = data[f'lora_weight_{index + 1}']
lora_hash = data[f'lora_hash_{index + 1}']
# 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)
width, height = eval(data['resolution'])
generation_params = {
self.fooocus_to_a1111['performance']: data['performance'],
self.fooocus_to_a1111['steps']: data['steps'],
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{heigth}',
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'],
# TODO load model by name / hash
self.fooocus_to_a1111['base_model']: Path(data['base_model']).stem,
self.fooocus_to_a1111['base_model_hash']: data['base_model_hash']
self.fooocus_to_a1111['base_model_hash']: self.base_model_hash,
}
if 'refiner_model' in data and data['refiner_model'] != 'None' and 'refiner_model_hash' in data:
# TODO evaluate if this should always be added
if self.refiner_model_name not in ['', 'None']:
generation_params |= {
self.fooocus_to_a1111['refiner_model']: Path(data['refiner_model']).stem,
self.fooocus_to_a1111['refiner_model_hash']: data['refiner_model_hash']
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 generation_params.items() if v is not None])
# TODO check if multiline positive prompt is correctly processed
positive_prompt_resolved = ', '.join(data['full_prompt']) #TODO add loras to positive prompt if even possible
negative_prompt_resolved = ', '.join(data['full_negative_prompt']) #TODO add loras to negative prompt if even possible
positive_prompt_resolved = ', '.join(self.full_prompt) # TODO add loras to positive prompt if even possible
negative_prompt_resolved = ', '.join(
self.full_negative_prompt) # TODO add loras to negative prompt if even possible
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()
@@ -200,11 +232,11 @@ class FooocusMetadataParser(MetadataParser):
lora_filenames.remove(modules.config.downloading_sdxl_lcm_lora())
for key, value in metadata.items():
if value == '' or value == 'None':
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_', 'lora_name_')):
elif key.startswith('lora_combined_'):
metadata[key] = self.replace_value_with_filename(key, value, lora_filenames)
else:
continue
@@ -212,20 +244,33 @@ class FooocusMetadataParser(MetadataParser):
return metadata
def parse_string(self, metadata: list) -> str:
# remove model folder paths from metadata
for li, (label, key, value, show_in_log, copy_in_log) in enumerate(metadata):
if value == '' or value == 'None':
continue
if key in ['base_model', 'refiner_model'] or key.startswith(('lora_combined_', 'lora_name_')):
if key.startswith('lora_combined_'):
name, weight = value.split(' : ')
name = Path(name).stem
value = f'{name} : {weight}'
else:
value = Path(value).stem
metadata[li] = (label, key, value, show_in_log, copy_in_log)
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)
return json.dumps({k: v for _, k, v, _, _ in metadata})
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(res)
@staticmethod
def replace_value_with_filename(key, value, filenames):