feat: map basic information for scheme A1111
This commit is contained in:
Manuel Schmid
parent
ee21c2b6bc
commit
e19596c2df
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@ -203,16 +203,16 @@ def worker():
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modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end = 0.0
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steps = 8
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if save_metadata_to_images:
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base_model_path = os.path.join(modules.config.path_checkpoints, base_model_name)
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base_model_hash = calculate_sha256(base_model_path)[0:10]
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base_model_path = os.path.join(modules.config.path_checkpoints, base_model_name)
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base_model_hash = calculate_sha256(base_model_path)[0:10]
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lora_hashes = []
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for (n, w) in loras:
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if n != 'None':
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lora_path = os.path.join(modules.config.path_loras, n)
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lora_hashes.append(f'{n.split(".")[0]}: {calculate_sha256(lora_path)[0:10]}')
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lora_hashes_string = ", ".join(lora_hashes)
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refiner_model_path = os.path.join(modules.config.path_checkpoints, refiner_model_name)
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refiner_model_hash = calculate_sha256(refiner_model_path)[0:10] if refiner_model_name != 'None' else ''
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lora_hashes = []
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for (n, w) in loras:
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lora_path = os.path.join(modules.config.path_loras, n) if n != 'None' else ''
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lora_hashes.append(calculate_sha256(lora_path)[0:10] if n != 'None' else '')
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modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg
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print(f'[Parameters] Adaptive CFG = {modules.patch.adaptive_cfg}')
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@ -812,7 +812,9 @@ def worker():
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modules.patch.negative_adm_scale,
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modules.patch.adm_scaler_end)), True, True),
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('Base Model', 'base_model', base_model_name, True, True),
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('Base Model Hash', 'base_model_hash', base_model_hash, False, False),
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('Refiner Model', 'refiner_model', refiner_model_name, True, True),
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('Refiner Model Hash', 'refiner_model_hash', refiner_model_hash, False, False),
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('Refiner Switch', 'refiner_switch', refiner_switch, True, True),
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('Sampler', 'sampler', sampler_name, True, True),
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('Scheduler', 'scheduler', scheduler_name, True, True),
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@ -821,8 +823,9 @@ def worker():
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for li, (n, w) in enumerate(loras):
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if n != 'None':
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d.append((f'LoRA {li + 1}', f'lora{li + 1}_combined', f'{n} : {w}', True, True))
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# d.append((f'LoRA {li + 1} Name', f'lora{li + 1}_name', n, False, False))
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# d.append((f'LoRA {li + 1} Weight', f'lora{li + 1}_weight', n, False, False))
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d.append((f'LoRA {li + 1} Name', f'lora_name_{li + 1}', n, False, False))
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d.append((f'LoRA {li + 1} Weight', f'lora_weight_{li + 1}', w, False, False))
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d.append((f'LoRA {li + 1} Hash', f'lora_hash_{li + 1}', lora_hashes[li], False, False))
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d.append(('Version', 'version', 'v' + fooocus_version.version, True, True))
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log(x, d, save_metadata_to_images, metadata_scheme)
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@ -1,4 +1,5 @@
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import json
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import re
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from abc import ABC, abstractmethod
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from enum import Enum
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from PIL import Image
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@ -6,7 +7,11 @@ from PIL import Image
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import modules.config
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import fooocus_version
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# import advanced_parameters
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from modules.util import quote, is_json
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from modules.util import quote, unquote, is_json
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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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class MetadataScheme(Enum):
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@ -16,7 +21,7 @@ class MetadataScheme(Enum):
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class MetadataParser(ABC):
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@abstractmethod
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def parse_json(self, metadata: dict):
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def parse_json(self, metadata: dict) -> dict:
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raise NotImplementedError
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# TODO add data to parse
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@ -27,9 +32,67 @@ class MetadataParser(ABC):
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class A1111MetadataParser(MetadataParser):
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def parse_json(self, metadata: dict):
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fooocus_to_a1111 = {
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'negative_prompt': 'Negative prompt',
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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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'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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'version': 'Version'
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}
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def parse_json(self, metadata: str) -> dict:
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# TODO add correct mapping
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pass
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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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data = {
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'prompt': prompt,
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'negative_prompt': negative_prompt
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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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# TODO check
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data[f"{k}-1"] = m.group(1)
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data[f"{k}-2"] = m.group(2)
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else:
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key = list(self.fooocus_to_a1111.keys())[list(self.fooocus_to_a1111.values()).index(k)]
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data[key] = v
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except Exception:
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print(f"Error parsing \"{k}: {v}\"")
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return data
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def parse_string(self, metadata: dict) -> str:
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# TODO add correct mapping
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@ -39,30 +102,54 @@ class A1111MetadataParser(MetadataParser):
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# TODO check if correct
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width, heigth = data['resolution'].split(', ')
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lora_hashes = []
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for index in range(5):
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name = f'lora_name_{index + 1}'
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if name in data:
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# weight = f'lora_weight_{index}'
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hash = data[f'lora_hash_{index + 1}']
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lora_hashes.append(f'{name.split(".")[0]}: {hash}')
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lora_hashes_string = ", ".join(lora_hashes)
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# set static defaults
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generation_params = {
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"Steps": data['steps'],
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"Sampler": data['sampler'],
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"CFG scale": data['guidance_scale'],
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"Seed": data['seed'],
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"Size": f"{width}x{heigth}",
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# "Model hash": base_model_hash,
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"Model": data['base_model'].split('.')[0],
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# "Lora hashes": lora_hashes_string,
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'styles': [],
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}
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generation_params |= {
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self.fooocus_to_a1111['steps']: data['steps'],
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self.fooocus_to_a1111['sampler']: data['sampler'],
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self.fooocus_to_a1111['guidance_scale']: data['guidance_scale'],
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self.fooocus_to_a1111['seed']: data['seed'],
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self.fooocus_to_a1111['resolution']: f'{width}x{heigth}',
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self.fooocus_to_a1111['base_model']: data['base_model'].split('.')[0],
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self.fooocus_to_a1111['base_model_hash']: data['base_model_hash']
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}
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if 'refiner_model' in data and data['refiner_model'] != 'None' and 'refiner_model_hash' in data:
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generation_params |= {
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self.fooocus_to_a1111['refiner_model']: data['refiner_model'].split('.')[0],
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self.fooocus_to_a1111['refiner_model_hash']: data['refiner_model_hash'],
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}
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generation_params |= {
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self.fooocus_to_a1111['lora_hashes']: lora_hashes_string,
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# "Denoising strength": data['denoising_strength'],
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"Version": f"Fooocus {data['version']}"
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self.fooocus_to_a1111['version']: f"Fooocus {data['version']}"
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}
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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 generation_params.items() if v is not None])
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positive_prompt_resolved = ', '.join(data['full_prompt'])
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negative_prompt_resolved = ', '.join(data['full_negative_prompt'])
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# TODO check if multiline positive prompt is correctly processed
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positive_prompt_resolved = ', '.join(data['full_prompt']) #TODO add loras to positive prompt if even possible
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negative_prompt_resolved = ', '.join(data['full_negative_prompt']) #TODO add loras to positive prompt if even possible
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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 parse_json(self, metadata: dict):
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def parse_json(self, metadata: dict) -> dict:
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# TODO add mapping if necessary
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return metadata
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@ -140,6 +227,7 @@ class FooocusMetadataParser(MetadataParser):
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# metadata |= {
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# 'software': f'Fooocus v{fooocus_version.version}',
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# }
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# TODO add metadata_created_by
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# if modules.config.metadata_created_by != '':
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# metadata |= {
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# 'created_by': modules.config.metadata_created_by
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@ -197,6 +197,16 @@ def quote(text):
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return json.dumps(text, ensure_ascii=False)
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def unquote(text):
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if len(text) == 0 or text[0] != '"' or text[-1] != '"':
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return text
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try:
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return json.loads(text)
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except Exception:
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return text
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def is_json(data: str) -> bool:
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try:
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loaded_json = json.loads(data)
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