mirror of
https://github.com/lllyasviel/Fooocus.git
synced 2026-08-16 13:13:16 +02:00
feat: add metadata to images (#1940)
* feat: add metadata logging for images inspired by https://github.com/MoonRide303/Fooocus-MRE * feat: add config and checkbox for save_metadata_to_images * feat: add argument disable_metadata * feat: add support for A1111 metadata schema https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/cf2772fab0af5573da775e7437e6acdca424f26e/modules/processing.py#L672 * feat: add model hash support for a1111 * feat: use resolved prompts with included expansion and styles for a1111 metadata * fix: code cleanup and resolved prompt fixes * feat: add config metadata_created_by * fix: use stting isntead of quote wrap for A1111 created_by * fix: correctlyy hide/show metadata schema on app start * fix: do not generate hashes when arg --disable-metadata is used * refactor: rename metadata_schema to metadata_scheme * fix: use pnginfo "parameters" insteadf of "Comments" see https://github.com/RupertAvery/DiffusionToolkit/issues/202 and https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/cf2772fab0af5573da775e7437e6acdca424f26e/modules/processing.py#L939 * feat: add resolved prompts to metadata * fix: use correct default value in metadata check for created_by * wip: add metadata mapping, reading and writing applying data after reading currently not functional for A1111 * feat: rename metadata tab and import button label * feat: map basic information for scheme A1111 * wip: optimize handling for metadata in Gradio calls * feat: add enums for Performance, Steps and StepsUOV also move MetadataSchema enum to prevent circular dependency * fix: correctly map resolution, use empty styles for A1111 * chore: code cleanup * feat: add A1111 prompt style detection only detects one style as Fooocus doesn't wrap {prompt} with the whole style, but has a separate prompt string for each style * wip: add prompt style extraction for A1111 scheme * feat: sort styles after metadata import * refactor: use central flag for LoRA count * refactor: use central flag for ControlNet image count * fix: use correct LoRA mapping, add fallback for backwards compatibility * feat: add created_by again * feat: add prefix "Fooocus" to version * wip: code cleanup, update todos * fix: use correct order to read LoRA in meta parser * wip: code cleanup, update todos * feat: make sha256 with length 10 default * feat: add lora handling to A1111 scheme * feat: override existing LoRA values when importing, would cause images to differ * fix: correctly extract prompt style when only prompt expansion is selected * feat: allow model / LoRA loading from subfolders * feat: code cleanup, do not queue metadata preview on image upload * refactor: add flag for refiner_swap_method * feat: add metadata handling for all non-img2img parameters * refactor: code cleanup * chore: use str as return type in calculate_sha256 * feat: add hash cache to metadata * chore: code cleanup * feat: add method get_scheme to Metadata * fix: align handling for scheme Fooocus by removing lcm lora from json parsing * refactor: add step before parsing to set data in parser - 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) * feat: sort metadata attributes before writing to image * feat: add translations and hint for image prompt parameters * chore: check and remove ToDo's * refactor: merge metadata.py into meta_parser.py * fix: add missing refiner in A1111 parse_json * wip: add TODO for ultiline prompt style resolution * fix: remove sorting for A1111, change performance key position fixes https://github.com/lllyasviel/Fooocus/pull/1940#issuecomment-1924444633 * fix: add workaround for multiline prompts * feat: add sampler mapping * feat: prevent config reset by renaming metadata_scheme to match config options * chore: remove remaining todos after analysis refiner is added when set restoring multiline prompts has been resolved by using separate parameters "raw_prompt" and "raw_negative_prompt" * chore: specify too broad exception types * feat: add mapping for _gpu samplers to cpu samplers gpu samplers are less deterministic than cpu but in general similar, see https://www.reddit.com/r/comfyui/comments/15hayzo/comment/juqcpep/ * feat: add better handling for image import with empty metadata * fix: parse adaptive_cfg as float instead of string * chore: loosen strict type for parse_json, fix indent * chore: make steps enums more strict * feat: only override steps if metadata value is not in steps enum or in steps enum and performance is not the same * fix: handle empty strings in metadata e.g. raw negative prompt when none is set
This commit is contained in:
Manuel Schmid
+447
-82
@@ -1,45 +1,113 @@
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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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import modules.sdxl_styles
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from modules.flags import MetadataScheme, Performance, Steps
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from modules.flags import SAMPLERS, CIVITAI_NO_KARRAS
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from modules.util import quote, unquote, extract_styles_from_prompt, is_json, get_file_from_folder_list, 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_prompt_txt, is_generating):
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loaded_parameter_dict = json.loads(raw_prompt_txt)
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def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
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loaded_parameter_dict = raw_metadata
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if isinstance(raw_metadata, str):
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loaded_parameter_dict = json.loads(raw_metadata)
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assert isinstance(loaded_parameter_dict, dict)
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results = [True, 1]
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results = [len(loaded_parameter_dict) > 0, 1]
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get_str('prompt', 'Prompt', loaded_parameter_dict, results)
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get_str('negative_prompt', 'Negative Prompt', loaded_parameter_dict, results)
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get_list('styles', 'Styles', loaded_parameter_dict, results)
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get_str('performance', 'Performance', loaded_parameter_dict, results)
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get_steps('steps', 'Steps', loaded_parameter_dict, results)
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get_float('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results)
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get_resolution('resolution', 'Resolution', loaded_parameter_dict, results)
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get_float('guidance_scale', 'Guidance Scale', loaded_parameter_dict, results)
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get_float('sharpness', 'Sharpness', loaded_parameter_dict, results)
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get_adm_guidance('adm_guidance', 'ADM Guidance', loaded_parameter_dict, results)
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get_str('refiner_swap_method', 'Refiner Swap Method', loaded_parameter_dict, results)
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get_float('adaptive_cfg', 'CFG Mimicking from TSNR', loaded_parameter_dict, results)
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get_str('base_model', 'Base Model', loaded_parameter_dict, results)
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get_str('refiner_model', 'Refiner Model', loaded_parameter_dict, results)
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get_float('refiner_switch', 'Refiner Switch', loaded_parameter_dict, results)
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get_str('sampler', 'Sampler', loaded_parameter_dict, results)
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get_str('scheduler', 'Scheduler', loaded_parameter_dict, results)
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get_seed('seed', 'Seed', loaded_parameter_dict, results)
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if is_generating:
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results.append(gr.update())
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else:
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results.append(gr.update(visible=True))
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results.append(gr.update(visible=False))
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get_freeu('freeu', 'FreeU', loaded_parameter_dict, results)
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for i in range(modules.config.default_max_lora_number):
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get_lora(f'lora_combined_{i + 1}', f'LoRA {i + 1}', loaded_parameter_dict, results)
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return results
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def get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
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try:
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h = loaded_parameter_dict.get('Prompt', None)
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h = source_dict.get(key, source_dict.get(fallback, default))
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Negative Prompt', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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def get_list(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
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try:
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h = loaded_parameter_dict.get('Styles', None)
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h = source_dict.get(key, source_dict.get(fallback, default))
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h = eval(h)
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assert isinstance(h, list)
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results.append(h)
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except:
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results.append(gr.update())
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def get_float(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
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try:
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h = loaded_parameter_dict.get('Performance', None)
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assert isinstance(h, str)
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h = source_dict.get(key, source_dict.get(fallback, default))
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assert h is not None
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h = float(h)
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results.append(h)
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except:
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results.append(gr.update())
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def get_steps(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
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try:
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h = loaded_parameter_dict.get('Resolution', None)
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h = source_dict.get(key, source_dict.get(fallback, default))
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assert h is not None
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h = int(h)
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# if not in steps or in steps and performance is not the same
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if h not in iter(Steps) or Steps(h).name.casefold() != source_dict.get('performance', '').replace(' ', '_').casefold():
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results.append(h)
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return
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results.append(-1)
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except:
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results.append(-1)
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def get_resolution(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
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try:
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h = source_dict.get(key, source_dict.get(fallback, default))
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width, height = eval(h)
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formatted = modules.config.add_ratio(f'{width}*{height}')
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if formatted in modules.config.available_aspect_ratios:
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@@ -55,24 +123,22 @@ def load_parameter_button_click(raw_prompt_txt, is_generating):
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results.append(gr.update())
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results.append(gr.update())
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def get_seed(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
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try:
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h = loaded_parameter_dict.get('Sharpness', None)
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h = source_dict.get(key, source_dict.get(fallback, default))
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assert h is not None
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h = float(h)
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h = int(h)
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results.append(False)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Guidance Scale', None)
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assert h is not None
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h = float(h)
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results.append(h)
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except:
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results.append(gr.update())
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def get_adm_guidance(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
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try:
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h = loaded_parameter_dict.get('ADM Guidance', None)
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h = source_dict.get(key, source_dict.get(fallback, default))
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p, n, e = eval(h)
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results.append(float(p))
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results.append(float(n))
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@@ -82,69 +148,368 @@ def load_parameter_button_click(raw_prompt_txt, is_generating):
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results.append(gr.update())
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Base Model', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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def get_freeu(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
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try:
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h = loaded_parameter_dict.get('Refiner Model', None)
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assert isinstance(h, str)
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results.append(h)
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h = source_dict.get(key, source_dict.get(fallback, default))
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b1, b2, s1, s2 = eval(h)
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results.append(True)
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results.append(float(b1))
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results.append(float(b2))
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results.append(float(s1))
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results.append(float(s2))
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Refiner Switch', None)
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assert h is not None
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h = float(h)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Sampler', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Scheduler', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Seed', None)
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assert h is not None
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h = int(h)
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results.append(False)
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results.append(h)
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results.append(gr.update())
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results.append(gr.update())
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results.append(gr.update())
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results.append(gr.update())
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def get_lora(key: str, fallback: str | None, source_dict: dict, results: list):
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try:
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n, w = source_dict.get(key, source_dict.get(fallback)).split(' : ')
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w = float(w)
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results.append(True)
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results.append(n)
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results.append(w)
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except:
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results.append(gr.update())
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results.append(gr.update())
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results.append(True)
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results.append('None')
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results.append(1)
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if is_generating:
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results.append(gr.update())
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else:
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results.append(gr.update(visible=True))
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results.append(gr.update(visible=False))
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for i in range(1, modules.config.default_max_lora_number + 1):
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try:
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n, w = loaded_parameter_dict.get(f'LoRA {i}', ' : ').split(' : ')
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w = float(w)
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results.append(True)
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results.append(n)
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results.append(w)
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except:
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results.append(True)
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results.append('None')
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results.append(1.0)
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def get_sha256(filepath):
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global hash_cache
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return results
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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.raw_prompt: str = ''
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self.full_prompt: str = ''
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self.raw_negative_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 | str) -> 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, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name, refiner_model_name, loras):
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self.raw_prompt = raw_prompt
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self.full_prompt = full_prompt
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self.raw_negative_prompt = raw_negative_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 = get_file_from_folder_list(base_model_name, modules.config.paths_checkpoints)
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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 = get_file_from_folder_list(refiner_model_name, modules.config.paths_checkpoints)
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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 = get_file_from_folder_list(lora_name, modules.config.paths_loras)
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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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'raw_prompt': 'Raw prompt',
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'raw_negative_prompt': 'Raw negative prompt',
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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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'scheduler': 'Scheduler',
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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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metadata_prompt = ''
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metadata_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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metadata_negative_prompt += ('' if metadata_negative_prompt == '' else "\n") + line
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else:
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metadata_prompt += ('' if metadata_prompt == '' else "\n") + line
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found_styles, prompt, negative_prompt = extract_styles_from_prompt(metadata_prompt, metadata_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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}
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for k, v in re_param.findall(lastline):
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try:
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if v != '' and 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['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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# workaround for multiline prompts
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if 'raw_prompt' in data:
|
||||
data['prompt'] = data['raw_prompt']
|
||||
raw_prompt = data['raw_prompt'].replace("\n", ', ')
|
||||
if metadata_prompt != raw_prompt and modules.sdxl_styles.fooocus_expansion not in found_styles:
|
||||
found_styles.append(modules.sdxl_styles.fooocus_expansion)
|
||||
|
||||
if 'raw_negative_prompt' in data:
|
||||
data['negative_prompt'] = data['raw_negative_prompt']
|
||||
|
||||
data['styles'] = str(found_styles)
|
||||
|
||||
# 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 ValueError | KeyError:
|
||||
pass
|
||||
|
||||
if 'sampler' in data:
|
||||
data['sampler'] = data['sampler'].replace(' Karras', '')
|
||||
# get key
|
||||
for k, v in SAMPLERS.items():
|
||||
if v == data['sampler']:
|
||||
data['sampler'] = k
|
||||
break
|
||||
|
||||
for key in ['base_model', 'refiner_model']:
|
||||
if key in data:
|
||||
for filename in modules.config.model_filenames:
|
||||
path = Path(filename)
|
||||
if data[key] == path.stem:
|
||||
data[key] = 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'])
|
||||
|
||||
sampler = data['sampler']
|
||||
scheduler = data['scheduler']
|
||||
if sampler in SAMPLERS and SAMPLERS[sampler] != '':
|
||||
sampler = SAMPLERS[sampler]
|
||||
if sampler not in CIVITAI_NO_KARRAS and scheduler == 'karras':
|
||||
sampler += f' Karras'
|
||||
|
||||
generation_params = {
|
||||
self.fooocus_to_a1111['steps']: self.steps,
|
||||
self.fooocus_to_a1111['sampler']: 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,
|
||||
|
||||
self.fooocus_to_a1111['performance']: data['performance'],
|
||||
self.fooocus_to_a1111['scheduler']: scheduler,
|
||||
# workaround for multiline prompts
|
||||
self.fooocus_to_a1111['raw_prompt']: self.raw_prompt,
|
||||
self.fooocus_to_a1111['raw_negative_prompt']: self.raw_negative_prompt,
|
||||
}
|
||||
|
||||
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 generation_params.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
|
||||
|
||||
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 ValueError:
|
||||
metadata_scheme = None
|
||||
|
||||
# broad fallback
|
||||
if isinstance(parameters, dict):
|
||||
metadata_scheme = MetadataScheme.FOOOCUS
|
||||
|
||||
if isinstance(parameters, str):
|
||||
metadata_scheme = MetadataScheme.A1111
|
||||
|
||||
return parameters, items, metadata_scheme
|
||||
|
||||
Reference in New Issue
Block a user