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feat: add metadata to images (#1940)

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* 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
2024-02-26 14:27:57 +01:00
committed by GitHub
parent d3113f5c3f
commit ba9eadbcda
9 changed files with 871 additions and 185 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/async_worker.py
+55 -46
View File
@@ -19,6 +19,7 @@ async_tasks = []
def worker():
global async_tasks
import os
import traceback
import math
import numpy as np
@@ -39,6 +40,7 @@ def worker():
import extras.ip_adapter as ip_adapter
import extras.face_crop
import fooocus_version
import args_manager
from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays
from modules.private_logger import log
@@ -46,6 +48,8 @@ def worker():
from modules.util import remove_empty_str, HWC3, resize_image, \
get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix
from modules.upscaler import perform_upscale
from modules.flags import Performance
from modules.meta_parser import get_metadata_parser, MetadataScheme
pid = os.getpid()
print(f'Started worker with PID {pid}')
@@ -135,7 +139,7 @@ def worker():
prompt = args.pop()
negative_prompt = args.pop()
style_selections = args.pop()
performance_selection = args.pop()
performance_selection = Performance(args.pop())
aspect_ratios_selection = args.pop()
image_number = args.pop()
image_seed = args.pop()
@@ -153,6 +157,7 @@ def worker():
inpaint_input_image = args.pop()
inpaint_additional_prompt = args.pop()
inpaint_mask_image_upload = args.pop()
disable_preview = args.pop()
disable_intermediate_results = args.pop()
disable_seed_increment = args.pop()
@@ -190,8 +195,11 @@ def worker():
invert_mask_checkbox = args.pop()
inpaint_erode_or_dilate = args.pop()
save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False
metadata_scheme = MetadataScheme(args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
cn_tasks = {x: [] for x in flags.ip_list}
for _ in range(4):
for _ in range(flags.controlnet_image_count):
cn_img = args.pop()
cn_stop = args.pop()
cn_weight = args.pop()
@@ -216,17 +224,9 @@ def worker():
print(f'Refiner disabled because base model and refiner are same.')
refiner_model_name = 'None'
assert performance_selection in ['Speed', 'Quality', 'Extreme Speed']
steps = performance_selection.steps()
steps = 30
if performance_selection == 'Speed':
steps = 30
if performance_selection == 'Quality':
steps = 60
if performance_selection == 'Extreme Speed':
if performance_selection == Performance.EXTREME_SPEED:
print('Enter LCM mode.')
progressbar(async_task, 1, 'Downloading LCM components ...')
loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]
@@ -244,7 +244,6 @@ def worker():
adm_scaler_positive = 1.0
adm_scaler_negative = 1.0
adm_scaler_end = 0.0
steps = 8
print(f'[Parameters] Adaptive CFG = {adaptive_cfg}')
print(f'[Parameters] Sharpness = {sharpness}')
@@ -305,16 +304,7 @@ def worker():
if 'fast' in uov_method:
skip_prompt_processing = True
else:
steps = 18
if performance_selection == 'Speed':
steps = 18
if performance_selection == 'Quality':
steps = 36
if performance_selection == 'Extreme Speed':
steps = 8
steps = performance_selection.steps_uov()
progressbar(async_task, 1, 'Downloading upscale models ...')
modules.config.downloading_upscale_model()
@@ -830,31 +820,50 @@ def worker():
img_paths = []
for x in imgs:
d = [
('Prompt', task['log_positive_prompt']),
('Negative Prompt', task['log_negative_prompt']),
('Fooocus V2 Expansion', task['expansion']),
('Styles', str(raw_style_selections)),
('Performance', performance_selection),
('Resolution', str((width, height))),
('Sharpness', sharpness),
('Guidance Scale', guidance_scale),
('ADM Guidance', str((
modules.patch.patch_settings[pid].positive_adm_scale,
modules.patch.patch_settings[pid].negative_adm_scale,
modules.patch.patch_settings[pid].adm_scaler_end))),
('Base Model', base_model_name),
('Refiner Model', refiner_model_name),
('Refiner Switch', refiner_switch),
('Sampler', sampler_name),
('Scheduler', scheduler_name),
('Seed', task['task_seed']),
]
d = [('Prompt', 'prompt', task['log_positive_prompt']),
('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),
('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']),
('Styles', 'styles', str(raw_style_selections)),
('Performance', 'performance', performance_selection.value),
('Resolution', 'resolution', str((width, height))),
('Guidance Scale', 'guidance_scale', guidance_scale),
('Sharpness', 'sharpness', sharpness),
('ADM Guidance', 'adm_guidance', str((
modules.patch.patch_settings[pid].positive_adm_scale,
modules.patch.patch_settings[pid].negative_adm_scale,
modules.patch.patch_settings[pid].adm_scaler_end))),
('Base Model', 'base_model', base_model_name),
('Refiner Model', 'refiner_model', refiner_model_name),
('Refiner Switch', 'refiner_switch', refiner_switch)]
if refiner_model_name != 'None':
if overwrite_switch > 0:
d.append(('Overwrite Switch', 'overwrite_switch', overwrite_switch))
if refiner_swap_method != flags.refiner_swap_method:
d.append(('Refiner Swap Method', 'refiner_swap_method', refiner_swap_method))
if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr:
d.append(('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
d.append(('Sampler', 'sampler', sampler_name))
d.append(('Scheduler', 'scheduler', scheduler_name))
d.append(('Seed', 'seed', task['task_seed']))
if freeu_enabled:
d.append(('FreeU', 'freeu', str((freeu_b1, freeu_b2, freeu_s1, freeu_s2))))
metadata_parser = None
if save_metadata_to_images:
metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
task['log_negative_prompt'], task['negative'],
steps, base_model_name, refiner_model_name, loras)
for li, (n, w) in enumerate(loras):
if n != 'None':
d.append((f'LoRA {li + 1}', f'{n} : {w}'))
d.append(('Version', 'v' + fooocus_version.version))
img_paths.append(log(x, d))
d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}'))
d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version))
img_paths.append(log(x, d, metadata_parser))
yield_result(async_task, img_paths, do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results)
except ldm_patched.modules.model_management.InterruptProcessingException as e: