mirror of https://github.com/vladmandic/automatic
switch cmdflags to settings
Vladimir Mandic
parent
831f562394
commit
81b8294e93
45
README.md
45
README.md
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@ -5,6 +5,12 @@
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Fork is as close as up-to-date with origin as time allows
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All code changes are merged upstream whenever possible
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The idea behind the fork is to enable latest technologies and advances in text-to-image generation
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*Sometimes this is not the same as "as simple as possible to use"*
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If you are looking an amazing simple-to-use Stable Diffusion tool, I'd suggest [InvokeAI](https://invoke-ai.github.io/InvokeAI/) specifically due to its automated installer and ease of use
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<br>
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<br>
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@ -13,7 +19,9 @@ All code changes are merged upstream whenever possible
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### Fork does differ in few things
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- New logger
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- New error and exception handlers
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- Built-in performance profiler
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- Updated **Python** libraries to latest known compatible versions
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e.g. `accelerate`, `transformers`, `numpy`, etc.
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- Includes opinionated **System** and **Options** configuration
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@ -22,7 +30,6 @@ All code changes are merged upstream whenever possible
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- Optimized startup
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Gradio web server will be initialized much earlier which model load is done in the background
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Faster model loading plus ability to fallback on corrupt models
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- Includes **SD2** configuration files
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- Uses simplified folder structure
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e.g. `/train`, `/outputs/*`, `/models/*`, etc.
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- Enhanced training templates
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@ -37,13 +44,10 @@ All code changes are merged upstream whenever possible
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- Optimized for `Torch` 2.0
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- Runs with `SDP` memory attention enabled by default if supported by system
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Fallback to `XFormers` if SDP is not supported
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If either `SDP` or `XFormers` are not supported, falls back to usual cmd line arguments
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### Removed
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- Drops compatibility with `python` **3.7** and requires **3.9**
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Recommended is **Python 3.10**
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- Drops compatibility with older versions of `python` and requires **3.9**
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- Drops localizations
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- Drops automated tests
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@ -69,41 +73,36 @@ Fork adds extra functionality:
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- [Steps Animation](https://github.com/vladmandic/sd-extension-steps-animation)
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- [Seed Travel](https://github.com/yownas/seed_travel)
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*Note*: Extensions are automatically updated to latest version on `install`
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<br>
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<br>
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## Install
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### Install
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1. Install `Python`, `Git`
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1. Install first:
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`Python`, `Git`
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2. Clone repository
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`git clone https://github.com/vladmandic/automatic`
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> git clone https://github.com/vladmandic/automatic
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### Run
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## Run
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Run desired startup script to install dependencies and extensions and start server:
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- `launch.py`:
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Main startup script
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Run `python launch.py --help` for available options
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- `launch-venv.bat` and `launch.venv.sh`:
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- `webui.bat` and `webui.sh`:
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Platform specific wrapper scripts that starts `launch.py` in Python virtual environment
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*Note*: Server can run without virtual environment, but it is recommended to use it
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**If you're unsure which launcher to use, this is the one you want**
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- `launch.py`:
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Main startup script
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Can be used directly to start server in manually activated `venv` or to run it without `venv`
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Run `python launch.py --help` for available options
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- `setup.py`:
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Main installer, used by `launch.py`
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Can also be used directly to update repository or extensions
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If running manually, make sure to activate `venv` first (if used)
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Run `python setup.py --help` for available options
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Setup details are logged to `setup.log`
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- `webui.py`:
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Main server script
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Run `python webui.py --help` for available options
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<br>
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15
TODO.md
15
TODO.md
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@ -5,16 +5,9 @@
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Stuff to be fixed...
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- Reconnect WebUI
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- Skip Torch
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- Update requirements
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- Update README
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- Fix CSS
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- Fix Firefox
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- Implement installer in Python
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- piexif, rich, kornia
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- Move cross-optimization to settings
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- Fix mediapipe
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- Redo Extensions tab
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- Fix Firefox CSS issues
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- Integrate CSS into single file
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## Integration
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@ -44,4 +37,4 @@ Tech that can be integrated as part of the core workflow...
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## Random
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- Bunch of stuff:<https://pharmapsychotic.com/tools.html>
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- Bunch of stuff: <https://pharmapsychotic.com/tools.html>
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@ -1 +1,2 @@
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mediapipe
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colormap
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@ -266,7 +266,6 @@ async def check(params):
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log.info({ 'checking server options' })
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options['training_xattention_optimizations'] = False
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options['training_image_repeats_per_epoch'] = 1
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if params.skipmodel:
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@ -105,7 +105,6 @@ def prepare_server():
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server_options.options.save_optimizer_state = False
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server_options.options.training_image_repeats_per_epoch = args.repeats
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server_options.options.training_write_csv_every = 0
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server_options.options.training_xattention_optimizations = False
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sdapi.postsync('/sdapi/v1/options', server_options.options)
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console.log(f'updated server options')
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18
config.json
18
config.json
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@ -38,7 +38,7 @@
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"dimensions_and_batch_together": true,
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"directories_filename_pattern": "",
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"directories_max_prompt_words": 8,
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"disable_weights_auto_swap": false,
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"disable_weights_auto_swap": true,
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"disabled_extensions": [
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"ScuNET"
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],
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@ -133,7 +133,6 @@
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"memmon_poll_rate": 1,
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"multiple_tqdm": false,
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"n_rows": -1,
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"no_dpmpp_sde_batch_determinism": false,
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"outdir_extras_samples": "outputs/extras",
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"outdir_grids": "",
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"outdir_img2img_grids": "outputs/grids",
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@ -160,7 +159,6 @@
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"samplers_in_dropdown": true,
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"samples_filename_pattern": "",
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"samples_format": "jpg",
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"samples_log_stdout": false,
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"samples_save": true,
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"save_images_add_number": true,
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"save_images_before_color_correction": true,
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@ -172,7 +170,7 @@
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"save_training_settings_to_txt": false,
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"save_txt": false,
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"sd_checkpoint_cache": 0,
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"sd_checkpoint_hash": "6ce0161689b3853acaa03779ec93eafe75a02f4ced659bee03f50797806fa2fa",
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"sd_checkpoint_hash": "cc6cb27103417325ff94f52b7a5d2dde45a7515b25c255d8e396c90014281516",
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"sd_hypernetwork_strength": 1.0,
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"sd_hypernetwork": "None",
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"sd_lora": "",
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@ -197,16 +195,13 @@
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"training_tensorboard_flush_every": 120,
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"training_tensorboard_save_images": false,
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"training_write_csv_every": 0.0,
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"training_xattention_optimizations": false,
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"ui_extra_networks_tab_reorder": "",
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"ui_reorder": "sampler, dimensions, cfg, seed, checkboxes, hires_fix, batch, scripts",
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"unload_models_when_training": false,
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"upcast_attn": false,
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"upscaler_for_img2img": "SwinIR_4x",
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"upscaling_max_images_in_cache": 5,
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"use_old_emphasis_implementation": false,
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"use_old_hires_fix_width_height": false,
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"use_old_karras_scheduler_sigmas": false,
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"use_original_name_batch": true,
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"use_save_to_dirs_for_ui": false,
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"use_upscaler_name_as_suffix": true,
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@ -231,5 +226,12 @@
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"image_browser_use_thumbnail": false,
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"image_browser_thumbnail_size": 200.0,
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"disable_all_extensions": "none",
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"openpose3d_use_online_version": false
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"openpose3d_use_online_version": false,
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"cross_attention": "xFormers for cross-attention layers",
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"cross_attention_optimization": "Scaled-Dot-Product",
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"cross_attention_options": [],
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"disable_nan_check": false,
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"opt_channelslast": false,
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"cudnn_benchmark": false,
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"image_browser_debug_level": "0 - none"
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}
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@ -40,7 +40,7 @@ class LDSR:
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model = model.to(shared.device)
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if half_attention:
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model = model.half()
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if shared.cmd_opts.opt_channelslast:
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if shared.opts.opt_channelslast:
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model = model.to(memory_format=torch.channels_last)
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sd_hijack.model_hijack.hijack(model) # apply optimization
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@ -1 +1 @@
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Subproject commit 8f18058bf6d061f64047c1babab57e2293d3d1e3
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Subproject commit 5d3465c4b2d5dacfdd6caa38b71b23b4f88401c0
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@ -1 +1 @@
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Subproject commit 687bfc2be7291a63d78c72c08c9f263b3cc4d47d
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Subproject commit 8a7df4a911469637cab61cd7722fbaa6d6a2a77d
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@ -1 +1 @@
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Subproject commit 241c05f8c9d3c5abe637187e3c4bb46f17447029
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Subproject commit e1885108055726638f0204aadf668064faf7eebc
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@ -1 +1 @@
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Subproject commit 59973b8dd532df46e62a014589b2574a5709deea
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Subproject commit 972cc439225ae6993b8c43bf4b677c9f16e3ead1
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@ -1 +1 @@
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Subproject commit 4b9755bb946ddcce4b89021271504f032f59f9ec
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Subproject commit 57040c311fc540bca9ca9d5ada2a9dd521f03f95
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30
launch.py
30
launch.py
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def is_installed(package):
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try:
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spec = importlib.util.find_spec(package)
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except ModuleNotFoundError:
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return False
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return spec is not None
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return setup.installed(package)
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def repo_dir(name):
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def git_clone(url, dir, name, commithash=None):
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if os.path.exists(dir):
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if commithash is None:
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return
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current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip()
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if current_hash == commithash:
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return
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run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
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run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
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return
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run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
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if commithash is not None:
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run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
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setup.clone(url, dir, commithash)
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def run_extension_installer(dir):
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setup.run_extension_installer(dir)
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if __name__ == "__main__":
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setup.run_setup(False)
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setup.set_environment()
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# setup.check_torch()
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print(f"Launching {'API server' if '--nowebui' in sys.argv else 'Web UI'} with arguments: {' '.join(sys.argv[1:])}")
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setup.log.info(f"Server Arguments: {sys.argv[1:]}")
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import webui
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if '--nowebui' in sys.argv:
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webui.api_only()
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else:
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webui.webui()
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webui.webui()
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@ -609,7 +609,7 @@ class Api:
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def train_embedding(self, args: dict):
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try:
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shared.state.begin()
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apply_optimizations = shared.opts.training_xattention_optimizations
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apply_optimizations = False
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error = None
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filename = ''
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if not apply_optimizations:
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@ -631,7 +631,7 @@ class Api:
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try:
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shared.state.begin()
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shared.loaded_hypernetworks = []
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apply_optimizations = shared.opts.training_xattention_optimizations
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apply_optimizations = False
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error = None
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filename = ''
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if not apply_optimizations:
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@ -2,90 +2,59 @@ import argparse
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import os
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from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file
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parser = argparse.ArgumentParser()
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parser = argparse.ArgumentParser(description="Stable Diffusion", formatter_class=lambda prog: argparse.HelpFormatter(prog,max_help_position=55,indent_increment=2,width=200))
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parser.add_argument("-f", action='store_true', help=argparse.SUPPRESS) # allows running as root; implemented outside of webui
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parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
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parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
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parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
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parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
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parser.add_argument("--vae-dir", type=str, default=None, help="Path to directory with VAE files")
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parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
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parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
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parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
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parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
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parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
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parser.add_argument("--max-batch-count", type=int, default=32, help="maximum batch count value for the UI")
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parser.add_argument("--embeddings-dir", type=str, default=os.path.join(data_path, 'models/embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
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parser.add_argument("--textual-inversion-templates-dir", type=str, default=os.path.join(script_path, 'train/templates'), help="directory with textual inversion templates")
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parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
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parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
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parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
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parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
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parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
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parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
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parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
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parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
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parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
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parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
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parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
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parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
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parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
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parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
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parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
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parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
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parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
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parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
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parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
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parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
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parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers", default=True)
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parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
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parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
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parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
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parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
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parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
|
||||
parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
|
||||
parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
|
||||
parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
|
||||
parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
|
||||
parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
|
||||
parser.add_argument("--disable-sdp-attention", action='store_true', help="disable scaled dot product cross-attention layer optimization", default=False)
|
||||
parser.add_argument("--opt-sdp-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization", default=True)
|
||||
parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization without memory efficient attention, makes image generation deterministic; requires PyTorch 2.*")
|
||||
parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
|
||||
parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI", default=True)
|
||||
parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
|
||||
parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
|
||||
parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
|
||||
parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(data_path, 'ui-config.json'))
|
||||
parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
|
||||
parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False)
|
||||
parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(data_path, 'config.json'))
|
||||
parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
|
||||
parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
|
||||
parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
|
||||
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
|
||||
parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
|
||||
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=True)
|
||||
parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
|
||||
parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
|
||||
parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=True)
|
||||
parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)", default=True)
|
||||
parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
|
||||
parser.add_argument("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests")
|
||||
parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
|
||||
parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
|
||||
parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list (no spaces)", default=None)
|
||||
parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
|
||||
parser.add_argument("--ui-settings-file", type=str, help=argparse.SUPPRESS, default=os.path.join(data_path, 'config.json'))
|
||||
parser.add_argument("--ui-config-file", type=str, help=argparse.SUPPRESS, default=os.path.join(data_path, 'ui-config.json'))
|
||||
parser.add_argument("--config", type=str, default=sd_default_config, help=argparse.SUPPRESS)
|
||||
parser.add_argument("--theme", type=str, help=argparse.SUPPRESS, default='dark')
|
||||
|
||||
parser.add_argument("--no-half", action='store_true', help="Do not switch the model to 16-bit floats")
|
||||
parser.add_argument("--no-half-vae", action='store_true', help="Do not switch the VAE model to 16-bit floats")
|
||||
parser.add_argument("--precision", type=str, help="Evaluate at this precision", choices=["full", "autocast"], default="autocast")
|
||||
|
||||
parser.add_argument("--medvram", action='store_true', help="Enable model optimizations for sacrificing a little speed for low memory usage")
|
||||
parser.add_argument("--lowvram", action='store_true', help="Enable model optimizations for sacrificing a lot of speed for lowest memory usage")
|
||||
parser.add_argument("--lowram", action='store_true', help="Load checkpoint weights to VRAM instead of RAM")
|
||||
|
||||
parser.add_argument("--ckpt", type=str, default=sd_model_file, help="Path to checkpoint of stable diffusion model to load immediately",)
|
||||
parser.add_argument('--vae', type=str, help='Path to checkpoint of stable diffusion VAE model to load immediately', default=None)
|
||||
parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="Base path where all user data is stored")
|
||||
# parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
|
||||
# parser.add_argument("--vae-dir", type=str, default=None, help="Path to directory with VAE files")
|
||||
# parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
|
||||
# parser.add_argument("--embeddings-dir", type=str, default=os.path.join(data_path, 'models/embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
|
||||
# parser.add_argument("--embeddings-templates-dir", type=str, default=os.path.join(script_path, 'train/templates'), help="directory with textual inversion templates")
|
||||
# parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
|
||||
# parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
|
||||
# parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
|
||||
# parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
|
||||
# parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
|
||||
# parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
|
||||
# parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
|
||||
|
||||
parser.add_argument("--allow-code", action='store_true', help="Allow custom script execution")
|
||||
parser.add_argument("--share", action='store_true', help="Enable to make the UI accessible through Gradio site")
|
||||
parser.add_argument("--enable-insecure", action='store_true', help="Enable extensions tab regardless of other options")
|
||||
parser.add_argument("--use-cpu", nargs='+', help="Force use CPU for specified modules", default=[], type=str.lower)
|
||||
parser.add_argument("--listen", action='store_true', help="Launch web server using public IP address")
|
||||
parser.add_argument("--port", type=int, help="Launch web server with given server port", default=None)
|
||||
parser.add_argument("--hide-ui-dir-config", action='store_true', help="Hide directory configuration from UI", default=False)
|
||||
parser.add_argument("--freeze-settings", action='store_true', help="Disable editing settings", default=False)
|
||||
parser.add_argument("--gradio-auth", type=str, help='Set Gradio authentication like "username:password,username:password""', default=None)
|
||||
parser.add_argument("--gradio-auth-path", type=str, help='Set Gradio authentication using file', default=None)
|
||||
parser.add_argument("--autolaunch", action='store_true', help="Open the UI URL in the system's default browser upon launch", default=False)
|
||||
parser.add_argument("--disable-console-progressbars", action='store_true', help="Do not output progressbars to console", default=True)
|
||||
parser.add_argument("--disable-safe-unpickle", action='store_true', help="Disable checking models for malicious code", default=True)
|
||||
parser.add_argument("--api-auth", type=str, help='Set API authentication', default=None)
|
||||
parser.add_argument("--api-log", action='store_true', help="Enable logging of all API requests")
|
||||
parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use", default=None)
|
||||
parser.add_argument("--cors-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list", default=None)
|
||||
parser.add_argument("--cors-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
|
||||
parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
|
||||
parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
|
||||
parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
|
||||
parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
|
||||
parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
|
||||
parser.add_argument("--profile", action='store_true', help="run profiler")
|
||||
|
||||
# used by setup.py
|
||||
parser.add_argument('--quick', default = False, action='store_true', help = "Skip installing if setup.log is newer than repo timestamp, default: %(default)s")
|
||||
parser.add_argument('--upgrade', default = False, action='store_true', help = "Upgrade main repository to latest version, default: %(default)s")
|
||||
parser.add_argument('--update', default = False, action='store_true', help = "Update all extensions and submodules, default: %(default)s")
|
||||
parser.add_argument('--skip-extensions', default = False, action='store_true', help = "Skips running individual extension installers, default: %(default)s")
|
||||
parser.add_argument("--no-hashing", action='store_true', help="Disable sha256 hashing of checkpoints", default=False)
|
||||
parser.add_argument("--no-download-sd-model", action='store_true', help="Disable download of default model even if no model is found", default=False)
|
||||
parser.add_argument("--profile", action='store_true', help="Run profiler, default: %(default)s")
|
||||
|
|
|
|||
|
|
@ -62,16 +62,19 @@ def torch_gc():
|
|||
|
||||
def enable_tf32():
|
||||
if torch.cuda.is_available():
|
||||
|
||||
# enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
|
||||
# see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
|
||||
if any([torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())]):
|
||||
torch.backends.cudnn.benchmark = True
|
||||
|
||||
torch.backends.cuda.matmul.allow_tf32 = True
|
||||
torch.backends.cudnn.allow_tf32 = True
|
||||
|
||||
|
||||
def enable_cudnn_benchmark():
|
||||
from modules import shared
|
||||
|
||||
if shared.opts.cudnn_benchmark:
|
||||
torch.backends.cudnn.benchmark = True
|
||||
else:
|
||||
torch.backends.cudnn.benchmark = False
|
||||
|
||||
|
||||
|
||||
errors.run(enable_tf32, "Enabling TF32")
|
||||
|
||||
|
|
@ -106,13 +109,10 @@ def randn_without_seed(shape):
|
|||
|
||||
def autocast(disable=False):
|
||||
from modules import shared
|
||||
|
||||
if disable:
|
||||
return contextlib.nullcontext()
|
||||
|
||||
if dtype == torch.float32 or shared.cmd_opts.precision == "full":
|
||||
return contextlib.nullcontext()
|
||||
|
||||
return torch.autocast("cuda")
|
||||
|
||||
|
||||
|
|
@ -126,27 +126,19 @@ class NansException(Exception):
|
|||
|
||||
def test_for_nans(x, where):
|
||||
from modules import shared
|
||||
|
||||
if shared.cmd_opts.disable_nan_check:
|
||||
if shared.opts.disable_nan_check:
|
||||
return
|
||||
|
||||
if not torch.all(torch.isnan(x)).item():
|
||||
return
|
||||
|
||||
if where == "unet":
|
||||
message = "A tensor with all NaNs was produced in Unet."
|
||||
|
||||
if not shared.cmd_opts.no_half:
|
||||
message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try setting the \"Upcast cross attention layer to float32\" option in Settings > Stable Diffusion or using the --no-half commandline argument to fix this."
|
||||
|
||||
elif where == "vae":
|
||||
message = "A tensor with all NaNs was produced in VAE."
|
||||
|
||||
if not shared.cmd_opts.no_half and not shared.cmd_opts.no_half_vae:
|
||||
message += " This could be because there's not enough precision to represent the picture. Try adding --no-half-vae commandline argument to fix this."
|
||||
else:
|
||||
message = "A tensor with all NaNs was produced."
|
||||
|
||||
message += " Use --disable-nan-check commandline argument to disable this check."
|
||||
|
||||
raise NansException(message)
|
||||
|
|
|
|||
|
|
@ -223,7 +223,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
|
|||
if re.search(regex, key):
|
||||
theta_0.pop(key, None)
|
||||
|
||||
ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
|
||||
ckpt_dir = shared.opts.ckpt_dir or sd_models.model_path
|
||||
|
||||
filename = filename_generator() if custom_name == '' else custom_name
|
||||
filename += ".inpainting" if result_is_inpainting_model else ""
|
||||
|
|
|
|||
|
|
@ -468,7 +468,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
|
|||
name = "".join( x for x in name if (x.isalnum() or x in "._- "))
|
||||
assert name, "Name cannot be empty!"
|
||||
|
||||
fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
|
||||
fn = os.path.join(shared.opts.hypernetwork_dir, f"{name}.pt")
|
||||
if not overwrite_old:
|
||||
assert not os.path.exists(fn), f"file {fn} already exists"
|
||||
|
||||
|
|
@ -515,7 +515,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
|
|||
shared.state.job_count = steps
|
||||
|
||||
hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
|
||||
filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
|
||||
filename = os.path.join(shared.opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
|
||||
|
||||
log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
|
||||
unload = shared.opts.unload_models_when_training
|
||||
|
|
@ -780,7 +780,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
|
|||
|
||||
|
||||
|
||||
filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
|
||||
filename = os.path.join(shared.opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
|
||||
hypernetwork.optimizer_name = optimizer_name
|
||||
if shared.opts.save_optimizer_state:
|
||||
hypernetwork.optimizer_state_dict = optimizer.state_dict()
|
||||
|
|
|
|||
|
|
@ -587,20 +587,6 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
|
|||
|
||||
image.already_saved_as = fullfn
|
||||
|
||||
oversize = image.width > opts.target_side_length or image.height > opts.target_side_length
|
||||
if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > opts.img_downscale_threshold * 1024 * 1024):
|
||||
ratio = image.width / image.height
|
||||
|
||||
if oversize and ratio > 1:
|
||||
image = image.resize((round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)), LANCZOS)
|
||||
elif oversize:
|
||||
image = image.resize((round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)), LANCZOS)
|
||||
|
||||
try:
|
||||
_atomically_save_image(image, fullfn_without_extension, ".jpg")
|
||||
except Exception as e:
|
||||
errors.display(e, "saving image as downscaled JPG")
|
||||
|
||||
if opts.save_txt and info is not None:
|
||||
txt_fullfn = f"{fullfn_without_extension}.txt"
|
||||
with open(txt_fullfn, "w", encoding="utf8") as file:
|
||||
|
|
|
|||
|
|
@ -153,9 +153,6 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
|
|||
p.scripts = modules.scripts.scripts_txt2img
|
||||
p.script_args = args
|
||||
|
||||
if shared.cmd_opts.enable_console_prompts:
|
||||
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
|
||||
|
||||
if mask:
|
||||
p.extra_generation_params["Mask blur"] = mask_blur
|
||||
|
||||
|
|
@ -175,8 +172,6 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
|
|||
shared.total_tqdm.clear()
|
||||
|
||||
generation_info_js = processed.js()
|
||||
if opts.samples_log_stdout:
|
||||
print(generation_info_js)
|
||||
|
||||
if opts.do_not_show_images:
|
||||
processed.images = []
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
import sys
|
||||
from modules import shared
|
||||
|
||||
# this will break any attempt to import xformers which will prevent stability diffusion repo from trying to use it
|
||||
if "--xformers" not in "".join(sys.argv):
|
||||
if shared.opts.cross_attention_optimization != "xFormers":
|
||||
sys.modules["xformers"] = None
|
||||
|
|
|
|||
|
|
@ -108,9 +108,9 @@ class InterrogateModels:
|
|||
import clip
|
||||
|
||||
if self.running_on_cpu:
|
||||
model, preprocess = clip.load(clip_model_name, device="cpu", download_root=shared.cmd_opts.clip_models_path)
|
||||
model, preprocess = clip.load(clip_model_name, device="cpu", download_root=shared.opts.clip_models_path)
|
||||
else:
|
||||
model, preprocess = clip.load(clip_model_name, download_root=shared.cmd_opts.clip_models_path)
|
||||
model, preprocess = clip.load(clip_model_name, download_root=shared.opts.clip_models_path)
|
||||
|
||||
model.eval()
|
||||
model = model.to(devices.device_interrogate)
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
Subproject commit b5c60d7d62d6bb4a174ac09327dc517fc4446523
|
||||
Subproject commit 5050971ac687dca70ba0486a583d283e8ae324e2
|
||||
|
|
@ -1 +1 @@
|
|||
Subproject commit ec1b758ab416e9d2f3590d5dd961b9ec27a0dc60
|
||||
Subproject commit 66a7fafad3cbb14378fe548fca0153709cc59968
|
||||
|
|
@ -1,26 +0,0 @@
|
|||
from pyngrok import ngrok, conf, exception
|
||||
|
||||
def connect(token, port, region):
|
||||
account = None
|
||||
if token is None:
|
||||
token = 'None'
|
||||
else:
|
||||
if ':' in token:
|
||||
# token = authtoken:username:password
|
||||
account = token.split(':')[1] + ':' + token.split(':')[-1]
|
||||
token = token.split(':')[0]
|
||||
|
||||
config = conf.PyngrokConfig(
|
||||
auth_token=token, region=region
|
||||
)
|
||||
try:
|
||||
if account is None:
|
||||
public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url
|
||||
else:
|
||||
public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True, auth=account).public_url
|
||||
except exception.PyngrokNgrokError:
|
||||
print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
|
||||
f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
|
||||
else:
|
||||
print(f'ngrok connected to localhost:{port}! URL: {public_url}\n'
|
||||
'You can use this link after the launch is complete.')
|
||||
|
|
@ -565,7 +565,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
def infotext(iteration=0, position_in_batch=0):
|
||||
return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
|
||||
|
||||
if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
|
||||
if os.path.exists(opts.embeddings_dir) and not p.do_not_reload_embeddings:
|
||||
model_hijack.embedding_db.load_textual_inversion_embeddings()
|
||||
|
||||
if p.scripts is not None:
|
||||
|
|
|
|||
|
|
@ -46,7 +46,7 @@ class UpscalerRealESRGAN(Upscaler):
|
|||
scale=info.scale,
|
||||
model_path=info.local_data_path,
|
||||
model=info.model(),
|
||||
half=not cmd_opts.no_half and not cmd_opts.upcast_sampling,
|
||||
half=not cmd_opts.no_half and not opts.upcast_sampling,
|
||||
tile=opts.ESRGAN_tile,
|
||||
tile_pad=opts.ESRGAN_tile_overlap,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -5,7 +5,8 @@ from types import MethodType
|
|||
import modules.textual_inversion.textual_inversion
|
||||
from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
|
||||
from modules.hypernetworks import hypernetwork
|
||||
from modules.shared import cmd_opts
|
||||
from modules.shared import cmd_opts, opts
|
||||
from modules.shared_items import list_crossattention
|
||||
from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
|
||||
from rich import print
|
||||
|
||||
|
|
@ -38,37 +39,40 @@ def apply_optimizations():
|
|||
|
||||
optimization_method = None
|
||||
|
||||
can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(getattr(torch.nn.functional, "scaled_dot_product_attention")) and not cmd_opts.disable_sdp_attention # not everyone has torch 2.x to use sdp
|
||||
can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(getattr(torch.nn.functional, "scaled_dot_product_attention"))
|
||||
|
||||
if cmd_opts.opt_sdp_no_mem_attention and can_use_sdp:
|
||||
print("Applying scaled dot product cross attention optimization (without memory efficient attention).")
|
||||
if opts.cross_attention_optimization == "Disable cross-attention layer optimization":
|
||||
print("Cross-attention optimization disabled")
|
||||
optimization_method = 'none'
|
||||
if can_use_sdp and opts.cross_attention_optimization == "Scaled-Dot-Product" and 'SDP disable memory attention' in opts.cross_attention_options:
|
||||
print("Applying scaled dot product cross attention optimization (without memory efficient attention)")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_no_mem_attention_forward
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_no_mem_attnblock_forward
|
||||
optimization_method = 'sdp-no-mem'
|
||||
elif cmd_opts.opt_sdp_attention and can_use_sdp:
|
||||
print("Applying scaled dot product cross attention optimization.")
|
||||
elif can_use_sdp and opts.cross_attention_optimization == "Scaled-Dot-Product":
|
||||
print("Applying scaled dot product cross attention optimization")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_attention_forward
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_attnblock_forward
|
||||
optimization_method = 'sdp'
|
||||
elif cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
|
||||
print("Applying xformers cross attention optimization.")
|
||||
if shared.xformers_available and opts.cross_attention_optimization == "xFormers":
|
||||
print("Applying xformers cross attention optimization")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
|
||||
optimization_method = 'xformers'
|
||||
elif cmd_opts.opt_sub_quad_attention:
|
||||
print("Applying sub-quadratic cross attention optimization.")
|
||||
if opts.cross_attention_optimization == "Sub-quadratic":
|
||||
print("Applying sub-quadratic cross attention optimization")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sub_quad_attnblock_forward
|
||||
optimization_method = 'sub-quadratic'
|
||||
elif cmd_opts.opt_split_attention_v1:
|
||||
print("Applying v1 cross attention optimization.")
|
||||
if opts.cross_attention_optimization == "Split attention":
|
||||
print("Applying split attention optimization")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
|
||||
optimization_method = 'v1'
|
||||
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not cmd_opts.opt_split_attention and not torch.cuda.is_available()):
|
||||
print("Applying cross attention optimization (InvokeAI).")
|
||||
if opts.cross_attention_optimization == "InvokeAI's":
|
||||
print("Applying InvokeAI's cross attention optimization")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
|
||||
optimization_method = 'invokeai'
|
||||
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
|
||||
if opts.cross_attention_optimization == "Doggettx's":
|
||||
print("Applying cross attention optimization (Doggettx).")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
|
||||
|
|
@ -149,7 +153,7 @@ class StableDiffusionModelHijack:
|
|||
embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
|
||||
|
||||
def __init__(self):
|
||||
self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
|
||||
self.embedding_db.add_embedding_dir(opts.embeddings_dir)
|
||||
|
||||
def hijack(self, m):
|
||||
if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
|
||||
|
|
|
|||
|
|
@ -205,10 +205,6 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
|
|||
is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
|
||||
"""
|
||||
|
||||
if opts.use_old_emphasis_implementation:
|
||||
import modules.sd_hijack_clip_old
|
||||
return modules.sd_hijack_clip_old.forward_old(self, texts)
|
||||
|
||||
batch_chunks, token_count = self.process_texts(texts)
|
||||
|
||||
used_embeddings = {}
|
||||
|
|
|
|||
|
|
@ -17,8 +17,6 @@ class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWit
|
|||
self.id_pad = 0
|
||||
|
||||
def tokenize(self, texts):
|
||||
assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
|
||||
|
||||
tokenized = [tokenizer.encode(text) for text in texts]
|
||||
|
||||
return tokenized
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ from modules.hypernetworks import hypernetwork
|
|||
from .sub_quadratic_attention import efficient_dot_product_attention
|
||||
|
||||
|
||||
if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
|
||||
if shared.opts.cross_attention_optimization == "xFormers":
|
||||
try:
|
||||
import xformers.ops
|
||||
shared.xformers_available = True
|
||||
|
|
@ -258,7 +258,7 @@ def sub_quad_attention_forward(self, x, context=None, mask=None):
|
|||
if shared.opts.upcast_attn:
|
||||
q, k = q.float(), k.float()
|
||||
|
||||
x = sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
|
||||
x = sub_quad_attention(q, k, v, q_chunk_size=shared.opts.sub_quad_q_chunk_size, kv_chunk_size=shared.opts.sub_quad_kv_chunk_size, chunk_threshold=shared.opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
|
||||
|
||||
x = x.to(dtype)
|
||||
|
||||
|
|
@ -307,7 +307,7 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_
|
|||
|
||||
|
||||
def get_xformers_flash_attention_op(q, k, v):
|
||||
if not shared.cmd_opts.xformers_flash_attention:
|
||||
if 'xFormers enable flash Attention' not in shared.opts.cross_attention_options:
|
||||
return None
|
||||
|
||||
try:
|
||||
|
|
@ -506,7 +506,7 @@ def sub_quad_attnblock_forward(self, x):
|
|||
q = q.contiguous()
|
||||
k = k.contiguous()
|
||||
v = v.contiguous()
|
||||
out = sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
|
||||
out = sub_quad_attention(q, k, v, q_chunk_size=shared.opts.sub_quad_q_chunk_size, kv_chunk_size=shared.opts.sub_quad_kv_chunk_size, chunk_threshold=shared.opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
|
||||
out = rearrange(out, 'b (h w) c -> b c h w', h=h)
|
||||
out = self.proj_out(out)
|
||||
return x + out
|
||||
|
|
|
|||
|
|
@ -33,8 +33,8 @@ class CheckpointInfo:
|
|||
self.filename = filename
|
||||
abspath = os.path.abspath(filename)
|
||||
|
||||
if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
|
||||
name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
|
||||
if shared.opts.ckpt_dir is not None and abspath.startswith(shared.opts.ckpt_dir):
|
||||
name = abspath.replace(shared.opts.ckpt_dir, '')
|
||||
elif abspath.startswith(model_path):
|
||||
name = abspath.replace(model_path, '')
|
||||
else:
|
||||
|
|
@ -115,7 +115,7 @@ def list_models():
|
|||
else:
|
||||
model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
|
||||
|
||||
model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
|
||||
model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
|
||||
|
||||
if os.path.exists(cmd_ckpt):
|
||||
checkpoint_info = CheckpointInfo(cmd_ckpt)
|
||||
|
|
@ -171,8 +171,8 @@ def select_checkpoint():
|
|||
if shared.cmd_opts.ckpt is not None:
|
||||
print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
|
||||
print(f" - directory {model_path}", file=sys.stderr)
|
||||
if shared.cmd_opts.ckpt_dir is not None:
|
||||
print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
|
||||
if shared.opts.ckpt_dir is not None:
|
||||
print(f" - directory {os.path.abspath(shared.opts.ckpt_dir)}", file=sys.stderr)
|
||||
print("Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations. The program will exit.", file=sys.stderr)
|
||||
exit(1)
|
||||
|
||||
|
|
@ -293,7 +293,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
|
|||
# cache newly loaded model
|
||||
checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
|
||||
|
||||
if shared.cmd_opts.opt_channelslast:
|
||||
if shared.opts.opt_channelslast:
|
||||
model.to(memory_format=torch.channels_last)
|
||||
timer.record("channels")
|
||||
|
||||
|
|
@ -305,7 +305,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
|
|||
if shared.cmd_opts.no_half_vae:
|
||||
model.first_stage_model = None
|
||||
# with --upcast-sampling, don't convert the depth model weights to float16
|
||||
if shared.cmd_opts.upcast_sampling and depth_model:
|
||||
if shared.opts.upcast_sampling and depth_model:
|
||||
model.depth_model = None
|
||||
|
||||
model.half()
|
||||
|
|
@ -316,7 +316,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
|
|||
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
|
||||
devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
|
||||
devices.dtype_unet = model.model.diffusion_model.dtype
|
||||
devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
|
||||
devices.unet_needs_upcast = shared.opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
|
||||
|
||||
model.first_stage_model.to(devices.dtype_vae)
|
||||
|
||||
|
|
@ -388,7 +388,7 @@ def repair_config(sd_config):
|
|||
|
||||
if shared.cmd_opts.no_half:
|
||||
sd_config.model.params.unet_config.params.use_fp16 = False
|
||||
elif shared.cmd_opts.upcast_sampling:
|
||||
elif shared.opts.upcast_sampling:
|
||||
sd_config.model.params.unet_config.params.use_fp16 = True
|
||||
|
||||
if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
|
||||
|
|
@ -416,8 +416,10 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
|
|||
current_checkpoint_info = shared.sd_model.sd_checkpoint_info
|
||||
sd_hijack.model_hijack.undo_hijack(shared.sd_model)
|
||||
shared.sd_model = None
|
||||
gc.collect()
|
||||
devices.torch_gc()
|
||||
|
||||
devices.enable_cudnn_benchmark()
|
||||
gc.collect()
|
||||
devices.torch_gc()
|
||||
|
||||
if already_loaded_state_dict is not None:
|
||||
state_dict = already_loaded_state_dict
|
||||
|
|
|
|||
|
|
@ -271,7 +271,7 @@ class KDiffusionSampler:
|
|||
if p.sampler_noise_scheduler_override:
|
||||
sigmas = p.sampler_noise_scheduler_override(steps)
|
||||
elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
|
||||
sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
|
||||
sigma_min, sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
|
||||
|
||||
sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
|
||||
else:
|
||||
|
|
@ -283,10 +283,6 @@ class KDiffusionSampler:
|
|||
return sigmas
|
||||
|
||||
def create_noise_sampler(self, x, sigmas, p):
|
||||
"""For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
|
||||
if shared.opts.no_dpmpp_sde_batch_determinism:
|
||||
return None
|
||||
|
||||
from k_diffusion.sampling import BrownianTreeNoiseSampler
|
||||
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
|
||||
current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ import glob
|
|||
from copy import deepcopy
|
||||
from rich import print
|
||||
|
||||
vae_path = os.path.abspath(os.path.join(paths.models_path, "VAE"))
|
||||
vae_path = shared.opts.vae_dir
|
||||
vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
|
||||
vae_dict = {}
|
||||
|
||||
|
|
@ -64,18 +64,18 @@ def refresh_vae_list():
|
|||
os.path.join(vae_path, '**/*.safetensors'),
|
||||
]
|
||||
|
||||
if shared.cmd_opts.ckpt_dir is not None and os.path.isdir(shared.cmd_opts.ckpt_dir):
|
||||
if shared.opts.ckpt_dir is not None and os.path.isdir(shared.opts.ckpt_dir):
|
||||
paths += [
|
||||
os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.ckpt'),
|
||||
os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.pt'),
|
||||
os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.safetensors'),
|
||||
os.path.join(shared.opts.ckpt_dir, '**/*.vae.ckpt'),
|
||||
os.path.join(shared.opts.ckpt_dir, '**/*.vae.pt'),
|
||||
os.path.join(shared.opts.ckpt_dir, '**/*.vae.safetensors'),
|
||||
]
|
||||
|
||||
if shared.cmd_opts.vae_dir is not None and os.path.isdir(shared.cmd_opts.vae_dir):
|
||||
if shared.opts.vae_dir is not None and os.path.isdir(shared.opts.vae_dir):
|
||||
paths += [
|
||||
os.path.join(shared.cmd_opts.vae_dir, '**/*.ckpt'),
|
||||
os.path.join(shared.cmd_opts.vae_dir, '**/*.pt'),
|
||||
os.path.join(shared.cmd_opts.vae_dir, '**/*.safetensors'),
|
||||
os.path.join(shared.opts.vae_dir, '**/*.ckpt'),
|
||||
os.path.join(shared.opts.vae_dir, '**/*.pt'),
|
||||
os.path.join(shared.opts.vae_dir, '**/*.safetensors'),
|
||||
]
|
||||
|
||||
candidates = []
|
||||
|
|
@ -97,8 +97,8 @@ def find_vae_near_checkpoint(checkpoint_file):
|
|||
|
||||
|
||||
def resolve_vae(checkpoint_file):
|
||||
if shared.cmd_opts.vae_path is not None:
|
||||
return shared.cmd_opts.vae_path, 'from commandline argument'
|
||||
if shared.cmd_opts.vae is not None:
|
||||
return shared.cmd_opts.vae, 'from commandline argument'
|
||||
|
||||
is_automatic = shared.opts.sd_vae in {"Automatic", "auto"} # "auto" for people with old config
|
||||
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ import modules.interrogate
|
|||
import modules.memmon
|
||||
import modules.styles
|
||||
import modules.devices as devices
|
||||
from modules import localization, script_loading, errors, ui_components, shared_items, cmd_args
|
||||
from modules import script_loading, errors, ui_components, shared_items, cmd_args
|
||||
from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir
|
||||
|
||||
demo = None
|
||||
|
|
@ -63,34 +63,22 @@ ui_reorder_categories = [
|
|||
"scripts",
|
||||
]
|
||||
|
||||
cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
|
||||
cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure
|
||||
|
||||
devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
|
||||
(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
|
||||
|
||||
device = devices.device
|
||||
|
||||
batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
|
||||
parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
|
||||
xformers_available = False
|
||||
config_filename = cmd_opts.ui_settings_file
|
||||
|
||||
os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
|
||||
hypernetworks = {}
|
||||
loaded_hypernetworks = []
|
||||
sd_upscalers = []
|
||||
sd_model = None
|
||||
clip_model = None
|
||||
|
||||
|
||||
def reload_hypernetworks():
|
||||
from modules.hypernetworks import hypernetwork
|
||||
global hypernetworks
|
||||
hypernetworks = hypernetwork.list_hypernetworks(opts.hypernetwork_dir)
|
||||
|
||||
hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
|
||||
|
||||
sd_upscalers = []
|
||||
|
||||
sd_model = None
|
||||
|
||||
clip_model = None
|
||||
|
||||
class State:
|
||||
skipped = False
|
||||
|
|
@ -190,8 +178,7 @@ class State:
|
|||
state = State()
|
||||
state.server_start = time.time()
|
||||
|
||||
styles_filename = cmd_opts.styles_file
|
||||
prompt_styles = modules.styles.StyleDatabase(styles_filename)
|
||||
prompt_styles = modules.styles.StyleDatabase('styles.csv')
|
||||
|
||||
interrogator = modules.interrogate.InterrogateModels("interrogate")
|
||||
|
||||
|
|
@ -235,98 +222,6 @@ tab_names = []
|
|||
|
||||
options_templates = {}
|
||||
|
||||
options_templates.update(options_section(('saving-images', "Saving images/grids"), {
|
||||
"samples_save": OptionInfo(True, "Always save all generated images"),
|
||||
"samples_format": OptionInfo('png', 'File format for images'),
|
||||
"samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs),
|
||||
"save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
|
||||
|
||||
"grid_save": OptionInfo(True, "Always save all generated image grids"),
|
||||
"grid_format": OptionInfo('png', 'File format for grids'),
|
||||
"grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
|
||||
"grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
|
||||
"grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
|
||||
"n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
|
||||
|
||||
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
|
||||
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
|
||||
"save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
|
||||
"save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
|
||||
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
|
||||
"save_mask": OptionInfo(False, "For inpainting, save a copy of the greyscale mask"),
|
||||
"save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
|
||||
"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
|
||||
"webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
|
||||
"export_for_4chan": OptionInfo(True, "If the saved image file size is above the limit, or its either width or height are above the limit, save a downscaled copy as JPG"),
|
||||
"img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
|
||||
"target_side_length": OptionInfo(4000, "Width/height limit for the above option, in pixels", gr.Number),
|
||||
"img_max_size_mp": OptionInfo(200, "Maximum image size, in megapixels", gr.Number),
|
||||
|
||||
"use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
|
||||
"use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
|
||||
"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
|
||||
"do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
|
||||
|
||||
"temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default"),
|
||||
"clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
|
||||
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('saving-paths', "Paths for saving"), {
|
||||
"outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
|
||||
"outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
|
||||
"outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
|
||||
"outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab', component_args=hide_dirs),
|
||||
"outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs),
|
||||
"outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
|
||||
"outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
|
||||
"outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
|
||||
"save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
|
||||
"grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
|
||||
"use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
|
||||
"directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
|
||||
"directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('upscaling', "Upscaling"), {
|
||||
"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
|
||||
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
|
||||
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
|
||||
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('face-restoration', "Face restoration"), {
|
||||
"face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
|
||||
"code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
||||
"face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('system', "System"), {
|
||||
"show_warnings": OptionInfo(False, "Show warnings in console."),
|
||||
"memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
|
||||
"samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
|
||||
"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
|
||||
"print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('training', "Training"), {
|
||||
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
|
||||
"pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
|
||||
"save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
|
||||
"save_training_settings_to_txt": OptionInfo(True, "Save textual inversion and hypernet settings to a text file whenever training starts."),
|
||||
"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
|
||||
"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
|
||||
"training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
|
||||
"training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
|
||||
"training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
|
||||
"training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging."),
|
||||
"training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard."),
|
||||
"training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
||||
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
|
||||
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
|
||||
|
|
@ -344,25 +239,121 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
|||
"comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
|
||||
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
|
||||
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
|
||||
"cross_attention_optimization": OptionInfo("Scaled-Dot-Product", "Cross-attention optimization method", gr.Radio, lambda: {"choices": shared_items.list_crossattention() }),
|
||||
"cross_attention_options": OptionInfo([], "Cross-attention advanced options", gr.CheckboxGroup, lambda: {"choices": ['xFormers enable flash Attention', 'SDP disable memory attention']}),
|
||||
"disable_nan_check": OptionInfo(False, "Do not check if produced images/latent spaces have NaN values"),
|
||||
"opt_channelslast": OptionInfo(False, "Use channels last as torch memory format "),
|
||||
"cudnn_benchmark": OptionInfo(False, "Enable CUDA cuDNN benchmark feature"),
|
||||
"sub_quad_q_chunk_size": OptionInfo(512, "Sub-quadratic cross-attention query chunk size for the layer optimization to use", gr.Slider, {"minimum": 16, "maximum": 8192, "step": 8}),
|
||||
"sub_quad_kv_chunk_size": OptionInfo(512, "Sub-quadratic cross-attentionkv chunk size for the sub-quadratic cross-attention layer optimization to use", gr.Slider, {"minimum": 0, "maximum": 8192, "step": 8}),
|
||||
"sub_quad_chunk_threshold": OptionInfo(80, "Sub-quadratic cross-attention percentage of VRAM chunking threshold", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}),
|
||||
"always_batch_cond_uncond": OptionInfo(False, "Disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram"),
|
||||
"upcast_sampling": OptionInfo(False, "Enable upcast sampling. Usually produces similar results to --no-half with better performance while using less memory"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('compatibility', "Compatibility"), {
|
||||
"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
|
||||
"use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
|
||||
"no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
|
||||
options_templates.update(options_section(('system-paths', "System Paths"), {
|
||||
"ckpt_dir": OptionInfo(os.path.join(models_path, 'Stable-diffusion'), "Path to directory with stable diffusion checkpoints"),
|
||||
"vae_dir": OptionInfo(os.path.join(models_path, 'VAE'), "Path to directory with VAE files"),
|
||||
"embeddings_dir": OptionInfo(os.path.join(models_path, 'embeddings'), "Embeddings directory for textual inversion"),
|
||||
"embeddings_templates_dir": OptionInfo(os.path.join(script_path, 'train/templates'), "Embeddings train templates directory"),
|
||||
"hypernetwork_dir": OptionInfo(os.path.join(models_path, 'hypernetworks'), "Hypernetwork directory"),
|
||||
"codeformer_models_path": OptionInfo(os.path.join(models_path, 'Codeformer'), "Path to directory with codeformer model file(s)."),
|
||||
"gfpgan_models_path": OptionInfo(os.path.join(models_path, 'GFPGAN'), "Path to directory with GFPGAN model file(s)"),
|
||||
"esrgan_models_path": OptionInfo(os.path.join(models_path, 'ESRGAN'), "Path to directory with ESRGAN model file(s)"),
|
||||
"bsrgan_models_path": OptionInfo(os.path.join(models_path, 'BSRGAN'), "Path to directory with BSRGAN model file(s)"),
|
||||
"realesrgan_models_path": OptionInfo(os.path.join(models_path, 'RealESRGAN'), "Path to directory with RealESRGAN model file(s)"),
|
||||
"clip_models_path": OptionInfo(os.path.join(models_path, 'CLIP'), "Path to directory with CLIP model file(s)"),
|
||||
# "gfpgan_model": OptionInfo("", "GFPGAN model file name"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('saving-images', "Image options"), {
|
||||
"samples_save": OptionInfo(True, "Always save all generated images"),
|
||||
"samples_format": OptionInfo('jpg', 'File format for images'),
|
||||
"samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs),
|
||||
"save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
|
||||
"grid_save": OptionInfo(True, "Always save all generated image grids"),
|
||||
"grid_format": OptionInfo('jpg', 'File format for grids'),
|
||||
"grid_extended_filename": OptionInfo(True, "Add extended info (seed, prompt) to filename when saving grid"),
|
||||
"grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
|
||||
"grid_prevent_empty_spots": OptionInfo(True, "Prevent empty spots in grid (when set to autodetect)"),
|
||||
"n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
|
||||
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
|
||||
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
|
||||
"save_images_before_face_restoration": OptionInfo(True, "Save a copy of image before doing face restoration."),
|
||||
"save_images_before_highres_fix": OptionInfo(True, "Save a copy of image before applying highres fix."),
|
||||
"save_images_before_color_correction": OptionInfo(True, "Save a copy of image before applying color correction to img2img results"),
|
||||
"save_mask": OptionInfo(False, "For inpainting, save a copy of the greyscale mask"),
|
||||
"save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
|
||||
"jpeg_quality": OptionInfo(85, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
|
||||
"webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
|
||||
"img_max_size_mp": OptionInfo(200, "Maximum image size, in megapixels", gr.Number),
|
||||
"use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
|
||||
"use_upscaler_name_as_suffix": OptionInfo(True, "Use upscaler name as filename suffix in the extras tab"),
|
||||
"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
|
||||
"save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
|
||||
"grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
|
||||
"use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
|
||||
"directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
|
||||
"directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
|
||||
"temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default"),
|
||||
"clean_temp_dir_at_start": OptionInfo(True, "Cleanup non-default temporary directory when starting webui"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('saving-paths', "Image Paths"), {
|
||||
"outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
|
||||
"outdir_txt2img_samples": OptionInfo("outputs/text", 'Output directory for txt2img images', component_args=hide_dirs),
|
||||
"outdir_img2img_samples": OptionInfo("outputs/image", 'Output directory for img2img images', component_args=hide_dirs),
|
||||
"outdir_extras_samples": OptionInfo("outputs/extras", 'Output directory for images from extras tab', component_args=hide_dirs),
|
||||
"outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs),
|
||||
"outdir_txt2img_grids": OptionInfo("outputs/grids", 'Output directory for txt2img grids', component_args=hide_dirs),
|
||||
"outdir_img2img_grids": OptionInfo("outputs/grids", 'Output directory for img2img grids', component_args=hide_dirs),
|
||||
"outdir_save": OptionInfo("outputs/save", "Directory for saving images using the Save button", component_args=hide_dirs),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('upscaling', "Upscaling"), {
|
||||
"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
|
||||
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
|
||||
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
|
||||
"upscaler_for_img2img": OptionInfo("SwinIR_4x", "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
|
||||
"use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('face-restoration', "Face restoration"), {
|
||||
"face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
|
||||
"code_former_weight": OptionInfo(0.2, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
||||
"face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('system', "System"), {
|
||||
"memmon_poll_rate": OptionInfo(2, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
|
||||
"multiple_tqdm": OptionInfo(False, "Add a second progress bar to the console that shows progress for an entire job."),
|
||||
"print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('training', "Training"), {
|
||||
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
|
||||
"pin_memory": OptionInfo(True, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
|
||||
"save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
|
||||
"save_training_settings_to_txt": OptionInfo(True, "Save textual inversion and hypernet settings to a text file whenever training starts."),
|
||||
"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
|
||||
"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
|
||||
"training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
|
||||
"training_write_csv_every": OptionInfo(0, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
|
||||
"training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging."),
|
||||
"training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard."),
|
||||
"training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('interrogate', "Interrogate Options"), {
|
||||
"interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
|
||||
"interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
|
||||
"interrogate_return_ranks": OptionInfo(True, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
|
||||
"interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
|
||||
"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
|
||||
"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
|
||||
"interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),
|
||||
"interrogate_clip_skip_categories": OptionInfo([], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
|
||||
"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
||||
"deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
|
||||
"interrogate_clip_min_length": OptionInfo(32, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
|
||||
"interrogate_clip_max_length": OptionInfo(192, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
|
||||
"interrogate_clip_dict_limit": OptionInfo(2048, "CLIP: maximum number of lines in text file (0 = No limit)"),
|
||||
"interrogate_clip_skip_categories": OptionInfo(["artists", "movements", "flavors"], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
|
||||
"interrogate_deepbooru_score_threshold": OptionInfo(0.65, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
||||
"deepbooru_sort_alpha": OptionInfo(False, "Interrogate: deepbooru sort alphabetically"),
|
||||
"deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
|
||||
"deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
|
||||
"deepbooru_filter_tags": OptionInfo("", "filter out those tags from deepbooru output (separated by comma)"),
|
||||
|
|
@ -390,30 +381,27 @@ options_templates.update(options_section(('ui', "User interface"), {
|
|||
"font": OptionInfo("", "Font for image grids that have text"),
|
||||
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
|
||||
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
|
||||
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
|
||||
"samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"),
|
||||
"dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"),
|
||||
"show_progress_in_title": OptionInfo(False, "Show generation progress in window title."),
|
||||
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
||||
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
||||
"quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
|
||||
"hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
|
||||
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
|
||||
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
|
||||
"localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "Live previews"), {
|
||||
"show_progressbar": OptionInfo(True, "Show progressbar"),
|
||||
"live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
|
||||
"show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
|
||||
"show_progress_every_n_steps": OptionInfo(10, "Show new live preview image every N sampling steps. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
|
||||
"show_progress_type": OptionInfo("Approx NN", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
|
||||
"live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
|
||||
"show_progress_every_n_steps": OptionInfo(-1, "Show new live preview image every N sampling steps. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
|
||||
"show_progress_type": OptionInfo("Full", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
|
||||
"live_preview_content": OptionInfo("Combined", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
|
||||
"live_preview_refresh_period": OptionInfo(1000, "Progressbar/preview update period, in milliseconds")
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
|
||||
"hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
|
||||
"hide_samplers": OptionInfo(["Euler", "LMS", "Heun", "DPM2", "DPM2 a", "DPM++ 2M", "DPM fast", "DPM adaptive", "DPM++ 2S a Karras", "DPM++ 2S a", "DPM++ SDE Karras", "DPM2 a Karras", "LMS Karras"], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
|
||||
"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
|
||||
|
|
@ -589,6 +577,16 @@ class Options:
|
|||
|
||||
|
||||
opts = Options()
|
||||
|
||||
batch_cond_uncond = opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
|
||||
parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
|
||||
xformers_available = False
|
||||
config_filename = cmd_opts.ui_settings_file
|
||||
|
||||
os.makedirs(opts.hypernetwork_dir, exist_ok=True)
|
||||
hypernetworks = {}
|
||||
loaded_hypernetworks = []
|
||||
|
||||
if os.path.exists(config_filename):
|
||||
opts.load(config_filename)
|
||||
|
||||
|
|
|
|||
|
|
@ -21,3 +21,17 @@ def refresh_vae_list():
|
|||
import modules.sd_vae
|
||||
|
||||
modules.sd_vae.refresh_vae_list()
|
||||
|
||||
def list_crossattention():
|
||||
return [
|
||||
"Disable cross-attention layer optimization",
|
||||
"xFormers",
|
||||
"Scaled-Dot-Product",
|
||||
"Doggettx's",
|
||||
"InvokeAI's",
|
||||
"Sub-quadratic",
|
||||
"Split attention"
|
||||
]
|
||||
# parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
|
||||
# parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
|
||||
# parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ textual_inversion_templates = {}
|
|||
def list_textual_inversion_templates():
|
||||
textual_inversion_templates.clear()
|
||||
|
||||
for root, dirs, fns in os.walk(shared.cmd_opts.textual_inversion_templates_dir):
|
||||
for root, dirs, fns in os.walk(shared.opts.embeddings_templates_dir):
|
||||
for fn in fns:
|
||||
path = os.path.join(root, fn)
|
||||
|
||||
|
|
@ -270,7 +270,7 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
|
|||
|
||||
# Remove illegal characters from name.
|
||||
name = "".join( x for x in name if (x.isalnum() or x in "._- "))
|
||||
fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
|
||||
fn = os.path.join(shared.opts.embeddings_dir, f"{name}.pt")
|
||||
if not overwrite_old:
|
||||
assert not os.path.exists(fn), f"file {fn} already exists"
|
||||
|
||||
|
|
@ -365,7 +365,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
|
|||
shared.state.textinfo = "Initializing textual inversion training..."
|
||||
shared.state.job_count = steps
|
||||
|
||||
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
|
||||
filename = os.path.join(shared.opts.embeddings_dir, f'{embedding_name}.pt')
|
||||
|
||||
log_directory = os.path.join(log_directory, embedding_name)
|
||||
unload = shared.opts.unload_models_when_training
|
||||
|
|
@ -623,7 +623,7 @@ Last saved embedding: {html.escape(last_saved_file)}<br/>
|
|||
Last saved image: {html.escape(last_saved_image)}<br/>
|
||||
</p>
|
||||
"""
|
||||
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
|
||||
filename = os.path.join(shared.opts.embeddings_dir, f'{embedding_name}.pt')
|
||||
save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)
|
||||
except Exception:
|
||||
shared.exception()
|
||||
|
|
|
|||
|
|
@ -25,7 +25,7 @@ def train_embedding(*args):
|
|||
|
||||
assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
|
||||
|
||||
apply_optimizations = shared.opts.training_xattention_optimizations
|
||||
apply_optimizations = False
|
||||
try:
|
||||
if not apply_optimizations:
|
||||
sd_hijack.undo_optimizations()
|
||||
|
|
|
|||
|
|
@ -46,9 +46,6 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
|
|||
p.scripts = modules.scripts.scripts_txt2img
|
||||
p.script_args = args
|
||||
|
||||
if cmd_opts.enable_console_prompts:
|
||||
print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
|
||||
|
||||
processed = modules.scripts.scripts_txt2img.run(p, *args)
|
||||
|
||||
if processed is None:
|
||||
|
|
@ -59,8 +56,6 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
|
|||
shared.total_tqdm.clear()
|
||||
|
||||
generation_info_js = processed.js()
|
||||
if opts.samples_log_stdout:
|
||||
print(generation_info_js)
|
||||
|
||||
if opts.do_not_show_images:
|
||||
processed.images = []
|
||||
|
|
|
|||
|
|
@ -42,7 +42,7 @@ import modules.hypernetworks.ui
|
|||
from modules.generation_parameters_copypaste import image_from_url_text
|
||||
import modules.extras
|
||||
|
||||
warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
|
||||
warnings.filterwarnings("ignore", category=UserWarning)
|
||||
|
||||
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
|
||||
mimetypes.init()
|
||||
|
|
@ -53,15 +53,6 @@ if not cmd_opts.share and not cmd_opts.listen:
|
|||
gradio.utils.version_check = lambda: None
|
||||
gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
|
||||
|
||||
if cmd_opts.ngrok is not None:
|
||||
import modules.ngrok as ngrok
|
||||
print('ngrok authtoken detected, trying to connect...')
|
||||
ngrok.connect(
|
||||
cmd_opts.ngrok,
|
||||
cmd_opts.port if cmd_opts.port is not None else 7860,
|
||||
cmd_opts.ngrok_region
|
||||
)
|
||||
|
||||
|
||||
def gr_show(visible=True):
|
||||
return {"visible": visible, "__type__": "update"}
|
||||
|
|
@ -108,7 +99,7 @@ def add_style(name: str, prompt: str, negative_prompt: str):
|
|||
shared.prompt_styles.styles[style.name] = style
|
||||
# Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
|
||||
# reserialize all styles every time we save them
|
||||
shared.prompt_styles.save_styles(shared.styles_filename)
|
||||
shared.prompt_styles.save_styles('styles.csv')
|
||||
|
||||
return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(2)]
|
||||
|
||||
|
|
@ -383,14 +374,9 @@ def create_output_panel(tabname, outdir):
|
|||
|
||||
|
||||
def create_sampler_and_steps_selection(choices, tabname):
|
||||
if opts.samplers_in_dropdown:
|
||||
with FormRow(elem_id=f"sampler_selection_{tabname}"):
|
||||
sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
|
||||
steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
|
||||
else:
|
||||
with FormGroup(elem_id=f"sampler_selection_{tabname}"):
|
||||
steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
|
||||
sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
|
||||
with FormRow(elem_id=f"sampler_selection_{tabname}"):
|
||||
sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
|
||||
steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
|
||||
|
||||
return steps, sampler_index
|
||||
|
||||
|
|
@ -461,11 +447,10 @@ def create_ui():
|
|||
with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
|
||||
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn")
|
||||
|
||||
if opts.dimensions_and_batch_together:
|
||||
with gr.Column(elem_id="txt2img_column_batch"):
|
||||
with FormRow(elem_id="txt2img_row_batch"):
|
||||
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
|
||||
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
|
||||
with gr.Column(elem_id="txt2img_column_batch"):
|
||||
with FormRow(elem_id="txt2img_row_batch"):
|
||||
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
|
||||
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
|
||||
|
||||
elif category == "cfg":
|
||||
with FormRow():
|
||||
|
|
@ -495,12 +480,6 @@ def create_ui():
|
|||
hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
|
||||
hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
|
||||
|
||||
elif category == "batch":
|
||||
if not opts.dimensions_and_batch_together:
|
||||
with FormRow(elem_id="txt2img_column_batch"):
|
||||
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
|
||||
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
|
||||
|
||||
elif category == "override_settings":
|
||||
with FormRow(elem_id="txt2img_override_settings_row") as row:
|
||||
override_settings = create_override_settings_dropdown('txt2img', row)
|
||||
|
|
@ -766,12 +745,6 @@ def create_ui():
|
|||
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
|
||||
tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
|
||||
|
||||
elif category == "batch":
|
||||
if not opts.dimensions_and_batch_together:
|
||||
with FormRow(elem_id="img2img_column_batch"):
|
||||
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
|
||||
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
|
||||
|
||||
elif category == "override_settings":
|
||||
with FormRow(elem_id="img2img_override_settings_row") as row:
|
||||
override_settings = create_override_settings_dropdown('img2img', row)
|
||||
|
|
@ -1426,8 +1399,6 @@ def create_ui():
|
|||
with gr.Row():
|
||||
with gr.Column(scale=6):
|
||||
settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
|
||||
with gr.Column():
|
||||
restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio")
|
||||
|
||||
result = gr.HTML(elem_id="settings_result")
|
||||
|
||||
|
|
@ -1474,7 +1445,6 @@ def create_ui():
|
|||
|
||||
with gr.TabItem("Actions"):
|
||||
request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
|
||||
download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
|
||||
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
|
||||
with gr.Row():
|
||||
unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
|
||||
|
|
@ -1511,13 +1481,6 @@ def create_ui():
|
|||
_js='function(){}'
|
||||
)
|
||||
|
||||
download_localization.click(
|
||||
fn=lambda: None,
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
_js='download_localization'
|
||||
)
|
||||
|
||||
def reload_scripts():
|
||||
modules.scripts.reload_script_body_only()
|
||||
reload_javascript() # need to refresh the html page
|
||||
|
|
@ -1528,16 +1491,6 @@ def create_ui():
|
|||
outputs=[]
|
||||
)
|
||||
|
||||
def request_restart():
|
||||
shared.state.interrupt()
|
||||
shared.state.need_restart = True
|
||||
|
||||
restart_gradio.click(
|
||||
fn=request_restart,
|
||||
_js='restart_reload',
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
interfaces = [
|
||||
(txt2img_interface, "From Text", "txt2img"),
|
||||
|
|
|
|||
|
|
@ -27,5 +27,5 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
|
|||
}
|
||||
|
||||
def allowed_directories_for_previews(self):
|
||||
return [v for v in [shared.cmd_opts.ckpt_dir, sd_models.model_path] if v is not None]
|
||||
return [v for v in [shared.opts.ckpt_dir, sd_models.model_path] if v is not None]
|
||||
|
||||
|
|
|
|||
|
|
@ -26,5 +26,5 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
|
|||
}
|
||||
|
||||
def allowed_directories_for_previews(self):
|
||||
return [shared.cmd_opts.hypernetwork_dir]
|
||||
return [shared.opts.hypernetwork_dir]
|
||||
|
||||
|
|
|
|||
|
|
@ -8,7 +8,6 @@ basicsr
|
|||
bitsandbytes
|
||||
blendmodes
|
||||
clean-fid
|
||||
colormap
|
||||
easydev
|
||||
extcolors
|
||||
facexlib
|
||||
|
|
@ -31,7 +30,6 @@ opencv-python
|
|||
piexif
|
||||
Pillow
|
||||
psutil
|
||||
pyngrok
|
||||
pyyaml
|
||||
requests
|
||||
resize-right
|
||||
|
|
|
|||
183
setup.py
183
setup.py
|
|
@ -4,37 +4,73 @@ import json
|
|||
import time
|
||||
import subprocess
|
||||
import argparse
|
||||
from modules.cmd_args import parser
|
||||
|
||||
|
||||
import logging
|
||||
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s | %(levelname)s | %(message)s', filename='setup.log', filemode='w')
|
||||
log = logging.getLogger("sd-setup")
|
||||
try:
|
||||
from rich.logging import RichHandler
|
||||
from rich.console import Console
|
||||
from rich.pretty import install as pretty_install
|
||||
from rich.traceback import install as traceback_install
|
||||
rh = RichHandler(show_time=True, omit_repeated_times=False, show_level=True, show_path=True, markup=True, rich_tracebacks=True, log_time_format='%H:%M:%S-%f')
|
||||
rh.setLevel(logging.INFO)
|
||||
log.addHandler(rh)
|
||||
# logging.basicConfig(level=logging.INFO, format='%(message)s', handlers=[RichHandler(show_time=True, omit_repeated_times=False, show_level=True, show_path=True, markup=True, rich_tracebacks=True, log_time_format='%H:%M:%S-%f')])
|
||||
console = Console(log_time=True, log_time_format='%H:%M:%S-%f')
|
||||
pretty_install(console=console)
|
||||
traceback_install(console=console, extra_lines=1, width=console.width, word_wrap=False, indent_guides=False, suppress=[])
|
||||
except:
|
||||
pass
|
||||
sh = logging.StreamHandler()
|
||||
sh.setLevel(logging.INFO)
|
||||
log.addHandler(sh)
|
||||
|
||||
parser = argparse.ArgumentParser(description = 'Setup for SD WebUI')
|
||||
# command line args
|
||||
# parser = argparse.ArgumentParser(description = 'Setup for SD WebUI')
|
||||
parser.add_argument('--debug', default = False, action='store_true', help = "Run installer with debug logging, default: %(default)s")
|
||||
parser.add_argument('--quick', default = False, action='store_true', help = "Skip installing if setup.log is newer than repo timestamp, default: %(default)s")
|
||||
parser.add_argument('--upgrade', default = False, action='store_true', help = "Upgrade main repository to latest version, default: %(default)s")
|
||||
parser.add_argument('--update', default = False, action='store_true', help = "Update all extensions and submodules, default: %(default)s")
|
||||
parser.add_argument('--noupdate', default = False, action='store_true', help = "Skip update extensions and submodules, default: %(default)s")
|
||||
parser.add_argument('--skip-extensions', default = False, action='store_true', help = "Skips running individual extension installers, default: %(default)s")
|
||||
args = parser.parse_args()
|
||||
|
||||
|
||||
# setup console and file logging
|
||||
if os.path.isfile('setup.log'):
|
||||
os.remove('setup.log')
|
||||
time.sleep(0.1) # prevent race condition
|
||||
import logging
|
||||
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s | %(levelname)s | %(pathname)s | %(message)s', filename='setup.log', filemode='a', encoding='utf-8', force=True)
|
||||
log = logging.getLogger("sd")
|
||||
print=print
|
||||
try: # we may not have rich on the first run
|
||||
from rich import print
|
||||
from rich.logging import RichHandler
|
||||
from rich.console import Console
|
||||
from rich.pretty import install as pretty_install
|
||||
from rich.traceback import install as traceback_install
|
||||
console = Console(log_time=True, log_time_format='%H:%M:%S-%f')
|
||||
pretty_install(console=console)
|
||||
traceback_install(console=console, extra_lines=1, width=console.width, word_wrap=False, indent_guides=False, suppress=[])
|
||||
rh = RichHandler(show_time=True, omit_repeated_times=False, show_level=True, show_path=False, markup=False, rich_tracebacks=True, log_time_format='%H:%M:%S-%f', level=logging.DEBUG if args.debug else logging.INFO, console=console)
|
||||
log.addHandler(rh)
|
||||
except:
|
||||
pass
|
||||
sh = logging.StreamHandler()
|
||||
sh.setLevel(logging.DEBUG if args.debug else logging.INFO)
|
||||
log.addHandler(sh)
|
||||
|
||||
|
||||
def installed(package):
|
||||
import pkg_resources
|
||||
ok = True
|
||||
try:
|
||||
pkgs = [p for p in package.split() if not p.startswith('-') and not p.startswith('git+') and not p.startswith('http') and not p.startswith('=')]
|
||||
for pkg in pkgs:
|
||||
p = pkg.split('==')
|
||||
spec = pkg_resources.working_set.by_key.get(p[0], None) # more reliable than importlib
|
||||
if spec is None:
|
||||
spec = pkg_resources.working_set.by_key.get(p[0].lower(), None) # check name variations
|
||||
if spec is None:
|
||||
spec = pkg_resources.working_set.by_key.get(p[0].replace('_', '-'), None) # check name variations
|
||||
ok = ok and spec is not None
|
||||
version = pkg_resources.get_distribution(p[0]).version
|
||||
if ok and len(p) > 1:
|
||||
ok = ok and version == p[1]
|
||||
if not ok:
|
||||
log.warning(f"Package wrong version found: {p[0]} {version} required {p[1]}")
|
||||
# if ok:
|
||||
# log.debug(f"Package already installed: {p[0]} {version}")
|
||||
# else:
|
||||
# log.debug(f"Package not installed: {p[0]} {version}")
|
||||
return ok
|
||||
except ModuleNotFoundError:
|
||||
log.debug(f"Package not installed: {pkgs}")
|
||||
return False
|
||||
|
||||
# install package using pip if not already installed
|
||||
def install(package):
|
||||
def pip(args: str):
|
||||
log.debug(f"Running pip: {args}")
|
||||
|
|
@ -47,37 +83,11 @@ def install(package):
|
|||
log.debug(f'Pip output: {txt}')
|
||||
return txt
|
||||
|
||||
def installed():
|
||||
import pkg_resources
|
||||
ok = True
|
||||
try:
|
||||
pkgs = [p for p in package.split() if not p.startswith('-') and not p.startswith('git+') and not p.startswith('http') and not p.startswith('=')]
|
||||
for pkg in pkgs:
|
||||
p = pkg.split('==')
|
||||
spec = pkg_resources.working_set.by_key.get(p[0], None)
|
||||
if spec is None:
|
||||
spec = pkg_resources.working_set.by_key.get(p[0].lower(), None)
|
||||
if spec is None:
|
||||
spec = pkg_resources.working_set.by_key.get(p[0].replace('_', '-'), None)
|
||||
ok = ok and spec is not None
|
||||
version = pkg_resources.get_distribution(p[0]).version
|
||||
if ok and len(p) > 1:
|
||||
ok = ok and version == p[1]
|
||||
if not ok:
|
||||
log.warning(f"Package wrong version found: {p[0]} {version} required {p[1]}")
|
||||
# if ok:
|
||||
# log.debug(f"Package already installed: {p[0]} {version}")
|
||||
# else:
|
||||
# log.debug(f"Package not installed: {p[0]} {version}")
|
||||
return ok
|
||||
except ModuleNotFoundError:
|
||||
log.debug(f"Package not installed: {pkgs}")
|
||||
return False
|
||||
|
||||
if not installed():
|
||||
if not installed(package):
|
||||
pip(f"install --upgrade {package}")
|
||||
|
||||
|
||||
# execute git command
|
||||
def git(args: str):
|
||||
# log.debug(f"Running git: {args}")
|
||||
git_cmd = os.environ.get('GIT', "git")
|
||||
|
|
@ -91,6 +101,7 @@ def git(args: str):
|
|||
return txt
|
||||
|
||||
|
||||
# update switch to main branch as head can get detached and update repository
|
||||
def update(dir):
|
||||
branch = git(f'-C "{dir}" branch')
|
||||
if 'main' in branch:
|
||||
|
|
@ -102,10 +113,11 @@ def update(dir):
|
|||
else:
|
||||
log.warning(f'Unknown branch for: {dir}')
|
||||
git(f'-C "{dir}" pull --rebase --autostash')
|
||||
branch = git(f'-C "{dir}" branch')
|
||||
|
||||
|
||||
# clone git repository
|
||||
def clone(url, dir, commithash=None):
|
||||
|
||||
if os.path.exists(dir):
|
||||
if commithash is None:
|
||||
return
|
||||
|
|
@ -120,12 +132,7 @@ def clone(url, dir, commithash=None):
|
|||
git(f'-C "{dir}" checkout {commithash}')
|
||||
|
||||
|
||||
def parse_env():
|
||||
import shlex
|
||||
args = os.environ.get('COMMANDLINE_ARGS', "")
|
||||
sys.argv += shlex.split(args)
|
||||
|
||||
|
||||
# check python version
|
||||
def check_python():
|
||||
import platform
|
||||
supported_minors = [10] if platform.system() != "Windows" else [9, 10, 11]
|
||||
|
|
@ -134,6 +141,7 @@ def check_python():
|
|||
raise RuntimeError(f"Incompatible Python version: {sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro} required 3.9-3.11")
|
||||
|
||||
|
||||
# check torch version
|
||||
def check_torch():
|
||||
install(f'torch torchaudio torchvision --extra-index-url https://download.pytorch.org/whl/cu118')
|
||||
try:
|
||||
|
|
@ -153,6 +161,7 @@ def check_torch():
|
|||
pass
|
||||
|
||||
|
||||
# install required packages
|
||||
def install_packages():
|
||||
log.info('Installing packages')
|
||||
gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
|
||||
|
|
@ -165,6 +174,7 @@ def install_packages():
|
|||
install(f'--no-deps {xformers_package}')
|
||||
|
||||
|
||||
# clone required repositories
|
||||
def install_repositories():
|
||||
def dir(name):
|
||||
return os.path.join(os.path.dirname(__file__), 'repositories', name)
|
||||
|
|
@ -188,6 +198,26 @@ def install_repositories():
|
|||
clone(blip_repo, dir('BLIP'), blip_commit)
|
||||
|
||||
|
||||
# run extension installer
|
||||
def run_extension_installer(extension_dir):
|
||||
path_installer = os.path.join(extension_dir, "install.py")
|
||||
if not os.path.isfile(path_installer):
|
||||
return
|
||||
try:
|
||||
log.debug(f"Running extension installer: {path_installer}")
|
||||
env = os.environ.copy()
|
||||
env['PYTHONPATH'] = os.path.abspath(".")
|
||||
result = subprocess.run(f'"{sys.executable}" "{path_installer}"', shell=True, env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
|
||||
if result.returncode != 0:
|
||||
txt = result.stdout.decode(encoding="utf8", errors="ignore")
|
||||
if len(result.stderr) > 0:
|
||||
txt = txt + '\n' + result.stderr.decode(encoding="utf8", errors="ignore")
|
||||
log.error(f'Error running extension installer: {txt}')
|
||||
except Exception as e:
|
||||
log.error(f'Exception running extension installer: {e}')
|
||||
|
||||
|
||||
# run installer for each installed and enabled extension and optionally update them
|
||||
def install_extensions():
|
||||
def list_extensions(dir):
|
||||
settings = {}
|
||||
|
|
@ -203,29 +233,11 @@ def install_extensions():
|
|||
log.debug(f'Disabled extensions: {disabled_extensions}')
|
||||
return [x for x in os.listdir(dir) if x not in disabled_extensions and not x.startswith('.')]
|
||||
|
||||
def run_extension_installer(extension_dir):
|
||||
path_installer = os.path.join(extension_dir, "install.py")
|
||||
if not os.path.isfile(path_installer):
|
||||
return
|
||||
try:
|
||||
log.debug(f"Running extension installer: {path_installer}")
|
||||
env = os.environ.copy()
|
||||
env['PYTHONPATH'] = os.path.abspath(".")
|
||||
result = subprocess.run(f'"{sys.executable}" "{path_installer}"', shell=True, env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
|
||||
if result.returncode != 0:
|
||||
txt = result.stdout.decode(encoding="utf8", errors="ignore")
|
||||
if len(result.stderr) > 0:
|
||||
txt = txt + '\n' + result.stderr.decode(encoding="utf8", errors="ignore")
|
||||
log.error(f'Error running extension installer: {txt}')
|
||||
except Exception as e:
|
||||
log.error(f'Exception running extension installer: {e}')
|
||||
|
||||
|
||||
extensions_builtin_dir = os.path.join(os.path.dirname(__file__), 'extensions-builtin')
|
||||
extensions = list_extensions(extensions_builtin_dir)
|
||||
log.info(f'Built-in extensions: {extensions}')
|
||||
for ext in extensions:
|
||||
if args.update:
|
||||
if not args.noupdate:
|
||||
update(os.path.join(extensions_builtin_dir, ext))
|
||||
if not args.skip_extensions:
|
||||
run_extension_installer(os.path.join(extensions_builtin_dir, ext))
|
||||
|
|
@ -234,16 +246,17 @@ def install_extensions():
|
|||
extensions = list_extensions(extensions_dir)
|
||||
log.info(f'Enabled extensions: {extensions}')
|
||||
for ext in extensions:
|
||||
if args.update:
|
||||
if not args.noupdate:
|
||||
update(os.path.join(extensions_dir, ext))
|
||||
if not args.skip_extensions:
|
||||
run_extension_installer(os.path.join(extensions_dir, ext))
|
||||
|
||||
|
||||
# initialize and optionally update submodules
|
||||
def install_submodules():
|
||||
log.info('Installing submodules')
|
||||
git(f'submodule --quiet update --init --recursive')
|
||||
if args.update:
|
||||
if not args.noupdate:
|
||||
log.info('Updating submodules')
|
||||
submodules = git('submodule').splitlines()
|
||||
for submodule in submodules:
|
||||
|
|
@ -251,6 +264,7 @@ def install_submodules():
|
|||
update(name)
|
||||
|
||||
|
||||
# install requirements
|
||||
def install_requirements():
|
||||
log.info('Installing requirements')
|
||||
f = open('requirements.txt', 'r')
|
||||
|
|
@ -259,6 +273,7 @@ def install_requirements():
|
|||
install(line)
|
||||
|
||||
|
||||
# set environment variables controling the behavior of various libraries
|
||||
def set_environment():
|
||||
log.info('Setting environment tuning')
|
||||
os.environ.setdefault('TF_CPP_MIN_LOG_LEVEL', '2')
|
||||
|
|
@ -273,8 +288,10 @@ def set_environment():
|
|||
os.environ.setdefault('CUDA_DEVICE_DEFAULT_PERSISTING_L2_CACHE_PERCENTAGE_LIMIT', '0')
|
||||
os.environ.setdefault('GRADIO_ANALYTICS_ENABLED', 'False')
|
||||
os.environ.setdefault('SAFETENSORS_FAST_GPU', '1')
|
||||
os.environ.setdefault('NUMEXPR_MAX_THREADS', '16')
|
||||
|
||||
|
||||
# check version of the main repo and optionally upgrade it
|
||||
def check_version():
|
||||
ver = git('log -1 --pretty=format:"%h %ad"')
|
||||
log.info(f'Version: {ver}')
|
||||
|
|
@ -289,12 +306,13 @@ def check_version():
|
|||
log.info(f'Updated to version: {ver}')
|
||||
else:
|
||||
log.warning(f'Latest available version: {commits["commit"]["commit"]["author"]["date"]}')
|
||||
if args.update:
|
||||
if not args.noupdate:
|
||||
log.info('Updating Wiki')
|
||||
update(os.path.join(os.path.dirname(__file__), "wiki"))
|
||||
update(os.path.join(os.path.dirname(__file__), "wiki", "origin-wiki"))
|
||||
|
||||
|
||||
# check if we can run setup in quick mode
|
||||
def check_timestamp():
|
||||
if not os.path.isfile('setup.log'):
|
||||
return False
|
||||
|
|
@ -304,13 +322,15 @@ def check_timestamp():
|
|||
log.debug(f'Repository update time: {time.ctime(int(version_time))}')
|
||||
return setup_time >= version_time
|
||||
|
||||
|
||||
# entry method when used as module
|
||||
def run_setup(quick = False):
|
||||
parse_env()
|
||||
check_python()
|
||||
if (quick or args.quick) and check_timestamp():
|
||||
log.info('Attempting quick setup')
|
||||
return
|
||||
log.info("Running setup")
|
||||
log.debug(f"Args: {vars(args)}")
|
||||
install_requirements()
|
||||
check_version()
|
||||
install_packages()
|
||||
|
|
@ -319,6 +339,7 @@ def run_setup(quick = False):
|
|||
install_extensions()
|
||||
install_requirements()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
run_setup()
|
||||
set_environment()
|
||||
|
|
|
|||
92
webui.py
92
webui.py
|
|
@ -1,15 +1,11 @@
|
|||
import os
|
||||
import sys
|
||||
import time
|
||||
import importlib
|
||||
import signal
|
||||
import re
|
||||
import warnings
|
||||
from fastapi import FastAPI
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from fastapi.middleware.gzip import GZipMiddleware
|
||||
from packaging import version
|
||||
from rich import print
|
||||
from setup import log
|
||||
|
||||
import logging
|
||||
logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
|
||||
|
|
@ -42,7 +38,6 @@ import modules.codeformer_model as codeformer
|
|||
import modules.face_restoration
|
||||
import modules.gfpgan_model as gfpgan
|
||||
import modules.img2img
|
||||
|
||||
import modules.lowvram
|
||||
import modules.scripts
|
||||
import modules.sd_hijack
|
||||
|
|
@ -52,15 +47,13 @@ import modules.txt2img
|
|||
import modules.script_callbacks
|
||||
import modules.textual_inversion.textual_inversion
|
||||
import modules.progress
|
||||
|
||||
import modules.ui
|
||||
from modules import modelloader
|
||||
from modules.shared import cmd_opts
|
||||
from modules.shared import cmd_opts, opts
|
||||
import modules.hypernetworks.hypernetwork
|
||||
|
||||
startup_timer.record("libraries")
|
||||
|
||||
|
||||
if cmd_opts.server_name:
|
||||
server_name = cmd_opts.server_name
|
||||
else:
|
||||
|
|
@ -72,28 +65,23 @@ def initialize():
|
|||
if torch.version.cuda: cuda_version = f'CUDA {torch.version.cuda} cuDNN {torch.backends.cudnn.version()}'
|
||||
elif torch.version.hip: cuda_version = f'HIP {torch.version.hip}'
|
||||
else: cuda_version = ''
|
||||
print(f'Torch {getattr(torch, "__long_version__", torch.__version__)} {cuda_version}')
|
||||
log.info(f'Torch {getattr(torch, "__long_version__", torch.__version__)} {cuda_version}')
|
||||
for device in [torch.cuda.device(i) for i in range(torch.cuda.device_count())]:
|
||||
print(f'GPU {torch.cuda.get_device_name(device)} VRAM {round(torch.cuda.get_device_properties(device).total_memory / 1024 / 1024)} Arch {torch.cuda.get_device_capability(device)} Cores {torch.cuda.get_device_properties(device).multi_processor_count}')
|
||||
log.info(f'GPU {torch.cuda.get_device_name(device)} VRAM {round(torch.cuda.get_device_properties(device).total_memory / 1024 / 1024)} Arch {torch.cuda.get_device_capability(device)} Cores {torch.cuda.get_device_properties(device).multi_processor_count}')
|
||||
else:
|
||||
print(f'Torch {getattr(torch, "__long_version__", torch.__version__)} running on CPU')
|
||||
log.info(f'Torch {getattr(torch, "__long_version__", torch.__version__)} running on CPU')
|
||||
|
||||
extensions.list_extensions()
|
||||
startup_timer.record("extensions")
|
||||
|
||||
if cmd_opts.ui_debug_mode:
|
||||
shared.sd_upscalers = upscaler.UpscalerLanczos().scalers
|
||||
modules.scripts.load_scripts()
|
||||
return
|
||||
|
||||
modelloader.cleanup_models()
|
||||
modules.sd_models.setup_model()
|
||||
startup_timer.record("models")
|
||||
|
||||
codeformer.setup_model(cmd_opts.codeformer_models_path)
|
||||
codeformer.setup_model(opts.codeformer_models_path)
|
||||
startup_timer.record("codeformer")
|
||||
|
||||
gfpgan.setup_model(cmd_opts.gfpgan_models_path)
|
||||
gfpgan.setup_model(opts.gfpgan_models_path)
|
||||
startup_timer.record("gfpgan")
|
||||
|
||||
modelloader.list_builtin_upscalers()
|
||||
|
|
@ -123,28 +111,26 @@ def initialize():
|
|||
ui_extra_networks.register_page(ui_extra_networks_textual_inversion.ExtraNetworksPageTextualInversion())
|
||||
ui_extra_networks.register_page(ui_extra_networks_hypernets.ExtraNetworksPageHypernetworks())
|
||||
ui_extra_networks.register_page(ui_extra_networks_checkpoints.ExtraNetworksPageCheckpoints())
|
||||
|
||||
extra_networks.initialize()
|
||||
extra_networks.register_extra_network(extra_networks_hypernet.ExtraNetworkHypernet())
|
||||
startup_timer.record("extra networks")
|
||||
|
||||
if cmd_opts.tls_keyfile is not None and cmd_opts.tls_keyfile is not None:
|
||||
|
||||
try:
|
||||
if not os.path.exists(cmd_opts.tls_keyfile):
|
||||
print("Invalid path to TLS keyfile given")
|
||||
log.warning("Invalid path to TLS keyfile given")
|
||||
if not os.path.exists(cmd_opts.tls_certfile):
|
||||
print(f"Invalid path to TLS certfile: '{cmd_opts.tls_certfile}'")
|
||||
log.warning(f"Invalid path to TLS certfile: '{cmd_opts.tls_certfile}'")
|
||||
except TypeError:
|
||||
cmd_opts.tls_keyfile = cmd_opts.tls_certfile = None
|
||||
print("TLS setup invalid, running webui without TLS")
|
||||
log.warning("TLS setup invalid, running webui without TLS")
|
||||
else:
|
||||
print("Running with TLS")
|
||||
log.info("Running with TLS")
|
||||
startup_timer.record("TLS")
|
||||
|
||||
# make the program just exit at ctrl+c without waiting for anything
|
||||
def sigint_handler(_sig, _frame):
|
||||
print('Exiting')
|
||||
log.info('Exiting')
|
||||
os._exit(0)
|
||||
|
||||
signal.signal(signal.SIGINT, sigint_handler)
|
||||
|
|
@ -157,10 +143,10 @@ def load_model():
|
|||
modules.sd_models.load_model()
|
||||
except Exception as e:
|
||||
errors.display(e, "loading stable diffusion model")
|
||||
print("Stable diffusion model failed to load, exiting", file=sys.stderr)
|
||||
log.error(f"Stable diffusion model failed to load")
|
||||
exit(1)
|
||||
if shared.sd_model is None:
|
||||
print("No stable diffusion model loaded, exiting", file=sys.stderr)
|
||||
log.error("No stable diffusion model loaded")
|
||||
exit(1)
|
||||
shared.opts.data["sd_model_checkpoint"] = shared.sd_model.sd_checkpoint_info.title
|
||||
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
|
||||
|
|
@ -171,12 +157,12 @@ def load_model():
|
|||
def setup_middleware(app):
|
||||
app.middleware_stack = None # reset current middleware to allow modifying user provided list
|
||||
app.add_middleware(GZipMiddleware, minimum_size=1024)
|
||||
if cmd_opts.cors_allow_origins and cmd_opts.cors_allow_origins_regex:
|
||||
app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_allow_origins.split(','), allow_origin_regex=cmd_opts.cors_allow_origins_regex, allow_methods=['*'], allow_credentials=True, allow_headers=['*'])
|
||||
elif cmd_opts.cors_allow_origins:
|
||||
app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_allow_origins.split(','), allow_methods=['*'], allow_credentials=True, allow_headers=['*'])
|
||||
elif cmd_opts.cors_allow_origins_regex:
|
||||
app.add_middleware(CORSMiddleware, allow_origin_regex=cmd_opts.cors_allow_origins_regex, allow_methods=['*'], allow_credentials=True, allow_headers=['*'])
|
||||
if cmd_opts.cors_origins and cmd_opts.cors_regex:
|
||||
app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_origins.split(','), allow_origin_regex=cmd_opts.cors_regex, allow_methods=['*'], allow_credentials=True, allow_headers=['*'])
|
||||
elif cmd_opts.cors_origins:
|
||||
app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_origins.split(','), allow_methods=['*'], allow_credentials=True, allow_headers=['*'])
|
||||
elif cmd_opts.cors_regex:
|
||||
app.add_middleware(CORSMiddleware, allow_origin_regex=cmd_opts.cors_regex, allow_methods=['*'], allow_credentials=True, allow_headers=['*'])
|
||||
app.build_middleware_stack() # rebuild middleware stack on-the-fly
|
||||
|
||||
|
||||
|
|
@ -186,36 +172,16 @@ def create_api(app):
|
|||
return api
|
||||
|
||||
|
||||
def api_only():
|
||||
initialize()
|
||||
load_model()
|
||||
|
||||
app = FastAPI()
|
||||
setup_middleware(app)
|
||||
api = create_api(app)
|
||||
|
||||
modules.script_callbacks.app_started_callback(None, app)
|
||||
|
||||
print(f"Startup time: {startup_timer.summary()}")
|
||||
api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861)
|
||||
|
||||
|
||||
def webui():
|
||||
launch_api = cmd_opts.api
|
||||
initialize()
|
||||
|
||||
if shared.opts.clean_temp_dir_at_start:
|
||||
ui_tempdir.cleanup_tmpdr()
|
||||
startup_timer.record("cleanup")
|
||||
|
||||
modules.script_callbacks.before_ui_callback()
|
||||
startup_timer.record("scripts before_ui_callback")
|
||||
|
||||
shared.demo = modules.ui.create_ui()
|
||||
startup_timer.record("ui")
|
||||
|
||||
if not cmd_opts.no_gradio_queue:
|
||||
shared.demo.queue(16)
|
||||
shared.demo.queue(16)
|
||||
|
||||
gradio_auth_creds = []
|
||||
if cmd_opts.gradio_auth:
|
||||
|
|
@ -231,7 +197,7 @@ def webui():
|
|||
server_port=cmd_opts.port,
|
||||
ssl_keyfile=cmd_opts.tls_keyfile,
|
||||
ssl_certfile=cmd_opts.tls_certfile,
|
||||
debug=cmd_opts.gradio_debug,
|
||||
debug=False,
|
||||
auth=[tuple(cred.split(':')) for cred in gradio_auth_creds] if gradio_auth_creds else None,
|
||||
inbrowser=cmd_opts.autolaunch,
|
||||
prevent_thread_lock=True,
|
||||
|
|
@ -239,24 +205,19 @@ def webui():
|
|||
)
|
||||
# for dep in shared.demo.dependencies:
|
||||
# dep['show_progress'] = False # disable gradio css animation on component update
|
||||
|
||||
# app is instance of FastAPI server
|
||||
# shared.demo.server is instance of gradio class which inherits from uvicorn.Server
|
||||
# shared.demo.config is instance of uvicorn.Config
|
||||
# shared.demo.app is instance of ASGIApp
|
||||
|
||||
cmd_opts.autolaunch = False
|
||||
|
||||
startup_timer.record("gradio")
|
||||
|
||||
app.user_middleware = [x for x in app.user_middleware if x.cls.__name__ != 'CORSMiddleware']
|
||||
|
||||
setup_middleware(app)
|
||||
|
||||
modules.progress.setup_progress_api(app)
|
||||
|
||||
if launch_api:
|
||||
create_api(app)
|
||||
create_api(app)
|
||||
|
||||
ui_extra_networks.add_pages_to_demo(app)
|
||||
|
||||
|
|
@ -265,14 +226,11 @@ def webui():
|
|||
|
||||
load_model()
|
||||
|
||||
print(f"Startup time: {startup_timer.summary()}")
|
||||
log.info(f"Startup time: {startup_timer.summary()}")
|
||||
|
||||
while True:
|
||||
time.sleep(0.1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if cmd_opts.nowebui:
|
||||
api_only()
|
||||
else:
|
||||
webui()
|
||||
webui()
|
||||
|
|
|
|||
Loading…
Reference in New Issue