mirror of
https://github.com/vladmandic/automatic
synced 2026-09-19 09:14:35 +02:00
Merge branch 'master' into directml
This commit is contained in:
Seunghoon Lee
+37
-76
@@ -1,12 +1,10 @@
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import datetime
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import json
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import os
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import sys
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import time
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import json
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import datetime
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import gradio as gr
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import tqdm
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import modules.interrogate
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import modules.memmon
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import modules.styles
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@@ -20,7 +18,7 @@ errors.install(gr)
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demo: gr.Blocks = None
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log = setup_log
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parser = cmd_args.parser
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url = 'https://github.com/vladmandic/automatic'
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if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
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cmd_opts = parser.parse_args()
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else:
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@@ -52,10 +50,7 @@ ui_reorder_categories = [
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]
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cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure
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devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
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(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'])
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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'])
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device = devices.device
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is_device_dml = False
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sd_upscalers = []
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@@ -102,7 +97,6 @@ class State:
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def nextjob(self):
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if opts.live_previews_enable and opts.show_progress_every_n_steps == -1:
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self.do_set_current_image()
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self.job_no += 1
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self.sampling_step = 0
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self.current_image_sampling_step = 0
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@@ -134,13 +128,11 @@ class State:
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self.interrupted = False
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self.textinfo = None
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self.time_start = time.time()
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devices.torch_gc()
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def end(self):
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self.job = ""
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self.job_count = 0
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devices.torch_gc()
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def set_current_image(self):
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@@ -167,9 +159,7 @@ class State:
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state = State()
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state.server_start = time.time()
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interrogator = modules.interrogate.InterrogateModels("interrogate")
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face_restorers = []
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class OptionInfo:
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@@ -186,7 +176,6 @@ class OptionInfo:
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def options_section(section_identifier, options_dict):
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for _k, v in options_dict.items():
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v.section = section_identifier
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return options_dict
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@@ -232,26 +221,25 @@ def refresh_themes():
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hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
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tab_names = []
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options_templates = {}
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default_checkpoint = list_checkpoint_tiles()[0] if len(list_checkpoint_tiles()) > 0 else "model.ckpt"
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options_templates.update(options_section(('sd', "Stable Diffusion"), {
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"sd_model_checkpoint": OptionInfo(default_checkpoint, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
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"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
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"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
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"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
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"sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
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"sd_checkpoint_cache": OptionInfo(0, "Model checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
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"sd_vae_checkpoint_cache": OptionInfo(0, "VAE checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
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"sd_vae": OptionInfo("Automatic", "Select VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
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"sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them", gr.Checkbox, {"visible": False}),
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"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
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"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
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"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
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"img2img_fix_steps": OptionInfo(False, "For image processing do exactly the amount of steps as specified."),
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"img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", ui_components.FormColorPicker, {}),
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"enable_quantization": OptionInfo(True, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds."),
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"enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
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"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
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"enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention", gr.Checkbox, {"visible": False}),
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"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image", gr.Checkbox, {"visible": False}),
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"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 }),
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"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
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"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1, "visible": False}),
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"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
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"cross_attention_optimization": OptionInfo("Sub-quadratic" if is_device_dml else "Scaled-Dot-Product", "Cross-attention optimization method", gr.Radio, lambda: {"choices": shared_items.list_crossattention() }),
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"cross_attention_options": OptionInfo([], "Cross-attention advanced options", gr.CheckboxGroup, lambda: {"choices": ['xFormers enable flash Attention', 'SDP disable memory attention']}),
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@@ -259,6 +247,9 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
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"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}),
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"sub_quad_chunk_threshold": OptionInfo(80, "Sub-quadratic cross-attention percentage of VRAM chunking threshold", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}),
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"always_batch_cond_uncond": OptionInfo(False, "Disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram"),
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"multiple_tqdm": OptionInfo(False, "Add a second progress bar to the console that shows progress for an entire job.", gr.Checkbox, {"visible": False}),
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"print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console.", gr.Checkbox, {"visible": False}),
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"dimensions_and_batch_together": OptionInfo(True, "", gr.Checkbox, {"visible": False}),
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}))
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options_templates.update(options_section(('system-paths', "System Paths"), {
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@@ -268,6 +259,7 @@ options_templates.update(options_section(('system-paths', "System Paths"), {
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"vae_dir": OptionInfo(os.path.join(paths.models_path, 'VAE'), "Path to directory with VAE files"),
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"embeddings_dir": OptionInfo(os.path.join(paths.models_path, 'embeddings'), "Embeddings directory for textual inversion"),
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"embeddings_templates_dir": OptionInfo(os.path.join(paths.script_path, 'train/templates'), "Embeddings train templates directory"),
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"embeddings_train_log": OptionInfo(os.path.join(paths.script_path, 'train.csv'), "Embeddings train log file"),
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"hypernetwork_dir": OptionInfo(os.path.join(paths.models_path, 'hypernetworks'), "Hypernetwork directory"),
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"codeformer_models_path": OptionInfo(os.path.join(paths.models_path, 'Codeformer'), "Path to directory with codeformer model file(s)."),
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"gfpgan_models_path": OptionInfo(os.path.join(paths.models_path, 'GFPGAN'), "Path to directory with GFPGAN model file(s)"),
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@@ -327,11 +319,12 @@ options_templates.update(options_section(('saving-paths', "Image Paths"), {
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}))
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options_templates.update(options_section(('cuda', "CUDA Settings"), {
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"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}),
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"precision": OptionInfo("Autocast", "Precision type", gr.Radio, lambda: {"choices": ["Autocast", "Full"]}),
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"cuda_dtype": OptionInfo("FP16", "Device precision type", gr.Radio, lambda: {"choices": ["FP32", "FP16", "BF16"]}),
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"cuda_dtype": OptionInfo("FP32" if sys.platform == "darwin" else "FP16", "Device precision type", gr.Radio, lambda: {"choices": ["FP32", "FP16", "BF16"]}),
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"no_half": OptionInfo(True if is_device_dml else False, "Use full precision for model (--no-half)"),
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"no_half_vae": OptionInfo(True if is_device_dml else False, "Use full precision for VAE (--no-half-vae)"),
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"upcast_sampling": OptionInfo(False, "Enable upcast sampling. Usually produces similar results to --no-half with better performance while using less memory"),
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"upcast_sampling": OptionInfo(True if sys.platform == "darwin" else False, "Enable upcast sampling. Usually produces similar results to --no-half with better performance while using less memory"),
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"disable_nan_check": OptionInfo(True, "Do not check if produced images/latent spaces have NaN values"),
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"rollback_vae": OptionInfo(False, "Attempt to roll back VAE when produced NaN values, requires NaN check (experimental)"),
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"opt_channelslast": OptionInfo(False, "Use channels last as torch memory format "),
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@@ -343,11 +336,11 @@ options_templates.update(options_section(('cuda', "CUDA Settings"), {
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}))
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options_templates.update(options_section(('upscaling', "Upscaling"), {
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"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
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"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
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"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI.", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
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"upscaler_for_img2img": OptionInfo("SwinIR_4x", "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
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"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)."),
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"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers (0 = no tiling)", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
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"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap in pixels for ESRGAN upscalers", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
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"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Real-ESRGAN available models", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
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"upscaler_for_img2img": OptionInfo("None", "Default upscaler for image resize operations", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
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"use_old_hires_fix_width_height": OptionInfo(False, "Hires fix uses width & height to set final resolution rather than first pass"),
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"dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
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}))
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@@ -357,12 +350,6 @@ options_templates.update(options_section(('face-restoration', "Face restoration"
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"face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
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}))
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options_templates.update(options_section(('system', "System"), {
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"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}),
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"multiple_tqdm": OptionInfo(False, "Add a second progress bar to the console that shows progress for an entire job."),
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"print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
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}))
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options_templates.update(options_section(('training', "Training"), {
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"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
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"pin_memory": OptionInfo(True, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
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@@ -409,13 +396,13 @@ options_templates.update(options_section(('ui', "User interface"), {
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"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
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"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
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"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
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"disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
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"disable_weights_auto_swap": OptionInfo(True, "Do not change the selected model when reading generation parameters."),
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"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
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"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
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"font": OptionInfo("", "Font for image grids that have text"),
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"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
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"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
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"show_progress_in_title": OptionInfo(False, "Show generation progress in window title."),
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"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer", gr.Checkbox, {"visible": False}),
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"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer", gr.Checkbox, {"visible": False}),
|
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"show_progress_in_title": OptionInfo(False, "Show generation progress in window title.", gr.Checkbox, {"visible": False}),
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"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}),
|
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"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}),
|
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"quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
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@@ -436,6 +423,7 @@ options_templates.update(options_section(('ui', "Live previews"), {
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options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
|
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"show_samplers": OptionInfo(["Euler a", "UniPC", "DDIM", "DPM++ SDE", "DPM++ SDE", "DPM2 Karras", "DPM++ 2M Karras"], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
|
||||
"fallback_sampler": OptionInfo("Euler a", "Fallback sampler if primary sampler is not compatible", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
|
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"eta_ancestral": OptionInfo(1.0, "Noise multiplier for ancestral samplers (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"eta_ddim": OptionInfo(0.0, "Noise multiplier for DDIM (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad']}),
|
||||
@@ -490,17 +478,14 @@ class Options:
|
||||
def __setattr__(self, key, value):
|
||||
if self.data is not None:
|
||||
if key in self.data or key in self.data_labels:
|
||||
assert not cmd_opts.freeze_settings, "changing settings is disabled"
|
||||
|
||||
info = opts.data_labels.get(key, None)
|
||||
comp_args = info.component_args if info else None
|
||||
if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
|
||||
raise RuntimeError(f"not possible to set {key} because it is restricted")
|
||||
|
||||
if cmd_opts.freeze_settings:
|
||||
print(f'Settings are frozen: {key}')
|
||||
return
|
||||
if cmd_opts.hide_ui_dir_config and key in restricted_opts:
|
||||
raise RuntimeError(f"not possible to set {key} because it is restricted")
|
||||
|
||||
self.data[key] = value
|
||||
print(f'Settings key is restricted: {key}')
|
||||
return
|
||||
else:
|
||||
self.data[key] = value
|
||||
return
|
||||
|
||||
return super(Options, self).__setattr__(key, value)
|
||||
@@ -509,24 +494,19 @@ class Options:
|
||||
if self.data is not None:
|
||||
if item in self.data:
|
||||
return self.data[item]
|
||||
|
||||
if item in self.data_labels:
|
||||
return self.data_labels[item].default
|
||||
|
||||
return super(Options, self).__getattribute__(item)
|
||||
|
||||
def set(self, key, value):
|
||||
"""sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
|
||||
|
||||
oldval = self.data.get(key, None)
|
||||
if oldval == value:
|
||||
return False
|
||||
|
||||
try:
|
||||
setattr(self, key, value)
|
||||
except RuntimeError:
|
||||
return False
|
||||
|
||||
if self.data_labels[key].onchange is not None:
|
||||
try:
|
||||
self.data_labels[key].onchange()
|
||||
@@ -534,37 +514,30 @@ class Options:
|
||||
errors.display(e, f"changing setting {key} to {value}")
|
||||
setattr(self, key, oldval)
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def get_default(self, key):
|
||||
"""returns the default value for the key"""
|
||||
|
||||
data_label = self.data_labels.get(key)
|
||||
if data_label is None:
|
||||
return None
|
||||
|
||||
return data_label.default
|
||||
|
||||
def save(self, filename):
|
||||
assert not cmd_opts.freeze_settings, "saving settings is disabled"
|
||||
|
||||
with open(filename, "w", encoding="utf8") as file:
|
||||
json.dump(self.data, file, indent=4)
|
||||
|
||||
def same_type(self, x, y):
|
||||
if x is None or y is None:
|
||||
return True
|
||||
|
||||
type_x = self.typemap.get(type(x), type(x))
|
||||
type_y = self.typemap.get(type(y), type(y))
|
||||
|
||||
return type_x == type_y
|
||||
|
||||
def load(self, filename):
|
||||
with open(filename, "r", encoding="utf8") as file:
|
||||
self.data = json.load(file)
|
||||
|
||||
bad_settings = 0
|
||||
for k, v in self.data.items():
|
||||
info = self.data_labels.get(k, None)
|
||||
@@ -578,7 +551,6 @@ class Options:
|
||||
def onchange(self, key, func, call=True):
|
||||
item = self.data_labels.get(key)
|
||||
item.onchange = func
|
||||
|
||||
if call:
|
||||
func()
|
||||
|
||||
@@ -591,13 +563,11 @@ class Options:
|
||||
|
||||
def reorder(self):
|
||||
"""reorder settings so that all items related to section always go together"""
|
||||
|
||||
section_ids = {}
|
||||
settings_items = self.data_labels.items()
|
||||
for k, item in settings_items:
|
||||
if item.section not in section_ids:
|
||||
section_ids[item.section] = len(section_ids)
|
||||
|
||||
self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])}
|
||||
|
||||
def cast_value(self, key, value):
|
||||
@@ -623,27 +593,20 @@ class Options:
|
||||
return value
|
||||
|
||||
|
||||
|
||||
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)
|
||||
|
||||
cmd_opts = cmd_args.compatibility_args(opts, cmd_opts)
|
||||
prompt_styles = modules.styles.StyleDatabase(opts.styles_dir)
|
||||
|
||||
settings_components = None
|
||||
"""assinged from ui.py, a mapping on setting names to gradio components repsponsible for those settings"""
|
||||
|
||||
latent_upscale_default_mode = "Latent"
|
||||
latent_upscale_modes = {
|
||||
"Latent": {"mode": "bilinear", "antialias": False},
|
||||
@@ -653,11 +616,10 @@ latent_upscale_modes = {
|
||||
"Latent (nearest)": {"mode": "nearest", "antialias": False},
|
||||
"Latent (nearest-exact)": {"mode": "nearest-exact", "antialias": False},
|
||||
}
|
||||
|
||||
progress_print_out = sys.stdout
|
||||
|
||||
gradio_theme = gr.themes.Base()
|
||||
|
||||
|
||||
def reload_gradio_theme(theme_name=None):
|
||||
global gradio_theme # pylint: disable=global-statement
|
||||
if not theme_name:
|
||||
@@ -718,7 +680,6 @@ class TotalTQDM:
|
||||
|
||||
|
||||
total_tqdm = TotalTQDM()
|
||||
|
||||
mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
|
||||
mem_mon.start()
|
||||
|
||||
|
||||
Reference in New Issue
Block a user