d64b451681
added eta parameter to parameters output for generated images split eta settings into ancestral and ddim (because they have different default values)
340 lines
20 KiB
Python
340 lines
20 KiB
Python
import sys
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import argparse
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import json
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import os
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import gradio as gr
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import tqdm
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import datetime
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import modules.artists
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from modules.paths import script_path, sd_path
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from modules.devices import get_optimal_device
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import modules.styles
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import modules.interrogate
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import modules.memmon
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import modules.sd_models
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sd_model_file = os.path.join(script_path, 'model.ckpt')
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default_sd_model_file = sd_model_file
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parser = argparse.ArgumentParser()
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parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), 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; this checkpoint will be added to the list of checkpoints and loaded by default if you don't have a checkpoint selected in settings",)
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parser.add_argument("--ckpt-dir", type=str, default=os.path.join(script_path, 'models'), help="path to directory with stable diffusion checkpoints",)
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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-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=16, help="maximum batch count value for the UI")
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parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
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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("--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("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
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parser.add_argument("--esrgan-models-path", type=str, help="path to directory with ESRGAN models", default=os.path.join(script_path, 'ESRGAN'))
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parser.add_argument("--swinir-models-path", type=str, help="path to directory with SwinIR models", default=os.path.join(script_path, 'SwinIR'))
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parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
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parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
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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")
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parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
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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)
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parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
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parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(script_path, 'ui-config.json'))
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parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
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parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
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parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option")
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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)
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parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
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parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
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parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
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parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
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cmd_opts = parser.parse_args()
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device = get_optimal_device()
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batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
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parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
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config_filename = cmd_opts.ui_settings_file
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class State:
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interrupted = False
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job = ""
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job_no = 0
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job_count = 0
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job_timestamp = '0'
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sampling_step = 0
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sampling_steps = 0
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current_latent = None
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current_image = None
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current_image_sampling_step = 0
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def interrupt(self):
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self.interrupted = True
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def nextjob(self):
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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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def get_job_timestamp(self):
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return datetime.datetime.now().strftime("%Y%m%d%H%M%S")
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state = State()
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artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
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styles_filename = cmd_opts.styles_file
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prompt_styles = modules.styles.StyleDatabase(styles_filename)
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interrogator = modules.interrogate.InterrogateModels("interrogate")
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face_restorers = []
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modules.sd_models.list_models()
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def realesrgan_models_names():
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import modules.realesrgan_model
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return [x.name for x in modules.realesrgan_model.get_realesrgan_models()]
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class OptionInfo:
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def __init__(self, default=None, label="", component=None, component_args=None, onchange=None):
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self.default = default
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self.label = label
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self.component = component
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self.component_args = component_args
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self.onchange = onchange
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self.section = None
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def options_section(section_identifer, options_dict):
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for k, v in options_dict.items():
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v.section = section_identifer
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return options_dict
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hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
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options_templates = {}
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options_templates.update(options_section(('saving-images', "Saving images/grids"), {
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"samples_save": OptionInfo(True, "Always save all generated images"),
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"samples_format": OptionInfo('png', 'File format for images'),
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"samples_filename_pattern": OptionInfo("", "Images filename pattern"),
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"grid_save": OptionInfo(True, "Always save all generated image grids"),
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"grid_format": OptionInfo('png', 'File format for grids'),
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"grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
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"grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
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"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}),
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"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
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"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
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"save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
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"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
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"export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
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"use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
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"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
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}))
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options_templates.update(options_section(('saving-paths', "Paths for saving"), {
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"outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
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"outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
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"outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
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"outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab', component_args=hide_dirs),
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"outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs),
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"outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
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"outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
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"outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
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}))
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options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
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"save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
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"grid_save_to_dirs": OptionInfo(False, "Save grids to subdirectory"),
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"directories_filename_pattern": OptionInfo("", "Directory name pattern"),
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"directories_max_prompt_words": OptionInfo(8, "Max prompt words", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
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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(["Real-ESRGAN 4x plus", "Real-ESRGAN 4x plus anime 6B"], "Select which RealESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
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"SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),
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"SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
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"ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
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"ldsr_pre_down": OptionInfo(1, "LDSR Pre-process down-sample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
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"ldsr_post_down": OptionInfo(1, "LDSR Post-process down-sample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
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"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
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}))
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options_templates.update(options_section(('face-restoration', "Face restoration"), {
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"face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
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"code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
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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(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
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"samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
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"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. Broken in PyCharm console."),
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}))
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options_templates.update(options_section(('sd', "Stable Diffusion"), {
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"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": [x.title for x in modules.sd_models.checkpoints_list.values()]}),
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"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
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"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
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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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"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
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"enable_emphasis": OptionInfo(True, "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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"filter_nsfw": OptionInfo(False, "Filter NSFW content"),
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"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
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}))
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options_templates.update(options_section(('interrogate', "Interrogate Options"), {
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"interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
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"interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
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"interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
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"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
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"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
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"interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
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}))
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options_templates.update(options_section(('ui', "User interface"), {
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"show_progressbar": OptionInfo(True, "Show progressbar"),
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"show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
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"return_grid": OptionInfo(True, "Show grid 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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"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_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
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}))
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options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
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"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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"eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
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's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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}))
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class Options:
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data = None
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data_labels = options_templates
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typemap = {int: float}
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def __init__(self):
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self.data = {k: v.default for k, v in self.data_labels.items()}
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def __setattr__(self, key, value):
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if self.data is not None:
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if key in self.data:
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self.data[key] = value
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return super(Options, self).__setattr__(key, value)
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def __getattr__(self, item):
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if self.data is not None:
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if item in self.data:
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return self.data[item]
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if item in self.data_labels:
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return self.data_labels[item].default
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return super(Options, self).__getattribute__(item)
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def save(self, filename):
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with open(filename, "w", encoding="utf8") as file:
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json.dump(self.data, file)
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def same_type(self, x, y):
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if x is None or y is None:
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return True
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type_x = self.typemap.get(type(x), type(x))
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type_y = self.typemap.get(type(y), type(y))
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return type_x == type_y
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def load(self, filename):
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with open(filename, "r", encoding="utf8") as file:
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self.data = json.load(file)
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bad_settings = 0
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for k, v in self.data.items():
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info = self.data_labels.get(k, None)
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if info is not None and not self.same_type(info.default, v):
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print(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})", file=sys.stderr)
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bad_settings += 1
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if bad_settings > 0:
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print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
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def onchange(self, key, func):
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item = self.data_labels.get(key)
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item.onchange = func
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def dumpjson(self):
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d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
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return json.dumps(d)
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opts = Options()
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if os.path.exists(config_filename):
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opts.load(config_filename)
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sd_upscalers = []
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sd_model = None
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progress_print_out = sys.stdout
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class TotalTQDM:
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def __init__(self):
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self._tqdm = None
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def reset(self):
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self._tqdm = tqdm.tqdm(
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desc="Total progress",
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total=state.job_count * state.sampling_steps,
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position=1,
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file=progress_print_out
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)
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def update(self):
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if not opts.multiple_tqdm:
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return
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if self._tqdm is None:
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self.reset()
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self._tqdm.update()
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def updateTotal(self, new_total):
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if not opts.multiple_tqdm:
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return
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if self._tqdm is None:
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self.reset()
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self._tqdm.total=new_total
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def clear(self):
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if self._tqdm is not None:
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self._tqdm.close()
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self._tqdm = None
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total_tqdm = TotalTQDM()
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mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
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mem_mon.start()
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