diff options
Diffstat (limited to 'modules')
28 files changed, 805 insertions, 420 deletions
diff --git a/modules/api/api.py b/modules/api/api.py index 908c4514..fb2c2ce9 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -330,6 +330,7 @@ class Api: with self.queue_lock: with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p: + p.is_api = True p.scripts = script_runner p.outpath_grids = opts.outdir_txt2img_grids p.outpath_samples = opts.outdir_txt2img_samples @@ -390,6 +391,7 @@ class Api: with self.queue_lock: with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p: p.init_images = [decode_base64_to_image(x) for x in init_images] + p.is_api = True p.scripts = script_runner p.outpath_grids = opts.outdir_img2img_grids p.outpath_samples = opts.outdir_img2img_samples diff --git a/modules/api/models.py b/modules/api/models.py index 800c9b93..6a574771 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -50,10 +50,12 @@ class PydanticModelGenerator: additional_fields = None, ): def field_type_generator(k, v): - # field_type = str if not overrides.get(k) else overrides[k]["type"] - # print(k, v.annotation, v.default) field_type = v.annotation + if field_type == 'Image': + # images are sent as base64 strings via API + field_type = 'str' + return Optional[field_type] def merge_class_params(class_): @@ -63,7 +65,6 @@ class PydanticModelGenerator: parameters = {**parameters, **inspect.signature(classes.__init__).parameters} return parameters - self._model_name = model_name self._class_data = merge_class_params(class_instance) @@ -72,7 +73,7 @@ class PydanticModelGenerator: field=underscore(k), field_alias=k, field_type=field_type_generator(k, v), - field_value=v.default + field_value=None if isinstance(v.default, property) else v.default ) for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED ] diff --git a/modules/img2img.py b/modules/img2img.py index c7bbbac8..ac9fd3f8 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -116,7 +116,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal process_images(p)
-def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_name: str, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
+def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_name: str, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
override_settings = create_override_settings_dict(override_settings_texts)
is_batch = mode == 5
@@ -166,12 +166,6 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s prompt=prompt,
negative_prompt=negative_prompt,
styles=prompt_styles,
- seed=seed,
- subseed=subseed,
- subseed_strength=subseed_strength,
- seed_resize_from_h=seed_resize_from_h,
- seed_resize_from_w=seed_resize_from_w,
- seed_enable_extras=seed_enable_extras,
sampler_name=sampler_name,
batch_size=batch_size,
n_iter=n_iter,
diff --git a/modules/launch_utils.py b/modules/launch_utils.py index 2782872e..444f16fa 100644 --- a/modules/launch_utils.py +++ b/modules/launch_utils.py @@ -173,9 +173,12 @@ def git_clone(url, dir, name, commithash=None): if current_hash == commithash:
return
- run_git('fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}", autofix=False)
+ if run_git(dir, name, 'config --get remote.origin.url', None, f"Couldn't determine {name}'s origin URL", live=False).strip() != url:
+ run_git(dir, name, f'remote set-url origin "{url}"', None, f"Failed to set {name}'s origin URL", live=False)
- run_git('checkout', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
+ run_git(dir, name, 'fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}", autofix=False)
+
+ run_git(dir, name, f'checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
return
@@ -243,7 +246,7 @@ def list_extensions(settings_file): disabled_extensions = set(settings.get('disabled_extensions', []))
disable_all_extensions = settings.get('disable_all_extensions', 'none')
- if disable_all_extensions != 'none':
+ if disable_all_extensions != 'none' or args.disable_extra_extensions or args.disable_all_extensions:
return []
return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]
@@ -318,13 +321,13 @@ def prepare_environment(): blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
- stable_diffusion_xl_commit_hash = os.environ.get('STABLE_DIFFUSION_XL_COMMIT_HASH', "5c10deee76adad0032b412294130090932317a87")
- k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "c9fe758757e022f05ca5a53fa8fac28889e4f1cf")
+ stable_diffusion_xl_commit_hash = os.environ.get('STABLE_DIFFUSION_XL_COMMIT_HASH', "45c443b316737a4ab6e40413d7794a7f5657c19f")
+ k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "ab527a9a6d347f364e3d185ba6d714e22d80cb3c")
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
try:
- # the existance of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
+ # the existence of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
os.remove(os.path.join(script_path, "tmp", "restart"))
os.environ.setdefault('SD_WEBUI_RESTARTING', '1')
except OSError:
diff --git a/modules/mac_specific.py b/modules/mac_specific.py index bce527cc..89256c5b 100644 --- a/modules/mac_specific.py +++ b/modules/mac_specific.py @@ -52,9 +52,6 @@ def cumsum_fix(input, cumsum_func, *args, **kwargs): if has_mps: - # MPS fix for randn in torchsde - CondFunc('torchsde._brownian.brownian_interval._randn', lambda _, size, dtype, device, seed: torch.randn(size, dtype=dtype, device=torch.device("cpu"), generator=torch.Generator(torch.device("cpu")).manual_seed(int(seed))).to(device), lambda _, size, dtype, device, seed: device.type == 'mps') - if platform.mac_ver()[0].startswith("13.2."): # MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124) CondFunc('torch.nn.functional.linear', lambda _, input, weight, bias: (torch.matmul(input, weight.t()) + bias) if bias is not None else torch.matmul(input, weight.t()), lambda _, input, weight, bias: input.numel() > 10485760) diff --git a/modules/postprocessing.py b/modules/postprocessing.py index 136e9c88..cf04d38b 100644 --- a/modules/postprocessing.py +++ b/modules/postprocessing.py @@ -11,37 +11,32 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, shared.state.begin(job="extras")
- image_data = []
- image_names = []
outputs = []
- if extras_mode == 1:
- for img in image_folder:
- if isinstance(img, Image.Image):
- image = img
- fn = ''
- else:
- image = Image.open(os.path.abspath(img.name))
- fn = os.path.splitext(img.orig_name)[0]
- image_data.append(image)
- image_names.append(fn)
- elif extras_mode == 2:
- assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
- assert input_dir, 'input directory not selected'
-
- image_list = shared.listfiles(input_dir)
- for filename in image_list:
- try:
- image = Image.open(filename)
- except Exception:
- continue
- image_data.append(image)
- image_names.append(filename)
- else:
- assert image, 'image not selected'
-
- image_data.append(image)
- image_names.append(None)
+ def get_images(extras_mode, image, image_folder, input_dir):
+ if extras_mode == 1:
+ for img in image_folder:
+ if isinstance(img, Image.Image):
+ image = img
+ fn = ''
+ else:
+ image = Image.open(os.path.abspath(img.name))
+ fn = os.path.splitext(img.orig_name)[0]
+ yield image, fn
+ elif extras_mode == 2:
+ assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
+ assert input_dir, 'input directory not selected'
+
+ image_list = shared.listfiles(input_dir)
+ for filename in image_list:
+ try:
+ image = Image.open(filename)
+ except Exception:
+ continue
+ yield image, filename
+ else:
+ assert image, 'image not selected'
+ yield image, None
if extras_mode == 2 and output_dir != '':
outpath = output_dir
@@ -50,14 +45,16 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, infotext = ''
- for image, name in zip(image_data, image_names):
+ for image_data, name in get_images(extras_mode, image, image_folder, input_dir):
+ image_data: Image.Image
+
shared.state.textinfo = name
- parameters, existing_pnginfo = images.read_info_from_image(image)
+ parameters, existing_pnginfo = images.read_info_from_image(image_data)
if parameters:
existing_pnginfo["parameters"] = parameters
- pp = scripts_postprocessing.PostprocessedImage(image.convert("RGB"))
+ pp = scripts_postprocessing.PostprocessedImage(image_data.convert("RGB"))
scripts.scripts_postproc.run(pp, args)
@@ -78,6 +75,8 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, if extras_mode != 2 or show_extras_results:
outputs.append(pp.image)
+ image_data.close()
+
devices.torch_gc()
return outputs, ui_common.plaintext_to_html(infotext), ''
diff --git a/modules/processing.py b/modules/processing.py index efa6eafa..c983d001 100644..100755 --- a/modules/processing.py +++ b/modules/processing.py @@ -1,9 +1,11 @@ +from __future__ import annotations
import json
import logging
import math
import os
import sys
import hashlib
+from dataclasses import dataclass, field
import torch
import numpy as np
@@ -11,7 +13,7 @@ from PIL import Image, ImageOps import random
import cv2
from skimage import exposure
-from typing import Any, Dict, List
+from typing import Any
import modules.sd_hijack
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
@@ -57,7 +59,7 @@ def apply_color_correction(correction, original_image): image = blendLayers(image, original_image, BlendType.LUMINOSITY)
- return image
+ return image.convert('RGB')
def apply_overlay(image, paste_loc, index, overlays):
@@ -104,97 +106,165 @@ def txt2img_image_conditioning(sd_model, x, width, height): return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
+@dataclass(repr=False)
class StableDiffusionProcessing:
- """
- The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
- """
+ sd_model: object = None
+ outpath_samples: str = None
+ outpath_grids: str = None
+ prompt: str = ""
+ prompt_for_display: str = None
+ negative_prompt: str = ""
+ styles: list[str] = None
+ seed: int = -1
+ subseed: int = -1
+ subseed_strength: float = 0
+ seed_resize_from_h: int = -1
+ seed_resize_from_w: int = -1
+ seed_enable_extras: bool = True
+ sampler_name: str = None
+ batch_size: int = 1
+ n_iter: int = 1
+ steps: int = 50
+ cfg_scale: float = 7.0
+ width: int = 512
+ height: int = 512
+ restore_faces: bool = None
+ tiling: bool = None
+ do_not_save_samples: bool = False
+ do_not_save_grid: bool = False
+ extra_generation_params: dict[str, Any] = None
+ overlay_images: list = None
+ eta: float = None
+ do_not_reload_embeddings: bool = False
+ denoising_strength: float = 0
+ ddim_discretize: str = None
+ s_min_uncond: float = None
+ s_churn: float = None
+ s_tmax: float = None
+ s_tmin: float = None
+ s_noise: float = None
+ override_settings: dict[str, Any] = None
+ override_settings_restore_afterwards: bool = True
+ sampler_index: int = None
+ refiner_checkpoint: str = None
+ refiner_switch_at: float = None
+ token_merging_ratio = 0
+ token_merging_ratio_hr = 0
+ disable_extra_networks: bool = False
+
+ scripts_value: scripts.ScriptRunner = field(default=None, init=False)
+ script_args_value: list = field(default=None, init=False)
+ scripts_setup_complete: bool = field(default=False, init=False)
+
cached_uc = [None, None]
cached_c = [None, None]
- def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = None, tiling: bool = None, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = None, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
- if sampler_index is not None:
+ comments: dict = None
+ sampler: sd_samplers_common.Sampler | None = field(default=None, init=False)
+ is_using_inpainting_conditioning: bool = field(default=False, init=False)
+ paste_to: tuple | None = field(default=None, init=False)
+
+ is_hr_pass: bool = field(default=False, init=False)
+
+ c: tuple = field(default=None, init=False)
+ uc: tuple = field(default=None, init=False)
+
+ rng: rng.ImageRNG | None = field(default=None, init=False)
+ step_multiplier: int = field(default=1, init=False)
+ color_corrections: list = field(default=None, init=False)
+
+ all_prompts: list = field(default=None, init=False)
+ all_negative_prompts: list = field(default=None, init=False)
+ all_seeds: list = field(default=None, init=False)
+ all_subseeds: list = field(default=None, init=False)
+ iteration: int = field(default=0, init=False)
+ main_prompt: str = field(default=None, init=False)
+ main_negative_prompt: str = field(default=None, init=False)
+
+ prompts: list = field(default=None, init=False)
+ negative_prompts: list = field(default=None, init=False)
+ seeds: list = field(default=None, init=False)
+ subseeds: list = field(default=None, init=False)
+ extra_network_data: dict = field(default=None, init=False)
+
+ user: str = field(default=None, init=False)
+
+ sd_model_name: str = field(default=None, init=False)
+ sd_model_hash: str = field(default=None, init=False)
+ sd_vae_name: str = field(default=None, init=False)
+ sd_vae_hash: str = field(default=None, init=False)
+
+ is_api: bool = field(default=False, init=False)
+
+ def __post_init__(self):
+ if self.sampler_index is not None:
print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
- self.outpath_samples: str = outpath_samples
- self.outpath_grids: str = outpath_grids
- self.prompt: str = prompt
- self.prompt_for_display: str = None
- self.negative_prompt: str = (negative_prompt or "")
- self.styles: list = styles or []
- self.seed: int = seed
- self.subseed: int = subseed
- self.subseed_strength: float = subseed_strength
- self.seed_resize_from_h: int = seed_resize_from_h
- self.seed_resize_from_w: int = seed_resize_from_w
- self.sampler_name: str = sampler_name
- self.batch_size: int = batch_size
- self.n_iter: int = n_iter
- self.steps: int = steps
- self.cfg_scale: float = cfg_scale
- self.width: int = width
- self.height: int = height
- self.restore_faces: bool = restore_faces
- self.tiling: bool = tiling
- self.do_not_save_samples: bool = do_not_save_samples
- self.do_not_save_grid: bool = do_not_save_grid
- self.extra_generation_params: dict = extra_generation_params or {}
- self.overlay_images = overlay_images
- self.eta = eta
- self.do_not_reload_embeddings = do_not_reload_embeddings
- self.paste_to = None
- self.color_corrections = None
- self.denoising_strength: float = denoising_strength
+ self.comments = {}
+
+ if self.styles is None:
+ self.styles = []
+
self.sampler_noise_scheduler_override = None
- self.ddim_discretize = ddim_discretize or opts.ddim_discretize
- self.s_min_uncond = s_min_uncond or opts.s_min_uncond
- self.s_churn = s_churn or opts.s_churn
- self.s_tmin = s_tmin or opts.s_tmin
- self.s_tmax = (s_tmax if s_tmax is not None else opts.s_tmax) or float('inf')
- self.s_noise = s_noise if s_noise is not None else opts.s_noise
- self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
- self.override_settings_restore_afterwards = override_settings_restore_afterwards
- self.is_using_inpainting_conditioning = False
- self.disable_extra_networks = False
- self.token_merging_ratio = 0
- self.token_merging_ratio_hr = 0
-
- if not seed_enable_extras:
+ self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond
+ self.s_churn = self.s_churn if self.s_churn is not None else opts.s_churn
+ self.s_tmin = self.s_tmin if self.s_tmin is not None else opts.s_tmin
+ self.s_tmax = (self.s_tmax if self.s_tmax is not None else opts.s_tmax) or float('inf')
+ self.s_noise = self.s_noise if self.s_noise is not None else opts.s_noise
+
+ self.extra_generation_params = self.extra_generation_params or {}
+ self.override_settings = self.override_settings or {}
+ self.script_args = self.script_args or {}
+
+ self.refiner_checkpoint_info = None
+
+ if not self.seed_enable_extras:
self.subseed = -1
self.subseed_strength = 0
self.seed_resize_from_h = 0
self.seed_resize_from_w = 0
- self.scripts = None
- self.script_args = script_args
- self.all_prompts = None
- self.all_negative_prompts = None
- self.all_seeds = None
- self.all_subseeds = None
- self.iteration = 0
- self.is_hr_pass = False
- self.sampler = None
- self.main_prompt = None
- self.main_negative_prompt = None
-
- self.prompts = None
- self.negative_prompts = None
- self.extra_network_data = None
- self.seeds = None
- self.subseeds = None
-
- self.step_multiplier = 1
self.cached_uc = StableDiffusionProcessing.cached_uc
self.cached_c = StableDiffusionProcessing.cached_c
- self.uc = None
- self.c = None
- self.rng: rng.ImageRNG = None
-
- self.user = None
@property
def sd_model(self):
return shared.sd_model
+ @sd_model.setter
+ def sd_model(self, value):
+ pass
+
+ @property
+ def scripts(self):
+ return self.scripts_value
+
+ @scripts.setter
+ def scripts(self, value):
+ self.scripts_value = value
+
+ if self.scripts_value and self.script_args_value and not self.scripts_setup_complete:
+ self.setup_scripts()
+
+ @property
+ def script_args(self):
+ return self.script_args_value
+
+ @script_args.setter
+ def script_args(self, value):
+ self.script_args_value = value
+
+ if self.scripts_value and self.script_args_value and not self.scripts_setup_complete:
+ self.setup_scripts()
+
+ def setup_scripts(self):
+ self.scripts_setup_complete = True
+
+ self.scripts.setup_scrips(self, is_ui=not self.is_api)
+
+ def comment(self, text):
+ self.comments[text] = 1
+
def txt2img_image_conditioning(self, x, width=None, height=None):
self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
@@ -373,9 +443,10 @@ class StableDiffusionProcessing: negative_prompts = prompt_parser.SdConditioning(self.negative_prompts, width=self.width, height=self.height, is_negative_prompt=True)
sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
- self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
- self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)
- self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)
+ total_steps = sampler_config.total_steps(self.steps) if sampler_config else self.steps
+ self.step_multiplier = total_steps // self.steps
+ self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, total_steps, [self.cached_uc], self.extra_network_data)
+ self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, total_steps, [self.cached_c], self.extra_network_data)
def get_conds(self):
return self.c, self.uc
@@ -397,7 +468,7 @@ class Processed: self.subseed = subseed
self.subseed_strength = p.subseed_strength
self.info = info
- self.comments = comments
+ self.comments = "".join(f"{comment}\n" for comment in p.comments)
self.width = p.width
self.height = p.height
self.sampler_name = p.sampler_name
@@ -407,7 +478,10 @@ class Processed: self.batch_size = p.batch_size
self.restore_faces = p.restore_faces
self.face_restoration_model = opts.face_restoration_model if p.restore_faces else None
- self.sd_model_hash = shared.sd_model.sd_model_hash
+ self.sd_model_name = p.sd_model_name
+ self.sd_model_hash = p.sd_model_hash
+ self.sd_vae_name = p.sd_vae_name
+ self.sd_vae_hash = p.sd_vae_hash
self.seed_resize_from_w = p.seed_resize_from_w
self.seed_resize_from_h = p.seed_resize_from_h
self.denoising_strength = getattr(p, 'denoising_strength', None)
@@ -458,7 +532,10 @@ class Processed: "batch_size": self.batch_size,
"restore_faces": self.restore_faces,
"face_restoration_model": self.face_restoration_model,
+ "sd_model_name": self.sd_model_name,
"sd_model_hash": self.sd_model_hash,
+ "sd_vae_name": self.sd_vae_name,
+ "sd_vae_hash": self.sd_vae_hash,
"seed_resize_from_w": self.seed_resize_from_w,
"seed_resize_from_h": self.seed_resize_from_h,
"denoising_strength": self.denoising_strength,
@@ -577,10 +654,10 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Seed": p.all_seeds[0] if use_main_prompt else all_seeds[index],
"Face restoration": opts.face_restoration_model if p.restore_faces else None,
"Size": f"{p.width}x{p.height}",
- "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
- "Model": (None if not opts.add_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),
- "VAE hash": sd_vae.get_loaded_vae_hash() if opts.add_model_hash_to_info else None,
- "VAE": sd_vae.get_loaded_vae_name() if opts.add_model_name_to_info else None,
+ "Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
+ "Model": p.sd_model_name if opts.add_model_name_to_info else None,
+ "VAE hash": p.sd_vae_hash if opts.add_model_hash_to_info else None,
+ "VAE": p.sd_vae_name if opts.add_model_name_to_info else None,
"Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
@@ -669,11 +746,19 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if p.tiling is None:
p.tiling = opts.tiling
+ if p.refiner_checkpoint not in (None, "", "None"):
+ p.refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(p.refiner_checkpoint)
+ if p.refiner_checkpoint_info is None:
+ raise Exception(f'Could not find checkpoint with name {p.refiner_checkpoint}')
+
+ p.sd_model_name = shared.sd_model.sd_checkpoint_info.name_for_extra
+ p.sd_model_hash = shared.sd_model.sd_model_hash
+ p.sd_vae_name = sd_vae.get_loaded_vae_name()
+ p.sd_vae_hash = sd_vae.get_loaded_vae_hash()
+
modules.sd_hijack.model_hijack.apply_circular(p.tiling)
modules.sd_hijack.model_hijack.clear_comments()
- comments = {}
-
p.setup_prompts()
if type(seed) == list:
@@ -753,7 +838,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: p.setup_conds()
for comment in model_hijack.comments:
- comments[comment] = 1
+ p.comment(comment)
p.extra_generation_params.update(model_hijack.extra_generation_params)
@@ -882,7 +967,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: images_list=output_images,
seed=p.all_seeds[0],
info=infotexts[0],
- comments="".join(f"{comment}\n" for comment in comments),
subseed=p.all_subseeds[0],
index_of_first_image=index_of_first_image,
infotexts=infotexts,
@@ -906,49 +990,51 @@ def old_hires_fix_first_pass_dimensions(width, height): return width, height
+@dataclass(repr=False)
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
- sampler = None
+ enable_hr: bool = False
+ denoising_strength: float = 0.75
+ firstphase_width: int = 0
+ firstphase_height: int = 0
+ hr_scale: float = 2.0
+ hr_upscaler: str = None
+ hr_second_pass_steps: int = 0
+ hr_resize_x: int = 0
+ hr_resize_y: int = 0
+ hr_checkpoint_name: str = None
+ hr_sampler_name: str = None
+ hr_prompt: str = ''
+ hr_negative_prompt: str = ''
+
cached_hr_uc = [None, None]
cached_hr_c = [None, None]
- def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_checkpoint_name: str = None, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
- super().__init__(**kwargs)
- self.enable_hr = enable_hr
- self.denoising_strength = denoising_strength
- self.hr_scale = hr_scale
- self.hr_upscaler = hr_upscaler
- self.hr_second_pass_steps = hr_second_pass_steps
- self.hr_resize_x = hr_resize_x
- self.hr_resize_y = hr_resize_y
- self.hr_upscale_to_x = hr_resize_x
- self.hr_upscale_to_y = hr_resize_y
- self.hr_checkpoint_name = hr_checkpoint_name
- self.hr_checkpoint_info = None
- self.hr_sampler_name = hr_sampler_name
- self.hr_prompt = hr_prompt
- self.hr_negative_prompt = hr_negative_prompt
- self.all_hr_prompts = None
- self.all_hr_negative_prompts = None
- self.latent_scale_mode = None
-
- if firstphase_width != 0 or firstphase_height != 0:
+ hr_checkpoint_info: dict = field(default=None, init=False)
+ hr_upscale_to_x: int = field(default=0, init=False)
+ hr_upscale_to_y: int = field(default=0, init=False)
+ truncate_x: int = field(default=0, init=False)
+ truncate_y: int = field(default=0, init=False)
+ applied_old_hires_behavior_to: tuple = field(default=None, init=False)
+ latent_scale_mode: dict = field(default=None, init=False)
+ hr_c: tuple | None = field(default=None, init=False)
+ hr_uc: tuple | None = field(default=None, init=False)
+ all_hr_prompts: list = field(default=None, init=False)
+ all_hr_negative_prompts: list = field(default=None, init=False)
+ hr_prompts: list = field(default=None, init=False)
+ hr_negative_prompts: list = field(default=None, init=False)
+ hr_extra_network_data: list = field(default=None, init=False)
+
+ def __post_init__(self):
+ super().__post_init__()
+
+ if self.firstphase_width != 0 or self.firstphase_height != 0:
self.hr_upscale_to_x = self.width
self.hr_upscale_to_y = self.height
- self.width = firstphase_width
- self.height = firstphase_height
-
- self.truncate_x = 0
- self.truncate_y = 0
- self.applied_old_hires_behavior_to = None
-
- self.hr_prompts = None
- self.hr_negative_prompts = None
- self.hr_extra_network_data = None
+ self.width = self.firstphase_width
+ self.height = self.firstphase_height
self.cached_hr_uc = StableDiffusionProcessingTxt2Img.cached_hr_uc
self.cached_hr_c = StableDiffusionProcessingTxt2Img.cached_hr_c
- self.hr_c = None
- self.hr_uc = None
def calculate_target_resolution(self):
if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
@@ -1142,6 +1228,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
+ self.sampler = None
+ devices.torch_gc()
+
decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
self.is_hr_pass = False
@@ -1188,10 +1277,20 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): hr_prompts = prompt_parser.SdConditioning(self.hr_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y)
hr_negative_prompts = prompt_parser.SdConditioning(self.hr_negative_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y, is_negative_prompt=True)
- self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
- self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
+ sampler_config = sd_samplers.find_sampler_config(self.hr_sampler_name or self.sampler_name)
+ steps = self.hr_second_pass_steps or self.steps
+ total_steps = sampler_config.total_steps(steps) if sampler_config else steps
+
+ self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, total_steps, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
+ self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, total_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
def setup_conds(self):
+ if self.is_hr_pass:
+ # if we are in hr pass right now, the call is being made from the refiner, and we don't need to setup firstpass cons or switch model
+ self.hr_c = None
+ self.calculate_hr_conds()
+ return
+
super().setup_conds()
self.hr_uc = None
@@ -1216,7 +1315,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): return super().get_conds()
-
def parse_extra_network_prompts(self):
res = super().parse_extra_network_prompts()
@@ -1229,35 +1327,53 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): return res
+@dataclass(repr=False)
class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
- sampler = None
-
- def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = None, mask_blur_x: int = 4, mask_blur_y: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
- super().__init__(**kwargs)
-
- self.init_images = init_images
- self.resize_mode: int = resize_mode
- self.denoising_strength: float = denoising_strength
- self.image_cfg_scale: float = image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
- self.init_latent = None
- self.image_mask = mask
- self.latent_mask = None
- self.mask_for_overlay = None
- if mask_blur is not None:
- mask_blur_x = mask_blur
- mask_blur_y = mask_blur
- self.mask_blur_x = mask_blur_x
- self.mask_blur_y = mask_blur_y
- self.inpainting_fill = inpainting_fill
- self.inpaint_full_res = inpaint_full_res
- self.inpaint_full_res_padding = inpaint_full_res_padding
- self.inpainting_mask_invert = inpainting_mask_invert
- self.initial_noise_multiplier = opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier
+ init_images: list = None
+ resize_mode: int = 0
+ denoising_strength: float = 0.75
+ image_cfg_scale: float = None
+ mask: Any = None
+ mask_blur_x: int = 4
+ mask_blur_y: int = 4
+ mask_blur: int = None
+ inpainting_fill: int = 0
+ inpaint_full_res: bool = True
+ inpaint_full_res_padding: int = 0
+ inpainting_mask_invert: int = 0
+ initial_noise_multiplier: float = None
+ latent_mask: Image = None
+
+ image_mask: Any = field(default=None, init=False)
+
+ nmask: torch.Tensor = field(default=None, init=False)
+ image_conditioning: torch.Tensor = field(default=None, init=False)
+ init_img_hash: str = field(default=None, init=False)
+ mask_for_overlay: Image = field(default=None, init=False)
+ init_latent: torch.Tensor = field(default=None, init=False)
+
+ def __post_init__(self):
+ super().__post_init__()
+
+ self.image_mask = self.mask
self.mask = None
- self.nmask = None
- self.image_conditioning = None
+ self.initial_noise_multiplier = opts.initial_noise_multiplier if self.initial_noise_multiplier is None else self.initial_noise_multiplier
+
+ @property
+ def mask_blur(self):
+ if self.mask_blur_x == self.mask_blur_y:
+ return self.mask_blur_x
+ return None
+
+ @mask_blur.setter
+ def mask_blur(self, value):
+ if isinstance(value, int):
+ self.mask_blur_x = value
+ self.mask_blur_y = value
def init(self, all_prompts, all_seeds, all_subseeds):
+ self.image_cfg_scale: float = self.image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
+
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
crop_region = None
@@ -1271,13 +1387,13 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): if self.mask_blur_x > 0:
np_mask = np.array(image_mask)
- kernel_size = 2 * int(4 * self.mask_blur_x + 0.5) + 1
+ kernel_size = 2 * int(2.5 * self.mask_blur_x + 0.5) + 1
np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), self.mask_blur_x)
image_mask = Image.fromarray(np_mask)
if self.mask_blur_y > 0:
np_mask = np.array(image_mask)
- kernel_size = 2 * int(4 * self.mask_blur_y + 0.5) + 1
+ kernel_size = 2 * int(2.5 * self.mask_blur_y + 0.5) + 1
np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur_y)
image_mask = Image.fromarray(np_mask)
@@ -1383,7 +1499,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): elif self.inpainting_fill == 3:
self.init_latent = self.init_latent * self.mask
- self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, image_mask)
+ self.image_conditioning = self.img2img_image_conditioning(image * 2 - 1, self.init_latent, image_mask)
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
x = self.rng.next()
diff --git a/modules/processing_scripts/refiner.py b/modules/processing_scripts/refiner.py new file mode 100644 index 00000000..b389c4ef --- /dev/null +++ b/modules/processing_scripts/refiner.py @@ -0,0 +1,49 @@ +import gradio as gr
+
+from modules import scripts, sd_models
+from modules.ui_common import create_refresh_button
+from modules.ui_components import InputAccordion
+
+
+class ScriptRefiner(scripts.ScriptBuiltinUI):
+ section = "accordions"
+ create_group = False
+
+ def __init__(self):
+ pass
+
+ def title(self):
+ return "Refiner"
+
+ def show(self, is_img2img):
+ return scripts.AlwaysVisible
+
+ def ui(self, is_img2img):
+ with InputAccordion(False, label="Refiner", elem_id=self.elem_id("enable")) as enable_refiner:
+ with gr.Row():
+ refiner_checkpoint = gr.Dropdown(label='Checkpoint', elem_id=self.elem_id("checkpoint"), choices=sd_models.checkpoint_tiles(), value='', tooltip="switch to another model in the middle of generation")
+ create_refresh_button(refiner_checkpoint, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, self.elem_id("checkpoint_refresh"))
+
+ refiner_switch_at = gr.Slider(value=0.8, label="Switch at", minimum=0.01, maximum=1.0, step=0.01, elem_id=self.elem_id("switch_at"), tooltip="fraction of sampling steps when the switch to refiner model should happen; 1=never, 0.5=switch in the middle of generation")
+
+ def lookup_checkpoint(title):
+ info = sd_models.get_closet_checkpoint_match(title)
+ return None if info is None else info.title
+
+ self.infotext_fields = [
+ (enable_refiner, lambda d: 'Refiner' in d),
+ (refiner_checkpoint, lambda d: lookup_checkpoint(d.get('Refiner'))),
+ (refiner_switch_at, 'Refiner switch at'),
+ ]
+
+ return enable_refiner, refiner_checkpoint, refiner_switch_at
+
+ def setup(self, p, enable_refiner, refiner_checkpoint, refiner_switch_at):
+ # the actual implementation is in sd_samplers_common.py, apply_refiner
+
+ if not enable_refiner or refiner_checkpoint in (None, "", "None"):
+ p.refiner_checkpoint_info = None
+ p.refiner_switch_at = None
+ else:
+ p.refiner_checkpoint = refiner_checkpoint
+ p.refiner_switch_at = refiner_switch_at
diff --git a/modules/processing_scripts/seed.py b/modules/processing_scripts/seed.py new file mode 100644 index 00000000..6b6ff987 --- /dev/null +++ b/modules/processing_scripts/seed.py @@ -0,0 +1,111 @@ +import json
+
+import gradio as gr
+
+from modules import scripts, ui, errors
+from modules.shared import cmd_opts
+from modules.ui_components import ToolButton
+
+
+class ScriptSeed(scripts.ScriptBuiltinUI):
+ section = "seed"
+ create_group = False
+
+ def __init__(self):
+ self.seed = None
+ self.reuse_seed = None
+ self.reuse_subseed = None
+
+ def title(self):
+ return "Seed"
+
+ def show(self, is_img2img):
+ return scripts.AlwaysVisible
+
+ def ui(self, is_img2img):
+ with gr.Row(elem_id=self.elem_id("seed_row")):
+ if cmd_opts.use_textbox_seed:
+ self.seed = gr.Textbox(label='Seed', value="", elem_id=self.elem_id("seed"), min_width=100)
+ else:
+ self.seed = gr.Number(label='Seed', value=-1, elem_id=self.elem_id("seed"), min_width=100, precision=0)
+
+ random_seed = ToolButton(ui.random_symbol, elem_id=self.elem_id("random_seed"), label='Random seed')
+ reuse_seed = ToolButton(ui.reuse_symbol, elem_id=self.elem_id("reuse_seed"), label='Reuse seed')
+
+ seed_checkbox = gr.Checkbox(label='Extra', elem_id=self.elem_id("subseed_show"), value=False)
+
+ with gr.Group(visible=False, elem_id=self.elem_id("seed_extras")) as seed_extras:
+ with gr.Row(elem_id=self.elem_id("subseed_row")):
+ subseed = gr.Number(label='Variation seed', value=-1, elem_id=self.elem_id("subseed"), precision=0)
+ random_subseed = ToolButton(ui.random_symbol, elem_id=self.elem_id("random_subseed"))
+ reuse_subseed = ToolButton(ui.reuse_symbol, elem_id=self.elem_id("reuse_subseed"))
+ subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=self.elem_id("subseed_strength"))
+
+ with gr.Row(elem_id=self.elem_id("seed_resize_from_row")):
+ seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=self.elem_id("seed_resize_from_w"))
+ seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=self.elem_id("seed_resize_from_h"))
+
+ random_seed.click(fn=None, _js="function(){setRandomSeed('" + self.elem_id("seed") + "')}", show_progress=False, inputs=[], outputs=[])
+ random_subseed.click(fn=None, _js="function(){setRandomSeed('" + self.elem_id("subseed") + "')}", show_progress=False, inputs=[], outputs=[])
+
+ seed_checkbox.change(lambda x: gr.update(visible=x), show_progress=False, inputs=[seed_checkbox], outputs=[seed_extras])
+
+ self.infotext_fields = [
+ (self.seed, "Seed"),
+ (seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
+ (subseed, "Variation seed"),
+ (subseed_strength, "Variation seed strength"),
+ (seed_resize_from_w, "Seed resize from-1"),
+ (seed_resize_from_h, "Seed resize from-2"),
+ ]
+
+ self.on_after_component(lambda x: connect_reuse_seed(self.seed, reuse_seed, x.component, False), elem_id=f'generation_info_{self.tabname}')
+ self.on_after_component(lambda x: connect_reuse_seed(subseed, reuse_subseed, x.component, True), elem_id=f'generation_info_{self.tabname}')
+
+ return self.seed, seed_checkbox, subseed, subseed_strength, seed_resize_from_w, seed_resize_from_h
+
+ def setup(self, p, seed, seed_checkbox, subseed, subseed_strength, seed_resize_from_w, seed_resize_from_h):
+ p.seed = seed
+
+ if seed_checkbox and subseed_strength > 0:
+ p.subseed = subseed
+ p.subseed_strength = subseed_strength
+
+ if seed_checkbox and seed_resize_from_w > 0 and seed_resize_from_h > 0:
+ p.seed_resize_from_w = seed_resize_from_w
+ p.seed_resize_from_h = seed_resize_from_h
+
+
+
+def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, is_subseed):
+ """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
+ (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
+ was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
+
+ def copy_seed(gen_info_string: str, index):
+ res = -1
+
+ try:
+ gen_info = json.loads(gen_info_string)
+ index -= gen_info.get('index_of_first_image', 0)
+
+ if is_subseed and gen_info.get('subseed_strength', 0) > 0:
+ all_subseeds = gen_info.get('all_subseeds', [-1])
+ res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
+ else:
+ all_seeds = gen_info.get('all_seeds', [-1])
+ res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
+
+ except json.decoder.JSONDecodeError:
+ if gen_info_string:
+ errors.report(f"Error parsing JSON generation info: {gen_info_string}")
+
+ return [res, gr.update()]
+
+ reuse_seed.click(
+ fn=copy_seed,
+ _js="(x, y) => [x, selected_gallery_index()]",
+ show_progress=False,
+ inputs=[generation_info, seed],
+ outputs=[seed, seed]
+ )
diff --git a/modules/scripts.py b/modules/scripts.py index f7d060aa..fcab5d3a 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -3,6 +3,7 @@ import re import sys
import inspect
from collections import namedtuple
+from dataclasses import dataclass
import gradio as gr
@@ -21,6 +22,11 @@ class PostprocessBatchListArgs: self.images = images
+@dataclass
+class OnComponent:
+ component: gr.blocks.Block
+
+
class Script:
name = None
"""script's internal name derived from title"""
@@ -35,9 +41,13 @@ class Script: is_txt2img = False
is_img2img = False
+ tabname = None
group = None
- """A gr.Group component that has all script's UI inside it"""
+ """A gr.Group component that has all script's UI inside it."""
+
+ create_group = True
+ """If False, for alwayson scripts, a group component will not be created."""
infotext_fields = None
"""if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
@@ -52,6 +62,15 @@ class Script: api_info = None
"""Generated value of type modules.api.models.ScriptInfo with information about the script for API"""
+ on_before_component_elem_id = None
+ """list of callbacks to be called before a component with an elem_id is created"""
+
+ on_after_component_elem_id = None
+ """list of callbacks to be called after a component with an elem_id is created"""
+
+ setup_for_ui_only = False
+ """If true, the script setup will only be run in Gradio UI, not in API"""
+
def title(self):
"""this function should return the title of the script. This is what will be displayed in the dropdown menu."""
@@ -90,9 +109,16 @@ class Script: pass
+ def setup(self, p, *args):
+ """For AlwaysVisible scripts, this function is called when the processing object is set up, before any processing starts.
+ args contains all values returned by components from ui().
+ """
+ pass
+
+
def before_process(self, p, *args):
"""
- This function is called very early before processing begins for AlwaysVisible scripts.
+ This function is called very early during processing begins for AlwaysVisible scripts.
You can modify the processing object (p) here, inject hooks, etc.
args contains all values returned by components from ui()
"""
@@ -212,6 +238,29 @@ class Script: pass
+ def on_before_component(self, callback, *, elem_id):
+ """
+ Calls callback before a component is created. The callback function is called with a single argument of type OnComponent.
+
+ May be called in show() or ui() - but it may be too late in latter as some components may already be created.
+
+ This function is an alternative to before_component in that it also cllows to run before a component is created, but
+ it doesn't require to be called for every created component - just for the one you need.
+ """
+ if self.on_before_component_elem_id is None:
+ self.on_before_component_elem_id = []
+
+ self.on_before_component_elem_id.append((elem_id, callback))
+
+ def on_after_component(self, callback, *, elem_id):
+ """
+ Calls callback after a component is created. The callback function is called with a single argument of type OnComponent.
+ """
+ if self.on_after_component_elem_id is None:
+ self.on_after_component_elem_id = []
+
+ self.on_after_component_elem_id.append((elem_id, callback))
+
def describe(self):
"""unused"""
return ""
@@ -232,6 +281,19 @@ class Script: """
pass
+
+class ScriptBuiltinUI(Script):
+ setup_for_ui_only = True
+
+ def elem_id(self, item_id):
+ """helper function to generate id for a HTML element, constructs final id out of tab and user-supplied item_id"""
+
+ need_tabname = self.show(True) == self.show(False)
+ tabname = ('img2img' if self.is_img2img else 'txt2txt') + "_" if need_tabname else ""
+
+ return f'{tabname}{item_id}'
+
+
current_basedir = paths.script_path
@@ -250,7 +312,7 @@ postprocessing_scripts_data = [] ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])
-def list_scripts(scriptdirname, extension):
+def list_scripts(scriptdirname, extension, *, include_extensions=True):
scripts_list = []
basedir = os.path.join(paths.script_path, scriptdirname)
@@ -258,8 +320,9 @@ def list_scripts(scriptdirname, extension): for filename in sorted(os.listdir(basedir)):
scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))
- for ext in extensions.active():
- scripts_list += ext.list_files(scriptdirname, extension)
+ if include_extensions:
+ for ext in extensions.active():
+ scripts_list += ext.list_files(scriptdirname, extension)
scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
@@ -288,7 +351,7 @@ def load_scripts(): postprocessing_scripts_data.clear()
script_callbacks.clear_callbacks()
- scripts_list = list_scripts("scripts", ".py")
+ scripts_list = list_scripts("scripts", ".py") + list_scripts("modules/processing_scripts", ".py", include_extensions=False)
syspath = sys.path
@@ -349,10 +412,17 @@ class ScriptRunner: self.selectable_scripts = []
self.alwayson_scripts = []
self.titles = []
+ self.title_map = {}
self.infotext_fields = []
self.paste_field_names = []
self.inputs = [None]
+ self.on_before_component_elem_id = {}
+ """dict of callbacks to be called before an element is created; key=elem_id, value=list of callbacks"""
+
+ self.on_after_component_elem_id = {}
+ """dict of callbacks to be called after an element is created; key=elem_id, value=list of callbacks"""
+
def initialize_scripts(self, is_img2img):
from modules import scripts_auto_postprocessing
@@ -367,6 +437,7 @@ class ScriptRunner: script.filename = script_data.path
script.is_txt2img = not is_img2img
script.is_img2img = is_img2img
+ script.tabname = "img2img" if is_img2img else "txt2img"
visibility = script.show(script.is_img2img)
@@ -379,6 +450,28 @@ class ScriptRunner: self.scripts.append(script)
self.selectable_scripts.append(script)
+ self.apply_on_before_component_callbacks()
+
+ def apply_on_before_component_callbacks(self):
+ for script in self.scripts:
+ on_before = script.on_before_component_elem_id or []
+ on_after = script.on_after_component_elem_id or []
+
+ for elem_id, callback in on_before:
+ if elem_id not in self.on_before_component_elem_id:
+ self.on_before_component_elem_id[elem_id] = []
+
+ self.on_before_component_elem_id[elem_id].append((callback, script))
+
+ for elem_id, callback in on_after:
+ if elem_id not in self.on_after_component_elem_id:
+ self.on_after_component_elem_id[elem_id] = []
+
+ self.on_after_component_elem_id[elem_id].append((callback, script))
+
+ on_before.clear()
+ on_after.clear()
+
def create_script_ui(self, script):
import modules.api.models as api_models
@@ -429,15 +522,20 @@ class ScriptRunner: if script.alwayson and script.section != section:
continue
- with gr.Group(visible=script.alwayson) as group:
- self.create_script_ui(script)
+ if script.create_group:
+ with gr.Group(visible=script.alwayson) as group:
+ self.create_script_ui(script)
- script.group = group
+ script.group = group
+ else:
+ self.create_script_ui(script)
def prepare_ui(self):
self.inputs = [None]
def setup_ui(self):
+ all_titles = [wrap_call(script.title, script.filename, "title") or script.filename for script in self.scripts]
+ self.title_map = {title.lower(): script for title, script in zip(all_titles, self.scripts)}
self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
self.setup_ui_for_section(None)
@@ -484,6 +582,8 @@ class ScriptRunner: self.infotext_fields.append((dropdown, lambda x: gr.update(value=x.get('Script', 'None'))))
self.infotext_fields.extend([(script.group, onload_script_visibility) for script in self.selectable_scripts])
+ self.apply_on_before_component_callbacks()
+
return self.inputs
def run(self, p, *args):
@@ -577,6 +677,12 @@ class ScriptRunner: errors.report(f"Error running postprocess_image: {script.filename}", exc_info=True)
def before_component(self, component, **kwargs):
+ for callback, script in self.on_before_component_elem_id.get(kwargs.get("elem_id"), []):
+ try:
+ callback(OnComponent(component=component))
+ except Exception:
+ errors.report(f"Error running on_before_component: {script.filename}", exc_info=True)
+
for script in self.scripts:
try:
script.before_component(component, **kwargs)
@@ -584,12 +690,21 @@ class ScriptRunner: errors.report(f"Error running before_component: {script.filename}", exc_info=True)
def after_component(self, component, **kwargs):
+ for callback, script in self.on_after_component_elem_id.get(component.elem_id, []):
+ try:
+ callback(OnComponent(component=component))
+ except Exception:
+ errors.report(f"Error running on_after_component: {script.filename}", exc_info=True)
+
for script in self.scripts:
try:
script.after_component(component, **kwargs)
except Exception:
errors.report(f"Error running after_component: {script.filename}", exc_info=True)
+ def script(self, title):
+ return self.title_map.get(title.lower())
+
def reload_sources(self, cache):
for si, script in list(enumerate(self.scripts)):
args_from = script.args_from
@@ -608,7 +723,6 @@ class ScriptRunner: self.scripts[si].args_from = args_from
self.scripts[si].args_to = args_to
-
def before_hr(self, p):
for script in self.alwayson_scripts:
try:
@@ -617,6 +731,17 @@ class ScriptRunner: except Exception:
errors.report(f"Error running before_hr: {script.filename}", exc_info=True)
+ def setup_scrips(self, p, *, is_ui=True):
+ for script in self.alwayson_scripts:
+ if not is_ui and script.setup_for_ui_only:
+ continue
+
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.setup(p, *script_args)
+ except Exception:
+ errors.report(f"Error running setup: {script.filename}", exc_info=True)
+
scripts_txt2img: ScriptRunner = None
scripts_img2img: ScriptRunner = None
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 0e810eec..7f9e328d 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -1,6 +1,7 @@ from __future__ import annotations
import math
import psutil
+import platform
import torch
from torch import einsum
@@ -94,7 +95,10 @@ class SdOptimizationSdp(SdOptimizationSdpNoMem): class SdOptimizationSubQuad(SdOptimization):
name = "sub-quadratic"
cmd_opt = "opt_sub_quad_attention"
- priority = 10
+
+ @property
+ def priority(self):
+ return 1000 if shared.device.type == 'mps' else 10
def apply(self):
ldm.modules.attention.CrossAttention.forward = sub_quad_attention_forward
@@ -120,7 +124,7 @@ class SdOptimizationInvokeAI(SdOptimization): @property
def priority(self):
- return 1000 if not torch.cuda.is_available() else 10
+ return 1000 if shared.device.type != 'mps' and not torch.cuda.is_available() else 10
def apply(self):
ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI
@@ -427,7 +431,10 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_ qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
if chunk_threshold is None:
- chunk_threshold_bytes = int(get_available_vram() * 0.9) if q.device.type == 'mps' else int(get_available_vram() * 0.7)
+ if q.device.type == 'mps':
+ chunk_threshold_bytes = 268435456 * (2 if platform.processor() == 'i386' else bytes_per_token)
+ else:
+ chunk_threshold_bytes = int(get_available_vram() * 0.7)
elif chunk_threshold == 0:
chunk_threshold_bytes = None
else:
diff --git a/modules/sd_models.py b/modules/sd_models.py index a178adca..f6fbdcd6 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -147,6 +147,9 @@ re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$") def get_closet_checkpoint_match(search_string):
+ if not search_string:
+ return None
+
checkpoint_info = checkpoint_aliases.get(search_string, None)
if checkpoint_info is not None:
return checkpoint_info
diff --git a/modules/sd_samplers_cfg_denoiser.py b/modules/sd_samplers_cfg_denoiser.py index a532e013..bc9b97e4 100644 --- a/modules/sd_samplers_cfg_denoiser.py +++ b/modules/sd_samplers_cfg_denoiser.py @@ -45,18 +45,23 @@ class CFGDenoiser(torch.nn.Module): self.nmask = None
self.init_latent = None
self.steps = None
+ """number of steps as specified by user in UI"""
+
+ self.total_steps = None
+ """expected number of calls to denoiser calculated from self.steps and specifics of the selected sampler"""
+
self.step = 0
self.image_cfg_scale = None
self.padded_cond_uncond = False
self.sampler = sampler
self.model_wrap = None
self.p = None
+ self.mask_before_denoising = False
@property
def inner_model(self):
raise NotImplementedError()
-
def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
denoised_uncond = x_out[-uncond.shape[0]:]
denoised = torch.clone(denoised_uncond)
@@ -100,7 +105,7 @@ class CFGDenoiser(torch.nn.Module): assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
- if self.mask is not None:
+ if self.mask_before_denoising and self.mask is not None:
x = self.init_latent * self.mask + self.nmask * x
batch_size = len(conds_list)
@@ -202,6 +207,9 @@ class CFGDenoiser(torch.nn.Module): else:
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
+ if not self.mask_before_denoising and self.mask is not None:
+ denoised = self.init_latent * self.mask + self.nmask * denoised
+
self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
if opts.live_preview_content == "Prompt":
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index 35c4d657..8886de7e 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -7,7 +7,16 @@ from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, s from modules.shared import opts, state
import k_diffusion.sampling
-SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
+
+SamplerDataTuple = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
+
+
+class SamplerData(SamplerDataTuple):
+ def total_steps(self, steps):
+ if self.options.get("second_order", False):
+ steps = steps * 2
+
+ return steps
def setup_img2img_steps(p, steps=None):
@@ -83,7 +92,15 @@ def images_tensor_to_samples(image, approximation=None, model=None): model = shared.sd_model
image = image.to(shared.device, dtype=devices.dtype_vae)
image = image * 2 - 1
- x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
+ if len(image) > 1:
+ x_latent = torch.stack([
+ model.get_first_stage_encoding(
+ model.encode_first_stage(torch.unsqueeze(img, 0))
+ )[0]
+ for img in image
+ ])
+ else:
+ x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
return x_latent
@@ -131,31 +148,29 @@ def replace_torchsde_browinan(): replace_torchsde_browinan()
-def apply_refiner(sampler):
- completed_ratio = sampler.step / sampler.steps
+def apply_refiner(cfg_denoiser):
+ completed_ratio = cfg_denoiser.step / cfg_denoiser.total_steps
+ refiner_switch_at = cfg_denoiser.p.refiner_switch_at
+ refiner_checkpoint_info = cfg_denoiser.p.refiner_checkpoint_info
- if completed_ratio <= shared.opts.sd_refiner_switch_at:
+ if refiner_switch_at is not None and completed_ratio < refiner_switch_at:
return False
- if shared.opts.sd_refiner_checkpoint == "None":
+ if refiner_checkpoint_info is None or shared.sd_model.sd_checkpoint_info == refiner_checkpoint_info:
return False
- if shared.sd_model.sd_checkpoint_info.title == shared.opts.sd_refiner_checkpoint:
+ if getattr(cfg_denoiser.p, "enable_hr", False) and not cfg_denoiser.p.is_hr_pass:
return False
- refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(shared.opts.sd_refiner_checkpoint)
- if refiner_checkpoint_info is None:
- raise Exception(f'Could not find checkpoint with name {shared.opts.sd_refiner_checkpoint}')
-
- sampler.p.extra_generation_params['Refiner'] = refiner_checkpoint_info.short_title
- sampler.p.extra_generation_params['Refiner switch at'] = shared.opts.sd_refiner_switch_at
+ cfg_denoiser.p.extra_generation_params['Refiner'] = refiner_checkpoint_info.short_title
+ cfg_denoiser.p.extra_generation_params['Refiner switch at'] = refiner_switch_at
with sd_models.SkipWritingToConfig():
sd_models.reload_model_weights(info=refiner_checkpoint_info)
devices.torch_gc()
- sampler.p.setup_conds()
- sampler.update_inner_model()
+ cfg_denoiser.p.setup_conds()
+ cfg_denoiser.update_inner_model()
return True
@@ -192,7 +207,7 @@ class Sampler: self.sampler_noises = None
self.stop_at = None
self.eta = None
- self.config = None # set by the function calling the constructor
+ self.config: SamplerData = None # set by the function calling the constructor
self.last_latent = None
self.s_min_uncond = None
self.s_churn = 0.0
@@ -202,12 +217,14 @@ class Sampler: self.eta_option_field = 'eta_ancestral'
self.eta_infotext_field = 'Eta'
+ self.eta_default = 1.0
self.conditioning_key = shared.sd_model.model.conditioning_key
self.p = None
self.model_wrap_cfg = None
self.sampler_extra_args = None
+ self.options = {}
def callback_state(self, d):
step = d['i']
@@ -220,6 +237,7 @@ class Sampler: def launch_sampling(self, steps, func):
self.model_wrap_cfg.steps = steps
+ self.model_wrap_cfg.total_steps = self.config.total_steps(steps)
state.sampling_steps = steps
state.sampling_step = 0
@@ -256,7 +274,7 @@ class Sampler: extra_params_kwargs[param_name] = getattr(p, param_name)
if 'eta' in inspect.signature(self.func).parameters:
- if self.eta != 1.0:
+ if self.eta != self.eta_default:
p.extra_generation_params[self.eta_infotext_field] = self.eta
extra_params_kwargs['eta'] = self.eta
@@ -267,19 +285,19 @@ class Sampler: s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
s_noise = getattr(opts, 's_noise', p.s_noise)
- if s_churn != self.s_churn:
+ if 's_churn' in extra_params_kwargs and s_churn != self.s_churn:
extra_params_kwargs['s_churn'] = s_churn
p.s_churn = s_churn
p.extra_generation_params['Sigma churn'] = s_churn
- if s_tmin != self.s_tmin:
+ if 's_tmin' in extra_params_kwargs and s_tmin != self.s_tmin:
extra_params_kwargs['s_tmin'] = s_tmin
p.s_tmin = s_tmin
p.extra_generation_params['Sigma tmin'] = s_tmin
- if s_tmax != self.s_tmax:
+ if 's_tmax' in extra_params_kwargs and s_tmax != self.s_tmax:
extra_params_kwargs['s_tmax'] = s_tmax
p.s_tmax = s_tmax
p.extra_generation_params['Sigma tmax'] = s_tmax
- if s_noise != self.s_noise:
+ if 's_noise' in extra_params_kwargs and s_noise != self.s_noise:
extra_params_kwargs['s_noise'] = s_noise
p.s_noise = s_noise
p.extra_generation_params['Sigma noise'] = s_noise
@@ -296,5 +314,8 @@ class Sampler: current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
+ def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ raise NotImplementedError()
-
+ def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ raise NotImplementedError()
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index d10fe12e..08b9e740 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -16,19 +16,25 @@ samplers_k_diffusion = [ ('Euler', 'sample_euler', ['k_euler'], {}),
('LMS', 'sample_lms', ['k_lms'], {}),
('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
- ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
- ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
+ ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True, "second_order": True}),
+ ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
+ ('DPM++ 2M SDE Heun', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun'], {"brownian_noise": True, "solver_type": "heun"}),
+ ('DPM++ 2M SDE Heun Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_ka'], {'scheduler': 'karras', "brownian_noise": True, "solver_type": "heun"}),
+ ('DPM++ 2M SDE Heun Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_exp'], {'scheduler': 'exponential', "brownian_noise": True, "solver_type": "heun"}),
+ ('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde'], {'discard_next_to_last_sigma': True, "brownian_noise": True}),
+ ('DPM++ 3M SDE Karras', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
+ ('DPM++ 3M SDE Exponential', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_exp'], {'scheduler': 'exponential', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
- ('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras'}),
+ ('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras', "second_order": True}),
]
@@ -42,6 +48,12 @@ sampler_extra_params = { 'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+ 'sample_dpm_fast': ['s_noise'],
+ 'sample_dpm_2_ancestral': ['s_noise'],
+ 'sample_dpmpp_2s_ancestral': ['s_noise'],
+ 'sample_dpmpp_sde': ['s_noise'],
+ 'sample_dpmpp_2m_sde': ['s_noise'],
+ 'sample_dpmpp_3m_sde': ['s_noise'],
}
k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
@@ -64,9 +76,12 @@ class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser): class KDiffusionSampler(sd_samplers_common.Sampler):
- def __init__(self, funcname, sd_model):
+ def __init__(self, funcname, sd_model, options=None):
super().__init__(funcname)
+ self.extra_params = sampler_extra_params.get(funcname, [])
+
+ self.options = options or {}
self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
self.model_wrap_cfg = CFGDenoiserKDiffusion(self)
@@ -149,6 +164,9 @@ class KDiffusionSampler(sd_samplers_common.Sampler): noise_sampler = self.create_noise_sampler(x, sigmas, p)
extra_params_kwargs['noise_sampler'] = noise_sampler
+ if self.config.options.get('solver_type', None) == 'heun':
+ extra_params_kwargs['solver_type'] = 'heun'
+
self.model_wrap_cfg.init_latent = x
self.last_latent = x
self.sampler_extra_args = {
@@ -190,6 +208,9 @@ class KDiffusionSampler(sd_samplers_common.Sampler): noise_sampler = self.create_noise_sampler(x, sigmas, p)
extra_params_kwargs['noise_sampler'] = noise_sampler
+ if self.config.options.get('solver_type', None) == 'heun':
+ extra_params_kwargs['solver_type'] = 'heun'
+
self.last_latent = x
self.sampler_extra_args = {
'cond': conditioning,
@@ -198,6 +219,7 @@ class KDiffusionSampler(sd_samplers_common.Sampler): 'cond_scale': p.cfg_scale,
's_min_uncond': self.s_min_uncond
}
+
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
diff --git a/modules/sd_samplers_timesteps.py b/modules/sd_samplers_timesteps.py index 16572c7e..66e83ff7 100644 --- a/modules/sd_samplers_timesteps.py +++ b/modules/sd_samplers_timesteps.py @@ -49,12 +49,12 @@ class CFGDenoiserTimesteps(CFGDenoiser): super().__init__(sampler)
self.alphas = shared.sd_model.alphas_cumprod
+ self.mask_before_denoising = True
def get_pred_x0(self, x_in, x_out, sigma):
- ts = int(sigma.item())
+ ts = sigma.to(dtype=int)
- s_in = x_in.new_ones([x_in.shape[0]])
- a_t = self.alphas[ts].item() * s_in
+ a_t = self.alphas[ts][:, None, None, None]
sqrt_one_minus_at = (1 - a_t).sqrt()
pred_x0 = (x_in - sqrt_one_minus_at * x_out) / a_t.sqrt()
@@ -76,6 +76,7 @@ class CompVisSampler(sd_samplers_common.Sampler): self.eta_option_field = 'eta_ddim'
self.eta_infotext_field = 'Eta DDIM'
+ self.eta_default = 0.0
self.model_wrap_cfg = CFGDenoiserTimesteps(self)
diff --git a/modules/sd_samplers_timesteps_impl.py b/modules/sd_samplers_timesteps_impl.py index 48d7e649..a72daafd 100644 --- a/modules/sd_samplers_timesteps_impl.py +++ b/modules/sd_samplers_timesteps_impl.py @@ -11,21 +11,22 @@ from modules.models.diffusion.uni_pc import uni_pc def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
alphas = alphas_cumprod[timesteps]
- alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64)
+ alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
extra_args = {} if extra_args is None else extra_args
- s_in = x.new_ones([x.shape[0]])
+ s_in = x.new_ones((x.shape[0]))
+ s_x = x.new_ones((x.shape[0], 1, 1, 1))
for i in tqdm.trange(len(timesteps) - 1, disable=disable):
index = len(timesteps) - 1 - i
e_t = model(x, timesteps[index].item() * s_in, **extra_args)
- a_t = alphas[index].item() * s_in
- a_prev = alphas_prev[index].item() * s_in
- sigma_t = sigmas[index].item() * s_in
- sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_in
+ a_t = alphas[index].item() * s_x
+ a_prev = alphas_prev[index].item() * s_x
+ sigma_t = sigmas[index].item() * s_x
+ sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t
@@ -42,18 +43,19 @@ def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta= def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
alphas = alphas_cumprod[timesteps]
- alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64)
+ alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
+ s_x = x.new_ones((x.shape[0], 1, 1, 1))
old_eps = []
def get_x_prev_and_pred_x0(e_t, index):
# select parameters corresponding to the currently considered timestep
- a_t = alphas[index].item() * s_in
- a_prev = alphas_prev[index].item() * s_in
- sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_in
+ a_t = alphas[index].item() * s_x
+ a_prev = alphas_prev[index].item() * s_x
+ sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
# current prediction for x_0
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
diff --git a/modules/shared_items.py b/modules/shared_items.py index e4ec40a8..84d69c8d 100644 --- a/modules/shared_items.py +++ b/modules/shared_items.py @@ -69,10 +69,11 @@ def reload_hypernetworks(): ui_reorder_categories_builtin_items = [
"inpaint",
"sampler",
+ "accordions",
"checkboxes",
- "hires_fix",
"dimensions",
"cfg",
+ "denoising",
"seed",
"batch",
"override_settings",
@@ -86,7 +87,7 @@ def ui_reorder_categories(): sections = {}
for script in scripts.scripts_txt2img.scripts + scripts.scripts_img2img.scripts:
- if isinstance(script.section, str):
+ if isinstance(script.section, str) and script.section not in ui_reorder_categories_builtin_items:
sections[script.section] = 1
yield from sections
diff --git a/modules/shared_options.py b/modules/shared_options.py index 1e5b64ea..fc0de61f 100644 --- a/modules/shared_options.py +++ b/modules/shared_options.py @@ -140,8 +140,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
"randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU", "NV"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors; use NV to produce same picture as on NVidia videocards"),
"tiling": OptionInfo(False, "Tiling", infotext='Tiling').info("produce a tileable picture"),
- "sd_refiner_checkpoint": OptionInfo("None", "Refiner checkpoint", gr.Dropdown, lambda: {"choices": ["None"] + shared_items.list_checkpoint_tiles()}, refresh=shared_items.refresh_checkpoints, infotext="Refiner").info("switch to another model in the middle of generation"),
- "sd_refiner_switch_at": OptionInfo(1.0, "Refiner switch at", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}, infotext='Refiner switch at').info("fraction of sampling steps when the swtch to refiner model should happen; 1=never, 0.5=switch in the middle of generation"),
}))
options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
@@ -286,13 +284,13 @@ options_templates.update(options_section(('ui', "Live previews"), { options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
"hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in shared_items.list_samplers()]}).needs_reload_ui(),
- "eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta DDIM').info("noise multiplier; higher = more unperdictable results"),
- "eta_ancestral": OptionInfo(1.0, "Eta for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta').info("noise multiplier; applies to Euler a and other samplers that have a in them"),
+ "eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta DDIM').info("noise multiplier; higher = more unpredictable results"),
+ "eta_ancestral": OptionInfo(1.0, "Eta for k-diffusion samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta').info("noise multiplier; currently only applies to ancestral samplers (i.e. Euler a) and SDE samplers"),
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 100.0, "step": 0.01}, infotext='Sigma churn').info('amount of stochasticity; only applies to Euler, Heun, and DPM2'),
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 10.0, "step": 0.01}, infotext='Sigma tmin').info('enable stochasticity; start value of the sigma range; only applies to Euler, Heun, and DPM2'),
's_tmax': OptionInfo(0.0, "sigma tmax", gr.Slider, {"minimum": 0.0, "maximum": 999.0, "step": 0.01}, infotext='Sigma tmax').info("0 = inf; end value of the sigma range; only applies to Euler, Heun, and DPM2"),
- 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.1, "step": 0.001}, infotext='Sigma noise').info('amount of additional noise to counteract loss of detail during sampling; only applies to Euler, Heun, and DPM2'),
+ 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.1, "step": 0.001}, infotext='Sigma noise').info('amount of additional noise to counteract loss of detail during sampling'),
'k_sched_type': OptionInfo("Automatic", "Scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}, infotext='Schedule type').info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
'sigma_min': OptionInfo(0.0, "sigma min", gr.Number, infotext='Schedule max sigma').info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
'sigma_max': OptionInfo(0.0, "sigma max", gr.Number, infotext='Schedule min sigma').info("0 = default (~14.6); maximum noise strength for k-diffusion noise scheduler"),
diff --git a/modules/sub_quadratic_attention.py b/modules/sub_quadratic_attention.py index 497568eb..ae4ee4bb 100644 --- a/modules/sub_quadratic_attention.py +++ b/modules/sub_quadratic_attention.py @@ -58,7 +58,7 @@ def _summarize_chunk( scale: float, ) -> AttnChunk: attn_weights = torch.baddbmm( - torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + torch.zeros(1, 1, 1, device=query.device, dtype=query.dtype), query, key.transpose(1,2), alpha=scale, @@ -121,7 +121,7 @@ def _get_attention_scores_no_kv_chunking( scale: float, ) -> Tensor: attn_scores = torch.baddbmm( - torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + torch.zeros(1, 1, 1, device=query.device, dtype=query.dtype), query, key.transpose(1,2), alpha=scale, diff --git a/modules/txt2img.py b/modules/txt2img.py index 5ea96bba..1ee592ad 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -9,7 +9,7 @@ from modules.ui import plaintext_to_html import gradio as gr
-def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_name: str, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_checkpoint_name: str, hr_sampler_name: str, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args):
+def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_name: str, n_iter: int, batch_size: int, cfg_scale: float, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_checkpoint_name: str, hr_sampler_name: str, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args):
override_settings = create_override_settings_dict(override_settings_texts)
p = processing.StableDiffusionProcessingTxt2Img(
@@ -19,12 +19,6 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step prompt=prompt,
styles=prompt_styles,
negative_prompt=negative_prompt,
- seed=seed,
- subseed=subseed,
- subseed_strength=subseed_strength,
- seed_resize_from_h=seed_resize_from_h,
- seed_resize_from_w=seed_resize_from_w,
- seed_enable_extras=seed_enable_extras,
sampler_name=sampler_name,
batch_size=batch_size,
n_iter=n_iter,
diff --git a/modules/ui.py b/modules/ui.py index 05292734..a6b1f964 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1,5 +1,4 @@ import datetime
-import json
import mimetypes
import os
import sys
@@ -13,7 +12,7 @@ from PIL import Image, PngImagePlugin # noqa: F401 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
from modules import gradio_extensons # noqa: F401
-from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts, sd_samplers, processing, ui_extra_networks
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts, sd_samplers, processing, ui_extra_networks
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML, InputAccordion
from modules.paths import script_path
from modules.ui_common import create_refresh_button
@@ -142,45 +141,6 @@ def interrogate_deepbooru(image): return gr.update() if prompt is None else prompt
-def create_seed_inputs(target_interface):
- with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"):
- if cmd_opts.use_textbox_seed:
- seed = gr.Textbox(label='Seed', value="", elem_id=f"{target_interface}_seed")
- else:
- seed = gr.Number(label='Seed', value=-1, elem_id=f"{target_interface}_seed", precision=0)
-
- random_seed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_seed", label='Random seed')
- reuse_seed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_seed", label='Reuse seed')
-
- seed_checkbox = gr.Checkbox(label='Extra', elem_id=f"{target_interface}_subseed_show", value=False)
-
- # Components to show/hide based on the 'Extra' checkbox
- seed_extras = []
-
- with FormRow(visible=False, elem_id=f"{target_interface}_subseed_row") as seed_extra_row_1:
- seed_extras.append(seed_extra_row_1)
- subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed", precision=0)
- random_subseed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_subseed")
- reuse_subseed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_subseed")
- subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=f"{target_interface}_subseed_strength")
-
- with FormRow(visible=False) as seed_extra_row_2:
- seed_extras.append(seed_extra_row_2)
- seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=f"{target_interface}_seed_resize_from_w")
- seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=f"{target_interface}_seed_resize_from_h")
-
- random_seed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_seed')}", show_progress=False, inputs=[], outputs=[])
- random_subseed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_subseed')}", show_progress=False, inputs=[], outputs=[])
-
- def change_visibility(show):
- return {comp: gr_show(show) for comp in seed_extras}
-
- seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
-
- return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
-
-
-
def connect_clear_prompt(button):
"""Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
button.click(
@@ -191,39 +151,6 @@ def connect_clear_prompt(button): )
-def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
- """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
- (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
- was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
- def copy_seed(gen_info_string: str, index):
- res = -1
-
- try:
- gen_info = json.loads(gen_info_string)
- index -= gen_info.get('index_of_first_image', 0)
-
- if is_subseed and gen_info.get('subseed_strength', 0) > 0:
- all_subseeds = gen_info.get('all_subseeds', [-1])
- res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
- else:
- all_seeds = gen_info.get('all_seeds', [-1])
- res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
-
- except json.decoder.JSONDecodeError:
- if gen_info_string:
- errors.report(f"Error parsing JSON generation info: {gen_info_string}")
-
- return [res, gr_show(False)]
-
- reuse_seed.click(
- fn=copy_seed,
- _js="(x, y) => [x, selected_gallery_index()]",
- show_progress=False,
- inputs=[generation_info, dummy_component],
- outputs=[seed, dummy_component]
- )
-
-
def update_token_counter(text, steps):
try:
text, _ = extra_networks.parse_prompt(text)
@@ -429,44 +356,45 @@ def create_ui(): batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
elif category == "cfg":
- cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
-
- elif category == "seed":
- seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
+ with gr.Row():
+ cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
elif category == "checkboxes":
with FormRow(elem_classes="checkboxes-row", variant="compact"):
pass
- elif category == "hires_fix":
- with InputAccordion(False, label="Hires. fix") as enable_hr:
- with enable_hr.extra():
- hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False, min_width=0)
+ elif category == "accordions":
+ with gr.Row(elem_id="txt2img_accordions", elem_classes="accordions"):
+ with InputAccordion(False, label="Hires. fix", elem_id="txt2img_hr") as enable_hr:
+ with enable_hr.extra():
+ hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False, min_width=0)
- with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
- hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
- hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
- denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
+ with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
+ hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
+ hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
+ denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
- with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
- hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
- 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")
+ with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
+ hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
+ 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")
- with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
+ with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
- hr_checkpoint_name = gr.Dropdown(label='Hires checkpoint', elem_id="hr_checkpoint", choices=["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint")
- create_refresh_button(hr_checkpoint_name, modules.sd_models.list_models, lambda: {"choices": ["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True)}, "hr_checkpoint_refresh")
+ hr_checkpoint_name = gr.Dropdown(label='Hires checkpoint', elem_id="hr_checkpoint", choices=["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint")
+ create_refresh_button(hr_checkpoint_name, modules.sd_models.list_models, lambda: {"choices": ["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True)}, "hr_checkpoint_refresh")
- hr_sampler_name = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + sd_samplers.visible_sampler_names(), value="Use same sampler")
+ hr_sampler_name = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + sd_samplers.visible_sampler_names(), value="Use same sampler")
- with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
- with gr.Column(scale=80):
- with gr.Row():
- hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
- with gr.Column(scale=80):
- with gr.Row():
- hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
+ with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
+ with gr.Column(scale=80):
+ with gr.Row():
+ hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
+ with gr.Column(scale=80):
+ with gr.Row():
+ hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
+
+ scripts.scripts_txt2img.setup_ui_for_section(category)
elif category == "batch":
if not opts.dimensions_and_batch_together:
@@ -482,7 +410,7 @@ def create_ui(): with FormGroup(elem_id="txt2img_script_container"):
custom_inputs = scripts.scripts_txt2img.setup_ui()
- else:
+ if category not in {"accordions"}:
scripts.scripts_txt2img.setup_ui_for_section(category)
hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
@@ -506,9 +434,6 @@ def create_ui(): txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
- connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
- connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
txt2img_args = dict(
fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
_js="submit",
@@ -522,8 +447,6 @@ def create_ui(): batch_count,
batch_size,
cfg_scale,
- seed,
- subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
height,
width,
enable_hr,
@@ -574,15 +497,9 @@ def create_ui(): (steps, "Steps"),
(sampler_name, "Sampler"),
(cfg_scale, "CFG scale"),
- (seed, "Seed"),
(width, "Size-1"),
(height, "Size-2"),
(batch_size, "Batch size"),
- (seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
- (subseed, "Variation seed"),
- (subseed_strength, "Variation seed strength"),
- (seed_resize_from_w, "Seed resize from-1"),
- (seed_resize_from_h, "Seed resize from-2"),
(toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
(denoising_strength, "Denoising strength"),
(enable_hr, lambda d: "Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d)),
@@ -610,7 +527,7 @@ def create_ui(): steps,
sampler_name,
cfg_scale,
- seed,
+ scripts.scripts_txt2img.script('Seed').seed,
width,
height,
]
@@ -780,20 +697,22 @@ def create_ui(): 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 == "cfg":
- with FormGroup():
- with FormRow():
- cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
- image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
- denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
+ elif category == "denoising":
+ denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
- elif category == "seed":
- seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
+ elif category == "cfg":
+ with gr.Row():
+ cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+ image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
elif category == "checkboxes":
with FormRow(elem_classes="checkboxes-row", variant="compact"):
pass
+ elif category == "accordions":
+ with gr.Row(elem_id="img2img_accordions", elem_classes="accordions"):
+ scripts.scripts_img2img.setup_ui_for_section(category)
+
elif category == "batch":
if not opts.dimensions_and_batch_together:
with FormRow(elem_id="img2img_column_batch"):
@@ -836,14 +755,12 @@ def create_ui(): inputs=[],
outputs=[inpaint_controls, mask_alpha],
)
- else:
+
+ if category not in {"accordions"}:
scripts.scripts_img2img.setup_ui_for_section(category)
img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
- connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
- connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
img2img_args = dict(
fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
_js="submit_img2img",
@@ -870,8 +787,6 @@ def create_ui(): cfg_scale,
image_cfg_scale,
denoising_strength,
- seed,
- subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
selected_scale_tab,
height,
width,
@@ -958,15 +873,9 @@ def create_ui(): (sampler_name, "Sampler"),
(cfg_scale, "CFG scale"),
(image_cfg_scale, "Image CFG scale"),
- (seed, "Seed"),
(width, "Size-1"),
(height, "Size-2"),
(batch_size, "Batch size"),
- (seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
- (subseed, "Variation seed"),
- (subseed_strength, "Variation seed strength"),
- (seed_resize_from_w, "Seed resize from-1"),
- (seed_resize_from_h, "Seed resize from-2"),
(toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
(denoising_strength, "Denoising strength"),
(mask_blur, "Mask blur"),
diff --git a/modules/ui_common.py b/modules/ui_common.py index 99d19ff0..4c035f2a 100644 --- a/modules/ui_common.py +++ b/modules/ui_common.py @@ -137,13 +137,17 @@ Requested path was: {f} generation_info = None
with gr.Column():
with gr.Row(elem_id=f"image_buttons_{tabname}", elem_classes="image-buttons"):
- open_folder_button = gr.Button(folder_symbol, visible=not shared.cmd_opts.hide_ui_dir_config)
+ open_folder_button = ToolButton(folder_symbol, elem_id=f'{tabname}_open_folder', visible=not shared.cmd_opts.hide_ui_dir_config, tooltip="Open images output directory.")
if tabname != "extras":
- save = gr.Button('Save', elem_id=f'save_{tabname}')
- save_zip = gr.Button('Zip', elem_id=f'save_zip_{tabname}')
-
- buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
+ save = ToolButton('💾', elem_id=f'save_{tabname}', tooltip=f"Save the image to a dedicated directory ({shared.opts.outdir_save}).")
+ save_zip = ToolButton('🗃️', elem_id=f'save_zip_{tabname}', tooltip=f"Save zip archive with images to a dedicated directory ({shared.opts.outdir_save})")
+
+ buttons = {
+ 'img2img': ToolButton('🖼️', elem_id=f'{tabname}_send_to_img2img', tooltip="Send image and generation parameters to img2img tab."),
+ 'inpaint': ToolButton('🎨️', elem_id=f'{tabname}_send_to_inpaint', tooltip="Send image and generation parameters to img2img inpaint tab."),
+ 'extras': ToolButton('📐', elem_id=f'{tabname}_send_to_extras', tooltip="Send image and generation parameters to extras tab.")
+ }
open_folder_button.click(
fn=lambda: open_folder(shared.opts.outdir_samples or outdir),
diff --git a/modules/ui_components.py b/modules/ui_components.py index bfe2fbd9..d08b2b99 100644 --- a/modules/ui_components.py +++ b/modules/ui_components.py @@ -87,13 +87,23 @@ class InputAccordion(gr.Checkbox): self.accordion_id = f"input-accordion-{InputAccordion.global_index}"
InputAccordion.global_index += 1
- kwargs['elem_id'] = self.accordion_id + "-checkbox"
- kwargs['visible'] = False
- super().__init__(value, **kwargs)
+ kwargs_checkbox = {
+ **kwargs,
+ "elem_id": f"{self.accordion_id}-checkbox",
+ "visible": False,
+ }
+ super().__init__(value, **kwargs_checkbox)
self.change(fn=None, _js='function(checked){ inputAccordionChecked("' + self.accordion_id + '", checked); }', inputs=[self])
- self.accordion = gr.Accordion(kwargs.get('label', 'Accordion'), open=value, elem_id=self.accordion_id, elem_classes=['input-accordion'])
+ kwargs_accordion = {
+ **kwargs,
+ "elem_id": self.accordion_id,
+ "label": kwargs.get('label', 'Accordion'),
+ "elem_classes": ['input-accordion'],
+ "open": value,
+ }
+ self.accordion = gr.Accordion(**kwargs_accordion)
def extra(self):
"""Allows you to put something into the label of the accordion.
diff --git a/modules/ui_extra_networks_checkpoints.py b/modules/ui_extra_networks_checkpoints.py index 77885022..ebb5249f 100644 --- a/modules/ui_extra_networks_checkpoints.py +++ b/modules/ui_extra_networks_checkpoints.py @@ -19,6 +19,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage): return {
"name": checkpoint.name_for_extra,
"filename": checkpoint.filename,
+ "shorthash": checkpoint.shorthash,
"preview": self.find_preview(path),
"description": self.find_description(path),
"search_term": self.search_terms_from_path(checkpoint.filename) + " " + (checkpoint.sha256 or ""),
diff --git a/modules/ui_extra_networks_hypernets.py b/modules/ui_extra_networks_hypernets.py index 514a4562..4cedf085 100644 --- a/modules/ui_extra_networks_hypernets.py +++ b/modules/ui_extra_networks_hypernets.py @@ -2,6 +2,7 @@ import os from modules import shared, ui_extra_networks
from modules.ui_extra_networks import quote_js
+from modules.hashes import sha256_from_cache
class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
@@ -14,13 +15,16 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage): def create_item(self, name, index=None, enable_filter=True):
full_path = shared.hypernetworks[name]
path, ext = os.path.splitext(full_path)
+ sha256 = sha256_from_cache(full_path, f'hypernet/{name}')
+ shorthash = sha256[0:10] if sha256 else None
return {
"name": name,
"filename": full_path,
+ "shorthash": shorthash,
"preview": self.find_preview(path),
"description": self.find_description(path),
- "search_term": self.search_terms_from_path(path),
+ "search_term": self.search_terms_from_path(path) + " " + (sha256 or ""),
"prompt": quote_js(f"<hypernet:{name}:") + " + opts.extra_networks_default_multiplier + " + quote_js(">"),
"local_preview": f"{path}.preview.{shared.opts.samples_format}",
"sort_keys": {'default': index, **self.get_sort_keys(path + ext)},
diff --git a/modules/ui_extra_networks_textual_inversion.py b/modules/ui_extra_networks_textual_inversion.py index 73134698..55ef0ea7 100644 --- a/modules/ui_extra_networks_textual_inversion.py +++ b/modules/ui_extra_networks_textual_inversion.py @@ -19,9 +19,10 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage): return {
"name": name,
"filename": embedding.filename,
+ "shorthash": embedding.shorthash,
"preview": self.find_preview(path),
"description": self.find_description(path),
- "search_term": self.search_terms_from_path(embedding.filename),
+ "search_term": self.search_terms_from_path(embedding.filename) + " " + (embedding.hash or ""),
"prompt": quote_js(embedding.name),
"local_preview": f"{path}.preview.{shared.opts.samples_format}",
"sort_keys": {'default': index, **self.get_sort_keys(embedding.filename)},
diff --git a/modules/ui_extra_networks_user_metadata.py b/modules/ui_extra_networks_user_metadata.py index cda471e4..b11622a1 100644 --- a/modules/ui_extra_networks_user_metadata.py +++ b/modules/ui_extra_networks_user_metadata.py @@ -93,11 +93,13 @@ class UserMetadataEditor: item = self.page.items.get(name, {})
try:
filename = item["filename"]
+ shorthash = item.get("shorthash", None)
stats = os.stat(filename)
params = [
('Filename: ', os.path.basename(filename)),
('File size: ', sysinfo.pretty_bytes(stats.st_size)),
+ ('Hash: ', shorthash),
('Modified: ', datetime.datetime.fromtimestamp(stats.st_mtime).strftime('%Y-%m-%d %H:%M')),
]
@@ -115,7 +117,7 @@ class UserMetadataEditor: errors.display(e, f"reading metadata info for {name}")
params = []
- table = '<table class="file-metadata">' + "".join(f"<tr><th>{name}</th><td>{value}</td></tr>" for name, value in params) + '</table>'
+ table = '<table class="file-metadata">' + "".join(f"<tr><th>{name}</th><td>{value}</td></tr>" for name, value in params if value is not None) + '</table>'
return html.escape(name), user_metadata.get('description', ''), table, self.get_card_html(name), user_metadata.get('notes', '')
|