Merge branch 'dev' into extra-norm-module

extensions-builtin/Lora/extra_networks_lora.py

@@ -6,9 +6,14 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
     def __init__(self):
         super().__init__('lora')
 
+        self.errors = {}
+        """mapping of network names to the number of errors the network had during operation"""
+
     def activate(self, p, params_list):
         additional = shared.opts.sd_lora
 
+        self.errors.clear()
+
         if additional != "None" and additional in networks.available_networks and not any(x for x in params_list if x.items[0] == additional):
             p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
             params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
@@ -56,4 +61,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
                 p.extra_generation_params["Lora hashes"] = ", ".join(network_hashes)
 
     def deactivate(self, p):
-        pass
+        if self.errors:
+            p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items()))
+
+            self.errors.clear()

extensions-builtin/Lora/networks.py

+import logging
 import os
 import re
 
@@ -194,7 +195,7 @@ def load_network(name, network_on_disk):
         net.modules[key] = net_module
 
     if keys_failed_to_match:
-        print(f"Failed to match keys when loading network {network_on_disk.filename}: {keys_failed_to_match}")
+        logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}")
 
     return net
 
@@ -207,7 +208,6 @@ def purge_networks_from_memory():
     devices.torch_gc()
 
 
-
 def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
     already_loaded = {}
 
@@ -248,7 +248,7 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
 
         if net is None:
             failed_to_load_networks.append(name)
-            print(f"Couldn't find network with name {name}")
+            logging.info(f"Couldn't find network with name {name}")
             continue
 
         net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0
@@ -257,7 +257,7 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
         loaded_networks.append(net)
 
     if failed_to_load_networks:
-        sd_hijack.model_hijack.comments.append("Failed to find networks: " + ", ".join(failed_to_load_networks))
+        sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks))
 
     purge_networks_from_memory()
 
@@ -327,20 +327,25 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
         for net in loaded_networks:
             module = net.modules.get(network_layer_name, None)
             if module is not None and hasattr(self, 'weight'):
-                with torch.no_grad():
-                    updown, ex_bias = module.calc_updown(self.weight)
-
-                    if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
-                        # inpainting model. zero pad updown to make channel[1]  4 to 9
-                        updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
+                try:
+                    with torch.no_grad():
+                        updown, ex_bias = module.calc_updown(self.weight)
+
+                        if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
+                            # inpainting model. zero pad updown to make channel[1]  4 to 9
+                            updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
+
+                        self.weight += updown
+                        if ex_bias is not None and hasattr(self, 'bias'):
+                            if self.bias is None:
+                                self.bias = torch.nn.Parameter(ex_bias)
+                            else:
+                                self.bias += ex_bias
+                except RuntimeError as e:
+                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
+                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
 
-                    self.weight += updown
-                    if ex_bias is not None and hasattr(self, 'bias'):
-                        if self.bias is None:
-                            self.bias = torch.nn.Parameter(ex_bias)
-                        else:
-                            self.bias += ex_bias
-                    continue
+                continue
 
             module_q = net.modules.get(network_layer_name + "_q_proj", None)
             module_k = net.modules.get(network_layer_name + "_k_proj", None)
@@ -348,26 +353,33 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
             module_out = net.modules.get(network_layer_name + "_out_proj", None)
 
             if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
-                with torch.no_grad():
-                    updown_q, _ = module_q.calc_updown(self.in_proj_weight)
-                    updown_k, _ = module_k.calc_updown(self.in_proj_weight)
-                    updown_v, _ = module_v.calc_updown(self.in_proj_weight)
-                    updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
-                    updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight)
-
-                    self.in_proj_weight += updown_qkv
-                    self.out_proj.weight += updown_out
+                try:
+                    with torch.no_grad():
+                        updown_q, _ = module_q.calc_updown(self.in_proj_weight)
+                        updown_k, _ = module_k.calc_updown(self.in_proj_weight)
+                        updown_v, _ = module_v.calc_updown(self.in_proj_weight)
+                        updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
+                        updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight)
+
+                        self.in_proj_weight += updown_qkv
+                        self.out_proj.weight += updown_out
                     if ex_bias is not None:
                         if self.out_proj.bias is None:
                             self.out_proj.bias = torch.nn.Parameter(ex_bias)
                         else:
                             self.out_proj.bias += ex_bias
-                    continue
+
+                except RuntimeError as e:
+                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
+                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+                continue
 
             if module is None:
                 continue
 
-            print(f'failed to calculate network weights for layer {network_layer_name}')
+            logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation")
+            extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
 
         self.network_current_names = wanted_names
 
@@ -540,6 +552,7 @@ def infotext_pasted(infotext, params):
     if added:
         params["Prompt"] += "\n" + "".join(added)
 
+extra_network_lora = None
 
 available_networks = {}
 available_network_aliases = {}

extensions-builtin/Lora/scripts/lora_script.py

@@ -23,9 +23,9 @@ def unload():
 def before_ui():
     ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
 
-    extra_network = extra_networks_lora.ExtraNetworkLora()
-    extra_networks.register_extra_network(extra_network)
-    extra_networks.register_extra_network_alias(extra_network, "lyco")
+    networks.extra_network_lora = extra_networks_lora.ExtraNetworkLora()
+    extra_networks.register_extra_network(networks.extra_network_lora)
+    extra_networks.register_extra_network_alias(networks.extra_network_lora, "lyco")
 
 
 if not hasattr(torch.nn, 'Linear_forward_before_network'):

extensions-builtin/Lora/ui_extra_networks_lora.py

@@ -25,9 +25,10 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
         item = {
             "name": name,
             "filename": lora_on_disk.filename,
+            "shorthash": lora_on_disk.shorthash,
             "preview": self.find_preview(path),
             "description": self.find_description(path),
-            "search_term": self.search_terms_from_path(lora_on_disk.filename),
+            "search_term": self.search_terms_from_path(lora_on_disk.filename) + " " + (lora_on_disk.hash or ""),
             "local_preview": f"{path}.{shared.opts.samples_format}",
             "metadata": lora_on_disk.metadata,
             "sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)},

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
 
@@ -319,12 +322,12 @@ def prepare_environment():
 
     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")
+    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:

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)

modules/processing.py

+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
@@ -104,97 +106,160 @@ 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] = field(default_factory=list)
+    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)
+
+    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 = {}
+
         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)
+
+    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'}
 
@@ -398,7 +463,7 @@ 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
@@ -408,7 +473,10 @@ 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)
@@ -459,7 +527,10 @@ 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,
@@ -578,10 +649,10 @@ 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": p.loaded_vae_hash if opts.add_model_hash_to_info else None,
-        "VAE": p.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}"),
@@ -670,14 +741,19 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if p.tiling is None:
         p.tiling = opts.tiling
 
-    p.loaded_vae_name = sd_vae.get_loaded_vae_name()
-    p.loaded_vae_hash = sd_vae.get_loaded_vae_hash()
+    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:
@@ -757,7 +833,7 @@ 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)
 
@@ -886,7 +962,6 @@ 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,
@@ -910,49 +985,51 @@ 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):
@@ -1146,6 +1223,9 @@ 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
@@ -1230,7 +1310,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         return super().get_conds()
 
-
     def parse_extra_network_prompts(self):
         res = super().parse_extra_network_prompts()
 
@@ -1243,32 +1322,37 @@ 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
-        self.mask_blur_x = mask_blur_x
-        self.mask_blur_y = mask_blur_y
-        if mask_blur is not None:
-            self.mask_blur = mask_blur
-        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):
@@ -1278,15 +1362,13 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
     @mask_blur.setter
     def mask_blur(self, value):
-        self.mask_blur_x = value
-        self.mask_blur_y = value
-
-    @mask_blur.deleter
-    def mask_blur(self):
-        del self.mask_blur_x
-        del self.mask_blur_y
+        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
 

modules/processing_scripts/refiner.py

@@ -38,18 +38,12 @@ class ScriptRefiner(scripts.Script):
 
         return enable_refiner, refiner_checkpoint, refiner_switch_at
 
-    def before_process(self, p, 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
 
-        p.refiner_checkpoint_info = None
-        p.refiner_switch_at = None
-
         if not enable_refiner or refiner_checkpoint in (None, "", "None"):
-            return
-
-        refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(refiner_checkpoint)
-        if refiner_checkpoint_info is None:
-            raise Exception(f'Could not find checkpoint with name {refiner_checkpoint}')
-
-        p.refiner_checkpoint_info = refiner_checkpoint_info
-        p.refiner_switch_at = refiner_switch_at
+            p.refiner_checkpoint_info = None
+            p.refiner_switch_at = None
+        else:
+            p.refiner_checkpoint = refiner_checkpoint
+            p.refiner_switch_at = refiner_switch_at

modules/processing_scripts/seed.py

@@ -58,7 +58,7 @@ class ScriptSeed(scripts.ScriptBuiltin):
 
         return self.seed, subseed, subseed_strength
 
-    def before_process(self, p, seed, subseed, subseed_strength):
+    def setup(self, p, seed, subseed, subseed_strength):
         p.seed = seed
 
         if subseed_strength > 0:

modules/scripts.py

@@ -106,9 +106,16 @@ 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()
         """
@@ -706,6 +713,14 @@ class ScriptRunner:
             except Exception:
                 errors.report(f"Error running before_hr: {script.filename}", exc_info=True)
 
+    def setup_scrips(self, p):
+        for script in self.alwayson_scripts:
+            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

modules/sd_hijack_optimizations.py

 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:

modules/sd_samplers_common.py

@@ -92,7 +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
 
@@ -145,7 +153,7 @@ def apply_refiner(cfg_denoiser):
     refiner_switch_at = cfg_denoiser.p.refiner_switch_at
     refiner_checkpoint_info = cfg_denoiser.p.refiner_checkpoint_info
 
-    if refiner_switch_at is not None and completed_ratio <= refiner_switch_at:
+    if refiner_switch_at is not None and completed_ratio < refiner_switch_at:
         return False
 
     if refiner_checkpoint_info is None or shared.sd_model.sd_checkpoint_info == refiner_checkpoint_info:
@@ -276,19 +284,19 @@ 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
@@ -305,5 +313,8 @@ 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()

modules/sd_samplers_kdiffusion.py

@@ -22,6 +22,9 @@ samplers_k_diffusion = [
     ('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++ 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'}),
@@ -42,6 +45,12 @@ 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}
@@ -67,6 +76,8 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
     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)
 

modules/sd_samplers_timesteps_impl.py

@@ -11,7 +11,7 @@ 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()))
 
@@ -42,7 +42,7 @@ 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

modules/shared_options.py

@@ -285,12 +285,12 @@ 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_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"),

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,

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 ""),

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)},

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)},

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', '')
 

scripts/xyz_grid.py

@@ -175,14 +175,22 @@ def do_nothing(p, x, xs):
 def format_nothing(p, opt, x):
     return ""
 
+
 def format_remove_path(p, opt, x):
     return os.path.basename(x)
 
+
 def str_permutations(x):
     """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
     return x
 
 
+def list_to_csv_string(data_list):
+    with StringIO() as o:
+        csv.writer(o).writerow(data_list)
+        return o.getvalue().strip()
+
+
 class AxisOption:
     def __init__(self, label, type, apply, format_value=format_value_add_label, confirm=None, cost=0.0, choices=None):
         self.label = label
@@ -199,6 +207,7 @@ class AxisOptionImg2Img(AxisOption):
         super().__init__(*args, **kwargs)
         self.is_img2img = True
 
+
 class AxisOptionTxt2Img(AxisOption):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -286,11 +295,10 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
             cell_size = (processed_result.width, processed_result.height)
             if processed_result.images[0] is not None:
                 cell_mode = processed_result.images[0].mode
-                #This corrects size in case of batches:
+                # This corrects size in case of batches:
                 cell_size = processed_result.images[0].size
             processed_result.images[idx] = Image.new(cell_mode, cell_size)
 
-
     if first_axes_processed == 'x':
         for ix, x in enumerate(xs):
             if second_axes_processed == 'y':
@@ -348,9 +356,9 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
     if draw_legend:
         z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], title_texts, [[images.GridAnnotation()]])
     processed_result.images.insert(0, z_grid)
-    #TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal.
-    #processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
-    #processed_result.all_seeds.insert(0, processed_result.all_seeds[0])
+    # TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal.
+    # processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
+    # processed_result.all_seeds.insert(0, processed_result.all_seeds[0])
     processed_result.infotexts.insert(0, processed_result.infotexts[0])
 
     return processed_result
@@ -374,8 +382,8 @@ class SharedSettingsStackHelper(object):
 re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
 re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")
 
-re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
-re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")
+re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*])?\s*")
+re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*])?\s*")
 
 
 class Script(scripts.Script):
@@ -390,19 +398,19 @@ class Script(scripts.Script):
                 with gr.Row():
                     x_type = gr.Dropdown(label="X type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type"))
                     x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values"))
-                    x_values_dropdown = gr.Dropdown(label="X values",visible=False,multiselect=True,interactive=True)
+                    x_values_dropdown = gr.Dropdown(label="X values", visible=False, multiselect=True, interactive=True)
                     fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False)
 
                 with gr.Row():
                     y_type = gr.Dropdown(label="Y type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type"))
                     y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values"))
-                    y_values_dropdown = gr.Dropdown(label="Y values",visible=False,multiselect=True,interactive=True)
+                    y_values_dropdown = gr.Dropdown(label="Y values", visible=False, multiselect=True, interactive=True)
                     fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_y_tool_button", visible=False)
 
                 with gr.Row():
                     z_type = gr.Dropdown(label="Z type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("z_type"))
                     z_values = gr.Textbox(label="Z values", lines=1, elem_id=self.elem_id("z_values"))
-                    z_values_dropdown = gr.Dropdown(label="Z values",visible=False,multiselect=True,interactive=True)
+                    z_values_dropdown = gr.Dropdown(label="Z values", visible=False, multiselect=True, interactive=True)
                     fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False)
 
         with gr.Row(variant="compact", elem_id="axis_options"):
@@ -414,6 +422,9 @@ class Script(scripts.Script):
                 include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
             with gr.Column():
                 margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
+            with gr.Column():
+                csv_mode = gr.Checkbox(label='Use text inputs instead of dropdowns', value=False, elem_id=self.elem_id("csv_mode"))
+
 
         with gr.Row(variant="compact", elem_id="swap_axes"):
             swap_xy_axes_button = gr.Button(value="Swap X/Y axes", elem_id="xy_grid_swap_axes_button")
@@ -430,50 +441,71 @@ class Script(scripts.Script):
         xz_swap_args = [x_type, x_values, x_values_dropdown, z_type, z_values, z_values_dropdown]
         swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)
 
-        def fill(x_type):
-            axis = self.current_axis_options[x_type]
-            return axis.choices() if axis.choices else gr.update()
+        def fill(axis_type, csv_mode):
+            axis = self.current_axis_options[axis_type]
+            if axis.choices:
+                if csv_mode:
+                    return list_to_csv_string(axis.choices()), gr.update()
+                else:
+                    return gr.update(), axis.choices()
+            else:
+                return gr.update(), gr.update()
 
-        fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values_dropdown])
-        fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values_dropdown])
-        fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values_dropdown])
+        fill_x_button.click(fn=fill, inputs=[x_type, csv_mode], outputs=[x_values, x_values_dropdown])
+        fill_y_button.click(fn=fill, inputs=[y_type, csv_mode], outputs=[y_values, y_values_dropdown])
+        fill_z_button.click(fn=fill, inputs=[z_type, csv_mode], outputs=[z_values, z_values_dropdown])
 
-        def select_axis(axis_type,axis_values_dropdown):
+        def select_axis(axis_type, axis_values, axis_values_dropdown, csv_mode):
             choices = self.current_axis_options[axis_type].choices
             has_choices = choices is not None
-            current_values = axis_values_dropdown
+
+            current_values = axis_values
+            current_dropdown_values = axis_values_dropdown
             if has_choices:
                 choices = choices()
-                if isinstance(current_values,str):
-                    current_values = current_values.split(",")
-                current_values = list(filter(lambda x: x in choices, current_values))
-            return gr.Button.update(visible=has_choices),gr.Textbox.update(visible=not has_choices),gr.update(choices=choices if has_choices else None,visible=has_choices,value=current_values)
+                if csv_mode:
+                    current_dropdown_values = list(filter(lambda x: x in choices, current_dropdown_values))
+                    current_values = list_to_csv_string(current_dropdown_values)
+                else:
+                    current_dropdown_values = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(axis_values)))]
+                    current_dropdown_values = list(filter(lambda x: x in choices, current_dropdown_values))
+
+            return (gr.Button.update(visible=has_choices), gr.Textbox.update(visible=not has_choices or csv_mode, value=current_values),
+                    gr.update(choices=choices if has_choices else None, visible=has_choices and not csv_mode, value=current_dropdown_values))
+
+        x_type.change(fn=select_axis, inputs=[x_type, x_values, x_values_dropdown, csv_mode], outputs=[fill_x_button, x_values, x_values_dropdown])
+        y_type.change(fn=select_axis, inputs=[y_type, y_values, y_values_dropdown, csv_mode], outputs=[fill_y_button, y_values, y_values_dropdown])
+        z_type.change(fn=select_axis, inputs=[z_type, z_values, z_values_dropdown, csv_mode], outputs=[fill_z_button, z_values, z_values_dropdown])
+
+        def change_choice_mode(csv_mode, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown):
+            _fill_x_button, _x_values, _x_values_dropdown = select_axis(x_type, x_values, x_values_dropdown, csv_mode)
+            _fill_y_button, _y_values, _y_values_dropdown = select_axis(y_type, y_values, y_values_dropdown, csv_mode)
+            _fill_z_button, _z_values, _z_values_dropdown = select_axis(z_type, z_values, z_values_dropdown, csv_mode)
+            return _fill_x_button, _x_values, _x_values_dropdown, _fill_y_button, _y_values, _y_values_dropdown, _fill_z_button, _z_values, _z_values_dropdown
 
-        x_type.change(fn=select_axis, inputs=[x_type,x_values_dropdown], outputs=[fill_x_button,x_values,x_values_dropdown])
-        y_type.change(fn=select_axis, inputs=[y_type,y_values_dropdown], outputs=[fill_y_button,y_values,y_values_dropdown])
-        z_type.change(fn=select_axis, inputs=[z_type,z_values_dropdown], outputs=[fill_z_button,z_values,z_values_dropdown])
+        csv_mode.change(fn=change_choice_mode, inputs=[csv_mode, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown], outputs=[fill_x_button, x_values, x_values_dropdown, fill_y_button, y_values, y_values_dropdown, fill_z_button, z_values, z_values_dropdown])
 
-        def get_dropdown_update_from_params(axis,params):
+        def get_dropdown_update_from_params(axis, params):
             val_key = f"{axis} Values"
-            vals = params.get(val_key,"")
+            vals = params.get(val_key, "")
             valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
-            return gr.update(value = valslist)
+            return gr.update(value=valslist)
 
         self.infotext_fields = (
             (x_type, "X Type"),
             (x_values, "X Values"),
-            (x_values_dropdown, lambda params:get_dropdown_update_from_params("X",params)),
+            (x_values_dropdown, lambda params: get_dropdown_update_from_params("X", params)),
             (y_type, "Y Type"),
             (y_values, "Y Values"),
-            (y_values_dropdown, lambda params:get_dropdown_update_from_params("Y",params)),
+            (y_values_dropdown, lambda params: get_dropdown_update_from_params("Y", params)),
             (z_type, "Z Type"),
             (z_values, "Z Values"),
-            (z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)),
+            (z_values_dropdown, lambda params: get_dropdown_update_from_params("Z", params)),
         )
 
-        return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
+        return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size, csv_mode]
 
-    def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
+    def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size, csv_mode):
         if not no_fixed_seeds:
             modules.processing.fix_seed(p)
 
@@ -484,7 +516,7 @@ class Script(scripts.Script):
             if opt.label == 'Nothing':
                 return [0]
 
-            if opt.choices is not None:
+            if opt.choices is not None and not csv_mode:
                 valslist = vals_dropdown
             else:
                 valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
@@ -503,8 +535,8 @@ class Script(scripts.Script):
                         valslist_ext += list(range(start, end, step))
                     elif mc is not None:
                         start = int(mc.group(1))
-                        end   = int(mc.group(2))
-                        num   = int(mc.group(3)) if mc.group(3) is not None else 1
+                        end = int(mc.group(2))
+                        num = int(mc.group(3)) if mc.group(3) is not None else 1
 
                         valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()]
                     else:
@@ -525,8 +557,8 @@ class Script(scripts.Script):
                         valslist_ext += np.arange(start, end + step, step).tolist()
                     elif mc is not None:
                         start = float(mc.group(1))
-                        end   = float(mc.group(2))
-                        num   = int(mc.group(3)) if mc.group(3) is not None else 1
+                        end = float(mc.group(2))
+                        num = int(mc.group(3)) if mc.group(3) is not None else 1
 
                         valslist_ext += np.linspace(start=start, stop=end, num=num).tolist()
                     else:
@@ -545,22 +577,22 @@ class Script(scripts.Script):
             return valslist
 
         x_opt = self.current_axis_options[x_type]
-        if x_opt.choices is not None:
-            x_values = ",".join(x_values_dropdown)
+        if x_opt.choices is not None and not csv_mode:
+            x_values = list_to_csv_string(x_values_dropdown)
         xs = process_axis(x_opt, x_values, x_values_dropdown)
 
         y_opt = self.current_axis_options[y_type]
-        if y_opt.choices is not None:
-            y_values = ",".join(y_values_dropdown)
+        if y_opt.choices is not None and not csv_mode:
+            y_values = list_to_csv_string(y_values_dropdown)
         ys = process_axis(y_opt, y_values, y_values_dropdown)
 
         z_opt = self.current_axis_options[z_type]
-        if z_opt.choices is not None:
-            z_values = ",".join(z_values_dropdown)
+        if z_opt.choices is not None and not csv_mode:
+            z_values = list_to_csv_string(z_values_dropdown)
         zs = process_axis(z_opt, z_values, z_values_dropdown)
 
         # this could be moved to common code, but unlikely to be ever triggered anywhere else
-        Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
+        Image.MAX_IMAGE_PIXELS = None  # disable check in Pillow and rely on check below to allow large custom image sizes
         grid_mp = round(len(xs) * len(ys) * len(zs) * p.width * p.height / 1000000)
         assert grid_mp < opts.img_max_size_mp, f'Error: Resulting grid would be too large ({grid_mp} MPixels) (max configured size is {opts.img_max_size_mp} MPixels)'
 
@@ -720,7 +752,7 @@ class Script(scripts.Script):
             # Auto-save main and sub-grids:
             grid_count = z_count + 1 if z_count > 1 else 1
             for g in range(grid_count):
-                #TODO: See previous comment about intentional data misalignment.
+                # TODO: See previous comment about intentional data misalignment.
                 adj_g = g-1 if g > 0 else g
                 images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed)
 

webui-macos-env.sh

@@ -12,8 +12,6 @@ fi
 export install_dir="$HOME"
 export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate"
 export TORCH_COMMAND="pip install torch==2.0.1 torchvision==0.15.2"
-export K_DIFFUSION_REPO="https://github.com/brkirch/k-diffusion.git"
-export K_DIFFUSION_COMMIT_HASH="51c9778f269cedb55a4d88c79c0246d35bdadb71"
 export PYTORCH_ENABLE_MPS_FALLBACK=1
 
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