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_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', '')
 

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
 
 ####################################################################