Merge remote-tracking branch 'origin/master' into unipc

modules/api/api.py

@@ -498,7 +498,7 @@ class Api:
             if not apply_optimizations:
                 sd_hijack.undo_optimizations()
             try:
-                hypernetwork, filename = train_hypernetwork(*args)
+                hypernetwork, filename = train_hypernetwork(**args)
             except Exception as e:
                 error = e
             finally:

modules/esrgan_model_arch.py

@@ -1,5 +1,6 @@
 # this file is adapted from https://github.com/victorca25/iNNfer
 
+from collections import OrderedDict
 import math
 import functools
 import torch

modules/extensions.py

@@ -2,6 +2,7 @@ import os
 import sys
 import traceback
 
+import time
 import git
 
 from modules import paths, shared
@@ -25,6 +26,7 @@ class Extension:
         self.status = ''
         self.can_update = False
         self.is_builtin = is_builtin
+        self.version = ''
 
         repo = None
         try:
@@ -40,6 +42,10 @@ class Extension:
             try:
                 self.remote = next(repo.remote().urls, None)
                 self.status = 'unknown'
+                head = repo.head.commit
+                ts = time.asctime(time.gmtime(repo.head.commit.committed_date))
+                self.version = f'{head.hexsha[:8]} ({ts})'
+
             except Exception:
                 self.remote = None
 

modules/generation_parameters_copypaste.py

@@ -74,8 +74,8 @@ def image_from_url_text(filedata):
     return image
 
 
-def add_paste_fields(tabname, init_img, fields):
-    paste_fields[tabname] = {"init_img": init_img, "fields": fields}
+def add_paste_fields(tabname, init_img, fields, override_settings_component=None):
+    paste_fields[tabname] = {"init_img": init_img, "fields": fields, "override_settings_component": override_settings_component}
 
     # backwards compatibility for existing extensions
     import modules.ui
@@ -110,6 +110,7 @@ def connect_paste_params_buttons():
     for binding in registered_param_bindings:
         destination_image_component = paste_fields[binding.tabname]["init_img"]
         fields = paste_fields[binding.tabname]["fields"]
+        override_settings_component = binding.override_settings_component or paste_fields[binding.tabname]["override_settings_component"]
 
         destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
         destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
@@ -130,7 +131,7 @@ def connect_paste_params_buttons():
             )
 
         if binding.source_text_component is not None and fields is not None:
-            connect_paste(binding.paste_button, fields, binding.source_text_component, binding.override_settings_component, binding.tabname)
+            connect_paste(binding.paste_button, fields, binding.source_text_component, override_settings_component, binding.tabname)
 
         if binding.source_tabname is not None and fields is not None:
             paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else [])

modules/hypernetworks/hypernetwork.py

@@ -380,8 +380,8 @@ def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None):
         layer.hyper_k = hypernetwork_layers[0]
         layer.hyper_v = hypernetwork_layers[1]
 
-    context_k = hypernetwork_layers[0](context_k)
-    context_v = hypernetwork_layers[1](context_v)
+    context_k = devices.cond_cast_unet(hypernetwork_layers[0](devices.cond_cast_float(context_k)))
+    context_v = devices.cond_cast_unet(hypernetwork_layers[1](devices.cond_cast_float(context_v)))
     return context_k, context_v
 
 
@@ -496,7 +496,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
     shared.reload_hypernetworks()
 
 
-def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
@@ -554,7 +554,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
 
     pin_memory = shared.opts.pin_memory
 
-    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize)
+    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight)
 
     if shared.opts.save_training_settings_to_txt:
         saved_params = dict(
@@ -640,13 +640,19 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
                 
                 with devices.autocast():
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
+                    if use_weight:
+                        w = batch.weight.to(devices.device, non_blocking=pin_memory)
                     if tag_drop_out != 0 or shuffle_tags:
                         shared.sd_model.cond_stage_model.to(devices.device)
                         c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory)
                         shared.sd_model.cond_stage_model.to(devices.cpu)
                     else:
                         c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory)
-                    loss = shared.sd_model(x, c)[0] / gradient_step
+                    if use_weight:
+                        loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step
+                        del w
+                    else:
+                        loss = shared.sd_model.forward(x, c)[0] / gradient_step
                     del x
                     del c
 

modules/images.py

@@ -18,7 +18,7 @@ import string
 import json
 import hashlib
 
-from modules import sd_samplers, shared, script_callbacks
+from modules import sd_samplers, shared, script_callbacks, errors
 from modules.shared import opts, cmd_opts
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
@@ -553,6 +553,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         elif extension.lower() in (".jpg", ".jpeg", ".webp"):
             if image_to_save.mode == 'RGBA':
                 image_to_save = image_to_save.convert("RGB")
+            elif image_to_save.mode == 'I;16':
+                image_to_save = image_to_save.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")
 
             image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
 
@@ -575,17 +577,19 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
 
     image.already_saved_as = fullfn
 
-    target_side_length = 4000
-    oversize = image.width > target_side_length or image.height > target_side_length
-    if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > 4 * 1024 * 1024):
+    oversize = image.width > opts.target_side_length or image.height > opts.target_side_length
+    if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > opts.img_downscale_threshold * 1024 * 1024):
         ratio = image.width / image.height
 
         if oversize and ratio > 1:
-            image = image.resize((target_side_length, image.height * target_side_length // image.width), LANCZOS)
+            image = image.resize((round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)), LANCZOS)
         elif oversize:
-            image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS)
+            image = image.resize((round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)), LANCZOS)
 
-        _atomically_save_image(image, fullfn_without_extension, ".jpg")
+        try:
+            _atomically_save_image(image, fullfn_without_extension, ".jpg")
+        except Exception as e:
+            errors.display(e, "saving image as downscaled JPG")
 
     if opts.save_txt and info is not None:
         txt_fullfn = f"{fullfn_without_extension}.txt"

modules/img2img.py

@@ -73,6 +73,8 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
 
             if not save_normally:
                 os.makedirs(output_dir, exist_ok=True)
+                if processed_image.mode == 'RGBA':
+                    processed_image = processed_image.convert("RGB")
                 processed_image.save(os.path.join(output_dir, filename))
 
 

modules/processing.py

@@ -543,8 +543,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
-    _, extra_network_data = extra_networks.parse_prompts(p.all_prompts[0:1])
-
     if p.scripts is not None:
         p.scripts.process(p)
 
@@ -582,13 +580,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
                 sd_vae_approx.model()
 
-            if not p.disable_extra_networks:
-                extra_networks.activate(p, extra_network_data)
-
-        with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
-            processed = Processed(p, [], p.seed, "")
-            file.write(processed.infotext(p, 0))
-
         if state.job_count == -1:
             state.job_count = p.n_iter
 
@@ -609,11 +600,24 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if len(prompts) == 0:
                 break
 
-            prompts, _ = extra_networks.parse_prompts(prompts)
+            prompts, extra_network_data = extra_networks.parse_prompts(prompts)
+
+            if not p.disable_extra_networks:
+                with devices.autocast():
+                    extra_networks.activate(p, extra_network_data)
 
             if p.scripts is not None:
                 p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
 
+            # params.txt should be saved after scripts.process_batch, since the
+            # infotext could be modified by that callback
+            # Example: a wildcard processed by process_batch sets an extra model
+            # strength, which is saved as "Model Strength: 1.0" in the infotext
+            if n == 0:
+                with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
+                    processed = Processed(p, [], p.seed, "")
+                    file.write(processed.infotext(p, 0))
+
             uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
             c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
 

modules/script_callbacks.py

@@ -46,6 +46,18 @@ class CFGDenoiserParams:
         """Total number of sampling steps planned"""
 
 
+class CFGDenoisedParams:
+    def __init__(self, x, sampling_step, total_sampling_steps):
+        self.x = x
+        """Latent image representation in the process of being denoised"""
+
+        self.sampling_step = sampling_step
+        """Current Sampling step number"""
+
+        self.total_sampling_steps = total_sampling_steps
+        """Total number of sampling steps planned"""
+
+
 class UiTrainTabParams:
     def __init__(self, txt2img_preview_params):
         self.txt2img_preview_params = txt2img_preview_params
@@ -68,6 +80,7 @@ callback_map = dict(
     callbacks_before_image_saved=[],
     callbacks_image_saved=[],
     callbacks_cfg_denoiser=[],
+    callbacks_cfg_denoised=[],
     callbacks_before_component=[],
     callbacks_after_component=[],
     callbacks_image_grid=[],
@@ -150,6 +163,14 @@ def cfg_denoiser_callback(params: CFGDenoiserParams):
             report_exception(c, 'cfg_denoiser_callback')
 
 
+def cfg_denoised_callback(params: CFGDenoisedParams):
+    for c in callback_map['callbacks_cfg_denoised']:
+        try:
+            c.callback(params)
+        except Exception:
+            report_exception(c, 'cfg_denoised_callback')
+
+
 def before_component_callback(component, **kwargs):
     for c in callback_map['callbacks_before_component']:
         try:
@@ -283,6 +304,14 @@ def on_cfg_denoiser(callback):
     add_callback(callback_map['callbacks_cfg_denoiser'], callback)
 
 
+def on_cfg_denoised(callback):
+    """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
+    The callback is called with one argument:
+        - params: CFGDenoisedParams - parameters to be passed to the inner model and sampling state details.
+    """
+    add_callback(callback_map['callbacks_cfg_denoised'], callback)
+
+
 def on_before_component(callback):
     """register a function to be called before a component is created.
     The callback is called with arguments:

modules/sd_hijack.py

@@ -1,5 +1,6 @@
 import torch
 from torch.nn.functional import silu
+from types import MethodType
 
 import modules.textual_inversion.textual_inversion
 from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
@@ -76,6 +77,54 @@ def fix_checkpoint():
     pass
 
 
+def weighted_loss(sd_model, pred, target, mean=True):
+    #Calculate the weight normally, but ignore the mean
+    loss = sd_model._old_get_loss(pred, target, mean=False)
+    
+    #Check if we have weights available
+    weight = getattr(sd_model, '_custom_loss_weight', None)
+    if weight is not None:
+        loss *= weight
+    
+    #Return the loss, as mean if specified
+    return loss.mean() if mean else loss
+
+def weighted_forward(sd_model, x, c, w, *args, **kwargs):
+    try:
+        #Temporarily append weights to a place accessible during loss calc
+        sd_model._custom_loss_weight = w
+        
+        #Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely
+        #Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set
+        if not hasattr(sd_model, '_old_get_loss'):
+            sd_model._old_get_loss = sd_model.get_loss
+        sd_model.get_loss = MethodType(weighted_loss, sd_model)
+
+        #Run the standard forward function, but with the patched 'get_loss'
+        return sd_model.forward(x, c, *args, **kwargs)
+    finally:
+        try:
+            #Delete temporary weights if appended
+            del sd_model._custom_loss_weight
+        except AttributeError as e:
+            pass
+            
+        #If we have an old loss function, reset the loss function to the original one
+        if hasattr(sd_model, '_old_get_loss'):
+            sd_model.get_loss = sd_model._old_get_loss
+            del sd_model._old_get_loss
+
+def apply_weighted_forward(sd_model):
+    #Add new function 'weighted_forward' that can be called to calc weighted loss
+    sd_model.weighted_forward = MethodType(weighted_forward, sd_model)
+
+def undo_weighted_forward(sd_model):
+    try:
+        del sd_model.weighted_forward
+    except AttributeError as e:
+        pass
+
+
 class StableDiffusionModelHijack:
     fixes = None
     comments = []
@@ -104,6 +153,10 @@ class StableDiffusionModelHijack:
             m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
             m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
 
+        apply_weighted_forward(m)
+        if m.cond_stage_key == "edit":
+            sd_hijack_unet.hijack_ddpm_edit()
+
         self.optimization_method = apply_optimizations()
 
         self.clip = m.cond_stage_model
@@ -132,6 +185,7 @@ class StableDiffusionModelHijack:
             m.cond_stage_model = m.cond_stage_model.wrapped
 
         undo_optimizations()
+        undo_weighted_forward(m)
 
         self.apply_circular(False)
         self.layers = None

modules/sd_hijack_inpainting.py

@@ -11,6 +11,7 @@ import ldm.models.diffusion.plms
 from ldm.models.diffusion.ddpm import LatentDiffusion
 from ldm.models.diffusion.plms import PLMSSampler
 from ldm.models.diffusion.ddim import DDIMSampler, noise_like
+from ldm.models.diffusion.sampling_util import norm_thresholding
 
 
 @torch.no_grad()

modules/sd_hijack_unet.py

@@ -44,6 +44,7 @@ def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
     with devices.autocast():
         return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
 
+
 class GELUHijack(torch.nn.GELU, torch.nn.Module):
     def __init__(self, *args, **kwargs):
         torch.nn.GELU.__init__(self, *args, **kwargs)
@@ -53,6 +54,16 @@ class GELUHijack(torch.nn.GELU, torch.nn.Module):
         else:
             return torch.nn.GELU.forward(self, x)
 
+
+ddpm_edit_hijack = None
+def hijack_ddpm_edit():
+    global ddpm_edit_hijack
+    if not ddpm_edit_hijack:
+        CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
+        CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
+        ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
+
+
 unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
 CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
 CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)

modules/sd_models.py

@@ -105,9 +105,15 @@ def checkpoint_tiles():
 def list_models():
     checkpoints_list.clear()
     checkpoint_alisases.clear()
-    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], ext_blacklist=[".vae.safetensors"])
 
     cmd_ckpt = shared.cmd_opts.ckpt
+    if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
+        model_url = None
+    else:
+        model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
+
+    model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
+
     if os.path.exists(cmd_ckpt):
         checkpoint_info = CheckpointInfo(cmd_ckpt)
         checkpoint_info.register()

modules/sd_samplers_kdiffusion.py

@@ -8,6 +8,7 @@ from modules import prompt_parser, devices, sd_samplers_common
 from modules.shared import opts, state
 import modules.shared as shared
 from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
+from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
 
 samplers_k_diffusion = [
     ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
@@ -136,6 +137,9 @@ class CFGDenoiser(torch.nn.Module):
 
             x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
 
+        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
+        cfg_denoised_callback(denoised_params)
+
         devices.test_for_nans(x_out, "unet")
 
         if opts.live_preview_content == "Prompt":
@@ -269,6 +273,16 @@ class KDiffusionSampler:
 
         return sigmas
 
+    def create_noise_sampler(self, x, sigmas, p):
+        """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
+        if shared.opts.no_dpmpp_sde_batch_determinism:
+            return None
+
+        from k_diffusion.sampling import BrownianTreeNoiseSampler
+        sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+        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_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
 
@@ -278,18 +292,24 @@ class KDiffusionSampler:
         xi = x + noise * sigma_sched[0]
         
         extra_params_kwargs = self.initialize(p)
-        if 'sigma_min' in inspect.signature(self.func).parameters:
+        parameters = inspect.signature(self.func).parameters
+
+        if 'sigma_min' in parameters:
             ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
             extra_params_kwargs['sigma_min'] = sigma_sched[-2]
-        if 'sigma_max' in inspect.signature(self.func).parameters:
+        if 'sigma_max' in parameters:
             extra_params_kwargs['sigma_max'] = sigma_sched[0]
-        if 'n' in inspect.signature(self.func).parameters:
+        if 'n' in parameters:
             extra_params_kwargs['n'] = len(sigma_sched) - 1
-        if 'sigma_sched' in inspect.signature(self.func).parameters:
+        if 'sigma_sched' in parameters:
             extra_params_kwargs['sigma_sched'] = sigma_sched
-        if 'sigmas' in inspect.signature(self.func).parameters:
+        if 'sigmas' in parameters:
             extra_params_kwargs['sigmas'] = sigma_sched
 
+        if self.funcname == 'sample_dpmpp_sde':
+            noise_sampler = self.create_noise_sampler(x, sigmas, p)
+            extra_params_kwargs['noise_sampler'] = noise_sampler
+
         self.model_wrap_cfg.init_latent = x
         self.last_latent = x
         extra_args={
@@ -303,7 +323,7 @@ class KDiffusionSampler:
 
         return samples
 
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps = steps or p.steps
 
         sigmas = self.get_sigmas(p, steps)
@@ -311,14 +331,20 @@ class KDiffusionSampler:
         x = x * sigmas[0]
 
         extra_params_kwargs = self.initialize(p)
-        if 'sigma_min' in inspect.signature(self.func).parameters:
+        parameters = inspect.signature(self.func).parameters
+
+        if 'sigma_min' in parameters:
             extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
             extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
-            if 'n' in inspect.signature(self.func).parameters:
+            if 'n' in parameters:
                 extra_params_kwargs['n'] = steps
         else:
             extra_params_kwargs['sigmas'] = sigmas
 
+        if self.funcname == 'sample_dpmpp_sde':
+            noise_sampler = self.create_noise_sampler(x, sigmas, p)
+            extra_params_kwargs['noise_sampler'] = noise_sampler
+
         self.last_latent = x
         samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
             'cond': conditioning, 

modules/shared.py

@@ -81,6 +81,7 @@ parser.add_argument("--freeze-settings", action='store_true', help="disable edit
 parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(data_path, 'config.json'))
 parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
 parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
 parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
 parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
@@ -107,6 +108,7 @@ parser.add_argument("--server-name", type=str, help="Sets hostname of server", d
 parser.add_argument("--gradio-queue", action='store_true', help="Uses gradio queue; experimental option; breaks restart UI button")
 parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
 parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
+parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
 
 
 script_loading.preload_extensions(extensions.extensions_dir, parser)
@@ -325,7 +327,9 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
-    "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
+    "export_for_4chan": OptionInfo(True, "If the saved image file size is above the limit, or its either width or height are above the limit, save a downscaled copy as JPG"),
+    "img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
+    "target_side_length": OptionInfo(4000, "Width/height limit for the above option, in pixels", gr.Number),
 
     "use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
     "use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
@@ -364,7 +368,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {
-    "face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
+    "face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
     "code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
     "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
 }))
@@ -414,6 +418,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
 options_templates.update(options_section(('compatibility', "Compatibility"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
+    "no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
     "use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
 }))
 

modules/shared_items.py

@@ -20,4 +20,4 @@ def sd_vae_items():
 def refresh_vae_list():
     import modules.sd_vae
 
-    return modules.sd_vae.refresh_vae_list
+    modules.sd_vae.refresh_vae_list()

modules/textual_inversion/dataset.py

@@ -19,9 +19,10 @@ re_numbers_at_start = re.compile(r"^[-\d]+\s*")
 
 
 class DatasetEntry:
-    def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None):
+    def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None, weight=None):
         self.filename = filename
         self.filename_text = filename_text
+        self.weight = weight
         self.latent_dist = latent_dist
         self.latent_sample = latent_sample
         self.cond = cond
@@ -30,7 +31,7 @@ class DatasetEntry:
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False, use_weight=False):
         re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
 
         self.placeholder_token = placeholder_token
@@ -56,10 +57,16 @@ class PersonalizedBase(Dataset):
 
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
+            alpha_channel = None
             if shared.state.interrupted:
                 raise Exception("interrupted")
             try:
-                image = Image.open(path).convert('RGB')
+                image = Image.open(path)
+                #Currently does not work for single color transparency
+                #We would need to read image.info['transparency'] for that
+                if use_weight and 'A' in image.getbands():
+                    alpha_channel = image.getchannel('A')
+                image = image.convert('RGB')
                 if not varsize:
                     image = image.resize((width, height), PIL.Image.BICUBIC)
             except Exception:
@@ -87,17 +94,35 @@ class PersonalizedBase(Dataset):
             with devices.autocast():
                 latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
 
-            if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)):
-                latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
-                latent_sampling_method = "once"
-                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
-            elif latent_sampling_method == "deterministic":
-                # Works only for DiagonalGaussianDistribution
-                latent_dist.std = 0
-                latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
-                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
-            elif latent_sampling_method == "random":
-                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist)
+            #Perform latent sampling, even for random sampling.
+            #We need the sample dimensions for the weights
+            if latent_sampling_method == "deterministic":
+                if isinstance(latent_dist, DiagonalGaussianDistribution):
+                    # Works only for DiagonalGaussianDistribution
+                    latent_dist.std = 0
+                else:
+                    latent_sampling_method = "once"
+            latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
+
+            if use_weight and alpha_channel is not None:
+                channels, *latent_size = latent_sample.shape
+                weight_img = alpha_channel.resize(latent_size)
+                npweight = np.array(weight_img).astype(np.float32)
+                #Repeat for every channel in the latent sample
+                weight = torch.tensor([npweight] * channels).reshape([channels] + latent_size)
+                #Normalize the weight to a minimum of 0 and a mean of 1, that way the loss will be comparable to default.
+                weight -= weight.min()
+                weight /= weight.mean()
+            elif use_weight:
+                #If an image does not have a alpha channel, add a ones weight map anyway so we can stack it later
+                weight = torch.ones(latent_sample.shape)
+            else:
+                weight = None
+            
+            if latent_sampling_method == "random":
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight)
+            else:
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, weight=weight)
 
             if not (self.tag_drop_out != 0 or self.shuffle_tags):
                 entry.cond_text = self.create_text(filename_text)
@@ -110,6 +135,7 @@ class PersonalizedBase(Dataset):
             del torchdata
             del latent_dist
             del latent_sample
+            del weight
 
         self.length = len(self.dataset)
         self.groups = list(groups.values())
@@ -195,6 +221,10 @@ class BatchLoader:
         self.cond_text = [entry.cond_text for entry in data]
         self.cond = [entry.cond for entry in data]
         self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
+        if all(entry.weight is not None for entry in data):
+            self.weight = torch.stack([entry.weight for entry in data]).squeeze(1)
+        else:
+            self.weight = None
         #self.emb_index = [entry.emb_index for entry in data]
         #print(self.latent_sample.device)
 

modules/textual_inversion/textual_inversion.py

@@ -351,7 +351,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
         assert log_directory, "Log directory is empty"
 
 
-def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     save_embedding_every = save_embedding_every or 0
     create_image_every = create_image_every or 0
     template_file = textual_inversion_templates.get(template_filename, None)
@@ -410,7 +410,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
 
     pin_memory = shared.opts.pin_memory
 
-    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize)
+    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight)
 
     if shared.opts.save_training_settings_to_txt:
         save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})
@@ -480,6 +480,8 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
             
                 with devices.autocast():
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
+                    if use_weight:
+                        w = batch.weight.to(devices.device, non_blocking=pin_memory)
                     c = shared.sd_model.cond_stage_model(batch.cond_text)
 
                     if is_training_inpainting_model:
@@ -490,7 +492,11 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
                     else:
                         cond = c
 
-                    loss = shared.sd_model(x, cond)[0] / gradient_step
+                    if use_weight:
+                        loss = shared.sd_model.weighted_forward(x, cond, w)[0] / gradient_step
+                        del w
+                    else:
+                        loss = shared.sd_model.forward(x, cond)[0] / gradient_step
                     del x
 
                     _loss_step += loss.item()

modules/ui.py

@@ -631,9 +631,9 @@ def create_ui():
                 (hr_resize_y, "Hires resize-2"),
                 *modules.scripts.scripts_txt2img.infotext_fields
             ]
-            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
+            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
             parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
-                paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None, override_settings_component=override_settings,
+                paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None,
             ))
 
             txt2img_preview_params = [
@@ -963,10 +963,10 @@ def create_ui():
                 (mask_blur, "Mask blur"),
                 *modules.scripts.scripts_img2img.infotext_fields
             ]
-            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
-            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
+            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
             parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
-                paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None, override_settings_component=override_settings,
+                paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None,
             ))
 
     modules.scripts.scripts_current = None
@@ -1191,6 +1191,8 @@ def create_ui():
                         create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
                         save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
 
+                    use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
+
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
                     preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
 
@@ -1304,6 +1306,7 @@ def create_ui():
                 shuffle_tags,
                 tag_drop_out,
                 latent_sampling_method,
+                use_weight,
                 create_image_every,
                 save_embedding_every,
                 template_file,
@@ -1337,6 +1340,7 @@ def create_ui():
                 shuffle_tags,
                 tag_drop_out,
                 latent_sampling_method,
+                use_weight,
                 create_image_every,
                 save_embedding_every,
                 template_file,
@@ -1782,7 +1786,7 @@ def versions_html():
     return f"""
 python: <span title="{sys.version}">{python_version}</span>
  • 
-torch: {torch.__version__}
+torch: {getattr(torch, '__long_version__',torch.__version__)}
  • 
 xformers: {xformers_version}
  • 

modules/ui_extensions.py

@@ -80,6 +80,7 @@ def extension_table():
             <tr>
                 <th><abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr></th>
                 <th>URL</th>
+                <th><abbr title="Extension version">Version</abbr></th>
                 <th><abbr title="Use checkbox to mark the extension for update; it will be updated when you click apply button">Update</abbr></th>
             </tr>
         </thead>
@@ -87,11 +88,7 @@ def extension_table():
     """
 
     for ext in extensions.extensions:
-        remote = ""
-        if ext.is_builtin:
-            remote = "built-in"
-        elif ext.remote:
-            remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""
+        remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""
 
         if ext.can_update:
             ext_status = f"""<label><input class="gr-check-radio gr-checkbox" name="update_{html.escape(ext.name)}" checked="checked" type="checkbox">{html.escape(ext.status)}</label>"""
@@ -102,6 +99,7 @@ def extension_table():
             <tr>
                 <td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
                 <td>{remote}</td>
+                <td>{ext.version}</td>
                 <td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
             </tr>
     """

modules/ui_extra_networks.py

@@ -76,6 +76,10 @@ class ExtraNetworksPage:
                 while subdir.startswith("/"):
                     subdir = subdir[1:]
 
+                is_empty = len(os.listdir(x)) == 0
+                if not is_empty and not subdir.endswith("/"):
+                    subdir = subdir + "/"
+
                 subdirs[subdir] = 1
 
         if subdirs:
@@ -94,11 +98,13 @@ class ExtraNetworksPage:
             dirs = "".join([f"<li>{x}</li>" for x in self.allowed_directories_for_previews()])
             items_html = shared.html("extra-networks-no-cards.html").format(dirs=dirs)
 
+        self_name_id = self.name.replace(" ", "_")
+
         res = f"""
-<div id='{tabname}_{self.name}_subdirs' class='extra-network-subdirs extra-network-subdirs-{view}'>
+<div id='{tabname}_{self_name_id}_subdirs' class='extra-network-subdirs extra-network-subdirs-{view}'>
 {subdirs_html}
 </div>
-<div id='{tabname}_{self.name}_cards' class='extra-network-{view}'>
+<div id='{tabname}_{self_name_id}_cards' class='extra-network-{view}'>
 {items_html}
 </div>
 """