Merge branch 'master' into improved-hr-conflict-test

configs/instruct-pix2pix.yaml

@@ -20,8 +20,7 @@ model:
     conditioning_key: hybrid
     monitor: val/loss_simple_ema
     scale_factor: 0.18215
-    use_ema: true
-    load_ema: true
+    use_ema: false
 
     scheduler_config: # 10000 warmup steps
       target: ldm.lr_scheduler.LambdaLinearScheduler

javascript/ui.js

@@ -191,6 +191,28 @@ function confirm_clear_prompt(prompt, negative_prompt) {
     return [prompt, negative_prompt]
 }
 
+
+promptTokecountUpdateFuncs = {}
+
+function recalculatePromptTokens(name){
+    if(promptTokecountUpdateFuncs[name]){
+        promptTokecountUpdateFuncs[name]()
+    }
+}
+
+function recalculate_prompts_txt2img(){
+    recalculatePromptTokens('txt2img_prompt')
+    recalculatePromptTokens('txt2img_neg_prompt')
+    return args_to_array(arguments);
+}
+
+function recalculate_prompts_img2img(){
+    recalculatePromptTokens('img2img_prompt')
+    recalculatePromptTokens('img2img_neg_prompt')
+    return args_to_array(arguments);
+}
+
+
 opts = {}
 onUiUpdate(function(){
 	if(Object.keys(opts).length != 0) return;
@@ -232,14 +254,12 @@ onUiUpdate(function(){
             return
         }
 
-
         prompt.parentElement.insertBefore(counter, prompt)
         counter.classList.add("token-counter")
         prompt.parentElement.style.position = "relative"
 
-		textarea.addEventListener("input", function(){
-		    update_token_counter(id_button);
-		});
+		promptTokecountUpdateFuncs[id] = function(){ update_token_counter(id_button); }
+		textarea.addEventListener("input", promptTokecountUpdateFuncs[id]);
     }
 
     registerTextarea('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button')
@@ -273,7 +293,7 @@ onOptionsChanged(function(){
 
 let txt2img_textarea, img2img_textarea = undefined;
 let wait_time = 800
-let token_timeout;
+let token_timeouts = {};
 
 function update_txt2img_tokens(...args) {
 	update_token_counter("txt2img_token_button")
@@ -290,9 +310,9 @@ function update_img2img_tokens(...args) {
 }
 
 function update_token_counter(button_id) {
-	if (token_timeout)
-		clearTimeout(token_timeout);
-	token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
+	if (token_timeouts[button_id])
+		clearTimeout(token_timeouts[button_id]);
+	token_timeouts[button_id] = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
 }
 
 function restart_reload(){

launch.py

@@ -223,6 +223,7 @@ def prepare_environment():
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
     commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
 
+    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.16rc425')
     gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
     clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
     openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
@@ -282,7 +283,7 @@ def prepare_environment():
     if (not is_installed("xformers") or reinstall_xformers) and xformers:
         if platform.system() == "Windows":
             if platform.python_version().startswith("3.10"):
-                run_pip(f"install -U -I --no-deps xformers==0.0.16rc425", "xformers")
+                run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
             else:
                 print("Installation of xformers is not supported in this version of Python.")
                 print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")

modules/img2img.py

@@ -7,6 +7,7 @@ import numpy as np
 from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
 
 from modules import devices, sd_samplers
+from modules.generation_parameters_copypaste import create_override_settings_dict
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, state
 import modules.shared as shared
@@ -75,7 +76,9 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, *args):
+def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
+    override_settings = create_override_settings_dict(override_settings_texts)
+
     is_batch = mode == 5
 
     if mode == 0:  # img2img
@@ -142,6 +145,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
         inpaint_full_res=inpaint_full_res,
         inpaint_full_res_padding=inpaint_full_res_padding,
         inpainting_mask_invert=inpainting_mask_invert,
+        override_settings=override_settings,
     )
 
     p.scripts = modules.scripts.scripts_txt2img

modules/processing.py

@@ -455,7 +455,6 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "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}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
-        "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
         "Clip skip": None if clip_skip <= 1 else clip_skip,
         "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
     }

modules/sd_samplers_common.py

+from collections import namedtuple
+import numpy as np
+import torch
+from PIL import Image
+import torchsde._brownian.brownian_interval
+from modules import devices, processing, images, sd_vae_approx
+
+from modules.shared import opts, state
+import modules.shared as shared
+
+SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
+
+
+def setup_img2img_steps(p, steps=None):
+    if opts.img2img_fix_steps or steps is not None:
+        requested_steps = (steps or p.steps)
+        steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
+        t_enc = requested_steps - 1
+    else:
+        steps = p.steps
+        t_enc = int(min(p.denoising_strength, 0.999) * steps)
+
+    return steps, t_enc
+
+
+approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
+
+
+def single_sample_to_image(sample, approximation=None):
+    if approximation is None:
+        approximation = approximation_indexes.get(opts.show_progress_type, 0)
+
+    if approximation == 2:
+        x_sample = sd_vae_approx.cheap_approximation(sample)
+    elif approximation == 1:
+        x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
+    else:
+        x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
+
+    x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
+    x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+    x_sample = x_sample.astype(np.uint8)
+    return Image.fromarray(x_sample)
+
+
+def sample_to_image(samples, index=0, approximation=None):
+    return single_sample_to_image(samples[index], approximation)
+
+
+def samples_to_image_grid(samples, approximation=None):
+    return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
+
+
+def store_latent(decoded):
+    state.current_latent = decoded
+
+    if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
+        if not shared.parallel_processing_allowed:
+            shared.state.assign_current_image(sample_to_image(decoded))
+
+
+class InterruptedException(BaseException):
+    pass
+
+
+# MPS fix for randn in torchsde
+# XXX move this to separate file for MPS
+def torchsde_randn(size, dtype, device, seed):
+    if device.type == 'mps':
+        generator = torch.Generator(devices.cpu).manual_seed(int(seed))
+        return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+    else:
+        generator = torch.Generator(device).manual_seed(int(seed))
+        return torch.randn(size, dtype=dtype, device=device, generator=generator)
+
+
+torchsde._brownian.brownian_interval._randn = torchsde_randn
+

modules/sd_samplers_compvis.py

+import math
+import ldm.models.diffusion.ddim
+import ldm.models.diffusion.plms
+
+import numpy as np
+import torch
+
+from modules.shared import state
+from modules import sd_samplers_common, prompt_parser, shared
+
+
+samplers_data_compvis = [
+    sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
+    sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
+]
+
+
+class VanillaStableDiffusionSampler:
+    def __init__(self, constructor, sd_model):
+        self.sampler = constructor(sd_model)
+        self.is_plms = hasattr(self.sampler, 'p_sample_plms')
+        self.orig_p_sample_ddim = self.sampler.p_sample_plms if self.is_plms else self.sampler.p_sample_ddim
+        self.mask = None
+        self.nmask = None
+        self.init_latent = None
+        self.sampler_noises = None
+        self.step = 0
+        self.stop_at = None
+        self.eta = None
+        self.config = None
+        self.last_latent = None
+
+        self.conditioning_key = sd_model.model.conditioning_key
+
+    def number_of_needed_noises(self, p):
+        return 0
+
+    def launch_sampling(self, steps, func):
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except sd_samplers_common.InterruptedException:
+            return self.last_latent
+
+    def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
+        if state.interrupted or state.skipped:
+            raise sd_samplers_common.InterruptedException
+
+        if self.stop_at is not None and self.step > self.stop_at:
+            raise sd_samplers_common.InterruptedException
+
+        # Have to unwrap the inpainting conditioning here to perform pre-processing
+        image_conditioning = None
+        if isinstance(cond, dict):
+            image_conditioning = cond["c_concat"][0]
+            cond = cond["c_crossattn"][0]
+            unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
+
+        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
+        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
+
+        assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
+        cond = tensor
+
+        # for DDIM, shapes must match, we can't just process cond and uncond independently;
+        # filling unconditional_conditioning with repeats of the last vector to match length is
+        # not 100% correct but should work well enough
+        if unconditional_conditioning.shape[1] < cond.shape[1]:
+            last_vector = unconditional_conditioning[:, -1:]
+            last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
+            unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
+        elif unconditional_conditioning.shape[1] > cond.shape[1]:
+            unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
+
+        if self.mask is not None:
+            img_orig = self.sampler.model.q_sample(self.init_latent, ts)
+            x_dec = img_orig * self.mask + self.nmask * x_dec
+
+        # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
+        # Note that they need to be lists because it just concatenates them later.
+        if image_conditioning is not None:
+            cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
+        res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
+
+        if self.mask is not None:
+            self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
+        else:
+            self.last_latent = res[1]
+
+        sd_samplers_common.store_latent(self.last_latent)
+
+        self.step += 1
+        state.sampling_step = self.step
+        shared.total_tqdm.update()
+
+        return res
+
+    def initialize(self, p):
+        self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim
+        if self.eta != 0.0:
+            p.extra_generation_params["Eta DDIM"] = self.eta
+
+        for fieldname in ['p_sample_ddim', 'p_sample_plms']:
+            if hasattr(self.sampler, fieldname):
+                setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
+
+        self.mask = p.mask if hasattr(p, 'mask') else None
+        self.nmask = p.nmask if hasattr(p, 'nmask') else None
+
+    def adjust_steps_if_invalid(self, p, num_steps):
+        if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
+            valid_step = 999 / (1000 // num_steps)
+            if valid_step == math.floor(valid_step):
+                return int(valid_step) + 1
+        
+        return num_steps
+
+    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)
+        steps = self.adjust_steps_if_invalid(p, steps)
+        self.initialize(p)
+
+        self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
+        x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
+
+        self.init_latent = x
+        self.last_latent = x
+        self.step = 0
+
+        # Wrap the conditioning models with additional image conditioning for inpainting model
+        if image_conditioning is not None:
+            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
+        samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
+
+        return samples
+
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        self.initialize(p)
+
+        self.init_latent = None
+        self.last_latent = x
+        self.step = 0
+
+        steps = self.adjust_steps_if_invalid(p, steps or p.steps)
+
+        # Wrap the conditioning models with additional image conditioning for inpainting model
+        # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
+        if image_conditioning is not None:
+            conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
+            unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
+
+        samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
+
+        return samples_ddim

modules/shared.py

@@ -105,6 +105,8 @@ parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requ
 parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
 parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
 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")
+
 
 
 script_loading.preload_extensions(extensions.extensions_dir, parser)
@@ -127,12 +129,13 @@ restricted_opts = {
 ui_reorder_categories = [
     "inpaint",
     "sampler",
+    "checkboxes",
+    "hires_fix",
     "dimensions",
     "cfg",
     "seed",
-    "checkboxes",
-    "hires_fix",
     "batch",
+    "override_settings",
     "scripts",
 ]
 
@@ -346,10 +349,10 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
 }))
 
 options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
-    "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
-    "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
+    "save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
+    "grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
     "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
-    "directories_filename_pattern": OptionInfo("", "Directory name pattern", component_args=hide_dirs),
+    "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
     "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
 }))
 
@@ -440,7 +443,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
     "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
-    "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
+    "disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
     "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
     "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
     "font": OptionInfo("", "Font for image grids that have text"),
@@ -605,11 +608,37 @@ class Options:
 
         self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])}
 
+    def cast_value(self, key, value):
+        """casts an arbitrary to the same type as this setting's value with key
+        Example: cast_value("eta_noise_seed_delta", "12") -> returns 12 (an int rather than str)
+        """
+
+        if value is None:
+            return None
+
+        default_value = self.data_labels[key].default
+        if default_value is None:
+            default_value = getattr(self, key, None)
+        if default_value is None:
+            return None
+
+        expected_type = type(default_value)
+        if expected_type == bool and value == "False":
+            value = False
+        else:
+            value = expected_type(value)
+
+        return value
+
+
 
 opts = Options()
 if os.path.exists(config_filename):
     opts.load(config_filename)
 
+settings_components = None
+"""assinged from ui.py, a mapping on setting anmes to gradio components repsponsible for those settings"""
+
 latent_upscale_default_mode = "Latent"
 latent_upscale_modes = {
     "Latent": {"mode": "bilinear", "antialias": False},

modules/txt2img.py

 import modules.scripts
 from modules import sd_samplers
+from modules.generation_parameters_copypaste import create_override_settings_dict
 from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \
     StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, cmd_opts
@@ -8,7 +9,9 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, *args):
+
+def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, *args):
+    override_settings = create_override_settings_dict(override_settings_texts)
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -40,7 +43,8 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
         hr_resize_y=hr_resize_y,
         hr_sampler=sd_samplers.samplers_for_img2img[hr_sampler_index - 1].name if hr_sampler_index != 0 else '---',
         hr_prompt=hr_prompt,
-        hr_negative_prompt=hr_negative_prompt
+        hr_negative_prompt=hr_negative_prompt,
+        override_settings=override_settings,
     )
 
     p.scripts = modules.scripts.scripts_txt2img

modules/ui.py

@@ -380,6 +380,7 @@ def apply_setting(key, value):
     opts.save(shared.config_filename)
     return getattr(opts, key)
 
+
 def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
     def refresh():
         refresh_method()
@@ -433,6 +434,18 @@ def get_value_for_setting(key):
     return gr.update(value=value, **args)
 
 
+def create_override_settings_dropdown(tabname, row):
+    dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
+
+    dropdown.change(
+        fn=lambda x: gr.Dropdown.update(visible=len(x) > 0),
+        inputs=[dropdown],
+        outputs=[dropdown],
+    )
+
+    return dropdown
+
+
 def create_ui():
     import modules.img2img
     import modules.txt2img
@@ -514,6 +527,10 @@ def create_ui():
                                 batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
                                 batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
 
+                    elif category == "override_settings":
+                        with FormRow(elem_id="txt2img_override_settings_row") as row:
+                            override_settings = create_override_settings_dropdown('txt2img', row)
+
                     elif category == "scripts":
                         with FormGroup(elem_id="txt2img_script_container"):
                             custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
@@ -535,7 +552,6 @@ def create_ui():
                 )
 
             txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
-            parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -569,6 +585,8 @@ def create_ui():
                     hr_sampler_index,
                     hr_prompt,
                     hr_negative_prompt,
+                    override_settings,
+
                 ] + custom_inputs,
 
                 outputs=[
@@ -632,6 +650,9 @@ def create_ui():
                 *modules.scripts.scripts_txt2img.infotext_fields
             ]
             parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
+            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,
+            ))
 
             txt2img_preview_params = [
                 txt2img_prompt,
@@ -779,6 +800,10 @@ def create_ui():
                                 batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
                                 batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
 
+                    elif category == "override_settings":
+                        with FormRow(elem_id="img2img_override_settings_row") as row:
+                            override_settings = create_override_settings_dropdown('img2img', row)
+
                     elif category == "scripts":
                         with FormGroup(elem_id="img2img_script_container"):
                             custom_inputs = modules.scripts.scripts_img2img.setup_ui()
@@ -813,7 +838,6 @@ def create_ui():
                                 )
 
             img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
-            parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -866,7 +890,8 @@ def create_ui():
                     inpainting_mask_invert,
                     img2img_batch_input_dir,
                     img2img_batch_output_dir,
-                    img2img_batch_inpaint_mask_dir
+                    img2img_batch_inpaint_mask_dir,
+                    override_settings,
                 ] + custom_inputs,
                 outputs=[
                     img2img_gallery,
@@ -954,6 +979,9 @@ def create_ui():
             ]
             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.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,
+            ))
 
     modules.scripts.scripts_current = None
 
@@ -971,7 +999,11 @@ def create_ui():
                 html2 = gr.HTML()
                 with gr.Row():
                     buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
-                parameters_copypaste.bind_buttons(buttons, image, generation_info)
+
+                for tabname, button in buttons.items():
+                    parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+                        paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
+                    ))
 
         image.change(
             fn=wrap_gradio_call(modules.extras.run_pnginfo),
@@ -1380,6 +1412,7 @@ def create_ui():
 
     components = []
     component_dict = {}
+    shared.settings_components = component_dict
 
     script_callbacks.ui_settings_callback()
     opts.reorder()
@@ -1546,8 +1579,7 @@ def create_ui():
                 component = create_setting_component(k, is_quicksettings=True)
                 component_dict[k] = component
 
-        parameters_copypaste.integrate_settings_paste_fields(component_dict)
-        parameters_copypaste.run_bind()
+        parameters_copypaste.connect_paste_params_buttons()
 
         with gr.Tabs(elem_id="tabs") as tabs:
             for interface, label, ifid in interfaces:

modules/ui_common.py

@@ -198,5 +198,9 @@ Requested path was: {f}
                 html_info = gr.HTML(elem_id=f'html_info_{tabname}')
                 html_log = gr.HTML(elem_id=f'html_log_{tabname}')
 
-            parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
+            for paste_tabname, paste_button in buttons.items():
+                parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+                    paste_button=paste_button, tabname=paste_tabname, source_tabname="txt2img" if tabname == "txt2img" else None, source_image_component=result_gallery
+                ))
+
             return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log

modules/ui_extra_networks_checkpoints.py

@@ -14,6 +14,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
         shared.refresh_checkpoints()
 
     def list_items(self):
+        checkpoint: sd_models.CheckpointInfo
         for name, checkpoint in sd_models.checkpoints_list.items():
             path, ext = os.path.splitext(checkpoint.filename)
             previews = [path + ".png", path + ".preview.png"]
@@ -28,7 +29,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
                 "name": checkpoint.name_for_extra,
                 "filename": path,
                 "preview": preview,
-                "search_term": self.search_terms_from_path(checkpoint.filename),
+                "search_term": self.search_terms_from_path(checkpoint.filename) + " " + (checkpoint.sha256 or ""),
                 "onclick": '"' + html.escape(f"""return selectCheckpoint({json.dumps(name)})""") + '"',
                 "local_preview": path + ".png",
             }

webui.py

@@ -52,6 +52,9 @@ else:
 
 
 def check_versions():
+    if shared.cmd_opts.skip_version_check:
+        return
+
     expected_torch_version = "1.13.1"
 
     if version.parse(torch.__version__) < version.parse(expected_torch_version):
@@ -59,7 +62,10 @@ def check_versions():
 You are running torch {torch.__version__}.
 The program is tested to work with torch {expected_torch_version}.
 To reinstall the desired version, run with commandline flag --reinstall-torch.
-Beware that this will cause a lot of large files to be downloaded.
+Beware that this will cause a lot of large files to be downloaded, as well as
+there are reports of issues with training tab on the latest version.
+
+Use --skip-version-check commandline argument to disable this check.
         """.strip())
 
     expected_xformers_version = "0.0.16rc425"
@@ -71,6 +77,8 @@ Beware that this will cause a lot of large files to be downloaded.
 You are running xformers {xformers.__version__}.
 The program is tested to work with xformers {expected_xformers_version}.
 To reinstall the desired version, run with commandline flag --reinstall-xformers.
+
+Use --skip-version-check commandline argument to disable this check.
             """.strip())