Merge remote-tracking branch 'upstream/master' into ModelLoader

.gitignore

@@ -21,3 +21,5 @@ __pycache__
 /interrogate
 /user.css
 /.idea
+notification.mp3
+/SwinIR

javascript/dragdrop.js

@@ -68,13 +68,19 @@ window.addEventListener('paste', e => {
     if ( ! isValidImageList( files ) ) {
         return;
     }
-    [...gradioApp().querySelectorAll('input[type=file][accept="image/x-png,image/gif,image/jpeg"]')]
-        .filter(input => !input.matches('.\\!hidden input[type=file]'))
-        .forEach(input => {
-            input.files = files;
-            input.dispatchEvent(new Event('change'))
-        });
-    [...gradioApp().querySelectorAll('[data-testid="image"]')]
-        .filter(imgWrap => !imgWrap.closest('.\\!hidden'))
-        .forEach(imgWrap => dropReplaceImage( imgWrap, files ));
+
+    const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')]
+        .filter(el => uiElementIsVisible(el));
+    if ( ! visibleImageFields.length ) {
+        return;
+    }
+    
+    const firstFreeImageField = visibleImageFields
+        .filter(el => el.querySelector('input[type=file]'))?.[0];
+
+    dropReplaceImage(
+        firstFreeImageField ?
+        firstFreeImageField :
+        visibleImageFields[visibleImageFields.length - 1]
+    , files );
 });

javascript/notification.js

@@ -25,6 +25,9 @@ onUiUpdate(function(){
 
     lastHeadImg = headImg;
 
+    // play notification sound if available
+    gradioApp().querySelector('#audio_notification audio')?.play();
+
     if (document.hasFocus()) return;
 
     // Multiple copies of the images are in the DOM when one is selected. Dedup with a Set to get the real number generated.

javascript/ui.js

@@ -1,9 +1,8 @@
 // various functions for interation with ui.py not large enough to warrant putting them in separate files
 
 function selected_gallery_index(){
-    var gr = gradioApp()
-    var buttons = gradioApp().querySelectorAll(".gallery-item")
-    var button = gr.querySelector(".gallery-item.\\!ring-2")
+    var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem .gallery-item')
+    var button = gradioApp().querySelector('[style="display: block;"].tabitem .gallery-item.\\!ring-2')
 
     var result = -1
     buttons.forEach(function(v, i){ if(v==button) { result = i } })

modules/extras.py

@@ -3,6 +3,9 @@ import os
 import numpy as np
 from PIL import Image
 
+import torch
+import tqdm
+
 from modules import processing, shared, images, devices
 from modules.shared import opts
 import modules.gfpgan_model
@@ -137,3 +140,57 @@ def run_pnginfo(image):
         info = f"<div><p>{message}<p></div>"
 
     return '', geninfo, info
+
+
+def run_modelmerger(modelname_0, modelname_1, interp_method, interp_amount):
+    # Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
+    def weighted_sum(theta0, theta1, alpha):
+        return ((1 - alpha) * theta0) + (alpha * theta1)
+
+    # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
+    def sigmoid(theta0, theta1, alpha):
+        alpha = alpha * alpha * (3 - (2 * alpha))
+        return theta0 + ((theta1 - theta0) * alpha)
+
+    if os.path.exists(modelname_0):
+        model0_filename = modelname_0
+        modelname_0 = os.path.splitext(os.path.basename(modelname_0))[0]
+    else:
+        model0_filename = 'models/' + modelname_0 + '.ckpt'
+
+    if os.path.exists(modelname_1):
+        model1_filename = modelname_1
+        modelname_1 = os.path.splitext(os.path.basename(modelname_1))[0]
+    else:
+        model1_filename = 'models/' + modelname_1 + '.ckpt'
+
+    print(f"Loading {model0_filename}...")
+    model_0 = torch.load(model0_filename, map_location='cpu')
+
+    print(f"Loading {model1_filename}...")
+    model_1 = torch.load(model1_filename, map_location='cpu')
+    
+    theta_0 = model_0['state_dict']
+    theta_1 = model_1['state_dict']
+
+    theta_funcs = {
+        "Weighted Sum": weighted_sum,
+        "Sigmoid": sigmoid,
+    }
+    theta_func = theta_funcs[interp_method]
+
+    print(f"Merging...")
+    for key in tqdm.tqdm(theta_0.keys()):
+        if 'model' in key and key in theta_1:
+            theta_0[key] = theta_func(theta_0[key], theta_1[key], interp_amount)
+    
+    for key in theta_1.keys():
+        if 'model' in key and key not in theta_0:
+            theta_0[key] = theta_1[key]
+
+    output_modelname = 'models/' + modelname_0 + '-' + modelname_1 + '-merged.ckpt'
+    print(f"Saving to {output_modelname}...")
+    torch.save(model_0, output_modelname)
+
+    print(f"Checkpoint saved.")
+    return "Checkpoint saved to " + output_modelname

modules/processing.py

@@ -79,6 +79,13 @@ class StableDiffusionProcessing:
         self.color_corrections = None
         self.denoising_strength: float = 0
         
+        self.ddim_eta = opts.ddim_eta
+        self.ddim_discretize = opts.ddim_discretize
+        self.s_churn = opts.s_churn
+        self.s_tmin = opts.s_tmin
+        self.s_tmax = float('inf') # not representable as a standard ui option
+        self.s_noise = opts.s_noise
+        
         if not seed_enable_extras:
             self.subseed = -1
             self.subseed_strength = 0
@@ -117,6 +124,13 @@ class Processed:
         self.extra_generation_params = p.extra_generation_params
         self.index_of_first_image = index_of_first_image
 
+        self.ddim_eta = p.ddim_eta
+        self.ddim_discretize = p.ddim_discretize
+        self.s_churn = p.s_churn
+        self.s_tmin = p.s_tmin
+        self.s_tmax = p.s_tmax
+        self.s_noise = p.s_noise
+        
         self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
         self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
         self.seed = int(self.seed if type(self.seed) != list else self.seed[0])
@@ -406,7 +420,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 index_of_first_image = 1
 
             if opts.grid_save:
-                images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p)
+                images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
     devices.torch_gc()
     return Processed(p, output_images, all_seeds[0], infotext(), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image)

modules/sd_samplers.py

@@ -37,6 +37,11 @@ samplers = [
 ]
 samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
 
+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'],
+}
 
 def setup_img2img_steps(p, steps=None):
     if opts.img2img_fix_steps or steps is not None:
@@ -120,9 +125,9 @@ class VanillaStableDiffusionSampler:
 
         # existing code fails with cetain step counts, like 9
         try:
-            self.sampler.make_schedule(ddim_num_steps=steps, verbose=False)
+            self.sampler.make_schedule(ddim_num_steps=steps,  ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
         except Exception:
-            self.sampler.make_schedule(ddim_num_steps=steps+1, verbose=False)
+            self.sampler.make_schedule(ddim_num_steps=steps+1,ddim_eta=p.ddim_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)
 
@@ -149,9 +154,9 @@ class VanillaStableDiffusionSampler:
 
         # existing code fails with cetin step counts, like 9
         try:
-            samples_ddim, _ = 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)
+            samples_ddim, _ = 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=p.ddim_eta)
         except Exception:
-            samples_ddim, _ = self.sampler.sample(S=steps+1, 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)
+            samples_ddim, _ = self.sampler.sample(S=steps+1, 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=p.ddim_eta)
 
         return samples_ddim
 
@@ -224,6 +229,7 @@ class KDiffusionSampler:
         self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
         self.funcname = funcname
         self.func = getattr(k_diffusion.sampling, self.funcname)
+        self.extra_params = sampler_extra_params.get(funcname,[]) 
         self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
         self.sampler_noises = None
         self.sampler_noise_index = 0
@@ -269,7 +275,12 @@ class KDiffusionSampler:
         if self.sampler_noises is not None:
             k_diffusion.sampling.torch = TorchHijack(self)
 
-        return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
+        extra_params_kwargs = {}
+        for val in self.extra_params:
+          if hasattr(p,val):
+            extra_params_kwargs[val] = getattr(p,val)
+
+        return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
 
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
         steps = steps or p.steps
@@ -286,7 +297,12 @@ class KDiffusionSampler:
         if self.sampler_noises is not None:
             k_diffusion.sampling.torch = TorchHijack(self)
 
-        samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
+        extra_params_kwargs = {}
+        for val in self.extra_params:
+          if hasattr(p,val):
+            extra_params_kwargs[val] = getattr(p,val)
+
+        samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
 
         return samples
 

modules/shared.py

@@ -76,7 +76,7 @@ class State:
     job = ""
     job_no = 0
     job_count = 0
-    job_timestamp = 0
+    job_timestamp = '0'
     sampling_step = 0
     sampling_steps = 0
     current_latent = None
@@ -90,6 +90,7 @@ class State:
         self.job_no += 1
         self.sampling_step = 0
         self.current_image_sampling_step = 0
+        
     def get_job_timestamp(self):
         return datetime.datetime.now().strftime("%Y%m%d%H%M%S")
 
@@ -229,6 +230,13 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
 }))
 
+options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
+  "ddim_eta": OptionInfo(0.0, "DDIM eta", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+  "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": 1.0, "step": 0.01}),
+  's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+  's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+}))
 
 class Options:
     data = None

modules/ui.py

@@ -49,6 +49,7 @@ sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
 
 css_hide_progressbar = """
 .wrap .m-12 svg { display:none!important; }
+.wrap .m-12::before { content:"Loading..." }
 .progress-bar { display:none!important; }
 .meta-text { display:none!important; }
 """
@@ -393,7 +394,7 @@ def setup_progressbar(progressbar, preview, id_part):
     )
 
 
-def create_ui(txt2img, img2img, run_extras, run_pnginfo):
+def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
@@ -564,13 +565,13 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                     with gr.TabItem('Inpaint', id='inpaint'):
                         init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA")
 
-                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False)
-                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False)
+                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
+                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
 
                         mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
 
                         with gr.Row():
-                            mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask")
+                            mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
                             inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
 
                         inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index")
@@ -853,6 +854,33 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
             outputs=[html, generation_info, html2],
         )
 
+    with gr.Blocks() as modelmerger_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>/models</b> directory.</p>")
+                
+                modelname_0 = gr.Textbox(elem_id="modelmerger_modelname_0", label="Model Name (to)")
+                modelname_1 = gr.Textbox(elem_id="modelmerger_modelname_1", label="Model Name (from)")
+                interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid"], value="Weighted Sum", label="Interpolation Method")
+                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3)
+                submit = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
+            
+            with gr.Column(variant='panel'):
+                submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
+
+            submit.click(
+                fn=run_modelmerger,
+                inputs=[
+                    modelname_0,
+                    modelname_1,
+                    interp_method,
+                    interp_amount
+                ],
+                outputs=[
+                    submit_result,
+                ]
+            )
+
     def create_setting_component(key):
         def fun():
             return opts.data[key] if key in opts.data else opts.data_labels[key].default
@@ -950,6 +978,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
         (img2img_interface, "img2img", "img2img"),
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
+        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (settings_interface, "Settings", "settings"),
     ]
 
@@ -971,6 +1000,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                 with gr.TabItem(label, id=ifid):
                     interface.render()
         
+        if os.path.exists(os.path.join(script_path, "notification.mp3")):
+            audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+
         text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
         settings_submit.click(
             fn=run_settings,

script.js

@@ -39,3 +39,24 @@ document.addEventListener("DOMContentLoaded", function() {
     });
     mutationObserver.observe( gradioApp(), { childList:true, subtree:true })
 });
+
+/**
+ * checks that a UI element is not in another hidden element or tab content
+ */
+function uiElementIsVisible(el) {
+    let isVisible = !el.closest('.\\!hidden');
+    if ( ! isVisible ) {
+        return false;
+    }
+
+    while( isVisible = el.closest('.tabitem')?.style.display !== 'none' ) {
+        if ( ! isVisible ) {
+            return false;
+        } else if ( el.parentElement ) {
+            el = el.parentElement
+        } else {
+            break;
+        }
+    }
+    return isVisible;
+}

scripts/img2imgalt.py

@@ -59,7 +59,55 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
     return x / x.std()
 
 
-Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt"])
+Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt", "sigma_adjustment"])
+
+
+# Based on changes suggested by briansemrau in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/736
+def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
+    x = p.init_latent
+
+    s_in = x.new_ones([x.shape[0]])
+    dnw = K.external.CompVisDenoiser(shared.sd_model)
+    sigmas = dnw.get_sigmas(steps).flip(0)
+
+    shared.state.sampling_steps = steps
+
+    for i in trange(1, len(sigmas)):
+        shared.state.sampling_step += 1
+
+        x_in = torch.cat([x] * 2)
+        sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
+        cond_in = torch.cat([uncond, cond])
+
+        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
+
+        if i == 1:
+            t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
+        else:
+            t = dnw.sigma_to_t(sigma_in)
+
+        eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
+        denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
+
+        denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale
+
+        if i == 1:
+            d = (x - denoised) / (2 * sigmas[i])
+        else:
+            d = (x - denoised) / sigmas[i - 1]
+
+        dt = sigmas[i] - sigmas[i - 1]
+        x = x + d * dt
+
+        sd_samplers.store_latent(x)
+
+        # This shouldn't be necessary, but solved some VRAM issues
+        del x_in, sigma_in, cond_in, c_out, c_in, t,
+        del eps, denoised_uncond, denoised_cond, denoised, d, dt
+
+    shared.state.nextjob()
+
+    return x / sigmas[-1]
 
 
 class Script(scripts.Script):
@@ -78,9 +126,10 @@ class Script(scripts.Script):
         cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
         st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
         randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
-        return [original_prompt, original_negative_prompt, cfg, st, randomness]
+        sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False)
+        return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment]
 
-    def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness):
+    def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment):
         p.batch_size = 1
         p.batch_count = 1
 
@@ -88,7 +137,10 @@ class Script(scripts.Script):
         def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
             lat = (p.init_latent.cpu().numpy() * 10).astype(int)
 
-            same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st and self.cache.original_prompt == original_prompt and self.cache.original_negative_prompt == original_negative_prompt
+            same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \
+                                and self.cache.original_prompt == original_prompt \
+                                and self.cache.original_negative_prompt == original_negative_prompt \
+                                and self.cache.sigma_adjustment == sigma_adjustment
             same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100
 
             if same_everything:
@@ -97,8 +149,11 @@ class Script(scripts.Script):
                 shared.state.job_count += 1
                 cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
                 uncond = p.sd_model.get_learned_conditioning(p.batch_size * [original_negative_prompt])
-                rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
-                self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt)
+                if sigma_adjustment:
+                    rec_noise = find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg, st)
+                else:
+                    rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
+                self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
 
             rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])
             
@@ -121,6 +176,7 @@ class Script(scripts.Script):
         p.extra_generation_params["Decode CFG scale"] = cfg
         p.extra_generation_params["Decode steps"] = st
         p.extra_generation_params["Randomness"] = randomness
+        p.extra_generation_params["Sigma Adjustment"] = sigma_adjustment
 
         processed = processing.process_images(p)
 

scripts/xy_grid.py

@@ -2,6 +2,7 @@ from collections import namedtuple
 from copy import copy
 import random
 
+from PIL import Image
 import numpy as np
 
 import modules.scripts as scripts
@@ -86,7 +87,12 @@ axis_options = [
     AxisOption("Prompt S/R", str, apply_prompt, format_value),
     AxisOption("Sampler", str, apply_sampler, format_value),
     AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
-    AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label), #  as it is now all AxisOptionImg2Img items must go after AxisOption ones
+    AxisOption("Sigma Churn", float, apply_field("s_churn"),  format_value_add_label),
+    AxisOption("Sigma min",   float, apply_field("s_tmin"),   format_value_add_label),
+    AxisOption("Sigma max",   float, apply_field("s_tmax"),   format_value_add_label),
+    AxisOption("Sigma noise", float, apply_field("s_noise"),  format_value_add_label),
+    AxisOption("DDIM Eta",    float, apply_field("ddim_eta"), format_value_add_label),
+    AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),#  as it is now all AxisOptionImg2Img items must go after AxisOption ones
 ]
 
 
@@ -108,7 +114,10 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
             if first_pocessed is None:
                 first_pocessed = processed
 
-            res.append(processed.images[0])
+            try:
+              res.append(processed.images[0])
+            except:
+              res.append(Image.new(res[0].mode, res[0].size))
 
     grid = images.image_grid(res, rows=len(ys))
     if draw_legend:

webui.py

@@ -85,7 +85,8 @@ def webui():
         txt2img=wrap_gradio_gpu_call(modules.txt2img.txt2img),
         img2img=wrap_gradio_gpu_call(modules.img2img.img2img),
         run_extras=wrap_gradio_gpu_call(modules.extras.run_extras),
-        run_pnginfo=modules.extras.run_pnginfo
+        run_pnginfo=modules.extras.run_pnginfo,
+        run_modelmerger=modules.extras.run_modelmerger
     )
 
     demo.launch(