unconditional options init

.gitignore

@@ -2,9 +2,7 @@
 __pycache__
 .ruff_cache
 /cache.json
-/metadata.json
-/config.json
-/ui-config.json
+/*.json
 /params.txt
 /styles.csv
 /user.css
@@ -38,6 +36,7 @@ venv
 /*.lnk
 !webui.bat
 !webui.sh
+!package.json
 
 # all dynamic stuff
 /repositories/**/*

javascript/style.css

@@ -1,5 +1,6 @@
 :root, .dark{ --checkbox-label-gap: 0.25em 0.1em; --section-header-text-size: 12pt; --block-background-fill: transparent;}
 a { font-weight: bold; cursor: pointer; }
+h2 { margin-top: 1em !important; font-size: 1.4em !important; }
 div.gradio-container{ max-width: unset !important; padding: 8px !important; }
 div.tabitem { padding: 0 !important; }
 div.form{ border-width: 0; box-shadow: none; background: transparent; overflow: visible; gap: 0.5em; }

modules/generation_parameters_copypaste.py

@@ -308,8 +308,8 @@ def parse_generation_parameters(x: str):
     ('Discard penultimate sigma', 'always_discard_next_to_last_sigma'),
     ('UniPC variant', 'uni_pc_variant'),
     ('UniPC skip type', 'uni_pc_skip_type'),
-    ('UniPC order', 'uni_pc_order'),
-    ('UniPC lower order final', 'uni_pc_lower_order_final'),
+    ('UniPC order', 'schedulers_solver_order'),
+    ('UniPC lower order final', 'schedulers_use_loworder'),
     ('Token merging ratio', 'token_merging_ratio'),
     ('Token merging ratio hr', 'token_merging_ratio_hr'),
 ]

modules/models/diffusion/uni_pc/sampler.py

@@ -30,7 +30,7 @@ def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
         # value from the hires steps slider:
         num_inference_steps = t[0] + 1
         num_inference_steps / self.inflated_steps
-        self.denoise_steps = max(num_inference_steps, shared.opts.uni_pc_order)
+        self.denoise_steps = max(num_inference_steps, shared.opts.schedulers_solver_order)
 
         max(self.inflated_steps - self.denoise_steps, 0)
 
@@ -102,8 +102,8 @@ def decode(self, x_latent, conditioning, t_start, unconditional_guidance_scale=1
                 steps=self.denoise_steps,
                 skip_type=shared.opts.uni_pc_skip_type,
                 method="multistep",
-                order=shared.opts.uni_pc_order,
-                lower_order_final=shared.opts.uni_pc_lower_order_final,
+                order=shared.opts.schedulers_solver_order,
+                lower_order_final=shared.opts.schedulers_use_loworder,
                 denoise_to_zero=True,
                 timesteps=self.timesteps,
             )
@@ -187,6 +187,6 @@ def sample(self,
         )
 
         uni_pc = UniPC(model_fn, self.noise_schedule, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=self.before_sample, after_sample=self.after_sample, after_update=self.after_update)
-        x = uni_pc.sample(img, steps=S, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)
+        x = uni_pc.sample(img, steps=S, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.schedulers_solver_order, lower_order_final=shared.opts.schedulers_use_loworder)
 
         return x.to(device), None

modules/sd_samplers_compvis.py

@@ -149,8 +149,8 @@ def initialize(self, p):
             keys = [
                 ('UniPC variant', 'uni_pc_variant'),
                 ('UniPC skip type', 'uni_pc_skip_type'),
-                ('UniPC order', 'uni_pc_order'),
-                ('UniPC lower order final', 'uni_pc_lower_order_final'),
+                ('UniPC order', 'schedulers_solver_order'),
+                ('UniPC lower order final', 'schedulers_use_loworder'),
             ]
 
             for name, key in keys:
@@ -170,8 +170,8 @@ def initialize(self, p):
 
     def adjust_steps_if_invalid(self, p, num_steps):
         if ((self.config.name == 'DDIM') and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS') or (self.config.name == 'UniPC'):
-            if self.config.name == 'UniPC' and num_steps < shared.opts.uni_pc_order:
-                num_steps = shared.opts.uni_pc_order
+            if self.config.name == 'UniPC' and num_steps < shared.opts.schedulers_solver_order:
+                num_steps = shared.opts.schedulers_solver_order
             valid_step = 999 / (1000 // num_steps)
             if valid_step == math.floor(valid_step):
                 return min(int(valid_step) + 1, num_steps)

modules/shared.py

@@ -485,55 +485,38 @@ def refresh_themes():
     "logmonitor_refresh_period": OptionInfo(5000, "Log view update period, in milliseconds", gr.Slider, {"minimum": 0, "maximum": 30000, "step": 25}),
 }))
 
-
 options_templates.update(options_section(('sampler-params', "Sampler Settings"), {
     "show_samplers": OptionInfo(["Euler a", "UniPC", "DEIS", "DDIM", "DPM 1S", "DPM 2M", "DPM++ 2M SDE", "DPM++ 2M SDE Karras", "DPM2 Karras", "DPM++ 2M Karras"], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers() if x.name != "PLMS"]}),
     "fallback_sampler": OptionInfo("Euler a", "Secondary sampler", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
     "force_latent_sampler": OptionInfo("None", "Force latent upscaler sampler", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
     'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
     'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
-    'uni_pc_order': OptionInfo(3, "UniPC order (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
-    'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
+    'eta_noise_seed_delta': OptionInfo(0, "Noise seed delta (eta)", gr.Number, {"precision": 0}),
+
+    "schedulers_sep_diffusers": OptionInfo("<h2>Diffusers specific config</h2>", "", gr.HTML),
+    "schedulers_prediction_type": OptionInfo("default", "Samplers override model prediction type", gr.Radio, lambda: {"choices": ['default', 'epsilon', 'sample', 'v-prediction']}),
+    "schedulers_beta_schedule": OptionInfo("default", "Samplers override beta schedule", gr.Radio, lambda: {"choices": ['default', 'linear', 'scaled_linear', 'squaredcos_cap_v2']}),
+    "schedulers_solver_order": OptionInfo(2, "Samplers solver order where applicable", gr.Slider, {"minimum": 1, "maximum": 5, "step": 1}),
+    "schedulers_use_karras": OptionInfo(True, "Samplers should use Karras sigmas where applicable"),
+    "schedulers_use_loworder": OptionInfo(True, "Samplers should use use lower-order solvers in the final steps where applicable"),
+    "schedulers_use_thresholding": OptionInfo(False, "Samplers should use dynamic thresholding where applicable"),
+    "schedulers_dpm_solver": OptionInfo("sde-dpmsolver++", "Samplers DPM solver algorithm", gr.Radio, lambda: {"choices": ['dpmsolver', 'dpmsolver++', 'sde-dpmsolver++']}),
+
+    "schedulers_sep_kdiffusers": OptionInfo("<h2>K-Diffusion specific config</h2>", "", gr.HTML),
+    "always_batch_cond_uncond": OptionInfo(False, "Disable conditional batching enabled on low memory systems"),
+    "enable_quantization": OptionInfo(True, "Enable samplers quantization for sharper and cleaner results"),
+    "eta_ancestral": OptionInfo(1.0, "Noise multiplier for ancestral samplers (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    's_min_uncond': OptionInfo(0, "sigma negative guidance minimum ", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "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}),
+    'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
+
+    "schedulers_sep_compvis": OptionInfo("<h2>CompVis specific config</h2>", "", gr.HTML),
+    "ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad']}),
+    "eta_ddim": OptionInfo(0.0, "Noise multiplier for DDIM (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
 }))
 
-if backend == Backend.ORIGINAL:
-    options_templates.update(options_section(('sampler-params', "Sampler Settings"), {
-        "always_batch_cond_uncond": OptionInfo(False, "Disable conditional batching enabled on low memory systems"),
-        "enable_quantization": OptionInfo(True, "Enable samplers quantization for sharper and cleaner results"),
-        "eta_ancestral": OptionInfo(1.0, "Noise multiplier for ancestral samplers (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-        "eta_ddim": OptionInfo(0.0, "Noise multiplier for DDIM (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-        "ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad']}),
-        's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-        's_min_uncond': OptionInfo(0, "sigma negative guidance minimum ", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "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}),
-        'eta_noise_seed_delta': OptionInfo(0, "Noise seed delta (eta)", gr.Number, {"precision": 0}),
-        'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
-    }))
-elif backend == Backend.DIFFUSERS:
-    options_templates.update(options_section(('sampler-params', "Sampler Settings"), {
-        # hidden - included for compatibility only
-        "always_batch_cond_uncond": OptionInfo(False, "Disable conditional batching enabled on low memory systems", gr.Checkbox, { "visible": False}),
-        "enable_quantization": OptionInfo(True, "Enable samplers quantization for sharper and cleaner results", gr.Checkbox, { "visible": False}),
-        "eta_ancestral": OptionInfo(1.0, "Noise multiplier for ancestral samplers (eta)", gr.Number, { "visible": False}),
-        "eta_ddim": OptionInfo(0.0, "Noise multiplier for DDIM (eta)", gr.Number, { "visible": False}),
-        "ddim_discretize": OptionInfo('uniform', "", gr.Text, { "visible": False}),
-        's_churn': OptionInfo(0.0, "sigma churn", gr.Number, { "visible": False}),
-        's_min_uncond': OptionInfo(0, "sigma negative guidance minimum ", gr.Number, { "visible": False}),
-        's_tmin':  OptionInfo(0.0, "sigma tmin", gr.Number, { "visible": False}),
-        's_noise': OptionInfo(1.0, "sigma noise", gr.Number, { "visible": False}),
-        'eta_noise_seed_delta': OptionInfo(0, "Noise seed delta (eta)", gr.Number, {"precision": 0}, { "visible": False}),
-        'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma", gr.Checkbox, { "visible": False}),
-        # diffuser specific
-        "schedulers_prediction_type": OptionInfo("default", "Samplers override model prediction type", gr.Radio, lambda: {"choices": ['default', 'epsilon', 'sample', 'v-prediction']}),
-        "schedulers_beta_schedule": OptionInfo("default", "Samplers override beta schedule", gr.Radio, lambda: {"choices": ['default', 'linear', 'scaled_linear', 'squaredcos_cap_v2']}),
-        "schedulers_solver_order": OptionInfo(2, "Samplers solver order where applicable", gr.Slider, {"minimum": 1, "maximum": 5, "step": 1}),
-        "schedulers_use_karras": OptionInfo(True, "Samplers should use Karras sigmas where applicable"),
-        "schedulers_use_loworder": OptionInfo(True, "Samplers should use use lower-order solvers in the final steps where applicable"),
-        "schedulers_use_thresholding": OptionInfo(False, "Samplers should use dynamic thresholding where applicable"),
-        "schedulers_dpm_solver": OptionInfo("sde-dpmsolver++", "Samplers DPM solver algorithm", gr.Radio, lambda: {"choices": ['dpmsolver', 'dpmsolver++', 'sde-dpmsolver++']}),
-    }))
-
 options_templates.update(options_section(('postprocessing', "Postprocessing"), {
     'postprocessing_enable_in_main_ui': OptionInfo([], "Enable addtional postprocessing operations", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
     'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),

scripts/xyz_grid.py

@@ -131,8 +131,8 @@ def apply_fallback(p, x, xs):
         shared.opts.data["force_latent_sampler"] = sampler_name
 
 
-def apply_uni_pc_order(p, x, xs):
-    shared.opts.data["uni_pc_order"] = min(x, p.steps - 1)
+def apply_schedulers_solver_order(p, x, xs):
+    shared.opts.data["schedulers_solver_order"] = min(x, p.steps - 1)
 
 
 def apply_face_restore(p, opt, x):
@@ -231,7 +231,7 @@ def __init__(self, *args, **kwargs):
     AxisOption("Sampler Eta", float, apply_field("eta")),
     AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
     AxisOptionImg2Img("Image Mask Weight", float, apply_field("inpainting_mask_weight")),
-    AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),
+    AxisOption("Sampler Solver Order", int, apply_schedulers_solver_order, cost=0.5),
     AxisOption("Face restore", str, apply_face_restore, fmt=format_value),
     AxisOption("Token merging ratio", float, apply_override('token_merging_ratio')),
     AxisOption("Token merging ratio high-res", float, apply_override('token_merging_ratio_hr')),
@@ -345,7 +345,7 @@ class SharedSettingsStackHelper(object):
     def __enter__(self):
         #Save overridden settings so they can be restored later.
         self.vae = shared.opts.sd_vae
-        self.uni_pc_order = shared.opts.uni_pc_order
+        self.schedulers_solver_order = shared.opts.schedulers_solver_order
         self.token_merging_ratio_hr = shared.opts.token_merging_ratio_hr
         self.token_merging_ratio = shared.opts.token_merging_ratio
         self.sd_model_checkpoint = shared.opts.sd_model_checkpoint
@@ -356,7 +356,7 @@ def __enter__(self):
     def __exit__(self, exc_type, exc_value, tb):
         #Restore overriden settings after plot generation.
         shared.opts.data["sd_vae"] = self.vae
-        shared.opts.data["uni_pc_order"] = self.uni_pc_order
+        shared.opts.data["schedulers_solver_order"] = self.schedulers_solver_order
         shared.opts.data["token_merging_ratio_hr"] = self.token_merging_ratio_hr
         shared.opts.data["token_merging_ratio"] = self.token_merging_ratio
         shared.opts.data["force_latent_sampler"] = self.force_latent_sampler