fix settings again

DIFFUSERS.md

@@ -59,9 +59,8 @@ whats implemented so far?
 ## Todo
 
 - sdxl model  
-- new schedulers  
-- settings -> schedulers  
 - no idea if sd21 works out-of-the-box
+- hires fix?
 
 ## Limitations
 
@@ -143,15 +142,16 @@ will need to handle in the code before we get out of alpha
 | --- | --- | --- |
 | original | 7.99 / 7.93 / 8.83 / 9.14 / 9.2 | 6.7 / 7.2 |
 | original medvram | 6.23 / 7.16 / 8.41 / 9.24 / 9.68 | 8.4 / 6.8 |
-| original lowvram | | |
+| original lowvram | 1.05 / 1.94 / 3.2 / 4.81 / 6.46 | 8.8 / 5.2 |
+| original compile inductor | | |
 | diffusers | 9 / 7.4 / 8.2 / 8.4 / 7.0 | 4.3 / 9.0 |
 | diffusers medvram | 7.5 / 6.7 / 7.5 / 7.8 / 7.2 | 6.6 / 8.2 |
 | diffusers lowvram | 7.0 / 7.0 / 7.4 / 7.7 / 7.8 | 4.3 / 7.2 |
 | diffusers with safetensors | 8.9 / 7.3 / 8.1 / 8.4 / 7.1 | 5.9 / 9.0 |
-
+| diffusers compile | | |
 Notes:
 
-- Performance is measured for batch sizes 1, 2, 4, 8 16
+- Performance is measured for `batch-size` 1, 2, 4, 8 16
 - Test environment:
   - nVidia RTX 3060 GPU
   - Torch 2.1-nightly with CUDA 12.1

modules/shared.py

@@ -495,21 +495,21 @@ if backend == Backend.ORIGINAL:
 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", { "visible": False}),
-        "enable_quantization": OptionInfo(True, "Enable samplers quantization for sharper and cleaner results", { "visible": False}),
-        "eta_ancestral": OptionInfo(1.0, "Noise multiplier for ancestral samplers (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, { "visible": False}),
-        "eta_ddim": OptionInfo(0.0, "Noise multiplier for DDIM (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, { "visible": False}),
-        "ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad']}, { "visible": False}),
-        's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, { "visible": False}),
-        's_min_uncond': OptionInfo(0, "sigma negative guidance minimum ", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}, { "visible": False}),
-        's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, { "visible": False}),
-        's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, { "visible": False}),
+        "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", { "visible": False}),
-        'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}, { "visible": False}),
-        'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}, { "visible": False}),
-        'uni_pc_order': OptionInfo(3, "UniPC order (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}, { "visible": False}),
-        'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", { "visible": False}),
+        'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma", gr.Checkbox, { "visible": False}),
+        #'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}, { "visible": False}),
+        #'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}, { "visible": False}),
+        #'uni_pc_order': OptionInfo(3, "UniPC order (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}, { "visible": False}),
+        #'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", { "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']}),

modules/ui.py

@@ -903,9 +903,13 @@ def create_ui(startup_timer):
                     res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
                     create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
         else:
-            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+            try:
+                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+            except Exception as e:
+                modules.shared.log.error(f'Error creating setting: {key} {e}')
+                res = None
 
-        if not is_quicksettings:
+        if res is not None and not is_quicksettings:
             res.change(fn=None, inputs=res, _js=f'(val) => markIfModified("{key}", val)')
             dirty_indicator.click(fn=lambda: getattr(opts, key), outputs=res, show_progress=False)
             dirtyable_setting.__exit__()