samplers expose base/max seq len, improve sampler options save, copy defaults

Signed-off-by: Vladimir Mandic <[email protected]>

CHANGELOG.md

@@ -63,6 +63,9 @@ Plus quite a few other improvements and inevitable bug-fixes, see full changelog
   - processors: `DWPose`, `RTMW`, `RTMO`: image processor updates, thanks @kirtasshh  
     refactored to use `ONNX` instead of obsolete `mmpose` lib and with additional configurable settings  
   - lora: extend support for more model types
+  - scheduler: expose base and max image sequence lengths  
+    recommended to change when target resolution is significantly different from the base resolution  
+  - detailer: add option to skip specific classes using `[SKIP=name]` in the detailer prompt, thanks @kirtasshh
 - **UI**
   - massive improvement to built-in tooltips/hints  
     over 1,000 new entries, now at zero missing ui controls or settings  
@@ -94,7 +97,8 @@ Plus quite a few other improvements and inevitable bug-fixes, see full changelog
   - pixelsmith: vae tiling, thanks @li-lizhe  
   - preview: remove flicker when finishing generation
   - prompt cache: bypass when condition images are encoded
-  - sampler: when creating sampler, copy all valid options from default sampler
+  - scheduler: when creating sampler, copy all valid options from default sampler
+  - scheduler: debounce sampler options save
   - seedvr: handle temporal overap
   - styles: fix infotext parsing, thanks @obelisk-complex
   - temp file: gradio temp file error handling

modules/control/run.py

@@ -397,6 +397,7 @@ def control_run(state: str = '', # pylint: disable=keyword-arg-before-vararg
                 schedulers_base_shift: float | None = None, schedulers_max_shift: float | None = None, schedulers_rescale_betas: bool | None = None,
                 schedulers_timestep_spacing: str | None = None, schedulers_timesteps_range: int | None = None,
                 schedulers_sigma_adjust: float | None = None, schedulers_sigma_adjust_min: float | None = None, schedulers_sigma_adjust_max: float | None = None,
+                schedulers_base_image_seq_len: int | None = None, schedulers_max_image_seq_len: int | None = None,
                 scheduler_eta: float | None = None, eta_noise_seed_delta: int | None = None, enable_batch_seeds: bool | None = None,
                 diffusers_generator_device: str | None = None, nan_skip: bool | None = None,
                 sequential_seed: bool | None = None,
@@ -573,6 +574,7 @@ def control_run(state: str = '', # pylint: disable=keyword-arg-before-vararg
         schedulers_base_shift=schedulers_base_shift, schedulers_max_shift=schedulers_max_shift,
         schedulers_rescale_betas=schedulers_rescale_betas, schedulers_timestep_spacing=schedulers_timestep_spacing,
         schedulers_timesteps_range=schedulers_timesteps_range,
+        schedulers_base_image_seq_len=schedulers_base_image_seq_len, schedulers_max_image_seq_len=schedulers_max_image_seq_len,
         schedulers_sigma_adjust=schedulers_sigma_adjust, schedulers_sigma_adjust_min=schedulers_sigma_adjust_min,
         schedulers_sigma_adjust_max=schedulers_sigma_adjust_max,
         scheduler_eta=scheduler_eta, eta_noise_seed_delta=eta_noise_seed_delta,

modules/infotext.py

@@ -123,6 +123,8 @@ mapping = [
     ('Sampler beta end', 'schedulers_beta_end'),
     ('Sampler range', 'schedulers_timesteps_range'),
     ('Sampler shift', 'schedulers_shift'),
+    ('Sampler base seq', 'schedulers_base_image_seq_len'),
+    ('Sampler max seq', 'schedulers_max_image_seq_len'),
     ('Sampler dynamic shift', 'schedulers_dynamic_shift'),
     # Token Merging
     ('Mask weight', 'inpainting_mask_weight'),

modules/options.py

@@ -22,7 +22,7 @@ def options_section(section_identifier: tuple[str, str], options_dict: dict[str,
 
 class OptionInfo:
     def __init__(
-            self,
+            self, # pylint: disable=unused-argument
             default: Any = None,
             label="",
             component: type[Component] | type[DropdownEditable] | None = None,
@@ -35,8 +35,8 @@ class OptionInfo:
             comment_before='',
             comment_after='',
             category_id=None, # pylint: disable=unused-argument
-            *args, # pylint: disable=unused-argument
-            **kwargs, # pylint: disable=unused-argument
+            *args,
+            **kwargs,
         ): # pylint: disable=keyword-arg-before-vararg
         self.default = default
         self.label = label

modules/processing_class.py

@@ -239,6 +239,8 @@ class StableDiffusionProcessing:
                  schedulers_sigma_adjust: float | None = None,
                  schedulers_sigma_adjust_min: float | None = None,
                  schedulers_sigma_adjust_max: float | None = None,
+                 schedulers_base_image_seq_len: int | None = None,
+                 schedulers_max_image_seq_len: int | None = None,
                  scheduler_eta: float | None = None,
                  eta_noise_seed_delta: int | None = None,
                  enable_batch_seeds: bool | None = None,
@@ -526,6 +528,8 @@ class StableDiffusionProcessing:
         self.schedulers_sigma_adjust = schedulers_sigma_adjust
         self.schedulers_sigma_adjust_min = schedulers_sigma_adjust_min
         self.schedulers_sigma_adjust_max = schedulers_sigma_adjust_max
+        self.schedulers_base_image_seq_len = schedulers_base_image_seq_len
+        self.schedulers_max_image_seq_len = schedulers_max_image_seq_len
         self.scheduler_eta = scheduler_eta
         self.eta_noise_seed_delta = eta_noise_seed_delta
         self.enable_batch_seeds = enable_batch_seeds

modules/sd_samplers_diffusers.py

@@ -516,10 +516,10 @@ class DiffusionSampler:
         if 'beta_end' in self.config and get_override('schedulers_beta_end') > 0:
             self.config['beta_end'] = get_override('schedulers_beta_end')
         sched_shift = get_override('schedulers_shift')
-        if 'shift' in self.config:
-            self.config['shift'] = sched_shift if sched_shift > 0 else 3
-        if 'flow_shift' in self.config:
-            self.config['flow_shift'] = sched_shift if sched_shift > 0 else 3
+        if 'shift' in self.config and sched_shift > 0:
+            self.config['shift'] = sched_shift
+        if 'flow_shift' in self.config and sched_shift > 0:
+            self.config['flow_shift'] = sched_shift
         if 'use_dynamic_shifting' in self.config:
             self.config['use_dynamic_shifting'] = True if sched_shift == 0 else get_override('schedulers_dynamic_shift')
         if 'base_shift' in self.config:
@@ -530,6 +530,10 @@ class DiffusionSampler:
             self.config['use_beta_sigmas'] = 'StableDiffusion3' in model.__class__.__name__
         if 'rescale_betas_zero_snr' in self.config:
             self.config['rescale_betas_zero_snr'] = get_override('schedulers_rescale_betas')
+        if 'base_image_seq_len' in self.config and get_override('schedulers_base_image_seq_len') > 0:
+            self.config['base_image_seq_len'] = get_override('schedulers_base_image_seq_len')
+        if 'max_image_seq_len' in self.config and get_override('schedulers_max_image_seq_len') > 0:
+            self.config['max_image_seq_len'] = get_override('schedulers_max_image_seq_len')
         sched_ts_spacing = get_override('schedulers_timestep_spacing')
         if 'timestep_spacing' in self.config and sched_ts_spacing != 'default' and sched_ts_spacing is not None:
             self.config['timestep_spacing'] = sched_ts_spacing
@@ -558,7 +562,6 @@ class DiffusionSampler:
         for key in self.config.copy().keys():
             if key not in possible:
                 del self.config[key]
-        self.config['shift_terminal'] = 0.0
         debug_log(f'Sampler: name="{name}"')
         debug_log(f'Sampler: config={self.config}')
         debug_log(f'Sampler: signature={possible}')

modules/ui_definitions.py

@@ -858,6 +858,8 @@ def create_settings(cmd_opts):
                 "schedulers_shift": OptionInfo(3, "Sampler shift", gr.Slider, {"minimum": 0.1, "maximum": 10, "step": 0.1, "visible": False}),
                 "schedulers_fallback": OptionInfo(True, "Sampler fallback on invalid", gr.Checkbox, {"visible": False}),
                 "schedulers_dynamic_shift": OptionInfo(False, "Sampler dynamic shift", gr.Checkbox, {"visible": False}),
+                "schedulers_base_image_seq_len": OptionInfo(0, "Sampler base seq", gr.Slider, {"minimum": 0, "maximum": 1024, "step": 16, "visible": False}),
+                "schedulers_max_image_seq_len": OptionInfo(0, "Sampler max seq", gr.Slider, {"minimum": 0, "maximum": 16384, "step": 16, "visible": False}),
                 "schedulers_sigma_adjust": OptionInfo(1.0, "Sigma adjust", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01, "visible": False}),
                 "schedulers_sigma_adjust_min": OptionInfo(0.2, "Sigma adjust start", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01, "visible": False}),
                 "schedulers_sigma_adjust_max": OptionInfo(0.8, "Sigma adjust end", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01, "visible": False}),

modules/ui_sections.py

@@ -1,3 +1,4 @@
+import threading
 import gradio as gr
 from modules import shared, modelloader, ui_symbols, ui_common, sd_samplers
 from modules.logger import log
@@ -5,6 +6,10 @@ from modules.ui_components import ToolButton
 from modules.caption import caption
 
 
+_save_timer = None
+_save_lock = threading.Lock()
+
+
 def create_toprow(is_img2img: bool = False, id_part: str | None = None, generate_visible: bool = True, negative_visible: bool = True, reprocess_visible: bool = True):
     def apply_styles(prompt, prompt_neg, styles):
         prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles, wildcards=not shared.opts.extra_networks_apply_unparsed)
@@ -251,63 +256,88 @@ def create_sampler_and_steps_selection(choices, tabname, default_steps:int=20):
     return steps, sampler_index
 
 
+def save_with_debounce(delay=0.5):
+    global _save_timer # pylint: disable=global-statement
+    with _save_lock:
+        try:
+            if _save_timer is not None and _save_timer.is_alive():
+                _save_timer.cancel()
+        except Exception as e:
+            log.error(f'Sampler: save debounce cancel error: {e}')
+        def _do_save():
+            try:
+                shared.opts.save(silent=True)
+            except Exception as e:
+                log.error(f'Sampler: save execution error: {e}')
+
+        _save_timer = threading.Timer(delay, _do_save)
+        _save_timer.start()
+
+
 def create_sampler_options(tabname):
+
     def set_sampler_options(sampler_options):
         shared.opts.data['schedulers_dynamic_shift'] = 'dynamic' in sampler_options
         shared.opts.data['schedulers_use_thresholding'] = 'thresholding' in sampler_options
         shared.opts.data['schedulers_use_loworder'] = 'low order' in sampler_options
         shared.opts.data['schedulers_rescale_betas'] = 'rescale' in sampler_options
         log.debug(f'Sampler set options: {sampler_options}')
-        shared.opts.save(silent=True)
+        save_with_debounce()
 
     def set_sampler_fallback(fallback):
         log.debug(f'Sampler set options: fallback={fallback}')
         shared.opts.schedulers_fallback = fallback
-        shared.opts.save(silent=True)
+        save_with_debounce()
 
     def set_sampler_timesteps(timesteps):
         log.debug(f'Sampler set options: timesteps={timesteps}')
         shared.opts.schedulers_timesteps = timesteps
-        shared.opts.save(silent=True)
+        save_with_debounce()
 
     def set_sampler_spacing(spacing):
         log.debug(f'Sampler set options: spacing={spacing}')
         shared.opts.schedulers_timestep_spacing = spacing
-        shared.opts.save(silent=True)
+        save_with_debounce()
 
     def set_sampler_sigma(sampler_sigma):
         log.debug(f'Sampler set options: sigma={sampler_sigma}')
         shared.opts.schedulers_sigma = sampler_sigma
-        shared.opts.save(silent=True)
+        save_with_debounce()
 
     def set_sampler_order(sampler_order):
         log.debug(f'Sampler set options: order={sampler_order}')
         shared.opts.schedulers_solver_order = sampler_order
-        shared.opts.save(silent=True)
+        save_with_debounce()
 
     def set_sampler_prediction(sampler_prediction):
         log.debug(f'Sampler set options: prediction={sampler_prediction}')
         shared.opts.schedulers_prediction_type = sampler_prediction
-        shared.opts.save(silent=True)
+        save_with_debounce()
 
     def set_sampler_beta(sampler_beta):
         log.debug(f'Sampler set options: beta={sampler_beta}')
         shared.opts.schedulers_beta_schedule = sampler_beta
-        shared.opts.save(silent=True)
+        save_with_debounce()
 
     def set_sampler_shift(sampler_shift, sampler_base_shift, sampler_max_shift):
         log.debug(f'Sampler set options: shift={sampler_shift} base={sampler_base_shift} max={sampler_max_shift}')
         shared.opts.schedulers_shift = sampler_shift
         shared.opts.schedulers_base_shift = sampler_base_shift
         shared.opts.schedulers_max_shift = sampler_max_shift
-        shared.opts.save(silent=True)
+        save_with_debounce()
+
+    def set_sampler_seq_lens(base_image_seq_len, max_image_seq_len):
+        log.debug(f'Sampler set options: base_image_seq_len={base_image_seq_len} max_image_seq_len={max_image_seq_len}')
+        shared.opts.schedulers_base_image_seq_len = base_image_seq_len
+        shared.opts.schedulers_max_image_seq_len = max_image_seq_len
+        save_with_debounce()
 
     def set_sigma_adjust(val, start, end):
         log.debug(f'Sampler set options: sigma={val} min={start} max={end}')
         shared.opts.schedulers_sigma_adjust = val
         shared.opts.schedulers_sigma_adjust_min = start
         shared.opts.schedulers_sigma_adjust_max = end
-        shared.opts.save(silent=True)
+        save_with_debounce()
 
     # 'linear', 'scaled_linear', 'squaredcos_cap_v2'
     def set_sampler_preset(preset):
@@ -336,6 +366,9 @@ def create_sampler_options(tabname):
         sampler_shift = gr.Slider(minimum=0, maximum=10, step=0.1, label="Flow shift", value=shared.opts.schedulers_shift, elem_id=f"{tabname}_sampler_shift")
         sampler_base_shift = gr.Slider(minimum=0, maximum=10, step=0.01, label="Base shift", value=shared.opts.schedulers_base_shift, elem_id=f"{tabname}_sampler_base_shift")
         sampler_max_shift = gr.Slider(minimum=0, maximum=10, step=0.01, label="Max shift", value=shared.opts.schedulers_max_shift, elem_id=f"{tabname}_sampler_max_shift")
+    with gr.Row(elem_classes=['flex-break']):
+        sampler_base_image_seq_len = gr.Slider(minimum=0, maximum=1024, step=16, label='Sampler base seq', value=shared.opts.schedulers_base_image_seq_len, elem_id=f"{tabname}_sampler_base_image_seq_len")
+        sampler_max_image_seq_len = gr.Slider(minimum=0, maximum=16284, step=16, label='Sampler max seq', value=shared.opts.schedulers_max_image_seq_len, elem_id=f"{tabname}_sampler_max_image_seq_len")
     with gr.Row(elem_classes=['flex-break']):
         options = ['low order', 'thresholding', 'dynamic', 'rescale']
         values = []
@@ -362,6 +395,8 @@ def create_sampler_options(tabname):
     sampler_sigma_adjust_val.change(fn=set_sigma_adjust, inputs=[sampler_sigma_adjust_val, sampler_sigma_adjust_min, sampler_sigma_adjust_max], outputs=[])
     sampler_sigma_adjust_min.change(fn=set_sigma_adjust, inputs=[sampler_sigma_adjust_val, sampler_sigma_adjust_min, sampler_sigma_adjust_max], outputs=[])
     sampler_sigma_adjust_max.change(fn=set_sigma_adjust, inputs=[sampler_sigma_adjust_val, sampler_sigma_adjust_min, sampler_sigma_adjust_max], outputs=[])
+    sampler_base_image_seq_len.change(fn=set_sampler_seq_lens, inputs=[sampler_base_image_seq_len, sampler_max_image_seq_len], outputs=[])
+    sampler_max_image_seq_len.change(fn=set_sampler_seq_lens, inputs=[sampler_base_image_seq_len, sampler_max_image_seq_len], outputs=[])
 
 
 def create_hires_inputs(tab):

pipelines/flux/flux2_lora.py

@@ -393,6 +393,6 @@ def apply_patch():
                         pretrained_model_name_or_path_or_dict = apply_lora_alphas(sd)
                 except Exception:
                     pass
-        return original_lora_state_dict(cls, pretrained_model_name_or_path_or_dict, **kwargs)
+        return original_lora_state_dict(cls, pretrained_model_name_or_path_or_dict, **kwargs) # pylint: disable=too-many-function-args
 
     Flux2LoraLoaderMixin.lora_state_dict = patched_lora_state_dict

pipelines/krea2/pipeline_krea2_inpaint.py

@@ -69,6 +69,8 @@ class Krea2InpaintPipeline(Krea2Img2ImgPipeline):
         latents: Optional[torch.Tensor] = None,
         output_type: str = "pil",
         return_dict: bool = True,
+        dense_mask: bool = False,
+        attention_kwargs: dict | None = None,
         callback_on_step_end: Optional[Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
     ):

pipelines/lens/pipeline_image.py

@@ -115,13 +115,17 @@ class LensImg2ImgPipeline(LensPipeline):
         strength: float = 0.8,
         height: int = 1024,
         width: int = 1024,
+        base_resolution: Optional[int] = None,
+        aspect_ratio: Optional[str] = None,
         num_inference_steps: int = 50,
         guidance_scale: float = 4.0,
         num_images_per_prompt: int = 1,
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         latents: Optional[torch.Tensor] = None,
         prompt_embeds: Optional[List[torch.Tensor]] = None,
+        prompt_mask: Optional[torch.Tensor] = None,
         negative_prompt_embeds: Optional[List[torch.Tensor]] = None,
+        negative_prompt_mask: Optional[torch.Tensor] = None,
         output_type: str = "pil",
         return_dict: bool = True,
         callback_on_step_end: Optional[Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]] = None,
@@ -186,13 +190,17 @@ class LensInpaintPipeline(LensImg2ImgPipeline):
         strength: float = 0.8,
         height: int = 1024,
         width: int = 1024,
+        base_resolution: Optional[int] = None,
+        aspect_ratio: Optional[str] = None,
         num_inference_steps: int = 50,
         guidance_scale: float = 4.0,
         num_images_per_prompt: int = 1,
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         latents: Optional[torch.Tensor] = None,
         prompt_embeds: Optional[List[torch.Tensor]] = None,
+        prompt_mask: Optional[torch.Tensor] = None,
         negative_prompt_embeds: Optional[List[torch.Tensor]] = None,
+        negative_prompt_mask: Optional[torch.Tensor] = None,
         output_type: str = "pil",
         return_dict: bool = True,
         callback_on_step_end: Optional[Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]] = None,

test/fixtures/civitai-names.json

@@ -1229,4 +1229,4 @@
    }
   }
  }
-}
+}