rework diffusers args as and introduce second pass as replacement for hires

.eslintrc.json

@@ -16,21 +16,22 @@
   },
   "rules": {
     "max-len": [1, 275, 3],
-    "no-plusplus":"off",
-    "no-console":"off",
-    "no-unused-vars":"off",
-    "no-return-assign":"off",
-    "prefer-rest-params":"off",
-    "no-empty":"off",
-    "no-restricted-syntax":"off",
-    "no-param-reassign":"off",
-    "no-bitwise":"off",
-    "default-case":"off",
-    "no-restricted-globals":"off",
-    "no-mixed-operators":"off",
     "camelcase":"off",
+    "default-case":"off",
+    "no-bitwise":"off",
     "no-confusing-arrow":"off",
+    "no-console":"off",
+    "no-empty":"off",
+    "no-mixed-operators":"off",
+    "no-param-reassign":"off",
+    "no-plusplus":"off",
+    "no-restricted-globals":"off",
+    "no-restricted-syntax":"off",
+    "no-return-assign":"off",
+    "no-unused-vars":"off",
     "no-useless-escape":"off",
+    "object-curly-newline":"off",
+    "prefer-rest-params":"off",
     "radix":"off"
   },
   "globals": {

javascript/imageViewer.js

@@ -38,7 +38,6 @@ function modalSaveImage(event) {
 }
 
 function modalKeyHandler(event) {
-  console.log('HERE2', event.key);
   switch (event.key) {
     case 's':
       modalSaveImage();

modules/img2img.py

@@ -63,7 +63,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
         shared.log.debug(f'Processed: {len(images)} Memory: {memory_stats()} batch')
 
 
-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, image_cfg_scale: float, clip_skip: int, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, 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): # pylint: disable=unused-argument
+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, latent_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, image_cfg_scale: float, diffusers_guidance_rescale: float, refiner_denoise_start: float, refiner_denoise_end: float, clip_skip: int, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, 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): # pylint: disable=unused-argument
 
     if shared.sd_model is None:
         shared.log.warning('Model not loaded')
@@ -72,7 +72,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
     if init_img is None:
         shared.log.debug('Init image not set')
 
-    shared.log.debug(f'img2img: id_task={id_task}|mode={mode}|prompt={prompt}|negative_prompt={negative_prompt}|prompt_styles={prompt_styles}|init_img={init_img}|sketch={sketch}|init_img_with_mask={init_img_with_mask}|inpaint_color_sketch={inpaint_color_sketch}|inpaint_color_sketch_orig={inpaint_color_sketch_orig}|init_img_inpaint={init_img_inpaint}|init_mask_inpaint={init_mask_inpaint}|steps={steps}|sampler_index={sampler_index}|mask_blur={mask_blur}|mask_alpha={mask_alpha}|inpainting_fill={inpainting_fill}|restore_faces={restore_faces}|tiling={tiling}|n_iter={n_iter}|batch_size={batch_size}|cfg_scale={cfg_scale}|image_cfg_scale={image_cfg_scale}|clip_skip={clip_skip}|denoising_strength={denoising_strength}|seed={seed}|subseed{subseed}|subseed_strength={subseed_strength}|seed_resize_from_h={seed_resize_from_h}|seed_resize_from_w={seed_resize_from_w}|seed_enable_extras={seed_enable_extras}|selected_scale_tab={selected_scale_tab}|height={height}|width={width}|scale_by={scale_by}|resize_mode={resize_mode}|inpaint_full_res={inpaint_full_res}|inpaint_full_res_padding={inpaint_full_res_padding}|inpainting_mask_invert={inpainting_mask_invert}|img2img_batch_input_dir={img2img_batch_input_dir}|img2img_batch_output_dir={img2img_batch_output_dir}|img2img_batch_inpaint_mask_dir={img2img_batch_inpaint_mask_dir}|override_settings_texts={override_settings_texts}|args={args}')
+    shared.log.debug(f'img2img: id_task={id_task}|mode={mode}|prompt={prompt}|negative_prompt={negative_prompt}|prompt_styles={prompt_styles}|init_img={init_img}|sketch={sketch}|init_img_with_mask={init_img_with_mask}|inpaint_color_sketch={inpaint_color_sketch}|inpaint_color_sketch_orig={inpaint_color_sketch_orig}|init_img_inpaint={init_img_inpaint}|init_mask_inpaint={init_mask_inpaint}|steps={steps}|sampler_index={sampler_index}|latent_index={latent_index}|mask_blur={mask_blur}|mask_alpha={mask_alpha}|inpainting_fill={inpainting_fill}|restore_faces={restore_faces}|tiling={tiling}|n_iter={n_iter}|batch_size={batch_size}|cfg_scale={cfg_scale}|image_cfg_scale={image_cfg_scale}|clip_skip={clip_skip}|denoising_strength={denoising_strength}|seed={seed}|subseed{subseed}|subseed_strength={subseed_strength}|seed_resize_from_h={seed_resize_from_h}|seed_resize_from_w={seed_resize_from_w}|seed_enable_extras={seed_enable_extras}|selected_scale_tab={selected_scale_tab}|height={height}|width={width}|scale_by={scale_by}|resize_mode={resize_mode}|inpaint_full_res={inpaint_full_res}|inpaint_full_res_padding={inpaint_full_res_padding}|inpainting_mask_invert={inpainting_mask_invert}|img2img_batch_input_dir={img2img_batch_input_dir}|img2img_batch_output_dir={img2img_batch_output_dir}|img2img_batch_inpaint_mask_dir={img2img_batch_inpaint_mask_dir}|override_settings_texts={override_settings_texts}|args={args}')
 
     if sampler_index is None:
         shared.log.warning('Selected sampler is not enabled')
@@ -139,6 +139,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
         seed_resize_from_w=seed_resize_from_w,
         seed_enable_extras=True,
         sampler_name=sd_samplers.samplers_for_img2img[sampler_index].name,
+        latent_sampler=sd_samplers.samplers[latent_index].name,
         batch_size=batch_size,
         n_iter=n_iter,
         steps=steps,
@@ -155,6 +156,9 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
         resize_mode=resize_mode,
         denoising_strength=denoising_strength,
         image_cfg_scale=image_cfg_scale,
+        diffusers_guidance_rescale=diffusers_guidance_rescale,
+        refiner_denoise_start=refiner_denoise_start,
+        refiner_denoise_end=refiner_denoise_end,
         inpaint_full_res=inpaint_full_res,
         inpaint_full_res_padding=inpaint_full_res_padding,
         inpainting_mask_invert=inpainting_mask_invert,

modules/processing.py

@@ -89,7 +89,7 @@ class StableDiffusionProcessing:
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
     """
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, clip_skip: int = 1, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None): # pylint: disable=unused-argument
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, latent_sampler: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, image_cfg_scale: float = None, clip_skip: int = 1, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, diffusers_guidance_rescale: float = 0.7, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None): # pylint: disable=unused-argument
 
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -103,10 +103,13 @@ class StableDiffusionProcessing:
         self.seed_resize_from_h: int = seed_resize_from_h
         self.seed_resize_from_w: int = seed_resize_from_w
         self.sampler_name: str = sampler_name
+        self.latent_sampler: str = latent_sampler
         self.batch_size: int = batch_size
         self.n_iter: int = n_iter
         self.steps: int = steps
         self.cfg_scale: float = cfg_scale
+        self.image_cfg_scale = image_cfg_scale
+        self.diffusers_guidance_rescale = diffusers_guidance_rescale
         self.width: int = width
         self.height: int = height
         self.restore_faces: bool = restore_faces
@@ -148,6 +151,7 @@ class StableDiffusionProcessing:
         self.clip_skip = clip_skip
         self.iteration = 0
         self.is_hr_pass = False
+        self.refiner_denoise_start = 0
         opts.data['clip_skip'] = clip_skip
 
     @property
@@ -265,7 +269,7 @@ class Processed:
         self.height = p.height
         self.sampler_name = p.sampler_name
         self.cfg_scale = p.cfg_scale
-        self.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
+        self.image_cfg_scale = p.image_cfg_scale
         self.steps = p.steps
         self.batch_size = p.batch_size
         self.restore_faces = p.restore_faces
@@ -449,13 +453,15 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_su
     generation_params = {
         "Steps": p.steps,
         "Sampler": p.sampler_name,
+        "Latent sampler": p.latent_sampler,
         "CFG scale": p.cfg_scale,
-        "Image CFG scale": getattr(p, 'image_cfg_scale', None),
+        "Image CFG scale": p.image_cfg_scale,
         "Seed": all_seeds[index],
         "Face restoration": opts.face_restoration_model if p.restore_faces else None,
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', ''),
+        "Refiner": None if not opts.add_model_name_to_info or not shared.sd_refiner or not shared.sd_refiner.sd_checkpoint_info.model_name else shared.sd_refiner.sd_checkpoint_info.model_name.replace(',', '').replace(':', ''),
         "VAE": None if not opts.add_model_name_to_info or sd_vae.loaded_vae_file is None else os.path.splitext(os.path.basename(sd_vae.loaded_vae_file))[0],
         "Variation seed": None if p.subseed_strength == 0 else all_subseeds[index],
         "Variation seed strength": None if p.subseed_strength == 0 else p.subseed_strength,
@@ -497,7 +503,7 @@ def print_profile(profile, msg: str):
     except Exception:
         pass
     profile.disable()
-    stream = io.StringIO()
+    stream = io.StringIO() # pylint: disable=abstract-class-instantiated
     ps = pstats.Stats(profile, stream=stream)
     ps.sort_stats(pstats.SortKey.CUMULATIVE).print_stats(15)
     profile = None
@@ -618,7 +624,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         shared.state.sampling_step = step
         shared.state.sampling_steps = p.steps
         shared.state.current_latent = latents
-        shared.state.set_current_image()
 
     def set_pipeline_args(model, prompt, negative_prompt, **kwargs):
         args = {}
@@ -641,14 +646,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             args['output_type'] = 'np'
         if 'callback_steps' in possible:
             args['callback_steps'] = 1
-        if 'callback' in args:
+        if 'callback' in possible:
             args['callback'] = diffusers_callback
         if 'cross_attention_kwargs' in possible:
             args['cross_attention_kwargs'] = cross_attention_kwargs
         for arg in kwargs:
             if arg in possible:
                 args[arg] = kwargs[arg]
-        log.debug(f'Diffuser pipeline: {pipeline.__class__.__name__} args={args.keys()}')
+        # log.debug(f'Diffuser pipeline: {pipeline.__class__.__name__} possible={possible}')
+        log.debug(f'Diffuser pipeline: {pipeline.__class__.__name__} set={args.keys()}')
         return args
 
 
@@ -740,29 +746,46 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     # TODO(PVP): change out to latents once possible with `diffusers`
                     task_specific_kwargs = {"image": p.init_images[0], "mask_image": p.image_mask, "strength": p.denoising_strength}
 
-                # TODO Diffusers processing is not using p.sample so second pass is ignored
 
                 shared.sd_model.to(devices.device)
                 pipe_args = set_pipeline_args(
                     model=shared.sd_model,
                     prompt=prompts,
                     negative_prompt=negative_prompts,
-                    output_type='np' if shared.sd_refiner is None else 'latent',
+                    eta=shared.opts.eta_ddim,
+                    guidance_rescale=p.diffusers_guidance_rescale,
+                    # aesthetic_score=shared.opts.diffusers_aesthetics_score,
+                    output_type='np' if (shared.sd_refiner is None or p.enable_hr is False) else 'latent',
                     **task_specific_kwargs
                 )
                 output = shared.sd_model(**pipe_args) # pylint: disable=not-callable
-                if shared.sd_refiner is not None:
+
+                # TODO Diffusers processing is not using p.sample so second pass is ignored and we use this instead
+                if shared.sd_refiner is not None and p.enable_hr:
                     if shared.opts.diffusers_move_base:
                         shared.log.debug('Moving base model to CPU')
                         shared.sd_model.to('cpu')
+
+                    if (not hasattr(shared.sd_refiner.scheduler, 'name')) or (shared.sd_refiner.scheduler.name != p.latent_sampler):
+                        sampler = sd_samplers.all_samplers_map.get(p.latent_sampler, None)
+                        if sampler is None:
+                            sampler = sd_samplers.all_samplers_map.get("UniPC")
+                        shared.sd_refiner.scheduler = sd_samplers.create_sampler(sampler.name, shared.sd_refiner) # TODO(Patrick): For wrapped pipelines this is currently a no-op
+
                     shared.sd_refiner.to(devices.device)
                     devices.torch_gc()
                     pipe_args = set_pipeline_args(
                         model=shared.sd_refiner,
                         prompt=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts,
                         negative_prompt=[p.refiner_negative] if len(p.refiner_negative) > 0 else negative_prompts,
-                        num_inference_steps=p.refiner_steps,
-                        denoising_start=p.refiner_denoise,
+                        num_inference_steps=p.hr_second_pass_steps,
+                        eta=shared.opts.eta_ddim,
+                        strength=p.denoising_strength,
+                        guidance_scale=p.image_cfg_scale if p.image_cfg_scale is not None else p.cfg_scale,
+                        guidance_rescale=p.diffusers_guidance_rescale,
+                        # aesthetic_score=shared.opts.diffusers_aesthetics_score,
+                        denoising_start=p.refiner_denoise_start,
+                        denoising_end=p.refiner_denoise_end,
                         image=output.images[0],
                         output_type='np'
                     )
@@ -884,7 +907,7 @@ def old_hires_fix_first_pass_dimensions(width, height):
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, refiner_steps: int = 0, refiner_denoise: int = 0, refiner_prompt: str = '', refiner_negative: str = '', **kwargs):
+    def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, refiner_denoise_start: float = 0, refiner_denoise_end: float = 0, refiner_prompt: str = '', refiner_negative: str = '', **kwargs):
 
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
@@ -904,8 +927,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.truncate_x = 0
         self.truncate_y = 0
         self.applied_old_hires_behavior_to = None
-        self.refiner_steps = refiner_steps
-        self.refiner_denoise = refiner_denoise
+        self.refiner_denoise_start = refiner_denoise_start
+        self.refiner_denoise_end = refiner_denoise_end
         self.refiner_prompt = refiner_prompt
         self.refiner_negative = refiner_negative
 
@@ -1058,12 +1081,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
+    def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, refiner_denoise_start: float = 0, refiner_denoise_end: float = 0, refiner_prompt: str = '', refiner_negative: str = '', **kwargs):
         super().__init__(**kwargs)
         self.init_images = init_images
         self.resize_mode: int = resize_mode
         self.denoising_strength: float = denoising_strength
-        self.image_cfg_scale: float = image_cfg_scale if (shared.sd_model is not None) and hasattr(shared.sd_model, 'cond_stage_key') and (shared.sd_model.cond_stage_key == "edit") else None
+        self.image_cfg_scale: float = image_cfg_scale
         self.init_latent = None
         self.image_mask = mask
         self.latent_mask = None
@@ -1077,6 +1100,11 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.mask = None
         self.nmask = None
         self.image_conditioning = None
+        self.refiner_denoise_start = refiner_denoise_start
+        self.refiner_denoise_end = refiner_denoise_end
+        self.refiner_prompt = refiner_prompt
+        self.refiner_negative = refiner_negative
+
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         image_mask = self.image_mask

modules/sd_samplers_diffusers.py

@@ -14,6 +14,7 @@ try:
         # KDPM2DiscreteScheduler,
         PNDMScheduler,
         UniPCMultistepScheduler,
+        LMSDiscreteScheduler,
     )
 except Exception as e:
     import diffusers
@@ -32,6 +33,7 @@ config = {
     'PNDM': { 'skip_prk_steps': False, 'set_alpha_to_one': False, 'steps_offset': 0 },
     'DPM 1S': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False },
     'DPM 2M': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False },
+    'LMSD': { 'use_karras_sigmas': False, 'timestep_spacing': 'linspace', 'steps_offset': 0 },
 }
 
 samplers_data_diffusers = [
@@ -45,6 +47,7 @@ samplers_data_diffusers = [
     sd_samplers_common.SamplerData('Euler a', lambda model: DiffusionSampler('Euler a', EulerAncestralDiscreteScheduler, model), [], {}),
     sd_samplers_common.SamplerData('Heun', lambda model: DiffusionSampler('Heun', HeunDiscreteScheduler, model), [], {}),
     sd_samplers_common.SamplerData('PNDM', lambda model: DiffusionSampler('PNDM', PNDMScheduler, model), [], {}),
+    sd_samplers_common.SamplerData('LMSD', lambda model: DiffusionSampler('LMSD', LMSDiscreteScheduler, model), [], {}),
 ]
 
 class DiffusionSampler:

modules/shared.py

@@ -66,12 +66,12 @@ restricted_opts = {
 ui_reorder_categories = [
     "inpaint",
     "sampler",
-    "checkboxes",
-    "hires_fix",
     "dimensions",
     "cfg",
     "seed",
     "batch",
+    "checkboxes",
+    "second_pass",
     "override_settings",
     "scripts",
 ]
@@ -370,6 +370,8 @@ options_templates.update(options_section(('diffusers', "Diffusers Settings"), {
     "diffusers_vae_slicing": OptionInfo(False, "Enable VAE slicing"),
     "diffusers_vae_tiling": OptionInfo(False, "Enable VAE tiling"),
     "diffusers_attention_slicing": OptionInfo(False, "Enable attention slicing"),
+    # "diffusers_force_zeros": OptionInfo(False, "Force zeros for prompts when empty"),
+    # "diffusers_aesthetics_score": OptionInfo(6.0, "Require aesthetic score", gr.Slider, {"minimum": 0, "maximum": 10, "step": 0.1}),
 }))
 
 options_templates.update(options_section(('system-paths', "System Paths"), {
@@ -488,10 +490,11 @@ options_templates.update(options_section(('live-preview', "Live Previews"), {
 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()]}),
+    # "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"]}),
     'eta_noise_seed_delta': OptionInfo(0, "Noise seed delta (eta)", gr.Number, {"precision": 0}),
+    "eta_ddim": OptionInfo(0.0, "Noise multiplier for DDIM (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
 
     "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']}),
@@ -514,7 +517,6 @@ options_templates.update(options_section(('sampler-params', "Sampler Settings"),
 
     "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}),
 }))
 
 options_templates.update(options_section(('postprocessing', "Postprocessing"), {
@@ -746,7 +748,7 @@ cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or (cmd_o
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
 device = devices.device
 batch_cond_uncond = opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
-parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
+parallel_processing_allowed = not cmd_opts.lowvram
 mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
 mem_mon.start()
 if device.type == 'privateuseone':

modules/txt2img.py

@@ -5,9 +5,9 @@ from modules.ui import plaintext_to_html
 from modules.memstats import memory_stats
 
 
-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, clip_skip: int, 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, refiner_steps: int, refiner_denoise: float, refiner_prompt: str, refiner_negative: str, override_settings_texts, *args): # pylint: disable=unused-argument
+def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, latent_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, diffusers_guidance_rescale: float, clip_skip: int, 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, refiner_denoise_start: int, refiner_denoise_end: float, refiner_prompt: str, refiner_negative: str, override_settings_texts, *args): # pylint: disable=unused-argument
 
-    shared.log.debug(f'txt2img: id_task={id_task}|prompt={prompt}|negative_prompt={negative_prompt}|prompt_styles={prompt_styles}|steps={steps}|sampler_index={sampler_index}|restore_faces={restore_faces}|tiling={tiling}|n_iter={n_iter}|batch_size={batch_size}|cfg_scale={cfg_scale}|clip_skip={clip_skip}|seed={seed}|subseed={subseed}|subseed_strength={subseed_strength}|seed_resize_from_h={seed_resize_from_h}|seed_resize_from_w={seed_resize_from_w}|seed_enable_extras={seed_enable_extras}|height={height}|width={width}|enable_hr={enable_hr}|denoising_strength={denoising_strength}|hr_scale={hr_scale}|hr_upscaler={hr_upscaler}|hr_second_pass_steps={hr_second_pass_steps}|hr_resize_x={hr_resize_x}|hr_resize_y={hr_resize_y}|refiner_steps={refiner_steps}|refiner_denoise={refiner_denoise}|refiner_prompt={refiner_prompt}|refiner_negative={refiner_negative}|override_settings_texts={override_settings_texts}args={args}')
+    shared.log.debug(f'txt2img: id_task={id_task}|prompt={prompt}|negative_prompt={negative_prompt}|prompt_styles={prompt_styles}|steps={steps}|sampler_index={sampler_index}|latent_index={latent_index}|restore_faces={restore_faces}|tiling={tiling}|n_iter={n_iter}|batch_size={batch_size}|cfg_scale={cfg_scale}|clip_skip={clip_skip}|seed={seed}|subseed={subseed}|subseed_strength={subseed_strength}|seed_resize_from_h={seed_resize_from_h}|seed_resize_from_w={seed_resize_from_w}|seed_enable_extras={seed_enable_extras}|height={height}|width={width}|enable_hr={enable_hr}|denoising_strength={denoising_strength}|hr_scale={hr_scale}|hr_upscaler={hr_upscaler}|hr_second_pass_steps={hr_second_pass_steps}|hr_resize_x={hr_resize_x}|hr_resize_y={hr_resize_y}|image_cfg_scale={image_cfg_scale}|diffusers_guidance_rescale={diffusers_guidance_rescale}|refiner_denoise_start={refiner_denoise_start}|refiner_denoise_end={refiner_denoise_end}|refiner_prompt={refiner_prompt}|refiner_negative={refiner_negative}|override_settings_texts={override_settings_texts}args={args}')
     if sampler_index is None:
         shared.log.warning('Selected sampler is not enabled')
         sampler_index = 0
@@ -31,10 +31,13 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
         seed_resize_from_w=seed_resize_from_w,
         seed_enable_extras=True,
         sampler_name=sd_samplers.samplers[sampler_index].name,
+        latent_sampler=sd_samplers.samplers[latent_index].name,
         batch_size=batch_size,
         n_iter=n_iter,
         steps=steps,
         cfg_scale=cfg_scale,
+        image_cfg_scale=image_cfg_scale,
+        diffusers_guidance_rescale=diffusers_guidance_rescale,
         clip_skip=clip_skip,
         width=width,
         height=height,
@@ -47,8 +50,8 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
         hr_second_pass_steps=hr_second_pass_steps,
         hr_resize_x=hr_resize_x,
         hr_resize_y=hr_resize_y,
-        refiner_steps=refiner_steps,
-        refiner_denoise=refiner_denoise,
+        refiner_denoise_start=refiner_denoise_start,
+        refiner_denoise_end=refiner_denoise_end,
         refiner_prompt=refiner_prompt,
         refiner_negative=refiner_negative,
         override_settings=override_settings,

modules/ui.py

@@ -89,10 +89,12 @@ def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resiz
     from modules import processing, devices
     if not enable:
         return ""
+    if modules.shared.backend == modules.shared.Backend.DIFFUSERS:
+        return "Hires resize: disabled"
     p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y)
     with devices.autocast():
         p.init([""], [0], [0])
-    return f"resize: from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}</span>"
+    return f"Hires resize: from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}</span>"
 
 
 def resize_from_to_html(width, height, scale_by):
@@ -100,7 +102,9 @@ def resize_from_to_html(width, height, scale_by):
     target_height = int(height * scale_by)
     if not target_width or not target_height:
         return "no image selected"
-    return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
+    if modules.shared.backend == modules.shared.Backend.DIFFUSERS:
+        return "Hires resize: disabled"
+    return f"Hires resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
 
 
 def apply_styles(prompt, prompt_neg, styles):
@@ -367,43 +371,49 @@ def create_ui(startup_timer = None):
                                     batch_size = gr.Slider(minimum=1, maximum=32, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
                     elif category == "cfg":
                         with FormRow():
-                            cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=6.0, elem_id="txt2img_cfg_scale")
+                            cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.1, label='CFG Scale', value=6.0, elem_id="txt2img_cfg_scale")
                             clip_skip = gr.Slider(label='CLIP skip', value=1, minimum=1, maximum=14, step=1, elem_id='txt2img_clip_skip', interactive=True)
                     elif category == "seed":
                         seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
                     elif category == "checkboxes":
                         with FormRow(elem_classes="checkboxes-row", variant="compact"):
+                            second_pass = gr.Checkbox(label='Second pass', value=False, elem_id="txt2img_enable_hr")
                             restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(modules.shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
                             tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
-                            enable_hr = gr.Checkbox(label='Hires fix', value=False, elem_id="txt2img_enable_hr")
-                            enable_refiner = gr.Checkbox(label='Refiner', value=False, elem_id="txt2img_enable_refiner")
-                            hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False)
-                    elif category == "hires_fix":
-                        with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options:
+                    elif category == "second_pass":
+                        with FormGroup(visible=False, elem_id="txt2img_second_pass") as hr_options:
+                            hr_second_pass_steps, latent_index = create_sampler_and_steps_selection(modules.sd_samplers.samplers, "txt2img")
                             with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
                                 denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
-                                hr_second_pass_steps = gr.Slider(minimum=0, maximum=99, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
+
+                            with FormRow():
+                                hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False)
                             with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
                                 hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*modules.shared.latent_upscale_modes, *[x.name for x in modules.shared.sd_upscalers]], value=modules.shared.latent_upscale_default_mode)
                                 hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
                             with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact"):
                                 hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
                                 hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
-                        with FormGroup(visible=False, elem_id="txt2img_hires_fix") as refiner_options:
+
+                            with FormRow():
+                                hr_refiner = FormHTML(value="Refiner", elem_id="txtimg_hr_finalres", interactive=False)
                             with FormRow(elem_id="txt2img_refiner_row1", variant="compact"):
-                                refiner_steps = gr.Slider(minimum=1, maximum=99, step=1, label='Steps', value=5, elem_id="txt2img_refiner_steps")
-                                refiner_denoise = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Denoise start', value=0.5, elem_id="txt2img_refiner_denoise")
+                                image_cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.1, label='CFG Scale', value=6.0, elem_id="txt2img_image_cfg_scale")
+                                diffusers_guidance_rescale = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Guidance rescale', value=0.7, elem_id="txt2img_image_cfg_rescale")
+                                refiner_denoise_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Denoise start', value=0.0, elem_id="txt2img_refiner_denoise_start")
+                                refiner_denoise_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Denoise end', value=1.0, elem_id="txt2img_refiner_denoise_end")
                             with FormRow(elem_id="txt2img_refiner_row2", variant="compact"):
                                 refiner_prompt = gr.Textbox(value='', label='Prompt')
                             with FormRow(elem_id="txt2img_refiner_row3", variant="compact"):
                                 refiner_negative = gr.Textbox(value='', label='Negative prompt')
+
                     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()
-            hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
+            hr_resolution_preview_inputs = [second_pass, width, height, hr_scale, hr_resize_x, hr_resize_y]
             for preview_input in hr_resolution_preview_inputs:
                 preview_input.change(
                     fn=calc_resolution_hires,
@@ -427,26 +437,29 @@ def create_ui(startup_timer = None):
                     txt2img_prompt_styles,
                     steps,
                     sampler_index,
+                    latent_index,
                     restore_faces,
                     tiling,
                     batch_count,
                     batch_size,
                     cfg_scale,
+                    image_cfg_scale,
+                    diffusers_guidance_rescale,
                     clip_skip,
                     seed,
                     subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,
                     seed_checkbox,
                     height,
                     width,
-                    enable_hr,
+                    second_pass,
                     denoising_strength,
                     hr_scale,
                     hr_upscaler,
                     hr_second_pass_steps,
                     hr_resize_x,
                     hr_resize_y,
-                    refiner_steps,
-                    refiner_denoise,
+                    refiner_denoise_start,
+                    refiner_denoise_end,
                     refiner_prompt,
                     refiner_negative,
                     override_settings,
@@ -467,14 +480,17 @@ def create_ui(startup_timer = None):
 
             txt_prompt_img.change(fn=modules.images.image_data, inputs=[txt_prompt_img], outputs=[txt2img_prompt, txt_prompt_img])
 
-            enable_hr.change(fn=lambda x: gr_show(x), inputs=[enable_hr], outputs=[hr_options], show_progress = False)
-            enable_refiner.change(fn=lambda x: gr_show(x), inputs=[enable_refiner], outputs=[refiner_options], show_progress = False)
+            def enable_hr_change(visible: bool):
+                return {"visible": visible, "__type__": "update"}, f'Refiner{": disabled" if modules.shared.sd_refiner is None else ""}'
+
+            second_pass.change(enable_hr_change, inputs=[second_pass], outputs=[hr_options, hr_refiner], show_progress = False)
 
             txt2img_paste_fields = [
                 (txt2img_prompt, "Prompt"),
                 (txt2img_negative_prompt, "Negative prompt"),
                 (steps, "Steps"),
                 (sampler_index, "Sampler"),
+                (latent_index, "Latent sampler"),
                 (restore_faces, "Face restoration"),
                 (cfg_scale, "CFG scale"),
                 (clip_skip, "Clip skip"),
@@ -487,7 +503,7 @@ def create_ui(startup_timer = None):
                 (seed_resize_from_w, "Seed resize from-1"),
                 (seed_resize_from_h, "Seed resize from-2"),
                 (denoising_strength, "Denoising strength"),
-                (enable_hr, lambda d: "Denoising strength" in d),
+                (second_pass, lambda d: "Denoising strength" in d),
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
                 (hr_scale, "Hires upscale"),
                 (hr_upscaler, "Hires upscaler"),
@@ -658,14 +674,18 @@ def create_ui(startup_timer = None):
                         with FormRow(elem_id="img2img_column_batch"):
                             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")
+                            clip_skip = gr.Slider(label='CLIP skip', value=1, minimum=1, maximum=4, step=1, elem_id='img2img_clip_skip', interactive=True)
 
                     elif category == "cfg":
                         with FormGroup():
                             with FormRow():
                                 cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=6.0, elem_id="img2img_cfg_scale")
-                                image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
+                                image_cfg_scale = gr.Slider(minimum=0, maximum=30.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale")
+                                diffusers_guidance_rescale = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='CFG Scale', value=0.7, elem_id="txt2img_image_cfg_rescale")
+                            with FormRow():
                                 denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
-                                clip_skip = gr.Slider(label='CLIP skip', value=1, minimum=1, maximum=4, step=1, elem_id='img2img_clip_skip', interactive=True)
+                                refiner_denoise_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Denoise start', value=0.0, elem_id="txt2img_refiner_denoise_start")
+                                refiner_denoise_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Denoise end', value=1.0, elem_id="txt2img_refiner_denoise_end")
 
                     elif category == "seed":
                         seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
@@ -746,6 +766,7 @@ def create_ui(startup_timer = None):
                     init_mask_inpaint,
                     steps,
                     sampler_index,
+                    latent_index,
                     mask_blur,
                     mask_alpha,
                     inpainting_fill,
@@ -755,6 +776,9 @@ def create_ui(startup_timer = None):
                     batch_size,
                     cfg_scale,
                     image_cfg_scale,
+                    diffusers_guidance_rescale,
+                    refiner_denoise_start,
+                    refiner_denoise_end,
                     clip_skip,
                     denoising_strength,
                     seed,
@@ -1146,11 +1170,6 @@ def create_ui(startup_timer = None):
                 show_progress=info.refresh is not None,
             )
 
-        # TODO breaks pix2pix needs better detect if pix2pix and image_cfg_scale_visibility should be on model change, not on ui create
-        # image_cfg_scale_visibility = (modules.shared.sd_model is not None) and hasattr(modules.shared.sd_model, 'cond_stage_key') and (modules.shared.sd_model.cond_stage_key == "edit") # pix2pix
-        # text_settings.change(fn=lambda: gr.update(visible=image_cfg_scale_visibility), inputs=[], outputs=[image_cfg_scale])
-        # demo.load(fn=lambda: gr.update(visible=image_cfg_scale_visibility), inputs=[], outputs=[image_cfg_scale])
-
         button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
         button_set_checkpoint.click(
             fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'),

scripts/xyz_grid.py

@@ -123,14 +123,6 @@ def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
     p.styles.extend(x.split(','))
 
 
-def apply_fallback(p, x, xs):
-    sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
-    if sampler_name is None:
-        shared.log.warning(f"XYZ grid: unknown sampler: {x}")
-    else:
-        shared.opts.data["force_latent_sampler"] = sampler_name
-
-
 def apply_schedulers_solver_order(p, x, xs):
     shared.opts.data["schedulers_solver_order"] = min(x, p.steps - 1)
 
@@ -351,7 +343,6 @@ class SharedSettingsStackHelper(object):
         self.sd_model_checkpoint = shared.opts.sd_model_checkpoint
         self.sd_model_dict = shared.opts.sd_model_dict
         self.sd_vae_checkpoint = shared.opts.sd_vae
-        self.force_latent_sampler = shared.opts.force_latent_sampler
 
     def __exit__(self, exc_type, exc_value, tb):
         #Restore overriden settings after plot generation.
@@ -359,7 +350,6 @@ class SharedSettingsStackHelper(object):
         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
         if self.sd_model_dict != shared.opts.sd_model_dict:
             shared.opts.data["sd_model_dict"] = self.sd_model_dict
         if self.sd_model_checkpoint != shared.opts.sd_model_checkpoint: