Add dropdown for scheduler type

modules/img2img.py

@@ -78,7 +78,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-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, 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, enable_k_sched, sigma_min, sigma_max, rho, *args):
+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, 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, enable_k_sched, k_sched_type, sigma_min, sigma_max, rho, *args):
     override_settings = create_override_settings_dict(override_settings_texts)
 
     is_batch = mode == 5
@@ -156,6 +156,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
         inpainting_mask_invert=inpainting_mask_invert,
         override_settings=override_settings,
         enable_karras=enable_k_sched,
+        k_sched_type=k_sched_type,
         sigma_min=sigma_min,
         sigma_max=sigma_max,
         rho=rho

modules/processing.py

@@ -106,7 +106,7 @@ 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, 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, enable_karras: bool = False, sigma_min: float=0.1, sigma_max: float=10.0, rho: float=7.0):
+    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, 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, enable_karras: bool = False, k_sched_type: str = "karras", sigma_min: float=0.1, sigma_max: float=10.0, rho: float=7.0):
         if sampler_index is not None:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
@@ -147,6 +147,7 @@ class StableDiffusionProcessing:
         self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
         self.s_noise = s_noise or opts.s_noise
         self.enable_karras = enable_karras
+        self.k_sched_type = k_sched_type
         self.sigma_max = sigma_max
         self.sigma_min = sigma_min
         self.rho = rho
@@ -563,6 +564,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Steps": p.steps,
         "Sampler": p.sampler_name,
         "Enable Custom Karras Schedule": p.enable_karras,
+        "Karras Scheduler Type": p.k_sched_type,
         "Karras Scheduler sigma_max": p.sigma_max,
         "Karras Scheduler sigma_min": p.sigma_min,
         "Karras Scheduler rho": p.rho,

modules/sd_samplers_kdiffusion.py

@@ -44,6 +44,12 @@ sampler_extra_params = {
     'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
 }
 
+k_diffusion_scheduler = {
+    'karras': k_diffusion.sampling.get_sigmas_karras,
+    'exponential': k_diffusion.sampling.get_sigmas_exponential,
+    'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
+}
+
 
 class CFGDenoiser(torch.nn.Module):
     """
@@ -305,10 +311,15 @@ class KDiffusionSampler:
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
         elif p.enable_karras:
-            sigma_max = p.sigma_max
-            sigma_min = p.sigma_min
-            rho = p.rho
-            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho, device=shared.device)
+            print(p.k_sched_type, p.sigma_min, p.sigma_max, p.rho)
+            sigmas_func = k_diffusion_scheduler[p.k_sched_type]
+            sigmas_kwargs = {
+                'sigma_min': p.sigma_min,
+                'sigma_max': p.sigma_max
+            }
+            if p.k_sched_type != 'exponential':
+                sigmas_kwargs['rho'] = p.rho
+            sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
             sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
 

modules/txt2img.py

@@ -7,7 +7,7 @@ from modules.ui import plaintext_to_html
 
 
 
-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, 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, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, enable_k_sched, sigma_min, sigma_max, rho, *args):
+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, 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, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, enable_k_sched, k_sched_type, sigma_min, sigma_max, rho, *args):
     override_settings = create_override_settings_dict(override_settings_texts)
 
     p = processing.StableDiffusionProcessingTxt2Img(
@@ -44,6 +44,7 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
         hr_negative_prompt=hr_negative_prompt,
         override_settings=override_settings,
         enable_karras=enable_k_sched,
+        k_sched_type=k_sched_type,
         sigma_min=sigma_min,
         sigma_max=sigma_max,
         rho=rho

modules/ui.py

@@ -514,9 +514,10 @@ def create_ui():
                     elif category == "karras_scheduler":
                         with FormGroup(visible=False, elem_id="txt2img_karras_scheduler") as t2i_k_sched_options:
                             with FormRow(elem_id="txt2img_karras_scheduler_row1", variant="compact"):
-                                t2i_k_sched_sigma_max = gr.Slider(minimum=0.0, maximum=0.5, step=0.05, label='sigma min', value=0.1, elem_id="txt2img_sigma_min")
-                                t2i_k_sched_sigma_min = gr.Slider(minimum=5.0, maximum=50.0, step=0.1, label='sigma max', value=10.0, elem_id="txt2img_sigma_max")
-                                t2i_k_sched_rho = gr.Slider(minimum=3.0, maximum=10.0, step=0.5, label='rho', value=7.0, elem_id="txt2img_rho")
+                                t2i_k_sched_type = gr.Dropdown(label="Type", elem_id="t2i_k_sched_type", choices=['karras', 'exponential', 'polyexponential'], value='karras')
+                                t2i_k_sched_sigma_min = gr.Slider(minimum=0.0, maximum=0.5, step=0.05, label='sigma min', value=0.1, elem_id="txt2img_sigma_min")
+                                t2i_k_sched_sigma_max = gr.Slider(minimum=5.0, maximum=50.0, step=0.1, label='sigma max', value=10.0, elem_id="txt2img_sigma_max")
+                                t2i_k_sched_rho = gr.Slider(minimum=0.5, maximum=10.0, step=0.1, label='rho', value=7.0, elem_id="txt2img_rho")
 
                     elif category == "batch":
                         if not opts.dimensions_and_batch_together:
@@ -587,8 +588,9 @@ def create_ui():
                     hr_negative_prompt,
                     override_settings,
                     t2i_enable_k_sched,
-                    t2i_k_sched_sigma_max,
+                    t2i_k_sched_type,
                     t2i_k_sched_sigma_min,
+                    t2i_k_sched_sigma_max,
                     t2i_k_sched_rho
 
                 ] + custom_inputs,
@@ -674,7 +676,8 @@ def create_ui():
                 (hr_prompt, "Hires prompt"),
                 (hr_negative_prompt, "Hires negative prompt"),
                 (hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
-                (t2i_enable_k_sched, "Enable CustomKarras Schedule"),
+                (t2i_enable_k_sched, "Enable Custom Karras Schedule"),
+                (t2i_k_sched_type, "Karras Scheduler Type"),
                 (t2i_k_sched_sigma_max, "Karras Scheduler sigma_max"),
                 (t2i_k_sched_sigma_min, "Karras Scheduler sigma_min"),
                 (t2i_k_sched_rho, "Karras Scheduler rho"),
@@ -874,9 +877,10 @@ def create_ui():
                     elif category == "karras_scheduler":
                         with FormGroup(visible=False, elem_id="img2img_karras_scheduler") as i2i_k_sched_options:
                             with FormRow(elem_id="img2img_karras_scheduler_row1", variant="compact"):
-                                i2i_k_sched_sigma_max = gr.Slider(minimum=0.0, maximum=0.5, step=0.05, label='sigma min', value=0.1, elem_id="txt2img_sigma_min")
-                                i2i_k_sched_sigma_min = gr.Slider(minimum=5.0, maximum=50.0, step=0.1, label='sigma max', value=10.0, elem_id="txt2img_sigma_max")
-                                i2i_k_sched_rho = gr.Slider(minimum=3.0, maximum=10.0, step=0.5, label='rho', value=7.0, elem_id="txt2img_rho")
+                                i2i_k_sched_type = gr.Dropdown(label="Type", elem_id="t2i_k_sched_type", choices=['karras', 'exponential', 'polyexponential'], value='karras')
+                                i2i_k_sched_sigma_min = gr.Slider(minimum=0.0, maximum=0.5, step=0.05, label='sigma min', value=0.1, elem_id="txt2img_sigma_min")
+                                i2i_k_sched_sigma_max = gr.Slider(minimum=5.0, maximum=50.0, step=0.1, label='sigma max', value=10.0, elem_id="txt2img_sigma_max")
+                                i2i_k_sched_rho = gr.Slider(minimum=0.5, maximum=10.0, step=0.1, label='rho', value=7.0, elem_id="txt2img_rho")
 
                     elif category == "batch":
                         if not opts.dimensions_and_batch_together:
@@ -981,8 +985,9 @@ def create_ui():
                     img2img_batch_inpaint_mask_dir,
                     override_settings,
                     i2i_enable_k_sched,
-                    i2i_k_sched_sigma_max,
+                    i2i_k_sched_type,
                     i2i_k_sched_sigma_min,
+                    i2i_k_sched_sigma_max,
                     i2i_k_sched_rho
                 ] + custom_inputs,
                 outputs=[
@@ -1085,7 +1090,8 @@ def create_ui():
                 (steps, "Steps"),
                 (sampler_index, "Sampler"),
                 (restore_faces, "Face restoration"),
-                (i2i_enable_k_sched, "Enable Karras Schedule"),
+                (i2i_enable_k_sched, "Enable Custom Karras Schedule"),
+                (i2i_k_sched_type, "Karras Scheduler Type"),
                 (i2i_k_sched_sigma_max, "Karras Scheduler sigma_max"),
                 (i2i_k_sched_sigma_min, "Karras Scheduler sigma_min"),
                 (i2i_k_sched_rho, "Karras Scheduler rho"),

scripts/xyz_grid.py

@@ -10,7 +10,7 @@ import numpy as np
 import modules.scripts as scripts
 import gradio as gr
 
-from modules import images, sd_samplers, processing, sd_models, sd_vae
+from modules import images, sd_samplers, processing, sd_models, sd_vae, sd_samplers_kdiffusion
 from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
 from modules.shared import opts, state
 import modules.shared as shared
@@ -220,6 +220,7 @@ axis_options = [
     AxisOption("Sigma min", float, apply_field("s_tmin")),
     AxisOption("Sigma max", float, apply_field("s_tmax")),
     AxisOption("Sigma noise", float, apply_field("s_noise")),
+    AxisOption("Karras Scheduler Type", str, apply_field("k_sched_type"), choices=lambda: [x for x in sd_samplers_kdiffusion.k_diffusion_scheduler]),
     AxisOption("Karras Scheduler Sigma Min", float, apply_field("sigma_min")),
     AxisOption("Karras Scheduler Sigma Max", float, apply_field("sigma_max")),
     AxisOption("Karras Scheduler rho", float, apply_field("rho")),