integrate the new samplers PR

modules/processing.py

@@ -477,7 +477,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.firstphase_height_truncated = int(scale * self.height)
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-        self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model)
+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
@@ -520,7 +520,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         shared.state.nextjob()
 
-        self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model)
+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
+
         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
         # GC now before running the next img2img to prevent running out of memory
@@ -555,7 +556,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.nmask = None
 
     def init(self, all_prompts, all_seeds, all_subseeds):
-        self.sampler = sd_samplers.samplers_for_img2img[self.sampler_index].constructor(self.sd_model)
+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
         crop_region = None
 
         if self.image_mask is not None:

modules/sd_samplers.py

@@ -13,46 +13,46 @@ from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 
 
-SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases'])
+SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
 
 samplers_k_diffusion = [
-    ('Euler a', 'sample_euler_ancestral', ['k_euler_a']),
-    ('Euler', 'sample_euler', ['k_euler']),
-    ('LMS', 'sample_lms', ['k_lms']),
-    ('Heun', 'sample_heun', ['k_heun']),
-    ('DPM2', 'sample_dpm_2', ['k_dpm_2']),
-    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a']),
-    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast']),
-    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']),
+    ('Euler a', 'sample_euler_ancestral', ['k_euler_a'], {}),
+    ('Euler', 'sample_euler', ['k_euler'], {}),
+    ('LMS', 'sample_lms', ['k_lms'], {}),
+    ('Heun', 'sample_heun', ['k_heun'], {}),
+    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
+    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
+    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
+    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
+    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
+    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
 ]
 
-if opts.show_karras_scheduler_variants:
-    k_diffusion.sampling.sample_dpm_2_ka = k_diffusion.sampling.sample_dpm_2
-    k_diffusion.sampling.sample_dpm_2_ancestral_ka = k_diffusion.sampling.sample_dpm_2_ancestral
-    k_diffusion.sampling.sample_lms_ka = k_diffusion.sampling.sample_lms
-    samplers_k_diffusion_ka = [
-        ('LMS K Scheduling', 'sample_lms_ka', ['k_lms_ka']),
-        ('DPM2 K Scheduling', 'sample_dpm_2_ka', ['k_dpm_2_ka']),
-        ('DPM2 a K Scheduling', 'sample_dpm_2_ancestral_ka', ['k_dpm_2_a_ka']),
-    ]
-    samplers_k_diffusion.extend(samplers_k_diffusion_ka)
-
 samplers_data_k_diffusion = [
-    SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases)
-    for label, funcname, aliases in samplers_k_diffusion
+    SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
+    for label, funcname, aliases, options in samplers_k_diffusion
     if hasattr(k_diffusion.sampling, funcname)
 ]
 
 all_samplers = [
     *samplers_data_k_diffusion,
-    SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),
-    SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),
+    SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
+    SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
 ]
 
 samplers = []
 samplers_for_img2img = []
 
 
+def create_sampler_with_index(list_of_configs, index, model):
+    config = list_of_configs[index]
+    sampler = config.constructor(model)
+    sampler.config = config
+    
+    return sampler
+
+
 def set_samplers():
     global samplers, samplers_for_img2img
 
@@ -130,6 +130,7 @@ class VanillaStableDiffusionSampler:
         self.step = 0
         self.eta = None
         self.default_eta = 0.0
+        self.config = None
 
     def number_of_needed_noises(self, p):
         return 0
@@ -291,6 +292,7 @@ class KDiffusionSampler:
         self.stop_at = None
         self.eta = None
         self.default_eta = 1.0
+        self.config = None
 
     def callback_state(self, d):
         store_latent(d["denoised"])
@@ -356,10 +358,11 @@ class KDiffusionSampler:
 
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
-        elif self.funcname.endswith('ka'):
+        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
             sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
         else:
             sigmas = self.model_wrap.get_sigmas(steps)
+
         x = x * sigmas[0]
 
         extra_params_kwargs = self.initialize(p)

modules/shared.py

@@ -236,7 +236,6 @@ options_templates.update(options_section(('ui', "User interface"), {
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-    "show_karras_scheduler_variants": OptionInfo(True, "Show Karras scheduling variants for select samplers. Try these variants if your K sampled images suffer from excessive noise."),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {

scripts/alternate_sampler_noise_schedules.py

@@ -1,53 +0,0 @@
-import inspect
-from modules.processing import Processed, process_images
-import gradio as gr
-import modules.scripts as scripts
-import k_diffusion.sampling
-import torch
-
-
-class Script(scripts.Script):
-
-    def title(self):
-        return "Alternate Sampler Noise Schedules"
-
-    def ui(self, is_img2img):
-      noise_scheduler = gr.Dropdown(label="Noise Scheduler", choices=['Default','Karras','Exponential', 'Variance Preserving'], value='Default', type="index")
-      sched_smin     = gr.Slider(value=0.1,   label="Sigma min",                  minimum=0.0,   maximum=100.0, step=0.5,)
-      sched_smax     = gr.Slider(value=10.0,  label="Sigma max",                  minimum=0.0,   maximum=100.0, step=0.5)
-      sched_rho      = gr.Slider(value=7.0,   label="Sigma rho (Karras only)",    minimum=7.0,   maximum=100.0, step=0.5)
-      sched_beta_d   = gr.Slider(value=19.9,  label="Beta distribution (VP only)",minimum=0.0,   maximum=40.0, step=0.5)
-      sched_beta_min = gr.Slider(value=0.1,   label="Beta min (VP only)",         minimum=0.0,   maximum=40.0, step=0.1)
-      sched_eps_s    = gr.Slider(value=0.001, label="Epsilon (VP only)",          minimum=0.001, maximum=1.0,   step=0.001)
-
-      return [noise_scheduler, sched_smin, sched_smax, sched_rho, sched_beta_d, sched_beta_min, sched_eps_s]
-
-    def run(self, p, noise_scheduler, sched_smin, sched_smax, sched_rho, sched_beta_d, sched_beta_min, sched_eps_s):
-
-      noise_scheduler_func_name = ['-','get_sigmas_karras','get_sigmas_exponential','get_sigmas_vp'][noise_scheduler]
-
-      base_params = {
-        "sigma_min":sched_smin, 
-        "sigma_max":sched_smax, 
-        "rho":sched_rho, 
-        "beta_d":sched_beta_d, 
-        "beta_min":sched_beta_min, 
-        "eps_s":sched_eps_s,
-        "device":"cuda" if torch.cuda.is_available() else "cpu"
-      }
-
-      if hasattr(k_diffusion.sampling,noise_scheduler_func_name):
-
-        sigma_func  = getattr(k_diffusion.sampling,noise_scheduler_func_name)
-        sigma_func_kwargs = {}
-
-        for k,v in base_params.items():
-          if k in inspect.signature(sigma_func).parameters:
-            sigma_func_kwargs[k] = v
-
-        def substitute_noise_scheduler(n):
-          return sigma_func(n,**sigma_func_kwargs)
-
-        p.sampler_noise_scheduler_override = substitute_noise_scheduler
-
-      return process_images(p)

scripts/img2imgalt.py

@@ -8,7 +8,6 @@ import gradio as gr
 
 from modules import processing, shared, sd_samplers, prompt_parser
 from modules.processing import Processed
-from modules.sd_samplers import samplers
 from modules.shared import opts, cmd_opts, state
 
 import torch
@@ -159,7 +158,7 @@ class Script(scripts.Script):
             
             combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
             
-            sampler = samplers[p.sampler_index].constructor(p.sd_model)
+            sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, p.sampler_index, p.sd_model)
 
             sigmas = sampler.model_wrap.get_sigmas(p.steps)