integrate the new samplers PR
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AUTOMATIC
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@ -477,7 +477,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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self.firstphase_height_truncated = int(scale * self.height)
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
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self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model)
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self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
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if not self.enable_hr:
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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)
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@ -520,7 +520,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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shared.state.nextjob()
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self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model)
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self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
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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)
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# GC now before running the next img2img to prevent running out of memory
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@ -555,7 +556,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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self.nmask = None
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def init(self, all_prompts, all_seeds, all_subseeds):
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self.sampler = sd_samplers.samplers_for_img2img[self.sampler_index].constructor(self.sd_model)
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self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
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crop_region = None
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if self.image_mask is not None:
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@ -13,46 +13,46 @@ from modules.shared import opts, cmd_opts, state
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import modules.shared as shared
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SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases'])
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SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
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samplers_k_diffusion = [
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('Euler a', 'sample_euler_ancestral', ['k_euler_a']),
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('Euler', 'sample_euler', ['k_euler']),
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('LMS', 'sample_lms', ['k_lms']),
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('Heun', 'sample_heun', ['k_heun']),
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('DPM2', 'sample_dpm_2', ['k_dpm_2']),
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('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a']),
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('DPM fast', 'sample_dpm_fast', ['k_dpm_fast']),
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('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']),
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('Euler a', 'sample_euler_ancestral', ['k_euler_a'], {}),
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('Euler', 'sample_euler', ['k_euler'], {}),
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('LMS', 'sample_lms', ['k_lms'], {}),
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('Heun', 'sample_heun', ['k_heun'], {}),
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('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
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('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
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('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
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('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
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('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
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('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
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('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
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]
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if opts.show_karras_scheduler_variants:
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k_diffusion.sampling.sample_dpm_2_ka = k_diffusion.sampling.sample_dpm_2
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k_diffusion.sampling.sample_dpm_2_ancestral_ka = k_diffusion.sampling.sample_dpm_2_ancestral
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k_diffusion.sampling.sample_lms_ka = k_diffusion.sampling.sample_lms
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samplers_k_diffusion_ka = [
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('LMS K Scheduling', 'sample_lms_ka', ['k_lms_ka']),
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('DPM2 K Scheduling', 'sample_dpm_2_ka', ['k_dpm_2_ka']),
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('DPM2 a K Scheduling', 'sample_dpm_2_ancestral_ka', ['k_dpm_2_a_ka']),
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]
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samplers_k_diffusion.extend(samplers_k_diffusion_ka)
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samplers_data_k_diffusion = [
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SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases)
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for label, funcname, aliases in samplers_k_diffusion
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SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
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for label, funcname, aliases, options in samplers_k_diffusion
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if hasattr(k_diffusion.sampling, funcname)
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]
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all_samplers = [
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*samplers_data_k_diffusion,
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SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),
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SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),
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SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
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SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
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]
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samplers = []
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samplers_for_img2img = []
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def create_sampler_with_index(list_of_configs, index, model):
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config = list_of_configs[index]
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sampler = config.constructor(model)
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sampler.config = config
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return sampler
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def set_samplers():
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global samplers, samplers_for_img2img
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@ -130,6 +130,7 @@ class VanillaStableDiffusionSampler:
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self.step = 0
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self.eta = None
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self.default_eta = 0.0
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self.config = None
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def number_of_needed_noises(self, p):
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return 0
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@ -291,6 +292,7 @@ class KDiffusionSampler:
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self.stop_at = None
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self.eta = None
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self.default_eta = 1.0
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self.config = None
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def callback_state(self, d):
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store_latent(d["denoised"])
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@ -355,11 +357,12 @@ class KDiffusionSampler:
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steps = steps or p.steps
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if p.sampler_noise_scheduler_override:
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sigmas = p.sampler_noise_scheduler_override(steps)
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elif self.funcname.endswith('ka'):
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sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
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sigmas = p.sampler_noise_scheduler_override(steps)
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elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
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sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
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else:
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sigmas = self.model_wrap.get_sigmas(steps)
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sigmas = self.model_wrap.get_sigmas(steps)
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x = x * sigmas[0]
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extra_params_kwargs = self.initialize(p)
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@ -236,7 +236,6 @@ options_templates.update(options_section(('ui', "User interface"), {
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"font": OptionInfo("", "Font for image grids that have text"),
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"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
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"js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
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"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."),
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}))
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options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
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@ -1,53 +0,0 @@
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import inspect
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from modules.processing import Processed, process_images
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import gradio as gr
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import modules.scripts as scripts
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import k_diffusion.sampling
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import torch
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class Script(scripts.Script):
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def title(self):
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return "Alternate Sampler Noise Schedules"
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def ui(self, is_img2img):
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noise_scheduler = gr.Dropdown(label="Noise Scheduler", choices=['Default','Karras','Exponential', 'Variance Preserving'], value='Default', type="index")
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sched_smin = gr.Slider(value=0.1, label="Sigma min", minimum=0.0, maximum=100.0, step=0.5,)
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sched_smax = gr.Slider(value=10.0, label="Sigma max", minimum=0.0, maximum=100.0, step=0.5)
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sched_rho = gr.Slider(value=7.0, label="Sigma rho (Karras only)", minimum=7.0, maximum=100.0, step=0.5)
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sched_beta_d = gr.Slider(value=19.9, label="Beta distribution (VP only)",minimum=0.0, maximum=40.0, step=0.5)
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sched_beta_min = gr.Slider(value=0.1, label="Beta min (VP only)", minimum=0.0, maximum=40.0, step=0.1)
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sched_eps_s = gr.Slider(value=0.001, label="Epsilon (VP only)", minimum=0.001, maximum=1.0, step=0.001)
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return [noise_scheduler, sched_smin, sched_smax, sched_rho, sched_beta_d, sched_beta_min, sched_eps_s]
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def run(self, p, noise_scheduler, sched_smin, sched_smax, sched_rho, sched_beta_d, sched_beta_min, sched_eps_s):
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noise_scheduler_func_name = ['-','get_sigmas_karras','get_sigmas_exponential','get_sigmas_vp'][noise_scheduler]
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base_params = {
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"sigma_min":sched_smin,
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"sigma_max":sched_smax,
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"rho":sched_rho,
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"beta_d":sched_beta_d,
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"beta_min":sched_beta_min,
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"eps_s":sched_eps_s,
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"device":"cuda" if torch.cuda.is_available() else "cpu"
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}
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if hasattr(k_diffusion.sampling,noise_scheduler_func_name):
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sigma_func = getattr(k_diffusion.sampling,noise_scheduler_func_name)
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sigma_func_kwargs = {}
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for k,v in base_params.items():
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if k in inspect.signature(sigma_func).parameters:
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sigma_func_kwargs[k] = v
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def substitute_noise_scheduler(n):
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return sigma_func(n,**sigma_func_kwargs)
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p.sampler_noise_scheduler_override = substitute_noise_scheduler
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return process_images(p)
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@ -8,7 +8,6 @@ import gradio as gr
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from modules import processing, shared, sd_samplers, prompt_parser
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from modules.processing import Processed
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from modules.sd_samplers import samplers
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from modules.shared import opts, cmd_opts, state
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import torch
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@ -159,7 +158,7 @@ class Script(scripts.Script):
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combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
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sampler = samplers[p.sampler_index].constructor(p.sd_model)
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sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, p.sampler_index, p.sd_model)
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sigmas = sampler.model_wrap.get_sigmas(p.steps)
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