option to let users select which samplers they want to hide
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@ -11,9 +11,8 @@ import cv2
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from skimage import exposure
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import modules.sd_hijack
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from modules import devices, prompt_parser, masking
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from modules import devices, prompt_parser, masking, sd_samplers
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from modules.sd_hijack import model_hijack
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from modules.sd_samplers import samplers, samplers_for_img2img
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from modules.shared import opts, cmd_opts, state
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import modules.shared as shared
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import modules.face_restoration
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@ -110,7 +109,7 @@ class Processed:
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self.width = p.width
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self.height = p.height
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self.sampler_index = p.sampler_index
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self.sampler = samplers[p.sampler_index].name
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self.sampler = sd_samplers.samplers[p.sampler_index].name
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self.cfg_scale = p.cfg_scale
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self.steps = p.steps
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self.batch_size = p.batch_size
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@ -265,7 +264,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
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generation_params = {
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"Steps": p.steps,
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"Sampler": samplers[p.sampler_index].name,
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"Sampler": sd_samplers.samplers[p.sampler_index].name,
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"CFG scale": p.cfg_scale,
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"Seed": all_seeds[index],
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"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
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@ -478,7 +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 = samplers[self.sampler_index].constructor(self.sd_model)
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self.sampler = sd_samplers.samplers[self.sampler_index].constructor(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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@ -521,7 +520,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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shared.state.nextjob()
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self.sampler = samplers[self.sampler_index].constructor(self.sd_model)
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self.sampler = sd_samplers.samplers[self.sampler_index].constructor(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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@ -556,7 +555,7 @@ 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 = samplers_for_img2img[self.sampler_index].constructor(self.sd_model)
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self.sampler = sd_samplers.samplers_for_img2img[self.sampler_index].constructor(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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@ -32,12 +32,27 @@ samplers_data_k_diffusion = [
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if hasattr(k_diffusion.sampling, funcname)
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]
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samplers = [
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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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]
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samplers_for_img2img = [x for x in samplers if x.name not in ['PLMS', 'DPM fast', 'DPM adaptive']]
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samplers = []
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samplers_for_img2img = []
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def set_samplers():
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global samplers, samplers_for_img2img
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hidden = set(opts.hide_samplers)
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hidden_img2img = set(opts.hide_samplers + ['PLMS', 'DPM fast', 'DPM adaptive'])
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samplers = [x for x in all_samplers if x.name not in hidden]
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samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
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set_samplers()
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sampler_extra_params = {
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'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
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@ -13,6 +13,7 @@ import modules.memmon
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import modules.sd_models
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import modules.styles
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import modules.devices as devices
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from modules import sd_samplers
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from modules.paths import script_path, sd_path
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sd_model_file = os.path.join(script_path, 'model.ckpt')
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@ -238,14 +239,16 @@ options_templates.update(options_section(('ui', "User interface"), {
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}))
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options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
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"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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"eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
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's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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"hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in sd_samplers.all_samplers]}),
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"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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"eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
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's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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}))
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class Options:
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data = None
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data_labels = options_templates
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4
webui.py
4
webui.py
@ -2,7 +2,7 @@ import os
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import threading
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import time
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import importlib
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from modules import devices
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from modules import devices, sd_samplers
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from modules.paths import script_path
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import signal
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import threading
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@ -109,6 +109,8 @@ def webui():
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time.sleep(0.5)
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break
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sd_samplers.set_samplers()
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print('Reloading Custom Scripts')
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modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
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print('Reloading modules: modules.ui')
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