From c26732fbee2a57e621ac22bf70decf7496daa4cd Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 5 Oct 2022 23:16:27 +0300 Subject: added support for AND from https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/ --- modules/processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index bb94033b..d8c6b8d5 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -360,7 +360,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: #c = p.sd_model.get_learned_conditioning(prompts) with devices.autocast(): uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps) - c = prompt_parser.get_learned_conditioning(shared.sd_model, prompts, p.steps) + c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps) if len(model_hijack.comments) > 0: for comment in model_hijack.comments: -- cgit v1.2.3 From 5f24b7bcf4a074fbdec757617fcd1bc82e76551b Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 6 Oct 2022 12:08:48 +0300 Subject: option to let users select which samplers they want to hide --- modules/processing.py | 13 ++++++------- modules/sd_samplers.py | 19 +++++++++++++++++-- modules/shared.py | 15 +++++++++------ webui.py | 4 +++- 4 files changed, 35 insertions(+), 16 deletions(-) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index d8c6b8d5..e01c8b3f 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -11,9 +11,8 @@ import cv2 from skimage import exposure import modules.sd_hijack -from modules import devices, prompt_parser, masking +from modules import devices, prompt_parser, masking, sd_samplers from modules.sd_hijack import model_hijack -from modules.sd_samplers import samplers, samplers_for_img2img from modules.shared import opts, cmd_opts, state import modules.shared as shared import modules.face_restoration @@ -110,7 +109,7 @@ class Processed: self.width = p.width self.height = p.height self.sampler_index = p.sampler_index - self.sampler = samplers[p.sampler_index].name + self.sampler = sd_samplers.samplers[p.sampler_index].name self.cfg_scale = p.cfg_scale self.steps = p.steps self.batch_size = p.batch_size @@ -265,7 +264,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration generation_params = { "Steps": p.steps, - "Sampler": samplers[p.sampler_index].name, + "Sampler": sd_samplers.samplers[p.sampler_index].name, "CFG scale": p.cfg_scale, "Seed": all_seeds[index], "Face restoration": (opts.face_restoration_model if p.restore_faces else None), @@ -478,7 +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 = samplers[self.sampler_index].constructor(self.sd_model) + self.sampler = sd_samplers.samplers[self.sampler_index].constructor(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) @@ -521,7 +520,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): shared.state.nextjob() - self.sampler = samplers[self.sampler_index].constructor(self.sd_model) + self.sampler = sd_samplers.samplers[self.sampler_index].constructor(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 @@ -556,7 +555,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.nmask = None def init(self, all_prompts, all_seeds, all_subseeds): - self.sampler = samplers_for_img2img[self.sampler_index].constructor(self.sd_model) + self.sampler = sd_samplers.samplers_for_img2img[self.sampler_index].constructor(self.sd_model) crop_region = None if self.image_mask is not None: diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index d27c547b..2e1f7715 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -32,12 +32,27 @@ samplers_data_k_diffusion = [ if hasattr(k_diffusion.sampling, funcname) ] -samplers = [ +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), []), ] -samplers_for_img2img = [x for x in samplers if x.name not in ['PLMS', 'DPM fast', 'DPM adaptive']] + +samplers = [] +samplers_for_img2img = [] + + +def set_samplers(): + global samplers, samplers_for_img2img + + hidden = set(opts.hide_samplers) + hidden_img2img = set(opts.hide_samplers + ['PLMS', 'DPM fast', 'DPM adaptive']) + + samplers = [x for x in all_samplers if x.name not in hidden] + samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img] + + +set_samplers() sampler_extra_params = { 'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'], diff --git a/modules/shared.py b/modules/shared.py index bab0fe6e..ca2e4c74 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -13,6 +13,7 @@ import modules.memmon import modules.sd_models import modules.styles import modules.devices as devices +from modules import sd_samplers from modules.paths import script_path, sd_path sd_model_file = os.path.join(script_path, 'model.ckpt') @@ -238,14 +239,16 @@ options_templates.update(options_section(('ui', "User interface"), { })) options_templates.update(options_section(('sampler-params', "Sampler parameters"), { - "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), - "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), - "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}), - 's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), - 's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), - 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), + "hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in sd_samplers.all_samplers]}), + "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), + "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), + "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}), + 's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), + 's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), + 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), })) + class Options: data = None data_labels = options_templates diff --git a/webui.py b/webui.py index 47848ba5..9ef12427 100644 --- a/webui.py +++ b/webui.py @@ -2,7 +2,7 @@ import os import threading import time import importlib -from modules import devices +from modules import devices, sd_samplers from modules.paths import script_path import signal import threading @@ -109,6 +109,8 @@ def webui(): time.sleep(0.5) break + sd_samplers.set_samplers() + print('Reloading Custom Scripts') modules.scripts.reload_scripts(os.path.join(script_path, "scripts")) print('Reloading modules: modules.ui') -- cgit v1.2.3 From 5993df24a1026225cb8af89237547c1d9101ce69 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 6 Oct 2022 14:12:52 +0300 Subject: integrate the new samplers PR --- modules/processing.py | 7 ++-- modules/sd_samplers.py | 59 +++++++++++++++------------- modules/shared.py | 1 - scripts/alternate_sampler_noise_schedules.py | 53 ------------------------- scripts/img2imgalt.py | 3 +- 5 files changed, 36 insertions(+), 87 deletions(-) delete mode 100644 scripts/alternate_sampler_noise_schedules.py (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index e01c8b3f..e567956c 100644 --- a/modules/processing.py +++ b/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: diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 8d6eb762..497df943 100644 --- a/modules/sd_samplers.py +++ b/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"]) @@ -355,11 +357,12 @@ class KDiffusionSampler: steps = steps or p.steps if p.sampler_noise_scheduler_override: - sigmas = p.sampler_noise_scheduler_override(steps) - elif self.funcname.endswith('ka'): - sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device) + sigmas = p.sampler_noise_scheduler_override(steps) + 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) + sigmas = self.model_wrap.get_sigmas(steps) + x = x * sigmas[0] extra_params_kwargs = self.initialize(p) diff --git a/modules/shared.py b/modules/shared.py index 9e4860a2..ca2e4c74 100644 --- a/modules/shared.py +++ b/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"), { diff --git a/scripts/alternate_sampler_noise_schedules.py b/scripts/alternate_sampler_noise_schedules.py deleted file mode 100644 index 4f3ed8fb..00000000 --- a/scripts/alternate_sampler_noise_schedules.py +++ /dev/null @@ -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) diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py index 0ef137f7..f9894cb0 100644 --- a/scripts/img2imgalt.py +++ b/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) -- cgit v1.2.3