REMOVE

modules/processing.py

@@ -1112,9 +1112,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         img2img_sampler_name = self.hr_sampler_name or self.sampler_name
 
-        if self.sampler_name in ['PLMS', 'UniPC']:  # PLMS/UniPC do not support img2img so we just silently switch to DDIM
-            img2img_sampler_name = 'DDIM'
-
         self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
 
         if self.latent_scale_mode is not None:

modules/sd_hijack.py

@@ -5,7 +5,7 @@ from types import MethodType
 from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
 from modules.hypernetworks import hypernetwork
 from modules.shared import cmd_opts
-from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr, sd_hijack_inpainting
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
@@ -34,8 +34,6 @@ ldm.modules.diffusionmodules.model.print = shared.ldm_print
 ldm.util.print = shared.ldm_print
 ldm.models.diffusion.ddpm.print = shared.ldm_print
 
-sd_hijack_inpainting.do_inpainting_hijack()
-
 optimizers = []
 current_optimizer: sd_hijack_optimizations.SdOptimization = None
 

modules/sd_hijack_inpainting.py

-import torch
-
-import ldm.models.diffusion.ddpm
-import ldm.models.diffusion.ddim
-import ldm.models.diffusion.plms
-
-from ldm.models.diffusion.ddim import noise_like
-from ldm.models.diffusion.sampling_util import norm_thresholding
-
-
-@torch.no_grad()
-def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                  temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                  unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, dynamic_threshold=None):
-    b, *_, device = *x.shape, x.device
-
-    def get_model_output(x, t):
-        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-            e_t = self.model.apply_model(x, t, c)
-        else:
-            x_in = torch.cat([x] * 2)
-            t_in = torch.cat([t] * 2)
-
-            if isinstance(c, dict):
-                assert isinstance(unconditional_conditioning, dict)
-                c_in = {}
-                for k in c:
-                    if isinstance(c[k], list):
-                        c_in[k] = [
-                            torch.cat([unconditional_conditioning[k][i], c[k][i]])
-                            for i in range(len(c[k]))
-                        ]
-                    else:
-                        c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
-            else:
-                c_in = torch.cat([unconditional_conditioning, c])
-
-            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-        if score_corrector is not None:
-            assert self.model.parameterization == "eps"
-            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-        return e_t
-
-    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-
-    def get_x_prev_and_pred_x0(e_t, index):
-        # select parameters corresponding to the currently considered timestep
-        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-        # current prediction for x_0
-        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-        if quantize_denoised:
-            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-        if dynamic_threshold is not None:
-            pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
-        # direction pointing to x_t
-        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-        return x_prev, pred_x0
-
-    e_t = get_model_output(x, t)
-    if len(old_eps) == 0:
-        # Pseudo Improved Euler (2nd order)
-        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
-        e_t_next = get_model_output(x_prev, t_next)
-        e_t_prime = (e_t + e_t_next) / 2
-    elif len(old_eps) == 1:
-        # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
-        e_t_prime = (3 * e_t - old_eps[-1]) / 2
-    elif len(old_eps) == 2:
-        # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
-        e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
-    elif len(old_eps) >= 3:
-        # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
-        e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
-
-    x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
-
-    return x_prev, pred_x0, e_t
-
-
-def do_inpainting_hijack():
-    ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms

modules/sd_samplers.py

-from modules import sd_samplers_compvis, sd_samplers_kdiffusion, sd_samplers_timesteps, shared
+from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, shared
 
 # imports for functions that previously were here and are used by other modules
 from modules.sd_samplers_common import samples_to_image_grid, sample_to_image  # noqa: F401
 
 all_samplers = [
     *sd_samplers_kdiffusion.samplers_data_k_diffusion,
-    *sd_samplers_compvis.samplers_data_compvis,
     *sd_samplers_timesteps.samplers_data_timesteps,
 ]
 all_samplers_map = {x.name: x for x in all_samplers}
@@ -42,10 +41,8 @@ def set_samplers():
     global samplers, samplers_for_img2img
 
     hidden = set(shared.opts.hide_samplers)
-    hidden_img2img = set(shared.opts.hide_samplers + ['PLMS', 'UniPC'])
-
     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]
+    samplers_for_img2img = [x for x in all_samplers if x.name not in hidden]
 
     samplers_map.clear()
     for sampler in all_samplers:

modules/sd_samplers_cfg_denoiser.py

-from collections import deque
 import torch
 from modules import prompt_parser, devices, sd_samplers_common
 

modules/sd_samplers_compvis.py

-import math
-import ldm.models.diffusion.ddim
-import ldm.models.diffusion.plms
-
-import numpy as np
-import torch
-
-from modules.shared import state
-from modules import sd_samplers_common, prompt_parser, shared
-import modules.models.diffusion.uni_pc
-
-
-samplers_data_compvis = [
-    sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {"default_eta_is_0": True, "uses_ensd": True, "no_sdxl": True}),
-    sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {"no_sdxl": True}),
-    sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {"no_sdxl": True}),
-]
-
-
-class VanillaStableDiffusionSampler:
-    def __init__(self, constructor, sd_model):
-        self.sampler = constructor(sd_model)
-        self.is_ddim = hasattr(self.sampler, 'p_sample_ddim')
-        self.is_plms = hasattr(self.sampler, 'p_sample_plms')
-        self.is_unipc = isinstance(self.sampler, modules.models.diffusion.uni_pc.UniPCSampler)
-        self.orig_p_sample_ddim = None
-        if self.is_plms:
-            self.orig_p_sample_ddim = self.sampler.p_sample_plms
-        elif self.is_ddim:
-            self.orig_p_sample_ddim = self.sampler.p_sample_ddim
-        self.mask = None
-        self.nmask = None
-        self.init_latent = None
-        self.sampler_noises = None
-        self.step = 0
-        self.stop_at = None
-        self.eta = None
-        self.config = None
-        self.last_latent = None
-
-        self.conditioning_key = sd_model.model.conditioning_key
-
-    def number_of_needed_noises(self, p):
-        return 0
-
-    def launch_sampling(self, steps, func):
-        state.sampling_steps = steps
-        state.sampling_step = 0
-
-        try:
-            return func()
-        except sd_samplers_common.InterruptedException:
-            return self.last_latent
-
-    def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
-        x_dec, ts, cond, unconditional_conditioning = self.before_sample(x_dec, ts, cond, unconditional_conditioning)
-
-        res = self.orig_p_sample_ddim(x_dec, cond, ts, *args, unconditional_conditioning=unconditional_conditioning, **kwargs)
-
-        x_dec, ts, cond, unconditional_conditioning, res = self.after_sample(x_dec, ts, cond, unconditional_conditioning, res)
-
-        return res
-
-    def before_sample(self, x, ts, cond, unconditional_conditioning):
-        if state.interrupted or state.skipped:
-            raise sd_samplers_common.InterruptedException
-
-        if self.stop_at is not None and self.step > self.stop_at:
-            raise sd_samplers_common.InterruptedException
-
-        # Have to unwrap the inpainting conditioning here to perform pre-processing
-        image_conditioning = None
-        uc_image_conditioning = None
-        if isinstance(cond, dict):
-            if self.conditioning_key == "crossattn-adm":
-                image_conditioning = cond["c_adm"]
-                uc_image_conditioning = unconditional_conditioning["c_adm"]
-            else:
-                image_conditioning = cond["c_concat"][0]
-            cond = cond["c_crossattn"][0]
-            unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
-
-        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
-        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
-
-        assert all(len(conds) == 1 for conds in conds_list), 'composition via AND is not supported for DDIM/PLMS samplers'
-        cond = tensor
-
-        # for DDIM, shapes must match, we can't just process cond and uncond independently;
-        # filling unconditional_conditioning with repeats of the last vector to match length is
-        # not 100% correct but should work well enough
-        if unconditional_conditioning.shape[1] < cond.shape[1]:
-            last_vector = unconditional_conditioning[:, -1:]
-            last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
-            unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
-        elif unconditional_conditioning.shape[1] > cond.shape[1]:
-            unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
-
-        if self.mask is not None:
-            img_orig = self.sampler.model.q_sample(self.init_latent, ts)
-            x = img_orig * self.mask + self.nmask * x
-
-        # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
-        # Note that they need to be lists because it just concatenates them later.
-        if image_conditioning is not None:
-            if self.conditioning_key == "crossattn-adm":
-                cond = {"c_adm": image_conditioning, "c_crossattn": [cond]}
-                unconditional_conditioning = {"c_adm": uc_image_conditioning, "c_crossattn": [unconditional_conditioning]}
-            else:
-                cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
-                unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
-
-        return x, ts, cond, unconditional_conditioning
-
-    def update_step(self, last_latent):
-        if self.mask is not None:
-            self.last_latent = self.init_latent * self.mask + self.nmask * last_latent
-        else:
-            self.last_latent = last_latent
-
-        sd_samplers_common.store_latent(self.last_latent)
-
-        self.step += 1
-        state.sampling_step = self.step
-        shared.total_tqdm.update()
-
-    def after_sample(self, x, ts, cond, uncond, res):
-        if not self.is_unipc:
-            self.update_step(res[1])
-
-        return x, ts, cond, uncond, res
-
-    def unipc_after_update(self, x, model_x):
-        self.update_step(x)
-
-    def initialize(self, p):
-        if self.is_ddim:
-            self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim
-        else:
-            self.eta = 0.0
-
-        if self.eta != 0.0:
-            p.extra_generation_params["Eta DDIM"] = self.eta
-
-        if self.is_unipc:
-            keys = [
-                ('UniPC variant', 'uni_pc_variant'),
-                ('UniPC skip type', 'uni_pc_skip_type'),
-                ('UniPC order', 'uni_pc_order'),
-                ('UniPC lower order final', 'uni_pc_lower_order_final'),
-            ]
-
-            for name, key in keys:
-                v = getattr(shared.opts, key)
-                if v != shared.opts.get_default(key):
-                    p.extra_generation_params[name] = v
-
-        for fieldname in ['p_sample_ddim', 'p_sample_plms']:
-            if hasattr(self.sampler, fieldname):
-                setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
-        if self.is_unipc:
-            self.sampler.set_hooks(lambda x, t, c, u: self.before_sample(x, t, c, u), lambda x, t, c, u, r: self.after_sample(x, t, c, u, r), lambda x, mx: self.unipc_after_update(x, mx))
-
-        self.mask = p.mask if hasattr(p, 'mask') else None
-        self.nmask = p.nmask if hasattr(p, 'nmask') else None
-
-
-    def adjust_steps_if_invalid(self, p, num_steps):
-        if ((self.config.name == 'DDIM') and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS') or (self.config.name == 'UniPC'):
-            if self.config.name == 'UniPC' and num_steps < shared.opts.uni_pc_order:
-                num_steps = shared.opts.uni_pc_order
-            valid_step = 999 / (1000 // num_steps)
-            if valid_step == math.floor(valid_step):
-                return int(valid_step) + 1
-
-        return num_steps
-
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
-        steps = self.adjust_steps_if_invalid(p, steps)
-        self.initialize(p)
-
-        self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
-        x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
-
-        self.init_latent = x
-        self.last_latent = x
-        self.step = 0
-
-        # Wrap the conditioning models with additional image conditioning for inpainting model
-        if image_conditioning is not None:
-            if self.conditioning_key == "crossattn-adm":
-                conditioning = {"c_adm": image_conditioning, "c_crossattn": [conditioning]}
-                unconditional_conditioning = {"c_adm": torch.zeros_like(image_conditioning), "c_crossattn": [unconditional_conditioning]}
-            else:
-                conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
-                unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
-
-        samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
-
-        return samples
-
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        self.initialize(p)
-
-        self.init_latent = None
-        self.last_latent = x
-        self.step = 0
-
-        steps = self.adjust_steps_if_invalid(p, steps or p.steps)
-
-        # Wrap the conditioning models with additional image conditioning for inpainting model
-        # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
-        if image_conditioning is not None:
-            if self.conditioning_key == "crossattn-adm":
-                conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_adm": image_conditioning}
-                unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_adm": torch.zeros_like(image_conditioning)}
-            else:
-                conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
-                unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
-
-        samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
-
-        return samples_ddim

modules/sd_samplers_kdiffusion.py

-from collections import deque
 import torch
 import inspect
 import k_diffusion.sampling
-from modules import devices, sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser
+from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser
 
 from modules.shared import opts
 import modules.shared as shared

modules/sd_samplers_timesteps.py

@@ -7,9 +7,9 @@ from modules.shared import opts
 import modules.shared as shared
 
 samplers_timesteps = [
-    ('k_DDIM', sd_samplers_timesteps_impl.ddim, ['k_ddim'], {}),
-    ('k_PLMS', sd_samplers_timesteps_impl.plms, ['k_plms'], {}),
-    ('k_UniPC', sd_samplers_timesteps_impl.unipc, ['k_unipc'], {}),
+    ('DDIM', sd_samplers_timesteps_impl.ddim, ['ddim'], {}),
+    ('PLMS', sd_samplers_timesteps_impl.plms, ['plms'], {}),
+    ('UniPC', sd_samplers_timesteps_impl.unipc, ['unipc'], {}),
 ]