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_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'], {}),
 ]