add CFG denoiser implementation for DDIM, PLMS and UniPC (this is the commit when you can run both old and new implementations to compare them)

modules/sd_samplers.py

@@ -1,4 +1,4 @@
-from modules import sd_samplers_compvis, sd_samplers_kdiffusion, shared
+from modules import sd_samplers_compvis, 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
@@ -6,6 +6,7 @@ from modules.sd_samplers_common import samples_to_image_grid, sample_to_image  #
 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}
 

modules/sd_samplers_cfg_denoiser.py

@@ -39,7 +39,7 @@ class CFGDenoiser(torch.nn.Module):
     negative prompt.
     """
 
-    def __init__(self, model):
+    def __init__(self, model, sampler):
         super().__init__()
         self.inner_model = model
         self.mask = None
@@ -48,6 +48,7 @@ class CFGDenoiser(torch.nn.Module):
         self.step = 0
         self.image_cfg_scale = None
         self.padded_cond_uncond = False
+        self.sampler = sampler
 
     def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
         denoised_uncond = x_out[-uncond.shape[0]:]
@@ -65,6 +66,9 @@ class CFGDenoiser(torch.nn.Module):
 
         return denoised
 
+    def get_pred_x0(self, x_in, x_out, sigma):
+        return x_out
+
     def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
         if state.interrupted or state.skipped:
             raise sd_samplers_common.InterruptedException
@@ -78,6 +82,9 @@ class CFGDenoiser(torch.nn.Module):
 
         assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
 
+        if self.mask is not None:
+            x = self.init_latent * self.mask + self.nmask * x
+
         batch_size = len(conds_list)
         repeats = [len(conds_list[i]) for i in range(batch_size)]
 
@@ -170,11 +177,6 @@ class CFGDenoiser(torch.nn.Module):
 
         devices.test_for_nans(x_out, "unet")
 
-        if opts.live_preview_content == "Prompt":
-            sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
-        elif opts.live_preview_content == "Negative prompt":
-            sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
-
         if is_edit_model:
             denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
         elif skip_uncond:
@@ -182,8 +184,16 @@ class CFGDenoiser(torch.nn.Module):
         else:
             denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
 
-        if self.mask is not None:
+        self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
-            denoised = self.init_latent * self.mask + self.nmask * denoised
+
+        if opts.live_preview_content == "Prompt":
+            preview = self.sampler.last_latent
+        elif opts.live_preview_content == "Negative prompt":
+            preview = self.get_pred_x0(x_in[-uncond.shape[0]:], x_out[-uncond.shape[0]:], sigma)
+        else:
+            preview = self.get_pred_x0(torch.cat([x_in[i:i+1] for i in denoised_image_indexes]), torch.cat([denoised[i:i+1] for i in denoised_image_indexes]), sigma)
+
+        sd_samplers_common.store_latent(preview)
 
         after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
         cfg_after_cfg_callback(after_cfg_callback_params)
@@ -192,27 +202,3 @@ class CFGDenoiser(torch.nn.Module):
         self.step += 1
         return denoised
 
-
-class TorchHijack:
-    def __init__(self, sampler_noises):
-        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
-        # implementation.
-        self.sampler_noises = deque(sampler_noises)
-
-    def __getattr__(self, item):
-        if item == 'randn_like':
-            return self.randn_like
-
-        if hasattr(torch, item):
-            return getattr(torch, item)
-
-        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
-
-    def randn_like(self, x):
-        if self.sampler_noises:
-            noise = self.sampler_noises.popleft()
-            if noise.shape == x.shape:
-                return noise
-
-        return devices.randn_like(x)
-

modules/sd_samplers_common.py

@@ -1,9 +1,11 @@
-from collections import namedtuple
+import inspect
+from collections import namedtuple, deque
 import numpy as np
 import torch
 from PIL import Image
 from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared
 from modules.shared import opts, state
+import k_diffusion.sampling
 
 SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
 
@@ -127,3 +129,139 @@ def replace_torchsde_browinan():
 
 
 replace_torchsde_browinan()
+
+
+class TorchHijack:
+    def __init__(self, sampler_noises):
+        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
+        # implementation.
+        self.sampler_noises = deque(sampler_noises)
+
+    def __getattr__(self, item):
+        if item == 'randn_like':
+            return self.randn_like
+
+        if hasattr(torch, item):
+            return getattr(torch, item)
+
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
+
+    def randn_like(self, x):
+        if self.sampler_noises:
+            noise = self.sampler_noises.popleft()
+            if noise.shape == x.shape:
+                return noise
+
+        return devices.randn_like(x)
+
+
+class Sampler:
+    def __init__(self, funcname):
+        self.funcname = funcname
+        self.func = funcname
+        self.extra_params = []
+        self.sampler_noises = None
+        self.stop_at = None
+        self.eta = None
+        self.config = None  # set by the function calling the constructor
+        self.last_latent = None
+        self.s_min_uncond = None
+        self.s_churn = 0.0
+        self.s_tmin = 0.0
+        self.s_tmax = float('inf')
+        self.s_noise = 1.0
+
+        self.eta_option_field = 'eta_ancestral'
+        self.eta_infotext_field = 'Eta'
+
+        self.conditioning_key = shared.sd_model.model.conditioning_key
+
+        self.model_wrap = None
+        self.model_wrap_cfg = None
+
+    def callback_state(self, d):
+        step = d['i']
+
+        if self.stop_at is not None and step > self.stop_at:
+            raise InterruptedException
+
+        state.sampling_step = step
+        shared.total_tqdm.update()
+
+    def launch_sampling(self, steps, func):
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except RecursionError:
+            print(
+                'Encountered RecursionError during sampling, returning last latent. '
+                'rho >5 with a polyexponential scheduler may cause this error. '
+                'You should try to use a smaller rho value instead.'
+            )
+            return self.last_latent
+        except InterruptedException:
+            return self.last_latent
+
+    def number_of_needed_noises(self, p):
+        return p.steps
+
+    def initialize(self, p) -> dict:
+        self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
+        self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
+        self.model_wrap_cfg.step = 0
+        self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
+        self.eta = p.eta if p.eta is not None else getattr(opts, self.eta_option_field, 0.0)
+        self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
+
+        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
+
+        extra_params_kwargs = {}
+        for param_name in self.extra_params:
+            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
+                extra_params_kwargs[param_name] = getattr(p, param_name)
+
+        if 'eta' in inspect.signature(self.func).parameters:
+            if self.eta != 1.0:
+                p.extra_generation_params[self.eta_infotext_field] = self.eta
+
+            extra_params_kwargs['eta'] = self.eta
+
+        if len(self.extra_params) > 0:
+            s_churn = getattr(opts, 's_churn', p.s_churn)
+            s_tmin = getattr(opts, 's_tmin', p.s_tmin)
+            s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
+            s_noise = getattr(opts, 's_noise', p.s_noise)
+
+            if s_churn != self.s_churn:
+                extra_params_kwargs['s_churn'] = s_churn
+                p.s_churn = s_churn
+                p.extra_generation_params['Sigma churn'] = s_churn
+            if s_tmin != self.s_tmin:
+                extra_params_kwargs['s_tmin'] = s_tmin
+                p.s_tmin = s_tmin
+                p.extra_generation_params['Sigma tmin'] = s_tmin
+            if s_tmax != self.s_tmax:
+                extra_params_kwargs['s_tmax'] = s_tmax
+                p.s_tmax = s_tmax
+                p.extra_generation_params['Sigma tmax'] = s_tmax
+            if s_noise != self.s_noise:
+                extra_params_kwargs['s_noise'] = s_noise
+                p.s_noise = s_noise
+                p.extra_generation_params['Sigma noise'] = s_noise
+
+        return extra_params_kwargs
+
+    def create_noise_sampler(self, x, sigmas, p):
+        """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
+        if shared.opts.no_dpmpp_sde_batch_determinism:
+            return None
+
+        from k_diffusion.sampling import BrownianTreeNoiseSampler
+        sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+        current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
+        return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
+
+
+

modules/sd_samplers_kdiffusion.py

@@ -4,8 +4,7 @@ import inspect
 import k_diffusion.sampling
 from modules import devices, sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser
 
-from modules.processing import StableDiffusionProcessing
+from modules.shared import opts
-from modules.shared import opts, state
 import modules.shared as shared
 
 samplers_k_diffusion = [
@@ -54,133 +53,17 @@ k_diffusion_scheduler = {
 }
 
 
-class TorchHijack:
+class KDiffusionSampler(sd_samplers_common.Sampler):
-    def __init__(self, sampler_noises):
-        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
-        # implementation.
-        self.sampler_noises = deque(sampler_noises)
-
-    def __getattr__(self, item):
-        if item == 'randn_like':
-            return self.randn_like
-
-        if hasattr(torch, item):
-            return getattr(torch, item)
-
-        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
-
-    def randn_like(self, x):
-        if self.sampler_noises:
-            noise = self.sampler_noises.popleft()
-            if noise.shape == x.shape:
-                return noise
-
-        return devices.randn_like(x)
-
-
-class KDiffusionSampler:
     def __init__(self, funcname, sd_model):
-        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
 
-        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
+        super().__init__(funcname)
-        self.funcname = funcname
+
-        self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
         self.extra_params = sampler_extra_params.get(funcname, [])
-        self.model_wrap_cfg = sd_samplers_cfg_denoiser.CFGDenoiser(self.model_wrap)
+        self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
-        self.sampler_noises = None
-        self.stop_at = None
-        self.eta = None
-        self.config = None  # set by the function calling the constructor
-        self.last_latent = None
-        self.s_min_uncond = None
 
-        # NOTE: These are also defined in the StableDiffusionProcessing class.
+        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
-        # They should have been here to begin with but we're going to
+        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
-        # leave that class __init__ signature alone.
+        self.model_wrap_cfg = sd_samplers_cfg_denoiser.CFGDenoiser(self.model_wrap, self)
-        self.s_churn = 0.0
-        self.s_tmin = 0.0
-        self.s_tmax = float('inf')
-        self.s_noise = 1.0
-
-        self.conditioning_key = sd_model.model.conditioning_key
-
-    def callback_state(self, d):
-        step = d['i']
-        latent = d["denoised"]
-        if opts.live_preview_content == "Combined":
-            sd_samplers_common.store_latent(latent)
-        self.last_latent = latent
-
-        if self.stop_at is not None and step > self.stop_at:
-            raise sd_samplers_common.InterruptedException
-
-        state.sampling_step = step
-        shared.total_tqdm.update()
-
-    def launch_sampling(self, steps, func):
-        state.sampling_steps = steps
-        state.sampling_step = 0
-
-        try:
-            return func()
-        except RecursionError:
-            print(
-                'Encountered RecursionError during sampling, returning last latent. '
-                'rho >5 with a polyexponential scheduler may cause this error. '
-                'You should try to use a smaller rho value instead.'
-            )
-            return self.last_latent
-        except sd_samplers_common.InterruptedException:
-            return self.last_latent
-
-    def number_of_needed_noises(self, p):
-        return p.steps
-
-    def initialize(self, p: StableDiffusionProcessing):
-        self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
-        self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
-        self.model_wrap_cfg.step = 0
-        self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
-        self.eta = p.eta if p.eta is not None else opts.eta_ancestral
-        self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
-
-        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
-
-        extra_params_kwargs = {}
-        for param_name in self.extra_params:
-            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
-                extra_params_kwargs[param_name] = getattr(p, param_name)
-
-        if 'eta' in inspect.signature(self.func).parameters:
-            if self.eta != 1.0:
-                p.extra_generation_params["Eta"] = self.eta
-
-            extra_params_kwargs['eta'] = self.eta
-
-        if len(self.extra_params) > 0:
-            s_churn = getattr(opts, 's_churn', p.s_churn)
-            s_tmin = getattr(opts, 's_tmin', p.s_tmin)
-            s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
-            s_noise = getattr(opts, 's_noise', p.s_noise)
-
-            if s_churn != self.s_churn:
-                extra_params_kwargs['s_churn'] = s_churn
-                p.s_churn = s_churn
-                p.extra_generation_params['Sigma churn'] = s_churn
-            if s_tmin != self.s_tmin:
-                extra_params_kwargs['s_tmin'] = s_tmin
-                p.s_tmin = s_tmin
-                p.extra_generation_params['Sigma tmin'] = s_tmin
-            if s_tmax != self.s_tmax:
-                extra_params_kwargs['s_tmax'] = s_tmax
-                p.s_tmax = s_tmax
-                p.extra_generation_params['Sigma tmax'] = s_tmax
-            if s_noise != self.s_noise:
-                extra_params_kwargs['s_noise'] = s_noise
-                p.s_noise = s_noise
-                p.extra_generation_params['Sigma noise'] = s_noise
-
-        return extra_params_kwargs
 
     def get_sigmas(self, p, steps):
         discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
@@ -232,22 +115,12 @@ class KDiffusionSampler:
 
         return sigmas
 
-    def create_noise_sampler(self, x, sigmas, p):
-        """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
-        if shared.opts.no_dpmpp_sde_batch_determinism:
-            return None
-
-        from k_diffusion.sampling import BrownianTreeNoiseSampler
-        sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-        current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
-        return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
-
     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)
 
         sigmas = self.get_sigmas(p, steps)
-
         sigma_sched = sigmas[steps - t_enc - 1:]
+
         xi = x + noise * sigma_sched[0]
 
         extra_params_kwargs = self.initialize(p)
@@ -296,12 +169,14 @@ class KDiffusionSampler:
         extra_params_kwargs = self.initialize(p)
         parameters = inspect.signature(self.func).parameters
 
+        if 'n' in parameters:
+            extra_params_kwargs['n'] = steps
+
         if 'sigma_min' in parameters:
             extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
             extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
-            if 'n' in parameters:
+
-                extra_params_kwargs['n'] = steps
+        if 'sigmas' in parameters:
-        else:
             extra_params_kwargs['sigmas'] = sigmas
 
         if self.config.options.get('brownian_noise', False):
@@ -322,3 +197,4 @@ class KDiffusionSampler:
 
         return samples
 
+

modules/sd_samplers_timesteps.py

@@ -0,0 +1,147 @@
+import torch
+import inspect
+from modules import devices, sd_samplers_common, sd_samplers_timesteps_impl
+from modules.sd_samplers_cfg_denoiser import CFGDenoiser
+
+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'], {}),
+]
+
+
+samplers_data_timesteps = [
+    sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: CompVisSampler(funcname, model), aliases, options)
+    for label, funcname, aliases, options in samplers_timesteps
+]
+
+
+class CompVisTimestepsDenoiser(torch.nn.Module):
+    def __init__(self, model, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.inner_model = model
+
+    def forward(self, input, timesteps, **kwargs):
+        return self.inner_model.apply_model(input, timesteps, **kwargs)
+
+
+class CompVisTimestepsVDenoiser(torch.nn.Module):
+    def __init__(self, model, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.inner_model = model
+
+    def predict_eps_from_z_and_v(self, x_t, t, v):
+        return self.inner_model.sqrt_alphas_cumprod[t.to(torch.int), None, None, None] * v + self.inner_model.sqrt_one_minus_alphas_cumprod[t.to(torch.int), None, None, None] * x_t
+
+    def forward(self, input, timesteps, **kwargs):
+        model_output = self.inner_model.apply_model(input, timesteps, **kwargs)
+        e_t = self.predict_eps_from_z_and_v(input, timesteps, model_output)
+        return e_t
+
+
+class CFGDenoiserTimesteps(CFGDenoiser):
+
+    def __init__(self, model, sampler):
+        super().__init__(model, sampler)
+
+        self.alphas = model.inner_model.alphas_cumprod
+
+    def get_pred_x0(self, x_in, x_out, sigma):
+        ts = int(sigma.item())
+
+        s_in = x_in.new_ones([x_in.shape[0]])
+        a_t = self.alphas[ts].item() * s_in
+        sqrt_one_minus_at = (1 - a_t).sqrt()
+
+        pred_x0 = (x_in - sqrt_one_minus_at * x_out) / a_t.sqrt()
+
+        return pred_x0
+
+
+class CompVisSampler(sd_samplers_common.Sampler):
+    def __init__(self, funcname, sd_model):
+        super().__init__(funcname)
+
+        self.eta_option_field = 'eta_ddim'
+        self.eta_infotext_field = 'Eta DDIM'
+
+        denoiser = CompVisTimestepsVDenoiser if sd_model.parameterization == "v" else CompVisTimestepsDenoiser
+        self.model_wrap = denoiser(sd_model)
+        self.model_wrap_cfg = CFGDenoiserTimesteps(self.model_wrap, self)
+
+    def get_timesteps(self, p, steps):
+        discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
+        if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
+            discard_next_to_last_sigma = True
+            p.extra_generation_params["Discard penultimate sigma"] = True
+
+        steps += 1 if discard_next_to_last_sigma else 0
+
+        timesteps = torch.clip(torch.asarray(list(range(0, 1000, 1000 // steps)), device=devices.device) + 1, 0, 999)
+
+        return timesteps
+
+    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)
+
+        timesteps = self.get_timesteps(p, steps)
+        timesteps_sched = timesteps[:t_enc]
+
+        alphas_cumprod = shared.sd_model.alphas_cumprod
+        sqrt_alpha_cumprod = torch.sqrt(alphas_cumprod[timesteps[t_enc]])
+        sqrt_one_minus_alpha_cumprod = torch.sqrt(1 - alphas_cumprod[timesteps[t_enc]])
+
+        xi = x * sqrt_alpha_cumprod + noise * sqrt_one_minus_alpha_cumprod
+
+        extra_params_kwargs = self.initialize(p)
+        parameters = inspect.signature(self.func).parameters
+
+        if 'timesteps' in parameters:
+            extra_params_kwargs['timesteps'] = timesteps_sched
+        if 'is_img2img' in parameters:
+            extra_params_kwargs['is_img2img'] = True
+
+        self.model_wrap_cfg.init_latent = x
+        self.last_latent = x
+        extra_args = {
+            'cond': conditioning,
+            'image_cond': image_conditioning,
+            'uncond': unconditional_conditioning,
+            'cond_scale': p.cfg_scale,
+            's_min_uncond': self.s_min_uncond
+        }
+
+        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+        if self.model_wrap_cfg.padded_cond_uncond:
+            p.extra_generation_params["Pad conds"] = True
+
+        return samples
+
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        steps = steps or p.steps
+        timesteps = self.get_timesteps(p, steps)
+
+        extra_params_kwargs = self.initialize(p)
+        parameters = inspect.signature(self.func).parameters
+
+        if 'timesteps' in parameters:
+            extra_params_kwargs['timesteps'] = timesteps
+
+        self.last_latent = x
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
+            'cond': conditioning,
+            'image_cond': image_conditioning,
+            'uncond': unconditional_conditioning,
+            'cond_scale': p.cfg_scale,
+            's_min_uncond': self.s_min_uncond
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+        if self.model_wrap_cfg.padded_cond_uncond:
+            p.extra_generation_params["Pad conds"] = True
+
+        return samples
+

modules/sd_samplers_timesteps_impl.py

@@ -0,0 +1,135 @@
+import torch
+import tqdm
+import k_diffusion.sampling
+import numpy as np
+
+from modules import shared
+from modules.models.diffusion.uni_pc import uni_pc
+
+
+@torch.no_grad()
+def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
+    alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
+    alphas = alphas_cumprod[timesteps]
+    alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64)
+    sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
+    sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
+
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    for i in tqdm.trange(len(timesteps) - 1, disable=disable):
+        index = len(timesteps) - 1 - i
+
+        e_t = model(x, timesteps[index].item() * s_in, **extra_args)
+
+        a_t = alphas[index].item() * s_in
+        a_prev = alphas_prev[index].item() * s_in
+        sigma_t = sigmas[index].item() * s_in
+        sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_in
+
+        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+        dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t
+        noise = sigma_t * k_diffusion.sampling.torch.randn_like(x)
+        x = a_prev.sqrt() * pred_x0 + dir_xt + noise
+
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
+
+    return x
+
+
+@torch.no_grad()
+def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
+    alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
+    alphas = alphas_cumprod[timesteps]
+    alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64)
+    sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
+
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    old_eps = []
+
+    def get_x_prev_and_pred_x0(e_t, index):
+        # select parameters corresponding to the currently considered timestep
+        a_t = alphas[index].item() * s_in
+        a_prev = alphas_prev[index].item() * s_in
+        sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_in
+
+        # current prediction for x_0
+        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+
+        # direction pointing to x_t
+        dir_xt = (1. - a_prev).sqrt() * e_t
+        x_prev = a_prev.sqrt() * pred_x0 + dir_xt
+        return x_prev, pred_x0
+
+    for i in tqdm.trange(len(timesteps) - 1, disable=disable):
+        index = len(timesteps) - 1 - i
+        ts = timesteps[index].item() * s_in
+        t_next = timesteps[max(index - 1, 0)].item() * s_in
+
+        e_t = model(x, ts, **extra_args)
+
+        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 = model(x_prev, t_next, **extra_args)
+            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
+        else:
+            # 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)
+
+        old_eps.append(e_t)
+        if len(old_eps) >= 4:
+            old_eps.pop(0)
+
+        x = x_prev
+
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
+
+    return x
+
+
+class UniPCCFG(uni_pc.UniPC):
+    def __init__(self, cfg_model, extra_args, callback, *args, **kwargs):
+        super().__init__(None, *args, **kwargs)
+
+        def after_update(x, model_x):
+            callback({'x': x, 'i': self.index, 'sigma': 0, 'sigma_hat': 0, 'denoised': model_x})
+            self.index += 1
+
+        self.cfg_model = cfg_model
+        self.extra_args = extra_args
+        self.callback = callback
+        self.index = 0
+        self.after_update = after_update
+
+    def get_model_input_time(self, t_continuous):
+        return (t_continuous - 1. / self.noise_schedule.total_N) * 1000.
+
+    def model(self, x, t):
+        t_input = self.get_model_input_time(t)
+
+        res = self.cfg_model(x, t_input, **self.extra_args)
+
+        return res
+
+
+def unipc(model, x, timesteps, extra_args=None, callback=None, disable=None, is_img2img=False):
+    alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
+
+    ns = uni_pc.NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
+    t_start = timesteps[-1] / 1000 + 1 / 1000 if is_img2img else None  # this is likely off by a bit - if someone wants to fix it please by all means
+    unipc_sampler = UniPCCFG(model, extra_args, callback, ns, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant)
+    x = unipc_sampler.sample(x, steps=len(timesteps), t_start=t_start, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)
+
+    return x