From 8e7097d06a6a261580d34375c9d2a9e4ffc63ffa Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Wed, 19 Oct 2022 13:47:45 -0700
Subject: Added support for RunwayML inpainting model

---
 modules/sd_samplers.py | 50 ++++++++++++++++++++++++++++++++++++++------------
 1 file changed, 38 insertions(+), 12 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index b58e810b..9d3cf289 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -136,9 +136,15 @@ class VanillaStableDiffusionSampler:
         if self.stop_at is not None and self.step > self.stop_at:
             raise InterruptedException
 
+        # Have to unwrap the inpainting conditioning here to perform pre-preocessing
+        image_conditioning = None
+        if isinstance(cond, dict):
+            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)
+        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
@@ -157,6 +163,10 @@ class VanillaStableDiffusionSampler:
             img_orig = self.sampler.model.q_sample(self.init_latent, ts)
             x_dec = img_orig * self.mask + self.nmask * x_dec
 
+        if image_conditioning is not None:
+            cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
         res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
 
         if self.mask is not None:
@@ -182,7 +192,7 @@ class VanillaStableDiffusionSampler:
         self.mask = p.mask if hasattr(p, 'mask') else None
         self.nmask = p.nmask if hasattr(p, 'nmask') else None
 
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps, t_enc = setup_img2img_steps(p, steps)
 
         self.initialize(p)
@@ -202,7 +212,7 @@ class VanillaStableDiffusionSampler:
 
         return samples
 
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         self.initialize(p)
 
         self.init_latent = None
@@ -210,6 +220,11 @@ class VanillaStableDiffusionSampler:
 
         steps = steps or p.steps
 
+        # Wrap the conditioning models with additional image conditioning for inpainting model
+        if image_conditioning is not None:
+            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
         # existing code fails with certain step counts, like 9
         try:
             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])
@@ -228,7 +243,7 @@ class CFGDenoiser(torch.nn.Module):
         self.init_latent = None
         self.step = 0
 
-    def forward(self, x, sigma, uncond, cond, cond_scale):
+    def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
         if state.interrupted or state.skipped:
             raise InterruptedException
 
@@ -239,28 +254,29 @@ class CFGDenoiser(torch.nn.Module):
         repeats = [len(conds_list[i]) for i in range(batch_size)]
 
         x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+        image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
         sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
 
         if tensor.shape[1] == uncond.shape[1]:
             cond_in = torch.cat([tensor, uncond])
 
             if shared.batch_cond_uncond:
-                x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
+                x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
             else:
                 x_out = torch.zeros_like(x_in)
                 for batch_offset in range(0, x_out.shape[0], batch_size):
                     a = batch_offset
                     b = a + batch_size
-                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
         else:
             x_out = torch.zeros_like(x_in)
             batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
             for batch_offset in range(0, tensor.shape[0], batch_size):
                 a = batch_offset
                 b = min(a + batch_size, tensor.shape[0])
-                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b])
+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]})
 
-            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond)
+            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
 
         denoised_uncond = x_out[-uncond.shape[0]:]
         denoised = torch.clone(denoised_uncond)
@@ -361,7 +377,7 @@ class KDiffusionSampler:
 
         return extra_params_kwargs
 
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps, t_enc = setup_img2img_steps(p, steps)
 
         if p.sampler_noise_scheduler_override:
@@ -389,11 +405,16 @@ class KDiffusionSampler:
 
         self.model_wrap_cfg.init_latent = x
 
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
+            'cond': conditioning, 
+            'image_cond': image_conditioning, 
+            'uncond': unconditional_conditioning, 
+            'cond_scale': p.cfg_scale
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
         return samples
 
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
         steps = steps or p.steps
 
         if p.sampler_noise_scheduler_override:
@@ -414,7 +435,12 @@ class KDiffusionSampler:
         else:
             extra_params_kwargs['sigmas'] = sigmas
 
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+        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
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
         return samples
 
-- 
cgit v1.2.3


From dde9f960727bfe151d418e43685a2881cf580a17 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Wed, 19 Oct 2022 14:14:24 -0700
Subject: added support for ddim img2img

---
 modules/sd_samplers.py | 6 ++++++
 1 file changed, 6 insertions(+)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 9d3cf289..d270e4df 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -208,6 +208,12 @@ class VanillaStableDiffusionSampler:
         self.init_latent = x
         self.step = 0
 
+        # Wrap the conditioning models with additional image conditioning for inpainting model
+        if image_conditioning is not None:
+            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+            
+            
         samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
 
         return samples
-- 
cgit v1.2.3


From c418467c03db916c3e5312e6ac4a67365e196dbd Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Wed, 19 Oct 2022 15:09:43 -0700
Subject: Don't compute latent mask if were not using it. Also added support
 for fixed highres_fix generation.

---
 modules/sd_samplers.py | 4 ++++
 1 file changed, 4 insertions(+)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d270e4df..c21be26e 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -117,6 +117,8 @@ class VanillaStableDiffusionSampler:
         self.config = None
         self.last_latent = None
 
+        self.conditioning_key = sd_model.model.conditioning_key
+
     def number_of_needed_noises(self, p):
         return 0
 
@@ -328,6 +330,8 @@ class KDiffusionSampler:
         self.config = None
         self.last_latent = None
 
+        self.conditioning_key = sd_model.model.conditioning_key
+
     def callback_state(self, d):
         step = d['i']
         latent = d["denoised"]
-- 
cgit v1.2.3


From 92a17a7a4a13fceb3c3e25a2e854b2a7dd6eb5df Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Thu, 20 Oct 2022 09:45:03 -0700
Subject: Made dummy latents smaller. Minor code cleanups

---
 modules/sd_samplers.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index c21be26e..cc682593 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -138,7 +138,7 @@ class VanillaStableDiffusionSampler:
         if self.stop_at is not None and self.step > self.stop_at:
             raise InterruptedException
 
-        # Have to unwrap the inpainting conditioning here to perform pre-preocessing
+        # Have to unwrap the inpainting conditioning here to perform pre-processing
         image_conditioning = None
         if isinstance(cond, dict):
             image_conditioning = cond["c_concat"][0]
@@ -146,7 +146,7 @@ class VanillaStableDiffusionSampler:
             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)            
+        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
@@ -165,6 +165,8 @@ class VanillaStableDiffusionSampler:
             img_orig = self.sampler.model.q_sample(self.init_latent, ts)
             x_dec = img_orig * self.mask + self.nmask * x_dec
 
+        # 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:
             cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
             unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
-- 
cgit v1.2.3


From d23a46ceaa76af2847f11172f32c92665c268b1b Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Thu, 20 Oct 2022 23:49:14 +0300
Subject: Different approach to skip/interrupt with highres fix

---
 modules/sd_samplers.py | 4 ++++
 1 file changed, 4 insertions(+)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index b58e810b..7ff77c01 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -196,6 +196,7 @@ class VanillaStableDiffusionSampler:
         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
 
         samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
@@ -206,6 +207,7 @@ class VanillaStableDiffusionSampler:
         self.initialize(p)
 
         self.init_latent = None
+        self.last_latent = x
         self.step = 0
 
         steps = steps or p.steps
@@ -388,6 +390,7 @@ class KDiffusionSampler:
             extra_params_kwargs['sigmas'] = sigma_sched
 
         self.model_wrap_cfg.init_latent = x
+        self.last_latent = x
 
         samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
@@ -414,6 +417,7 @@ class KDiffusionSampler:
         else:
             extra_params_kwargs['sigmas'] = sigmas
 
+        self.last_latent = x
         samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
         return samples
-- 
cgit v1.2.3


From 4fdb53c1e9962507fc8336dad9a0fabfe6c418c0 Mon Sep 17 00:00:00 2001
From: Unnoen <Unnoen@users.noreply.github.com>
Date: Wed, 19 Oct 2022 21:38:10 +1100
Subject: Generate grid preview for progress image

---
 modules/sd_samplers.py | 26 +++++++++++++++++++++++++-
 1 file changed, 25 insertions(+), 1 deletion(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index f58a29b9..74a480e5 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -7,7 +7,7 @@ import inspect
 import k_diffusion.sampling
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
-from modules import prompt_parser, devices, processing
+from modules import prompt_parser, devices, processing, images
 
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -89,6 +89,30 @@ def sample_to_image(samples):
     x_sample = x_sample.astype(np.uint8)
     return Image.fromarray(x_sample)
 
+def samples_to_image_grid(samples):
+    progress_images = []
+    for i in range(len(samples)):
+        # Decode the samples individually to reduce VRAM usage at the cost of a bit of speed.
+        x_sample = processing.decode_first_stage(shared.sd_model, samples[i:i+1])[0]
+        x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
+        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+        x_sample = x_sample.astype(np.uint8)
+        progress_images.append(Image.fromarray(x_sample))
+
+    return images.image_grid(progress_images)
+
+def samples_to_image_grid_combined(samples):
+    progress_images = []
+    # Decode all samples at once to increase speed at the cost of VRAM usage.
+    x_samples = processing.decode_first_stage(shared.sd_model, samples)
+    x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
+
+    for x_sample in x_samples:
+        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+        x_sample = x_sample.astype(np.uint8)
+        progress_images.append(Image.fromarray(x_sample))
+
+    return images.image_grid(progress_images)
 
 def store_latent(decoded):
     state.current_latent = decoded
-- 
cgit v1.2.3


From d213d6ca6f90094cb45c11e2f3cb37d25a8d1f94 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 20:48:13 +0300
Subject: removed the option to use 2x more memory when generating previews
 added an option to always only show one image in previews removed duplicate
 code

---
 modules/sd_samplers.py | 35 ++++++++++-------------------------
 1 file changed, 10 insertions(+), 25 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 74a480e5..0b408a70 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -71,6 +71,7 @@ sampler_extra_params = {
     'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
 }
 
+
 def setup_img2img_steps(p, steps=None):
     if opts.img2img_fix_steps or steps is not None:
         steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
@@ -82,37 +83,21 @@ def setup_img2img_steps(p, steps=None):
     return steps, t_enc
 
 
-def sample_to_image(samples):
-    x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0]
+def single_sample_to_image(sample):
+    x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
     x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
     x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
     x_sample = x_sample.astype(np.uint8)
     return Image.fromarray(x_sample)
 
+
+def sample_to_image(samples):
+    return single_sample_to_image(samples[0])
+
+
 def samples_to_image_grid(samples):
-    progress_images = []
-    for i in range(len(samples)):
-        # Decode the samples individually to reduce VRAM usage at the cost of a bit of speed.
-        x_sample = processing.decode_first_stage(shared.sd_model, samples[i:i+1])[0]
-        x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
-        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-        x_sample = x_sample.astype(np.uint8)
-        progress_images.append(Image.fromarray(x_sample))
-
-    return images.image_grid(progress_images)
-
-def samples_to_image_grid_combined(samples):
-    progress_images = []
-    # Decode all samples at once to increase speed at the cost of VRAM usage.
-    x_samples = processing.decode_first_stage(shared.sd_model, samples)
-    x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
-
-    for x_sample in x_samples:
-        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-        x_sample = x_sample.astype(np.uint8)
-        progress_images.append(Image.fromarray(x_sample))
-
-    return images.image_grid(progress_images)
+    return images.image_grid([single_sample_to_image(sample) for sample in samples])
+
 
 def store_latent(decoded):
     state.current_latent = decoded
-- 
cgit v1.2.3


From b38370275275bf6e11575000f39c50c6e90b1f7a Mon Sep 17 00:00:00 2001
From: ritosonn <ritosonn@gmail.com>
Date: Fri, 21 Oct 2022 23:46:32 +0900
Subject: fix #3145 #3093

---
 modules/sd_samplers.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 0b408a70..3670b57d 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -228,7 +228,7 @@ class VanillaStableDiffusionSampler:
             unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
             
             
-        samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=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
 
@@ -429,7 +429,7 @@ class KDiffusionSampler:
         self.model_wrap_cfg.init_latent = x
         self.last_latent = x
 
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
+        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
             'cond': conditioning, 
             'image_cond': image_conditioning, 
             'uncond': unconditional_conditioning, 
-- 
cgit v1.2.3


From de1dc0d279a877d5d9f512befe30a7d7e5cf3881 Mon Sep 17 00:00:00 2001
From: Martin Cairns <4314538+MartinCairnsSQL@users.noreply.github.com>
Date: Sat, 29 Oct 2022 15:23:19 +0100
Subject: Add adjust_steps_if_invalid to find next valid step for ddim uniform
 sampler

---
 modules/sd_samplers.py | 28 +++++++++++++++-------------
 1 file changed, 15 insertions(+), 13 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 3670b57d..aca014e8 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -1,5 +1,6 @@
 from collections import namedtuple
 import numpy as np
+from math import floor
 import torch
 import tqdm
 from PIL import Image
@@ -205,17 +206,22 @@ class VanillaStableDiffusionSampler:
         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':
+            valid_step = 999 / (1000 // num_steps)
+            if valid_step == 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 = setup_img2img_steps(p, steps)
-
+        steps = self.adjust_steps_if_invalid(p, steps)
         self.initialize(p)
 
-        # existing code fails with certain step counts, like 9
-        try:
-            self.sampler.make_schedule(ddim_num_steps=steps,  ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
-        except Exception:
-            self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
-
+        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
@@ -239,18 +245,14 @@ class VanillaStableDiffusionSampler:
         self.last_latent = x
         self.step = 0
 
-        steps = steps or p.steps
+        steps = self.adjust_steps_if_invalid(p, steps or p.steps)
 
         # Wrap the conditioning models with additional image conditioning for inpainting model
         if image_conditioning is not None:
             conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
             unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
 
-        # existing code fails with certain step counts, like 9
-        try:
-            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])
-        except Exception:
-            samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps+1, 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])
+        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
 
-- 
cgit v1.2.3


From 34c86c12b0a9d650d4e7c5be478bca34ad8ed048 Mon Sep 17 00:00:00 2001
From: Martin Cairns <4314538+MartinCairnsSQL@users.noreply.github.com>
Date: Sun, 30 Oct 2022 11:04:27 +0000
Subject: Include PLMS in adjust steps as it also can fail in the same way

---
 modules/sd_samplers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index aca014e8..8772db56 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -208,7 +208,7 @@ class VanillaStableDiffusionSampler:
 
 
     def adjust_steps_if_invalid(self, p, num_steps):
-        if self.config.name == 'DDIM' and p.ddim_discretize == 'uniform':
+        if  (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
             valid_step = 999 / (1000 // num_steps)
             if valid_step == floor(valid_step):
                 return int(valid_step) + 1
-- 
cgit v1.2.3


From 8ae0ea9deaa5a09d1e0aa8b2f8e97c38d71cdbda Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 30 Oct 2022 23:48:33 +0000
Subject: Add callback to sd_samplers

---
 modules/sd_samplers.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 3670b57d..30cb5c4b 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -11,6 +11,7 @@ from modules import prompt_parser, devices, processing, images
 
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
+from modules.script_callbacks import CGFDenoiserParams, cfg_denoiser_callback
 
 
 SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
@@ -278,6 +279,8 @@ class CFGDenoiser(torch.nn.Module):
         image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
         sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
 
+        cfg_denoiser_callback(CGFDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps))
+
         if tensor.shape[1] == uncond.shape[1]:
             cond_in = torch.cat([tensor, uncond])
 
-- 
cgit v1.2.3


From 5b6bedf6f2ebacb7f1f5809af8e26a6a1af16e2a Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 2 Nov 2022 00:38:17 +0000
Subject: Update class name and assign back to vars

---
 modules/sd_samplers.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 30cb5c4b..ebc0d896 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -11,7 +11,7 @@ from modules import prompt_parser, devices, processing, images
 
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
-from modules.script_callbacks import CGFDenoiserParams, cfg_denoiser_callback
+from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
 
 
 SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
@@ -279,7 +279,11 @@ class CFGDenoiser(torch.nn.Module):
         image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
         sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
 
-        cfg_denoiser_callback(CGFDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps))
+        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
+        cfg_denoiser_callback(denoiser_params)
+        x_in = denoiser_params.x
+        image_cond_in = denoiser_params.image_cond
+        sigma_in = denoiser_params.sigma
 
         if tensor.shape[1] == uncond.shape[1]:
             cond_in = torch.cat([tensor, uncond])
-- 
cgit v1.2.3


From 9c67408004ed132637d10321bf44565f82055fd2 Mon Sep 17 00:00:00 2001
From: timntorres <116157310+timntorres@users.noreply.github.com>
Date: Wed, 2 Nov 2022 02:18:21 -0700
Subject: Allow saving "before-highres-fix. (#4150)

* Save image/s before doing highres fix.
---
 modules/sd_samplers.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 44d4c189..d7fa89a0 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -93,9 +93,8 @@ def single_sample_to_image(sample):
     return Image.fromarray(x_sample)
 
 
-def sample_to_image(samples):
-    return single_sample_to_image(samples[0])
-
+def sample_to_image(samples, index=0):
+    return single_sample_to_image(samples[index])
 
 def samples_to_image_grid(samples):
     return images.image_grid([single_sample_to_image(sample) for sample in samples])
-- 
cgit v1.2.3


From eb5e82c7ddf5e72fa13b83bd1f12d3a07a4de1a4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 2 Nov 2022 12:45:03 +0300
Subject: do not unnecessarily run VAE one more time when saving intermediate
 image with hires fix

---
 modules/sd_samplers.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d7fa89a0..c7c414ef 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -96,6 +96,7 @@ def single_sample_to_image(sample):
 def sample_to_image(samples, index=0):
     return single_sample_to_image(samples[index])
 
+
 def samples_to_image_grid(samples):
     return images.image_grid([single_sample_to_image(sample) for sample in samples])
 
-- 
cgit v1.2.3


From 6008c0773ea575353f9b87da8a58454e20cc7857 Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Fri, 4 Nov 2022 23:03:05 +0000
Subject: Add support for new DPM-Solver++ samplers

---
 modules/sd_samplers.py | 4 ++++
 1 file changed, 4 insertions(+)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index c7c414ef..7ece6556 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -29,6 +29,10 @@ samplers_k_diffusion = [
     ('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'}),
+    ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+    ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
+    ('DPM-Solver++(2S) Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+    ('DPM-Solver++(2M) Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
 ]
 
 samplers_data_k_diffusion = [
-- 
cgit v1.2.3


From f92dc505a013af9e385c7edbdf97539be62503d6 Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Fri, 4 Nov 2022 23:12:48 +0000
Subject: Fix name

---
 modules/sd_samplers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 7ece6556..b28a2e4c 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -31,7 +31,7 @@ samplers_k_diffusion = [
     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
     ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
     ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
-    ('DPM-Solver++(2S) Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+    ('DPM-Solver++(2S) a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
     ('DPM-Solver++(2M) Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
 ]
 
-- 
cgit v1.2.3


From 1b6c2fc749e12f12bbee4705e65f217d23fa9072 Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Fri, 4 Nov 2022 23:28:13 +0000
Subject: Reorder samplers

---
 modules/sd_samplers.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index b28a2e4c..1e88f7ee 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -24,13 +24,13 @@ samplers_k_diffusion = [
     ('Heun', 'sample_heun', ['k_heun'], {}),
     ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
     ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
+    ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+    ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
     ('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'}),
-    ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
-    ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
     ('DPM-Solver++(2S) a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
     ('DPM-Solver++(2M) Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
 ]
-- 
cgit v1.2.3


From 159475e072f2ed3db8235aab9c3fa18640b93b80 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 5 Nov 2022 18:32:22 +0300
Subject: tweak names a bit for new samplers

---
 modules/sd_samplers.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 1e88f7ee..783992d2 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -24,15 +24,15 @@ samplers_k_diffusion = [
     ('Heun', 'sample_heun', ['k_heun'], {}),
     ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
     ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
-    ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
-    ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
+    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
     ('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'}),
-    ('DPM-Solver++(2S) a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
-    ('DPM-Solver++(2M) Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
 ]
 
 samplers_data_k_diffusion = [
-- 
cgit v1.2.3


From cdc8020d13c5eef099c609b0a911ccf3568afc0d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 19 Nov 2022 12:01:51 +0300
Subject: change StableDiffusionProcessing to internally use sampler name
 instead of sampler index

---
 modules/sd_samplers.py | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 783992d2..4fe67854 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -46,16 +46,23 @@ all_samplers = [
     SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
     SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
 ]
+all_samplers_map = {x.name: x for x in all_samplers}
 
 samplers = []
 samplers_for_img2img = []
 
 
-def create_sampler_with_index(list_of_configs, index, model):
-    config = list_of_configs[index]
+def create_sampler(name, model):
+    if name is not None:
+        config = all_samplers_map.get(name, None)
+    else:
+        config = all_samplers[0]
+
+    assert config is not None, f'bad sampler name: {name}'
+
     sampler = config.constructor(model)
     sampler.config = config
-    
+
     return sampler
 
 
-- 
cgit v1.2.3


From 0a01f5089127f1ab86625036526082f544344a10 Mon Sep 17 00:00:00 2001
From: uservar <63248296+uservar@users.noreply.github.com>
Date: Tue, 22 Nov 2022 14:24:50 +0000
Subject: Add DPM++ SDE sampler

---
 modules/sd_samplers.py | 2 ++
 1 file changed, 2 insertions(+)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 4fe67854..80e91d62 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -26,6 +26,7 @@ samplers_k_diffusion = [
     ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
     ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
     ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
+    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
     ('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'}),
@@ -33,6 +34,7 @@ samplers_k_diffusion = [
     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
     ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
     ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
 ]
 
 samplers_data_k_diffusion = [
-- 
cgit v1.2.3


From c833d5bfaae05de41d8e795aba5b15822673ef04 Mon Sep 17 00:00:00 2001
From: Jay Smith <jayvsmith@gmail.com>
Date: Fri, 25 Nov 2022 20:12:23 -0600
Subject: fixes #3449 - VRAM leak when switching to/from inpainting model

---
 modules/sd_samplers.py | 33 +++++++++++++++------------------
 1 file changed, 15 insertions(+), 18 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 4fe67854..44112f99 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -1,4 +1,4 @@
-from collections import namedtuple
+from collections import namedtuple, deque
 import numpy as np
 from math import floor
 import torch
@@ -335,18 +335,28 @@ class CFGDenoiser(torch.nn.Module):
 
 
 class TorchHijack:
-    def __init__(self, kdiff_sampler):
-        self.kdiff_sampler = kdiff_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.kdiff_sampler.randn_like
+            return self.randn_like
 
         if hasattr(torch, item):
             return getattr(torch, item)
 
         raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
 
+    def randn_like(self, x):
+        if self.sampler_noises:
+            noise = self.sampler_noises.popleft()
+            if noise.shape == x.shape:
+                return noise
+
+        return torch.randn_like(x)
+
 
 class KDiffusionSampler:
     def __init__(self, funcname, sd_model):
@@ -356,7 +366,6 @@ class KDiffusionSampler:
         self.extra_params = sampler_extra_params.get(funcname, [])
         self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
         self.sampler_noises = None
-        self.sampler_noise_index = 0
         self.stop_at = None
         self.eta = None
         self.default_eta = 1.0
@@ -389,26 +398,14 @@ class KDiffusionSampler:
     def number_of_needed_noises(self, p):
         return p.steps
 
-    def randn_like(self, x):
-        noise = self.sampler_noises[self.sampler_noise_index] if self.sampler_noises is not None and self.sampler_noise_index < len(self.sampler_noises) else None
-
-        if noise is not None and x.shape == noise.shape:
-            res = noise
-        else:
-            res = torch.randn_like(x)
-
-        self.sampler_noise_index += 1
-        return res
-
     def initialize(self, p):
         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.step = 0
-        self.sampler_noise_index = 0
         self.eta = p.eta or opts.eta_ancestral
 
         if self.sampler_noises is not None:
-            k_diffusion.sampling.torch = TorchHijack(self)
+            k_diffusion.sampling.torch = TorchHijack(self.sampler_noises)
 
         extra_params_kwargs = {}
         for param_name in self.extra_params:
-- 
cgit v1.2.3


From ce6911158b5b2f9cf79b405a1f368f875492044d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 26 Nov 2022 16:10:46 +0300
Subject: Add support Stable Diffusion 2.0

---
 modules/sd_samplers.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 4fe67854..4edd8c60 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -127,7 +127,8 @@ class InterruptedException(BaseException):
 class VanillaStableDiffusionSampler:
     def __init__(self, constructor, sd_model):
         self.sampler = constructor(sd_model)
-        self.orig_p_sample_ddim = self.sampler.p_sample_ddim if hasattr(self.sampler, 'p_sample_ddim') else self.sampler.p_sample_plms
+        self.is_plms = hasattr(self.sampler, 'p_sample_plms')
+        self.orig_p_sample_ddim = self.sampler.p_sample_plms if self.is_plms else self.sampler.p_sample_ddim
         self.mask = None
         self.nmask = None
         self.init_latent = None
@@ -218,7 +219,6 @@ class VanillaStableDiffusionSampler:
         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'):
             valid_step = 999 / (1000 // num_steps)
@@ -227,7 +227,6 @@ class VanillaStableDiffusionSampler:
         
         return num_steps
 
-
     def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps, t_enc = setup_img2img_steps(p, steps)
         steps = self.adjust_steps_if_invalid(p, steps)
@@ -260,9 +259,10 @@ class VanillaStableDiffusionSampler:
         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:
-            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
-            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+            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])
 
@@ -350,7 +350,9 @@ class TorchHijack:
 
 class KDiffusionSampler:
     def __init__(self, funcname, sd_model):
-        self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
+        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)
         self.funcname = funcname
         self.func = getattr(k_diffusion.sampling, self.funcname)
         self.extra_params = sampler_extra_params.get(funcname, [])
-- 
cgit v1.2.3


From 10923f9b3a10a9af20429e51242614e259fbd434 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 13:43:10 +0300
Subject: calculate dictionary for sampler names only once

---
 modules/sd_samplers.py | 7 +++++++
 1 file changed, 7 insertions(+)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 43ce34eb..6f8ccf1d 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -52,6 +52,7 @@ all_samplers_map = {x.name: x for x in all_samplers}
 
 samplers = []
 samplers_for_img2img = []
+samplers_map = {}
 
 
 def create_sampler(name, model):
@@ -77,6 +78,12 @@ def set_samplers():
     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_map.clear()
+    for sampler in all_samplers:
+        samplers_map[sampler.name.lower()] = sampler.name
+        for alias in sampler.aliases:
+            samplers_map[alias.lower()] = sampler.name
+
 
 set_samplers()
 
-- 
cgit v1.2.3


From 506d529d19f135f57e142371271f84d4971b456f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 16:28:32 +0300
Subject: rework #5012 to also work for pictures dragged into the prompt and
 also add Clip skip + ENSD to parameters

---
 modules/sd_samplers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 2ca17d8b..5fefb227 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -18,7 +18,7 @@ from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
 SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
 
 samplers_k_diffusion = [
-    ('Euler a', 'sample_euler_ancestral', ['k_euler_a'], {}),
+    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
     ('Euler', 'sample_euler', ['k_euler'], {}),
     ('LMS', 'sample_lms', ['k_lms'], {}),
     ('Heun', 'sample_heun', ['k_heun'], {}),
-- 
cgit v1.2.3


From 21effd629d0fdfdbbff2b20a9f4a3767e7e8bd33 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 28 Nov 2022 21:24:06 -0500
Subject: Add workaround for using MPS with torchsde

---
 modules/sd_samplers.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 5fefb227..8b11f569 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -6,6 +6,7 @@ import tqdm
 from PIL import Image
 import inspect
 import k_diffusion.sampling
+import torchsde._brownian.brownian_interval
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
 from modules import prompt_parser, devices, processing, images
@@ -367,6 +368,19 @@ class TorchHijack:
         return torch.randn_like(x)
 
 
+# MPS fix for randn in torchsde
+def torchsde_randn(size, dtype, device, seed):
+    if device.type == 'mps':
+        generator = torch.Generator(devices.cpu).manual_seed(int(seed))
+        return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+    else:
+        generator = torch.Generator(device).manual_seed(int(seed))
+        return torch.randn(size, dtype=dtype, device=device, generator=generator)
+
+
+torchsde._brownian.brownian_interval._randn = torchsde_randn
+
+
 class KDiffusionSampler:
     def __init__(self, funcname, sd_model):
         denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
-- 
cgit v1.2.3


From 0fddb4a1c06a6e2122add7eee3b001a6d473baee Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Wed, 30 Nov 2022 08:02:39 -0500
Subject: Rework MPS randn fix, add randn_like fix

torch.manual_seed() already sets a CPU generator, so there is no reason to create a CPU generator manually. torch.randn_like also needs a MPS fix for k-diffusion, but a torch hijack with randn_like already exists so it can also be used for that.
---
 modules/sd_samplers.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 8b11f569..4c123d3b 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -365,7 +365,10 @@ class TorchHijack:
             if noise.shape == x.shape:
                 return noise
 
-        return torch.randn_like(x)
+        if x.device.type == 'mps':
+            return torch.randn_like(x, device=devices.cpu).to(x.device)
+        else:
+            return torch.randn_like(x)
 
 
 # MPS fix for randn in torchsde
@@ -429,8 +432,7 @@ class KDiffusionSampler:
         self.model_wrap.step = 0
         self.eta = p.eta or opts.eta_ancestral
 
-        if self.sampler_noises is not None:
-            k_diffusion.sampling.torch = TorchHijack(self.sampler_noises)
+        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:
-- 
cgit v1.2.3


From 8b0703b8fcdab153958b11f0dd5e5b6b58565fed Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin" <git_commits@alexgoodwin.dev>
Date: Fri, 16 Dec 2022 08:18:29 -0800
Subject: Add a workaround patch for DPM2 a issue

DPM2 a and DPM2 a Karras samplers are both affected by an issue described by https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/3483 and can be resolved by a workaround suggested by the k-diffusion author at https://github.com/crowsonkb/k-diffusion/issues/43#issuecomment-1305195666
---
 modules/sd_samplers.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 4c123d3b..b8e0ce53 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -494,6 +494,9 @@ class KDiffusionSampler:
 
         x = x * sigmas[0]
 
+        if self.funcname == "sample_dpm_2_ancestral": # workaround dpm2 a issue
+            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+
         extra_params_kwargs = self.initialize(p)
         if 'sigma_min' in inspect.signature(self.func).parameters:
             extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
-- 
cgit v1.2.3


From 180fdf7809ea18de2d3b04618846d5a4e33c002e Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin" <git_commits@alexgoodwin.dev>
Date: Fri, 16 Dec 2022 08:42:00 -0800
Subject: apply to DPM2 (non-ancestral) as well

---
 modules/sd_samplers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index b8e0ce53..ae3d8bfa 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -494,7 +494,7 @@ class KDiffusionSampler:
 
         x = x * sigmas[0]
 
-        if self.funcname == "sample_dpm_2_ancestral": # workaround dpm2 a issue
+        if self.funcname in ['sample_dpm_2_ancestral', 'sample_dpm_2']:
             sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
 
         extra_params_kwargs = self.initialize(p)
-- 
cgit v1.2.3


From 7ba9bc2fdbfae8115294962510492faafeb48573 Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin" <git_commits@alexgoodwin.dev>
Date: Sun, 18 Dec 2022 19:16:42 -0800
Subject: fix dpm2 in img2img as well

---
 modules/sd_samplers.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index ae3d8bfa..1a1b8919 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -454,6 +454,9 @@ class KDiffusionSampler:
         else:
             sigmas = self.model_wrap.get_sigmas(steps)
 
+        if self.funcname in ['sample_dpm_2_ancestral', 'sample_dpm_2']:
+            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+
         sigma_sched = sigmas[steps - t_enc - 1:]
         xi = x + noise * sigma_sched[0]
         
-- 
cgit v1.2.3


From 399b229783a7b5fddab0a258740b4d59d668ee12 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 09:03:45 +0300
Subject: eliminate duplicated code add an option to samplers for skipping next
 to last sigma

---
 modules/sd_samplers.py | 31 ++++++++++++++-----------------
 1 file changed, 14 insertions(+), 17 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 1a1b8919..d26e48dc 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -23,16 +23,16 @@ samplers_k_diffusion = [
     ('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'], {}),
+    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),
     ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
     ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
     ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
     ('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'}),
+    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
+    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
     ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
     ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
     ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
@@ -444,9 +444,7 @@ class KDiffusionSampler:
 
         return extra_params_kwargs
 
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps, t_enc = setup_img2img_steps(p, steps)
-
+    def get_sigmas(self, p, steps):
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
@@ -454,9 +452,16 @@ class KDiffusionSampler:
         else:
             sigmas = self.model_wrap.get_sigmas(steps)
 
-        if self.funcname in ['sample_dpm_2_ancestral', 'sample_dpm_2']:
+        if self.config is not None and self.config.options.get('discard_next_to_last_sigma', False):
             sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
 
+        return sigmas
+
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        steps, t_enc = setup_img2img_steps(p, steps)
+
+        sigmas = self.get_sigmas(p, steps)
+
         sigma_sched = sigmas[steps - t_enc - 1:]
         xi = x + noise * sigma_sched[0]
         
@@ -488,18 +493,10 @@ class KDiffusionSampler:
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
         steps = steps or p.steps
 
-        if p.sampler_noise_scheduler_override:
-            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.get_sigmas(p, steps)
 
         x = x * sigmas[0]
 
-        if self.funcname in ['sample_dpm_2_ancestral', 'sample_dpm_2']:
-            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
-
         extra_params_kwargs = self.initialize(p)
         if 'sigma_min' in inspect.signature(self.func).parameters:
             extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
-- 
cgit v1.2.3


From 11dd79e346bd780bc5c3119df962e7a9c20f2493 Mon Sep 17 00:00:00 2001
From: AbstractQbit <38468635+AbstractQbit@users.noreply.github.com>
Date: Sat, 24 Dec 2022 14:00:17 +0300
Subject: Add an option for faster low quality previews

---
 modules/sd_samplers.py | 23 ++++++++++++++++-------
 1 file changed, 16 insertions(+), 7 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d26e48dc..fbb56af4 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -106,20 +106,29 @@ def setup_img2img_steps(p, steps=None):
     return steps, t_enc
 
 
-def single_sample_to_image(sample):
-    x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
+def single_sample_to_image(sample, approximation=False):
+    if approximation:
+        # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2
+        coefs = torch.tensor(
+            [[ 0.298,  0.207,  0.208],
+             [ 0.187,  0.286,  0.173],
+             [-0.158,  0.189,  0.264],
+             [-0.184, -0.271, -0.473]]).to(sample.device)
+        x_sample = torch.einsum("lxy,lr -> rxy", sample, coefs)
+    else:
+        x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
     x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
     x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
     x_sample = x_sample.astype(np.uint8)
     return Image.fromarray(x_sample)
 
 
-def sample_to_image(samples, index=0):
-    return single_sample_to_image(samples[index])
+def sample_to_image(samples, index=0, approximation=False):
+    return single_sample_to_image(samples[index], approximation)
 
 
-def samples_to_image_grid(samples):
-    return images.image_grid([single_sample_to_image(sample) for sample in samples])
+def samples_to_image_grid(samples, approximation=False):
+    return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
 
 
 def store_latent(decoded):
@@ -127,7 +136,7 @@ def store_latent(decoded):
 
     if opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
         if not shared.parallel_processing_allowed:
-            shared.state.current_image = sample_to_image(decoded)
+            shared.state.current_image = sample_to_image(decoded, approximation=opts.show_progress_approximate)
 
 
 class InterruptedException(BaseException):
-- 
cgit v1.2.3


From 0b8acce6a9a1418fa88a506450cd1b92e2d48986 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 18:38:16 +0300
Subject: separate part of denoiser code into a function to make it easier for
 extensions to override it

---
 modules/sd_samplers.py | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d26e48dc..8efe74df 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -288,6 +288,16 @@ class CFGDenoiser(torch.nn.Module):
         self.init_latent = None
         self.step = 0
 
+    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+        denoised_uncond = x_out[-uncond.shape[0]:]
+        denoised = torch.clone(denoised_uncond)
+
+        for i, conds in enumerate(conds_list):
+            for cond_index, weight in conds:
+                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+
+        return denoised
+
     def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
         if state.interrupted or state.skipped:
             raise InterruptedException
@@ -329,12 +339,7 @@ class CFGDenoiser(torch.nn.Module):
 
             x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
 
-        denoised_uncond = x_out[-uncond.shape[0]:]
-        denoised = torch.clone(denoised_uncond)
-
-        for i, conds in enumerate(conds_list):
-            for cond_index, weight in conds:
-                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+        denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
 
         if self.mask is not None:
             denoised = self.init_latent * self.mask + self.nmask * denoised
-- 
cgit v1.2.3


From 56e557c6ff8a6480887c9c585bf908045ee8e791 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 22:39:00 +0300
Subject: added cheap NN approximation for VAE

---
 modules/sd_samplers.py | 29 ++++++++++++++++-------------
 1 file changed, 16 insertions(+), 13 deletions(-)

(limited to 'modules/sd_samplers.py')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 27ef4ff8..177b5338 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -9,7 +9,7 @@ import k_diffusion.sampling
 import torchsde._brownian.brownian_interval
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
-from modules import prompt_parser, devices, processing, images
+from modules import prompt_parser, devices, processing, images, sd_vae_approx
 
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -106,28 +106,31 @@ def setup_img2img_steps(p, steps=None):
     return steps, t_enc
 
 
-def single_sample_to_image(sample, approximation=False):