From 3366e494a1147e570d8527eea19da88edb3a1e0c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 22 May 2023 00:13:53 +0300
Subject: option to pad prompt/neg prompt to be same length

---
 modules/sd_samplers_kdiffusion.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 59982fc9..638e0ac9 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -125,6 +125,16 @@ class CFGDenoiser(torch.nn.Module):
             x_in = x_in[:-batch_size]
             sigma_in = sigma_in[:-batch_size]
 
+        # TODO add infotext entry
+        if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
+            empty = shared.sd_model.cond_stage_model_empty_prompt
+            num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
+
+            if num_repeats < 0:
+                tensor = torch.cat([tensor, empty.repeat((tensor.shape[0], -num_repeats, 1))], axis=1)
+            elif num_repeats > 0:
+                uncond = torch.cat([uncond, empty.repeat((uncond.shape[0], num_repeats, 1))], axis=1)
+
         if tensor.shape[1] == uncond.shape[1] or skip_uncond:
             if is_edit_model:
                 cond_in = torch.cat([tensor, uncond, uncond])
-- 
cgit v1.2.3


From a10487986925ca8fd07ee7ae7fc5034752298551 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Mon, 22 May 2023 21:52:46 +0800
Subject: Add custom karras scheduler

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

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 638e0ac9..eb6c760c 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -304,6 +304,12 @@ class KDiffusionSampler:
 
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
+        elif p.enable_karras:
+            sigma_max = p.sigma_max
+            sigma_min = p.sigma_min
+            rho = p.rho
+            print(f"\nsigma_min: {sigma_min}, sigma_max: {sigma_max}, rho: {rho}")
+            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho, device=shared.device)
         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
             sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
 
-- 
cgit v1.2.3


From 90ec557d60289a89b4ea6cd9b311658fbe682dc3 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Mon, 22 May 2023 22:06:13 +0800
Subject: remove debug print

---
 modules/sd_samplers_kdiffusion.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index eb6c760c..d428551d 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -308,7 +308,6 @@ class KDiffusionSampler:
             sigma_max = p.sigma_max
             sigma_min = p.sigma_min
             rho = p.rho
-            print(f"\nsigma_min: {sigma_min}, sigma_max: {sigma_max}, rho: {rho}")
             sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho, device=shared.device)
         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
             sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-- 
cgit v1.2.3


From e6269cba7fd84a76b2bd0012cb954f947a79b6a5 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Mon, 22 May 2023 23:02:05 +0800
Subject: Add dropdown for scheduler type

---
 modules/sd_samplers_kdiffusion.py | 19 +++++++++++++++----
 1 file changed, 15 insertions(+), 4 deletions(-)

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index d428551d..441c040e 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -44,6 +44,12 @@ sampler_extra_params = {
     'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
 }
 
+k_diffusion_scheduler = {
+    'karras': k_diffusion.sampling.get_sigmas_karras,
+    'exponential': k_diffusion.sampling.get_sigmas_exponential,
+    'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
+}
+
 
 class CFGDenoiser(torch.nn.Module):
     """
@@ -305,10 +311,15 @@ class KDiffusionSampler:
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
         elif p.enable_karras:
-            sigma_max = p.sigma_max
-            sigma_min = p.sigma_min
-            rho = p.rho
-            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho, device=shared.device)
+            print(p.k_sched_type, p.sigma_min, p.sigma_max, p.rho)
+            sigmas_func = k_diffusion_scheduler[p.k_sched_type]
+            sigmas_kwargs = {
+                'sigma_min': p.sigma_min,
+                'sigma_max': p.sigma_max
+            }
+            if p.k_sched_type != 'exponential':
+                sigmas_kwargs['rho'] = p.rho
+            sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
             sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
 
-- 
cgit v1.2.3


From 7882f76da45de7279c7db0dd17b6aca82b7ddf46 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Mon, 22 May 2023 23:26:28 +0800
Subject: Replace karras by k_diffusion, fix gen info

---
 modules/sd_samplers_kdiffusion.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 441c040e..4d8f57a7 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -44,6 +44,7 @@ sampler_extra_params = {
     'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
 }
 
+k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
 k_diffusion_scheduler = {
     'karras': k_diffusion.sampling.get_sigmas_karras,
     'exponential': k_diffusion.sampling.get_sigmas_exponential,
@@ -310,7 +311,7 @@ class KDiffusionSampler:
 
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
-        elif p.enable_karras:
+        elif p.enable_custom_k_sched:
             print(p.k_sched_type, p.sigma_min, p.sigma_max, p.rho)
             sigmas_func = k_diffusion_scheduler[p.k_sched_type]
             sigmas_kwargs = {
-- 
cgit v1.2.3


From 5dfb1f597b47b1028ee010df2ed8642e2beb6c1c Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Mon, 22 May 2023 23:36:16 +0800
Subject: remove not related code

---
 modules/sd_samplers_kdiffusion.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 4d8f57a7..ba0cf08e 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -312,7 +312,6 @@ class KDiffusionSampler:
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
         elif p.enable_custom_k_sched:
-            print(p.k_sched_type, p.sigma_min, p.sigma_max, p.rho)
             sigmas_func = k_diffusion_scheduler[p.k_sched_type]
             sigmas_kwargs = {
                 'sigma_min': p.sigma_min,
-- 
cgit v1.2.3


From 65a87ccc9bf92a0fd24a453e2837dd2d19bbf5ce Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Tue, 23 May 2023 00:09:49 +0800
Subject: Add error information for recursion error

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

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index ba0cf08e..e2f18b54 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -272,6 +272,12 @@ class KDiffusionSampler:
 
         try:
             return func()
+        except RecursionError:
+            print(
+                'rho>5 with polyexponential scheduler may cause this error.'
+                'You should try to use smaller rho instead.'
+            )
+            return self.last_latent
         except sd_samplers_common.InterruptedException:
             return self.last_latent
 
-- 
cgit v1.2.3


From 403b304162b670597f20b01f147bb042eb78ee5c Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Tue, 23 May 2023 00:29:38 +0800
Subject: use sigma_max/min in model if sigma_max/min is 0

---
 modules/sd_samplers_kdiffusion.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index e2f18b54..7364ed44 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -318,10 +318,11 @@ class KDiffusionSampler:
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
         elif p.enable_custom_k_sched:
+            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
             sigmas_func = k_diffusion_scheduler[p.k_sched_type]
             sigmas_kwargs = {
-                'sigma_min': p.sigma_min,
-                'sigma_max': p.sigma_max
+                'sigma_min': p.sigma_min or sigma_min,
+                'sigma_max': p.sigma_max or sigma_max
             }
             if p.k_sched_type != 'exponential':
                 sigmas_kwargs['rho'] = p.rho
-- 
cgit v1.2.3


From 51d672890d168fe46dd152305d2fde3f1bb9b531 Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Mon, 22 May 2023 13:06:57 -0400
Subject: Revert #10586

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

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 638e0ac9..dcec9e0e 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -20,7 +20,7 @@ samplers_k_diffusion = [
     ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
     ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
     ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
-    ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True, 'discard_next_to_last_sigma': True}),
+    ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
     ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
@@ -29,7 +29,7 @@ samplers_k_diffusion = [
     ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
     ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
     ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
-    ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True, 'discard_next_to_last_sigma': True}),
+    ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
 ]
 
 samplers_data_k_diffusion = [
-- 
cgit v1.2.3


From 38aaad654bec640e99beb42964d09357878179bd Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Tue, 23 May 2023 09:38:30 +0800
Subject: Better hint for user

Co-authored-by: catboxanon <122327233+catboxanon@users.noreply.github.com>
---
 modules/sd_samplers_kdiffusion.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 7364ed44..969ef02b 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -274,8 +274,9 @@ class KDiffusionSampler:
             return func()
         except RecursionError:
             print(
-                'rho>5 with polyexponential scheduler may cause this error.'
-                'You should try to use smaller rho instead.'
+                '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:
-- 
cgit v1.2.3


From 70650f87a42615a62568a896403156d0065621b4 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Tue, 23 May 2023 11:34:51 +0800
Subject: Use better way to impl

---
 modules/sd_samplers_kdiffusion.py | 19 +++++++++++++------
 1 file changed, 13 insertions(+), 6 deletions(-)

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 969ef02b..5fea08b0 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -295,6 +295,13 @@ class KDiffusionSampler:
 
         k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
 
+        if opts.custom_k_sched:
+            p.extra_generation_params["Enable Custom KDiffusion Schedule"] = True
+            p.extra_generation_params["KDiffusion Scheduler Type"] = opts.k_sched_type
+            p.extra_generation_params["KDiffusion Scheduler sigma_max"] = opts.sigma_max
+            p.extra_generation_params["KDiffusion Scheduler sigma_min"] = opts.sigma_min
+            p.extra_generation_params["KDiffusion Scheduler rho"] = opts.rho
+
         extra_params_kwargs = {}
         for param_name in self.extra_params:
             if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
@@ -318,15 +325,15 @@ class KDiffusionSampler:
 
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
-        elif p.enable_custom_k_sched:
+        elif opts.custom_k_sched:
             sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-            sigmas_func = k_diffusion_scheduler[p.k_sched_type]
+            sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
             sigmas_kwargs = {
-                'sigma_min': p.sigma_min or sigma_min,
-                'sigma_max': p.sigma_max or sigma_max
+                'sigma_min': opts.sigma_min or sigma_min,
+                'sigma_max': opts.sigma_max or sigma_max
             }
-            if p.k_sched_type != 'exponential':
-                sigmas_kwargs['rho'] = p.rho
+            if opts.k_sched_type != 'exponential':
+                sigmas_kwargs['rho'] = opts.rho
             sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
             sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-- 
cgit v1.2.3


From 72377b02518f96051a01a7e0ea30a6a14d8ec1de Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Tue, 23 May 2023 23:48:23 +0800
Subject: Use type to determine if it is enable

---
 modules/sd_samplers_kdiffusion.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 5fea08b0..eff2e32d 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -46,6 +46,7 @@ sampler_extra_params = {
 
 k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
 k_diffusion_scheduler = {
+    'None': None,
     'karras': k_diffusion.sampling.get_sigmas_karras,
     'exponential': k_diffusion.sampling.get_sigmas_exponential,
     'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
@@ -295,8 +296,7 @@ class KDiffusionSampler:
 
         k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
 
-        if opts.custom_k_sched:
-            p.extra_generation_params["Enable Custom KDiffusion Schedule"] = True
+        if opts.k_sched_type != "None":
             p.extra_generation_params["KDiffusion Scheduler Type"] = opts.k_sched_type
             p.extra_generation_params["KDiffusion Scheduler sigma_max"] = opts.sigma_max
             p.extra_generation_params["KDiffusion Scheduler sigma_min"] = opts.sigma_min
@@ -325,7 +325,7 @@ class KDiffusionSampler:
 
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
-        elif opts.custom_k_sched:
+        elif opts.k_sched_type != "None":
             sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
             sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
             sigmas_kwargs = {
-- 
cgit v1.2.3


From 1601fccebca2dc5a806a0d2f0d33aa2da81a28fb Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Wed, 24 May 2023 00:18:09 +0800
Subject: Use automatic instead of None/default

---
 modules/sd_samplers_kdiffusion.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index eff2e32d..a4c797c6 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -46,7 +46,7 @@ sampler_extra_params = {
 
 k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
 k_diffusion_scheduler = {
-    'None': None,
+    'Automatic': None,
     'karras': k_diffusion.sampling.get_sigmas_karras,
     'exponential': k_diffusion.sampling.get_sigmas_exponential,
     'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
@@ -296,7 +296,7 @@ class KDiffusionSampler:
 
         k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
 
-        if opts.k_sched_type != "None":
+        if opts.k_sched_type != "Automatic":
             p.extra_generation_params["KDiffusion Scheduler Type"] = opts.k_sched_type
             p.extra_generation_params["KDiffusion Scheduler sigma_max"] = opts.sigma_max
             p.extra_generation_params["KDiffusion Scheduler sigma_min"] = opts.sigma_min
@@ -325,7 +325,7 @@ class KDiffusionSampler:
 
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
-        elif opts.k_sched_type != "None":
+        elif opts.k_sched_type != "Automatic":
             sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
             sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
             sigmas_kwargs = {
-- 
cgit v1.2.3


From 4b88e24ebe776680b327e33fe96d7fcf38e2e5d2 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Wed, 24 May 2023 20:35:58 +0800
Subject: improvements

See:
https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/10649#issuecomment-1561047723
---
 modules/sd_samplers_kdiffusion.py | 27 +++++++++++++++++----------
 1 file changed, 17 insertions(+), 10 deletions(-)

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index a4c797c6..d2d172e4 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -296,12 +296,6 @@ class KDiffusionSampler:
 
         k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
 
-        if opts.k_sched_type != "Automatic":
-            p.extra_generation_params["KDiffusion Scheduler Type"] = opts.k_sched_type
-            p.extra_generation_params["KDiffusion Scheduler sigma_max"] = opts.sigma_max
-            p.extra_generation_params["KDiffusion Scheduler sigma_min"] = opts.sigma_min
-            p.extra_generation_params["KDiffusion Scheduler rho"] = opts.rho
-
         extra_params_kwargs = {}
         for param_name in self.extra_params:
             if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
@@ -326,14 +320,27 @@ class KDiffusionSampler:
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
         elif opts.k_sched_type != "Automatic":
-            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-            sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
+            m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+            sigma_min, sigma_max = (0.1, 10)
             sigmas_kwargs = {
-                'sigma_min': opts.sigma_min or sigma_min,
-                'sigma_max': opts.sigma_max or sigma_max
+                'sigma_min': sigma_min if opts.use_old_karras_scheduler_sigmas else m_sigma_min,
+                'sigma_max': sigma_max if opts.use_old_karras_scheduler_sigmas else m_sigma_max
             }
+
+            sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
+            p.extra_generation_params["KDiff Sched Type"] = opts.k_sched_type
+
+            if opts.sigma_min != 0.3:
+                # take 0.0 as model default
+                sigmas_kwargs['sigma_min'] = opts.sigma_min or m_sigma_min
+                p.extra_generation_params["KDiff Sched min sigma"] = opts.sigma_min
+            if opts.sigma_max != 14.6:
+                sigmas_kwargs['sigma_max'] = opts.sigma_max or m_sigma_max
+                p.extra_generation_params["KDiff Sched max sigma"] = opts.sigma_max
             if opts.k_sched_type != 'exponential':
                 sigmas_kwargs['rho'] = opts.rho
+                p.extra_generation_params["KDiff Sched rho"] = opts.rho
+
             sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
             sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-- 
cgit v1.2.3


From a69b71a37f1fd32a60fbd87beed13f4f280400bd Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Wed, 24 May 2023 20:40:37 +0800
Subject: use Schedule instead of Sched

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

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index d2d172e4..9c9d9f17 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -328,18 +328,18 @@ class KDiffusionSampler:
             }
 
             sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
-            p.extra_generation_params["KDiff Sched Type"] = opts.k_sched_type
+            p.extra_generation_params["KDiff Schedule Type"] = opts.k_sched_type
 
             if opts.sigma_min != 0.3:
                 # take 0.0 as model default
                 sigmas_kwargs['sigma_min'] = opts.sigma_min or m_sigma_min
-                p.extra_generation_params["KDiff Sched min sigma"] = opts.sigma_min
+                p.extra_generation_params["KDiff Schedule min sigma"] = opts.sigma_min
             if opts.sigma_max != 14.6:
                 sigmas_kwargs['sigma_max'] = opts.sigma_max or m_sigma_max
-                p.extra_generation_params["KDiff Sched max sigma"] = opts.sigma_max
+                p.extra_generation_params["KDiff Schedule max sigma"] = opts.sigma_max
             if opts.k_sched_type != 'exponential':
                 sigmas_kwargs['rho'] = opts.rho
-                p.extra_generation_params["KDiff Sched rho"] = opts.rho
+                p.extra_generation_params["KDiff Schedule rho"] = opts.rho
 
             sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
-- 
cgit v1.2.3


From e8e7fe11e903115a706187f8301df2e06fa018f8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 27 May 2023 19:53:09 +0300
Subject: updates for the noise schedule settings

---
 modules/sd_samplers_kdiffusion.py | 30 ++++++++++++++++--------------
 1 file changed, 16 insertions(+), 14 deletions(-)

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 9c9d9f17..e9ba2c61 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -321,25 +321,27 @@ class KDiffusionSampler:
             sigmas = p.sampler_noise_scheduler_override(steps)
         elif opts.k_sched_type != "Automatic":
             m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-            sigma_min, sigma_max = (0.1, 10)
+            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
             sigmas_kwargs = {
-                'sigma_min': sigma_min if opts.use_old_karras_scheduler_sigmas else m_sigma_min,
-                'sigma_max': sigma_max if opts.use_old_karras_scheduler_sigmas else m_sigma_max
+                'sigma_min': sigma_min,
+                'sigma_max': sigma_max,
             }
 
             sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
-            p.extra_generation_params["KDiff Schedule Type"] = opts.k_sched_type
-
-            if opts.sigma_min != 0.3:
-                # take 0.0 as model default
-                sigmas_kwargs['sigma_min'] = opts.sigma_min or m_sigma_min
-                p.extra_generation_params["KDiff Schedule min sigma"] = opts.sigma_min
-            if opts.sigma_max != 14.6:
-                sigmas_kwargs['sigma_max'] = opts.sigma_max or m_sigma_max
-                p.extra_generation_params["KDiff Schedule max sigma"] = opts.sigma_max
-            if opts.k_sched_type != 'exponential':
+            p.extra_generation_params["Schedule type"] = opts.k_sched_type
+
+            if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
+                sigmas_kwargs['sigma_min'] = opts.sigma_min
+                p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
+            if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
+                sigmas_kwargs['sigma_max'] = opts.sigma_max
+                p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
+
+            default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
+
+            if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
                 sigmas_kwargs['rho'] = opts.rho
-                p.extra_generation_params["KDiff Schedule rho"] = opts.rho
+                p.extra_generation_params["Schedule rho"] = opts.rho
 
             sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
-- 
cgit v1.2.3


From 4bd490c28dd8f17b7df943eb3963c34d725084fc Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 27 Jun 2023 06:18:43 +0300
Subject: add missing infotext entry for the pad cond/uncond option

---
 modules/sd_samplers_kdiffusion.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index f8a0c7ba..71581b76 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -69,6 +69,7 @@ class CFGDenoiser(torch.nn.Module):
         self.init_latent = None
         self.step = 0
         self.image_cfg_scale = None
+        self.padded_cond_uncond = False
 
     def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
         denoised_uncond = x_out[-uncond.shape[0]:]
@@ -133,15 +134,17 @@ class CFGDenoiser(torch.nn.Module):
             x_in = x_in[:-batch_size]
             sigma_in = sigma_in[:-batch_size]
 
-        # TODO add infotext entry
+        self.padded_cond_uncond = False
         if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
             empty = shared.sd_model.cond_stage_model_empty_prompt
             num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
 
             if num_repeats < 0:
                 tensor = torch.cat([tensor, empty.repeat((tensor.shape[0], -num_repeats, 1))], axis=1)
+                self.padded_cond_uncond = True
             elif num_repeats > 0:
                 uncond = torch.cat([uncond, empty.repeat((uncond.shape[0], num_repeats, 1))], axis=1)
+                self.padded_cond_uncond = True
 
         if tensor.shape[1] == uncond.shape[1] or skip_uncond:
             if is_edit_model:
@@ -405,6 +408,9 @@ class KDiffusionSampler:
 
         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):
@@ -438,5 +444,8 @@ class KDiffusionSampler:
             '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
 
-- 
cgit v1.2.3