From f741a98baccae100fcfb40c017b5c35c5cba1b0c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 10 May 2023 08:43:42 +0300
Subject: imports cleanup for ruff

---
 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 0fc9f456..3b8e9622 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -1,7 +1,6 @@
 from collections import deque
 import torch
 import inspect
-import einops
 import k_diffusion.sampling
 from modules import prompt_parser, devices, sd_samplers_common
 
-- 
cgit v1.2.3


From 028d3f6425d85f122027c127fba8bcbf4f66ee75 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 10 May 2023 11:05:02 +0300
Subject: ruff auto fixes

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

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 3b8e9622..2f733cf5 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -86,7 +86,7 @@ class CFGDenoiser(torch.nn.Module):
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
 
-        assert not is_edit_model or all([len(conds) == 1 for conds in conds_list]), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
+        assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
 
         batch_size = len(conds_list)
         repeats = [len(conds_list[i]) for i in range(batch_size)]
-- 
cgit v1.2.3


From 49a55b410b66b7dd9be9335d8a2e3a71e4f8b15c Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Thu, 11 May 2023 18:28:15 +0300
Subject: Autofix Ruff W (not W605) (mostly whitespace)

---
 modules/sd_samplers_kdiffusion.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 2f733cf5..e9e41818 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -93,10 +93,10 @@ class CFGDenoiser(torch.nn.Module):
 
         if shared.sd_model.model.conditioning_key == "crossattn-adm":
             image_uncond = torch.zeros_like(image_cond)
-            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm} 
+            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm}
         else:
             image_uncond = image_cond
-            make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]} 
+            make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
 
         if not is_edit_model:
             x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
@@ -316,7 +316,7 @@ class KDiffusionSampler:
 
         sigma_sched = sigmas[steps - t_enc - 1:]
         xi = x + noise * sigma_sched[0]
-        
+
         extra_params_kwargs = self.initialize(p)
         parameters = inspect.signature(self.func).parameters
 
@@ -339,9 +339,9 @@ class KDiffusionSampler:
         self.model_wrap_cfg.init_latent = x
         self.last_latent = x
         extra_args={
-            'cond': conditioning, 
-            'image_cond': image_conditioning, 
-            'uncond': unconditional_conditioning, 
+            'cond': conditioning,
+            'image_cond': image_conditioning,
+            'uncond': unconditional_conditioning,
             'cond_scale': p.cfg_scale,
             's_min_uncond': self.s_min_uncond
         }
@@ -374,9 +374,9 @@ class KDiffusionSampler:
 
         self.last_latent = x
         samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
-            'cond': conditioning, 
-            'image_cond': image_conditioning, 
-            'uncond': unconditional_conditioning, 
+            'cond': conditioning,
+            'image_cond': image_conditioning,
+            'uncond': unconditional_conditioning,
             'cond_scale': p.cfg_scale,
             's_min_uncond': self.s_min_uncond
         }, disable=False, callback=self.callback_state, **extra_params_kwargs))
-- 
cgit v1.2.3


From 3078001439d25b66ef5627c9e3d431aa23bbed73 Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Sun, 14 May 2023 01:49:41 +0000
Subject: Add/modify CFG callbacks

Required by self-attn guidance extension
https://github.com/ashen-sensored/sd_webui_SAG
---
 modules/sd_samplers_kdiffusion.py | 8 +++++++-
 1 file changed, 7 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 e9e41818..55f0d3a3 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -8,6 +8,7 @@ from modules.shared import opts, state
 import modules.shared as shared
 from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
 from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
+from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
 
 samplers_k_diffusion = [
     ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
@@ -160,7 +161,7 @@ class CFGDenoiser(torch.nn.Module):
             fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
             x_out = torch.cat([x_out, fake_uncond])  # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
 
-        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
+        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
         cfg_denoised_callback(denoised_params)
 
         devices.test_for_nans(x_out, "unet")
@@ -180,6 +181,11 @@ class CFGDenoiser(torch.nn.Module):
         if self.mask is not None:
             denoised = self.init_latent * self.mask + self.nmask * denoised
 
+        after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
+        cfg_after_cfg_callback(after_cfg_callback_params)
+        if after_cfg_callback_params.output_altered:
+            denoised = after_cfg_callback_params.x
+
         self.step += 1
         return denoised
 
-- 
cgit v1.2.3


From 005849331e82cded96f6f3e5ff828037c672c38d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 14 May 2023 08:15:22 +0300
Subject: remove output_altered flag from AfterCFGCallbackParams

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

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 55f0d3a3..61f23ad7 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -183,8 +183,7 @@ class CFGDenoiser(torch.nn.Module):
 
         after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
         cfg_after_cfg_callback(after_cfg_callback_params)
-        if after_cfg_callback_params.output_altered:
-            denoised = after_cfg_callback_params.x
+        denoised = after_cfg_callback_params.x
 
         self.step += 1
         return denoised
-- 
cgit v1.2.3


From cdac5ace1456ba779d5a0171ff8757f31955bfee Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 16 May 2023 11:54:02 +0300
Subject: suppress ENSD infotext for samplers that don't use it

---
 modules/sd_samplers_kdiffusion.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 61f23ad7..5455561a 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -11,21 +11,21 @@ from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
 from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
 
 samplers_k_diffusion = [
-    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
+    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
     ('Euler', 'sample_euler', ['k_euler'], {}),
     ('LMS', 'sample_lms', ['k_lms'], {}),
     ('Heun', 'sample_heun', ['k_heun'], {}),
     ('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'], {}),
+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
+    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True}),
     ('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'], {}),
+    ('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'}),
-    ('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'}),
+    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True}),
+    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True}),
+    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": 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'}),
 ]
-- 
cgit v1.2.3


From a61cbef02c7652f96d333b28e01f5230e225224e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 16 May 2023 12:36:15 +0300
Subject: add second_order field to sampler config

---
 modules/sd_samplers_kdiffusion.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

(limited to 'modules/sd_samplers_kdiffusion.py')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 5455561a..552c6c64 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -14,20 +14,20 @@ samplers_k_diffusion = [
     ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
     ('Euler', 'sample_euler', ['k_euler'], {}),
     ('LMS', 'sample_lms', ['k_lms'], {}),
-    ('Heun', 'sample_heun', ['k_heun'], {}),
+    ('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
     ('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, "uses_ensd": True}),
-    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True}),
+    ('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'], {}),
+    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": 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'}),
-    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True}),
-    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True}),
-    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True}),
+    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
+    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
+    ('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'}),
+    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True}),
 ]
 
 samplers_data_k_diffusion = [
-- 
cgit v1.2.3


From 31545abe145ac8833c9c15aa41300fb609dcb128 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 21 May 2023 07:31:39 +0300
Subject: add DPM-Solver++(2M) SDE from new k-diffusion

---
 modules/sd_samplers_kdiffusion.py | 14 ++++++++------
 1 file changed, 8 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 552c6c64..1838cf08 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -19,7 +19,8 @@ samplers_k_diffusion = [
     ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
     ('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}),
+    ('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}),
     ('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'}),
@@ -27,7 +28,8 @@ samplers_k_diffusion = [
     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
     ('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}),
+    ('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}),
 ]
 
 samplers_data_k_diffusion = [
@@ -228,7 +230,7 @@ class KDiffusionSampler:
         self.sampler_noises = None
         self.stop_at = None
         self.eta = None
-        self.config = None
+        self.config = None  # set by the function calling the constructor
         self.last_latent = None
         self.s_min_uncond = None
 
@@ -337,13 +339,13 @@ class KDiffusionSampler:
         if 'sigmas' in parameters:
             extra_params_kwargs['sigmas'] = sigma_sched
 
-        if self.funcname == 'sample_dpmpp_sde':
+        if self.config.options.get('brownian_noise', False):
             noise_sampler = self.create_noise_sampler(x, sigmas, p)
             extra_params_kwargs['noise_sampler'] = noise_sampler
 
         self.model_wrap_cfg.init_latent = x
         self.last_latent = x
-        extra_args={
+        extra_args = {
             'cond': conditioning,
             'image_cond': image_conditioning,
             'uncond': unconditional_conditioning,
@@ -373,7 +375,7 @@ class KDiffusionSampler:
         else:
             extra_params_kwargs['sigmas'] = sigmas
 
-        if self.funcname == 'sample_dpmpp_sde':
+        if self.config.options.get('brownian_noise', False):
             noise_sampler = self.create_noise_sampler(x, sigmas, p)
             extra_params_kwargs['noise_sampler'] = noise_sampler
 
-- 
cgit v1.2.3


From 9a442702d10ada51141307b653eef0f697d58012 Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Sun, 21 May 2023 01:01:59 -0400
Subject: Discard penultimate sigma for dpmpp_2m_sde

---
 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 1838cf08..59982fc9 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}),
+    ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True, 'discard_next_to_last_sigma': 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}),
+    ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True, 'discard_next_to_last_sigma': True}),
 ]
 
 samplers_data_k_diffusion = [
-- 
cgit v1.2.3


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