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