automatic1111-CFGDenoiser-and-script_callbacks-mod-for-SAG.patch

From cb129e420612813d74043aa4a6a49575b53e9c14 Mon Sep 17 00:00:00 2001
From: Ashen <git123@gmail.com>
Date: Fri, 21 Apr 2023 09:40:59 -0700
Subject: [PATCH] CFGDenoiser and script_callbacks mod for SAG

---
 modules/script_callbacks.py       | 34 +++++++++++++++++++++++++++++++
 modules/sd_samplers_kdiffusion.py |  7 ++++++-
 2 files changed, 40 insertions(+), 1 deletion(-)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 07911876..d3d3df14 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -53,6 +53,21 @@ class CFGDenoiserParams:
 
 
 class CFGDenoisedParams:
+    def __init__(self, x, sampling_step, total_sampling_steps, inner_model):
+        self.x = x
+        """Latent image representation in the process of being denoised"""
+
+        self.sampling_step = sampling_step
+        """Current Sampling step number"""
+
+        self.total_sampling_steps = total_sampling_steps
+        """Total number of sampling steps planned"""
+
+        self.inner_model = inner_model
+        """Inner model reference that is being used for denoising"""
+
+
+class AfterCFGCallbackParams:
     def __init__(self, x, sampling_step, total_sampling_steps):
         self.x = x
         """Latent image representation in the process of being denoised"""
@@ -63,6 +78,8 @@ class CFGDenoisedParams:
         self.total_sampling_steps = total_sampling_steps
         """Total number of sampling steps planned"""
 
+        self.output_altered = False
+        """A flag for CFGDenoiser that indicates whether the output has been altered by the callback"""
 
 class UiTrainTabParams:
     def __init__(self, txt2img_preview_params):
@@ -87,6 +104,7 @@ callback_map = dict(
     callbacks_image_saved=[],
     callbacks_cfg_denoiser=[],
     callbacks_cfg_denoised=[],
+    callbacks_cfg_after_cfg=[],
     callbacks_before_component=[],
     callbacks_after_component=[],
     callbacks_image_grid=[],
@@ -177,6 +195,14 @@ def cfg_denoised_callback(params: CFGDenoisedParams):
             report_exception(c, 'cfg_denoised_callback')
 
 
+def cfg_after_cfg_callback(params: AfterCFGCallbackParams):
+    for c in callback_map['callbacks_cfg_after_cfg']:
+        try:
+            c.callback(params)
+        except Exception:
+            report_exception(c, 'cfg_after_cfg_callback')
+
+
 def before_component_callback(component, **kwargs):
     for c in callback_map['callbacks_before_component']:
         try:
@@ -318,6 +344,14 @@ def on_cfg_denoised(callback):
     add_callback(callback_map['callbacks_cfg_denoised'], callback)
 
 
+def on_cfg_after_cfg(callback):
+    """register a function to be called in the kdiffussion cfg_denoiser method after cfg calculations has completed.
+    The callback is called with one argument:
+        - params: CFGDenoisedParams - parameters to be passed to the inner model and sampling state details.
+    """
+    add_callback(callback_map['callbacks_cfg_after_cfg'], callback)
+
+
 def on_before_component(callback):
     """register a function to be called before a component is created.
     The callback is called with arguments:
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index e9f08518..6ff55ba6 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -9,6 +9,7 @@ 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'], {}),
@@ -146,7 +147,7 @@ class CFGDenoiser(torch.nn.Module):
 
             x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
 
-        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")
@@ -164,6 +165,10 @@ 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
-- 
2.40.0