From f8ff8c0638997fd0aef217db1505598846f14782 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 8 Aug 2023 22:09:40 +0300
Subject: [PATCH] merge errors

---
 modules/sd_samplers_cfg_denoiser.py | 23 +++++++++++++++++++++--
 modules/sd_samplers_common.py       |  6 +++++-
 modules/sd_samplers_kdiffusion.py   | 17 ++++++++++++-----
 modules/sd_samplers_timesteps.py    | 27 +++++++++++++++++----------
 4 files changed, 55 insertions(+), 18 deletions(-)

diff --git a/modules/sd_samplers_cfg_denoiser.py b/modules/sd_samplers_cfg_denoiser.py
index d826222cd..a532e0137 100644
--- a/modules/sd_samplers_cfg_denoiser.py
+++ b/modules/sd_samplers_cfg_denoiser.py
@@ -38,16 +38,24 @@ class CFGDenoiser(torch.nn.Module):
     negative prompt.
     """
 
-    def __init__(self, model, sampler):
+    def __init__(self, sampler):
         super().__init__()
-        self.inner_model = model
+        self.model_wrap = None
         self.mask = None
         self.nmask = None
         self.init_latent = None
+        self.steps = None
         self.step = 0
         self.image_cfg_scale = None
         self.padded_cond_uncond = False
         self.sampler = sampler
+        self.model_wrap = None
+        self.p = None
+
+    @property
+    def inner_model(self):
+        raise NotImplementedError()
+
 
     def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
         denoised_uncond = x_out[-uncond.shape[0]:]
@@ -68,10 +76,21 @@ class CFGDenoiser(torch.nn.Module):
     def get_pred_x0(self, x_in, x_out, sigma):
         return x_out
 
+    def update_inner_model(self):
+        self.model_wrap = None
+
+        c, uc = self.p.get_conds()
+        self.sampler.sampler_extra_args['cond'] = c
+        self.sampler.sampler_extra_args['uncond'] = uc
+
     def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
         if state.interrupted or state.skipped:
             raise sd_samplers_common.InterruptedException
 
+        if sd_samplers_common.apply_refiner(self):
+            cond = self.sampler.sampler_extra_args['cond']
+            uncond = self.sampler.sampler_extra_args['uncond']
+
         # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
         # so is_edit_model is set to False to support AND composition.
         is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 15f279707..fa3614ff0 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -202,8 +202,9 @@ class Sampler:
 
         self.conditioning_key = shared.sd_model.model.conditioning_key
 
-        self.model_wrap = None
+        self.p = None
         self.model_wrap_cfg = None
+        self.sampler_extra_args = None
 
     def callback_state(self, d):
         step = d['i']
@@ -215,6 +216,7 @@ class Sampler:
         shared.total_tqdm.update()
 
     def launch_sampling(self, steps, func):
+        self.model_wrap_cfg.steps = steps
         state.sampling_steps = steps
         state.sampling_step = 0
 
@@ -234,6 +236,8 @@ class Sampler:
         return p.steps
 
     def initialize(self, p) -> dict:
+        self.p = p
+        self.model_wrap_cfg.p = 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_cfg.step = 0
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 3ff4b6345..95a43ceff 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -52,17 +52,24 @@ k_diffusion_scheduler = {
 }
 
 
+class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):
+    @property
+    def inner_model(self):
+        if self.model_wrap is None:
+            denoiser = k_diffusion.external.CompVisVDenoiser if shared.sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
+            self.model_wrap = denoiser(shared.sd_model, quantize=shared.opts.enable_quantization)
+
+        return self.model_wrap
+
+
 class KDiffusionSampler(sd_samplers_common.Sampler):
     def __init__(self, funcname, sd_model):
-
         super().__init__(funcname)
 
-        self.extra_params = sampler_extra_params.get(funcname, [])
         self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
 
-        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.model_wrap_cfg = sd_samplers_cfg_denoiser.CFGDenoiser(self.model_wrap, self)
+        self.model_wrap_cfg = CFGDenoiserKDiffusion(self)
+        self.model_wrap = self.model_wrap_cfg.inner_model
 
     def get_sigmas(self, p, steps):
         discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
diff --git a/modules/sd_samplers_timesteps.py b/modules/sd_samplers_timesteps.py
index d89d0efb3..965e61c67 100644
--- a/modules/sd_samplers_timesteps.py
+++ b/modules/sd_samplers_timesteps.py
@@ -44,10 +44,10 @@ class CompVisTimestepsVDenoiser(torch.nn.Module):
 
 class CFGDenoiserTimesteps(CFGDenoiser):
 
-    def __init__(self, model, sampler):
-        super().__init__(model, sampler)
+    def __init__(self, sampler):
+        super().__init__(sampler)
 
-        self.alphas = model.inner_model.alphas_cumprod
+        self.alphas = shared.sd_model.alphas_cumprod
 
     def get_pred_x0(self, x_in, x_out, sigma):
         ts = int(sigma.item())
@@ -60,6 +60,14 @@ class CFGDenoiserTimesteps(CFGDenoiser):
 
         return pred_x0
 
+    @property
+    def inner_model(self):
+        if self.model_wrap is None:
+            denoiser = CompVisTimestepsVDenoiser if shared.sd_model.parameterization == "v" else CompVisTimestepsDenoiser
+            self.model_wrap = denoiser(shared.sd_model)
+
+        return self.model_wrap
+
 
 class CompVisSampler(sd_samplers_common.Sampler):
     def __init__(self, funcname, sd_model):
@@ -68,9 +76,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
         self.eta_option_field = 'eta_ddim'
         self.eta_infotext_field = 'Eta DDIM'
 
-        denoiser = CompVisTimestepsVDenoiser if sd_model.parameterization == "v" else CompVisTimestepsDenoiser
-        self.model_wrap = denoiser(sd_model)
-        self.model_wrap_cfg = CFGDenoiserTimesteps(self.model_wrap, self)
+        self.model_wrap_cfg = CFGDenoiserTimesteps(self)
 
     def get_timesteps(self, p, steps):
         discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
@@ -106,7 +112,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
 
         self.model_wrap_cfg.init_latent = x
         self.last_latent = x
-        extra_args = {
+        self.sampler_extra_args = {
             'cond': conditioning,
             'image_cond': image_conditioning,
             'uncond': unconditional_conditioning,
@@ -114,7 +120,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
             's_min_uncond': self.s_min_uncond
         }
 
-        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))
+        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_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
@@ -132,13 +138,14 @@ class CompVisSampler(sd_samplers_common.Sampler):
             extra_params_kwargs['timesteps'] = timesteps
 
         self.last_latent = x
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
+        self.sampler_extra_args = {
             '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))
+        }
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_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
-- 
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