Use original CFGDenoiser if image_cfg_scale = 1

If image_cfg_scale is =1 then the original image is not used for the output. We can then use the original CFGDenoiser to get the same result to support AND functionality.

Maybe in the future AND can be supported with "Image CFG Scale"

modules/sd_samplers_kdiffusion.py

@@ -245,7 +245,7 @@ class KDiffusionSampler:
         self.funcname = funcname
         self.func = getattr(k_diffusion.sampling, self.funcname)
         self.extra_params = sampler_extra_params.get(funcname, [])
-        self.model_wrap_cfg = CFGDenoiser(self.model_wrap) if not shared.sd_model.cond_stage_key == "edit" else CFGDenoiserEdit(self.model_wrap)
+        self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
         self.sampler_noises = None
         self.stop_at = None
         self.eta = None
@@ -280,6 +280,9 @@ class KDiffusionSampler:
         return p.steps
 
     def initialize(self, p):
+        if shared.sd_model.cond_stage_key == "edit" and getattr(p, 'image_cfg_scale', None) != 1:
+            self.model_wrap_cfg = CFGDenoiserEdit(self.model_wrap)
+
         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
@@ -352,7 +355,7 @@ class KDiffusionSampler:
             'cond_scale': p.cfg_scale,
         }
 
-        if hasattr(p, 'image_cfg_scale'):
+        if hasattr(p, 'image_cfg_scale') and p.image_cfg_scale != 1 and p.image_cfg_scale != None:
             extra_args['image_cfg_scale'] = p.image_cfg_scale
 
         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))