Uni_PC: make max denoise behave more like other samplers.

On the KSamplers denoise of 1.0 is the same as txt2img but there was a
small difference on UniPC.

comfy/extra_samplers/uni_pc.py

@@ -833,7 +833,7 @@ def expand_dims(v, dims):
 
 
 
-def sample_unipc(model, noise, image, sigmas, sampling_function, extra_args=None, callback=None, disable=None, noise_mask=None, variant='bh1'):
+def sample_unipc(model, noise, image, sigmas, sampling_function, max_denoise, extra_args=None, callback=None, disable=None, noise_mask=None, variant='bh1'):
         to_zero = False
         if sigmas[-1] == 0:
             timesteps = torch.nn.functional.interpolate(sigmas[None,None,:-1], size=(len(sigmas),), mode='linear')[0][0]
@@ -847,7 +847,12 @@ def sample_unipc(model, noise, image, sigmas, sampling_function, extra_args=None
         ns = NoiseScheduleVP('discrete', alphas_cumprod=model.inner_model.alphas_cumprod)
 
         if image is not None:
-            img = image * ns.marginal_alpha(timesteps[0]) + noise * ns.marginal_std(timesteps[0])
+            img = image * ns.marginal_alpha(timesteps[0])
+            if max_denoise:
+                noise_mult = 1.0
+            else:
+                noise_mult = ns.marginal_std(timesteps[0])
+            img += noise * noise_mult
         else:
             img = noise
 

comfy/samplers.py

@@ -334,6 +334,7 @@ class KSampler:
         self.sigma_min=float(self.model_wrap.sigma_min)
         self.sigma_max=float(self.model_wrap.sigma_max)
         self.set_steps(steps, denoise)
+        self.denoise = denoise
 
     def _calculate_sigmas(self, steps):
         sigmas = None
@@ -417,11 +418,16 @@ class KSampler:
                         cond_concat.append(blank_inpaint_image_like(noise))
             extra_args["cond_concat"] = cond_concat
 
+        if sigmas[0] != self.sigmas[0] or (self.denoise is not None and self.denoise < 1.0):
+            max_denoise = False
+        else:
+            max_denoise = True
+
         with precision_scope(self.device):
             if self.sampler == "uni_pc":
-                samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, extra_args=extra_args, noise_mask=denoise_mask)
+                samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=max_denoise, extra_args=extra_args, noise_mask=denoise_mask)
             elif self.sampler == "uni_pc_bh2":
-                samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, extra_args=extra_args, noise_mask=denoise_mask, variant='bh2')
+                samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=max_denoise, extra_args=extra_args, noise_mask=denoise_mask, variant='bh2')
             else:
                 extra_args["denoise_mask"] = denoise_mask
                 self.model_k.latent_image = latent_image