Cleanup uni_pc inpainting.

This causes some small changes to the uni pc inpainting behavior but it
seems to improve results slightly.

comfy/extra_samplers/uni_pc.py

@@ -358,9 +358,6 @@ class UniPC:
         thresholding=False,
         max_val=1.,
         variant='bh1',
-        noise_mask=None,
-        masked_image=None,
-        noise=None,
     ):
         """Construct a UniPC. 
 
@@ -372,9 +369,6 @@ class UniPC:
         self.predict_x0 = predict_x0
         self.thresholding = thresholding
         self.max_val = max_val
-        self.noise_mask = noise_mask
-        self.masked_image = masked_image
-        self.noise = noise
 
     def dynamic_thresholding_fn(self, x0, t=None):
         """
@@ -391,9 +385,6 @@ class UniPC:
         """
         Return the noise prediction model.
         """
-        if self.noise_mask is not None:
-            return self.model(x, t) * self.noise_mask
-        else:
         return self.model(x, t)
 
     def data_prediction_fn(self, x, t):
@@ -409,8 +400,6 @@ class UniPC:
             s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
             s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
             x0 = torch.clamp(x0, -s, s) / s
-        if self.noise_mask is not None:
-            x0 = x0 * self.noise_mask + (1. - self.noise_mask) * self.masked_image
         return x0
 
     def model_fn(self, x, t):
@@ -723,8 +712,6 @@ class UniPC:
             assert timesteps.shape[0] - 1 == steps
             # with torch.no_grad():
             for step_index in trange(steps, disable=disable_pbar):
-                if self.noise_mask is not None:
-                    x = x * self.noise_mask + (1. - self.noise_mask) * (self.masked_image * self.noise_schedule.marginal_alpha(timesteps[step_index]) + self.noise * self.noise_schedule.marginal_std(timesteps[step_index]))
                 if step_index == 0:
                     vec_t = timesteps[0].expand((x.shape[0]))
                     model_prev_list = [self.model_fn(x, vec_t)]
@@ -766,7 +753,7 @@ class UniPC:
                                 model_x = self.model_fn(x, vec_t)
                             model_prev_list[-1] = model_x
                 if callback is not None:
-                    callback(step_index, model_prev_list[-1], x, steps)
+                    callback({'x': x, 'i': step_index, 'denoised': model_prev_list[-1]})
         else:
             raise NotImplementedError()
         # if denoise_to_zero:
@@ -858,7 +845,7 @@ def predict_eps_sigma(model, input, sigma_in, **kwargs):
     return  (input - model(input, sigma_in, **kwargs)) / sigma
 
 
-def sample_unipc(model, noise, image, sigmas, max_denoise, extra_args=None, callback=None, disable=False, noise_mask=None, variant='bh1'):
+def sample_unipc(model, noise, sigmas, extra_args=None, callback=None, disable=False, variant='bh1'):
         timesteps = sigmas.clone()
         if sigmas[-1] == 0:
             timesteps = sigmas[:]
@@ -867,16 +854,7 @@ def sample_unipc(model, noise, image, sigmas, max_denoise, extra_args=None, call
             timesteps = sigmas.clone()
         ns = SigmaConvert()
 
-        if image is not None:
-            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
-
+        noise = noise / torch.sqrt(1.0 + timesteps[0] ** 2.0)
         model_type = "noise"
 
         model_fn = model_wrapper(
@@ -888,7 +866,10 @@ def sample_unipc(model, noise, image, sigmas, max_denoise, extra_args=None, call
         )
 
         order = min(3, len(timesteps) - 2)
-        uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, noise_mask=noise_mask, masked_image=image, noise=noise, variant=variant)
-        x = uni_pc.sample(img, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable)
+        uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant=variant)
+        x = uni_pc.sample(noise, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable)
         x /= ns.marginal_alpha(timesteps[-1])
         return x
+
+def sample_unipc_bh2(model, noise, sigmas, extra_args=None, callback=None, disable=False):
+    return sample_unipc(model, noise, sigmas, extra_args, callback, disable, variant='bh2')
\ No newline at end of file

comfy/samplers.py

@@ -513,14 +513,6 @@ class Sampler:
         sigma = float(sigmas[0])
         return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma
 
-class UNIPC(Sampler):
-    def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
-        return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, disable=disable_pbar)
-
-class UNIPCBH2(Sampler):
-    def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
-        return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2', disable=disable_pbar)
-
 KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
                   "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
                   "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"]
@@ -640,9 +632,9 @@ def calculate_sigmas_scheduler(model, scheduler_name, steps):
 
 def sampler_object(name):
     if name == "uni_pc":
-        sampler = UNIPC()
+        sampler = KSAMPLER(uni_pc.sample_unipc)
     elif name == "uni_pc_bh2":
-        sampler = UNIPCBH2()
+        sampler = KSAMPLER(uni_pc.sample_unipc_bh2)
     elif name == "ddim":
         sampler = ksampler("euler", inpaint_options={"random": True})
     else: