Add uni_pc bh2 variant.

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):
+def sample_unipc(model, noise, image, sigmas, sampling_function, 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]
@@ -870,7 +870,7 @@ def sample_unipc(model, noise, image, sigmas, sampling_function, extra_args=None
             model_kwargs=extra_args,
         )
 
-        uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, noise_mask=noise_mask, masked_image=image, noise=noise)
+        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=3, lower_order_final=True)
         if not to_zero:
             x /= ns.marginal_alpha(timesteps[-1])

comfy/samplers.py

@@ -313,7 +313,7 @@ class KSampler:
     SCHEDULERS = ["karras", "normal", "simple"]
     SAMPLERS = ["sample_euler", "sample_euler_ancestral", "sample_heun", "sample_dpm_2", "sample_dpm_2_ancestral",
                 "sample_lms", "sample_dpm_fast", "sample_dpm_adaptive", "sample_dpmpp_2s_ancestral", "sample_dpmpp_sde",
-                "sample_dpmpp_2m", "uni_pc"]
+                "sample_dpmpp_2m", "uni_pc", "uni_pc_bh2"]
 
     def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None):
         self.model = model
@@ -420,6 +420,8 @@ class KSampler:
         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)
+            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')
             else:
                 extra_args["denoise_mask"] = denoise_mask
                 self.model_k.latent_image = latent_image