Use relative imports for k_diffusion.

comfy/samplers.py

-import k_diffusion.sampling
-import k_diffusion.external
+from .k_diffusion import sampling as k_diffusion_sampling
+from .k_diffusion import external as k_diffusion_external
 import torch
 import contextlib
 import model_management
@@ -185,9 +185,9 @@ class KSampler:
     def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None):
         self.model = model
         if self.model.parameterization == "v":
-            self.model_wrap = k_diffusion.external.CompVisVDenoiser(self.model, quantize=True)
+            self.model_wrap = k_diffusion_external.CompVisVDenoiser(self.model, quantize=True)
         else:
-            self.model_wrap = k_diffusion.external.CompVisDenoiser(self.model, quantize=True)
+            self.model_wrap = k_diffusion_external.CompVisDenoiser(self.model, quantize=True)
         self.model_k = CFGDenoiserComplex(self.model_wrap)
         self.device = device
         if scheduler not in self.SCHEDULERS:
@@ -209,7 +209,7 @@ class KSampler:
             discard_penultimate_sigma = True
 
         if self.scheduler == "karras":
-            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max, device=self.device)
+            sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max, device=self.device)
         elif self.scheduler == "normal":
             sigmas = self.model_wrap.get_sigmas(steps).to(self.device)
         elif self.scheduler == "simple":
@@ -269,9 +269,9 @@ class KSampler:
 
         with precision_scope(self.device):
             if self.sampler == "sample_dpm_fast":
-                samples = k_diffusion.sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], self.steps, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg})
+                samples = k_diffusion_sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], self.steps, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg})
             elif self.sampler == "sample_dpm_adaptive":
-                samples = k_diffusion.sampling.sample_dpm_adaptive(self.model_k, noise, sigma_min, sigmas[0], extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg})
+                samples = k_diffusion_sampling.sample_dpm_adaptive(self.model_k, noise, sigma_min, sigmas[0], extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg})
             else:
-                samples = getattr(k_diffusion.sampling, self.sampler)(self.model_k, noise, sigmas, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg})
+                samples = getattr(k_diffusion_sampling, self.sampler)(self.model_k, noise, sigmas, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg})
         return samples.to(torch.float32)