diff --git a/ldm/models/diffusion/kdiffusion.py b/ldm/models/diffusion/kdiffusion.py
new file mode 100644
index 0000000..da5670d
--- /dev/null
+++ b/ldm/models/diffusion/kdiffusion.py
@@ -0,0 +1,55 @@
+import k_diffusion as K
+import torch
+import torch.nn as nn
+
+class KDiffusionSampler:
+    def __init__(self, m, sampler, callback=None):
+        self.model = m
+        self.model_wrap = K.external.CompVisDenoiser(m)
+        self.schedule = sampler
+        self.generation_callback = callback
+    def get_sampler_name(self):
+        return self.schedule
+    def sample(self, S, conditioning, unconditional_guidance_scale, unconditional_conditioning, x_T):
+        sigmas = self.model_wrap.get_sigmas(S)
+        x = x_T * sigmas[0]
+        model_wrap_cfg = CFGDenoiser(self.model_wrap)
+        samples_ddim = None
+        samples_ddim = K.sampling.__dict__[f'sample_{self.schedule}'](
+            model_wrap_cfg, x, sigmas,
+            extra_args={'cond': conditioning, 'uncond': unconditional_conditioning,'cond_scale': unconditional_guidance_scale},
+            disable=False, callback=self.generation_callback)
+        #
+        return samples_ddim, None
+class CFGMaskedDenoiser(nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.inner_model = model
+
+    def forward(self, x, sigma, uncond, cond, cond_scale, mask, x0, xi):
+        x_in = x
+        x_in = torch.cat([x_in] * 2)
+        sigma_in = torch.cat([sigma] * 2)
+        cond_in = torch.cat([uncond, cond])
+        uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
+        denoised = uncond + (cond - uncond) * cond_scale
+
+        if mask is not None:
+            assert x0 is not None
+            img_orig = x0
+            mask_inv = 1. - mask
+            denoised = (img_orig * mask_inv) + (mask * denoised)
+
+        return denoised
+
+class CFGDenoiser(nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.inner_model = model
+
+    def forward(self, x, sigma, uncond, cond, cond_scale):
+        x_in = torch.cat([x] * 2)
+        sigma_in = torch.cat([sigma] * 2)
+        cond_in = torch.cat([uncond, cond])
+        uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
+        return uncond + (cond - uncond) * cond_scale
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