@@ -77,15 +77,15 @@ def __init__(self, x, t0, t1, seed=None, **kwargs):
         except TypeError:
             seed = [seed]
             self.batched = False
-        self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
+        self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
 
     @staticmethod
     def sort(a, b):
         return (a, b, 1) if a < b else (b, a, -1)
 
     def __call__(self, t0, t1):
         t0, t1, sign = self.sort(t0, t1)
-        w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
+        w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign)
         return w if self.batched else w[0]
 
 
@@ -543,7 +543,8 @@ def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None,
 def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
     """DPM-Solver++ (stochastic)."""
     sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
+    seed = extra_args.get("seed", None)
+    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed) if noise_sampler is None else noise_sampler
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     sigma_fn = lambda t: t.neg().exp()
@@ -613,8 +614,9 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     if solver_type not in {'heun', 'midpoint'}:
         raise ValueError('solver_type must be \'heun\' or \'midpoint\'')
 
+    seed = extra_args.get("seed", None)
     sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
+    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed) if noise_sampler is None else noise_sampler
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
 
 
@@ -65,7 +65,7 @@ def cleanup_additional_models(models):
     for m in models:
         m.cleanup()
 
-def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False):
+def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
     device = comfy.model_management.get_torch_device()
 
     if noise_mask is not None:
@@ -85,7 +85,7 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
 
     sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
 
-    samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar)
+    samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
     samples = samples.cpu()
 
     cleanup_additional_models(models)
 
@@ -13,7 +13,7 @@ def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
 
 #The main sampling function shared by all the samplers
 #Returns predicted noise
-def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={}):
+def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={}, seed=None):
         def get_area_and_mult(cond, x_in, cond_concat_in, timestep_in):
             area = (x_in.shape[2], x_in.shape[3], 0, 0)
             strength = 1.0
@@ -292,20 +292,20 @@ def __init__(self, model):
         super().__init__()
         self.inner_model = model
         self.alphas_cumprod = model.alphas_cumprod
-    def apply_model(self, x, timestep, cond, uncond, cond_scale, cond_concat=None, model_options={}):
-        out = sampling_function(self.inner_model.apply_model, x, timestep, uncond, cond, cond_scale, cond_concat, model_options=model_options)
+    def apply_model(self, x, timestep, cond, uncond, cond_scale, cond_concat=None, model_options={}, seed=None):
+        out = sampling_function(self.inner_model.apply_model, x, timestep, uncond, cond, cond_scale, cond_concat, model_options=model_options, seed=seed)
         return out
 
 
 class KSamplerX0Inpaint(torch.nn.Module):
     def __init__(self, model):
         super().__init__()
         self.inner_model = model
-    def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, cond_concat=None, model_options={}):
+    def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, cond_concat=None, model_options={}, seed=None):
         if denoise_mask is not None:
             latent_mask = 1. - denoise_mask
             x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask
-        out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, cond_concat=cond_concat, model_options=model_options)
+        out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, cond_concat=cond_concat, model_options=model_options, seed=seed)
         if denoise_mask is not None:
             out *= denoise_mask
 
@@ -542,7 +542,7 @@ def set_steps(self, steps, denoise=None):
             sigmas = self.calculate_sigmas(new_steps).to(self.device)
             self.sigmas = sigmas[-(steps + 1):]
 
-    def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False):
+    def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
         if sigmas is None:
             sigmas = self.sigmas
         sigma_min = self.sigma_min
@@ -589,7 +589,7 @@ def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=N
         if latent_image is not None:
             latent_image = self.model.process_latent_in(latent_image)
 
-        extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": self.model_options}
+        extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": self.model_options, "seed":seed}
 
         cond_concat = None
         if hasattr(self.model, 'concat_keys'): #inpaint
 
@@ -965,7 +965,7 @@ def callback(step, x0, x, total_steps):
 
     samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
                                   denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
-                                  force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback)
+                                  force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, seed=seed)
     out = latent.copy()
     out["samples"] = samples
     return (out, )