Merge branch 'comfyanonymous:master' into master

comfy/k_diffusion/sampling.py

@@ -44,6 +44,17 @@ def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
     return append_zero(sigmas)
 
 
+def get_sigmas_laplace(n, sigma_min, sigma_max, mu=0., beta=0.5, device='cpu'):
+    """Constructs the noise schedule proposed by Tiankai et al. (2024). """
+    epsilon = 1e-5 # avoid log(0)
+    x = torch.linspace(0, 1, n, device=device)
+    clamp = lambda x: torch.clamp(x, min=sigma_min, max=sigma_max)
+    lmb = mu - beta * torch.sign(0.5-x) * torch.log(1 - 2 * torch.abs(0.5-x) + epsilon)
+    sigmas = clamp(torch.exp(lmb))
+    return sigmas
+
+
+
 def to_d(x, sigma, denoised):
     """Converts a denoiser output to a Karras ODE derivative."""
     return (x - denoised) / utils.append_dims(sigma, x.ndim)

comfy/lora.py

@@ -414,7 +414,7 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
             weight *= strength_model
 
         if isinstance(v, list):
-            v = (calculate_weight(v[1:], v[0].clone(), key, intermediate_dtype=intermediate_dtype), )
+            v = (calculate_weight(v[1:], comfy.model_management.cast_to_device(v[0], weight.device, intermediate_dtype, copy=True), key, intermediate_dtype=intermediate_dtype), )
 
         if len(v) == 1:
             patch_type = "diff"

comfy_extras/nodes_custom_sampler.py

@@ -90,6 +90,27 @@ class PolyexponentialScheduler:
         sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)
         return (sigmas, )
 
+class LaplaceScheduler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                     "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+                     "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+                     "mu": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step":0.1, "round": False}),
+                     "beta": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step":0.1, "round": False}),
+                    }
+               }
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/schedulers"
+
+    FUNCTION = "get_sigmas"
+
+    def get_sigmas(self, steps, sigma_max, sigma_min, mu, beta):
+        sigmas = k_diffusion_sampling.get_sigmas_laplace(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, mu=mu, beta=beta)
+        return (sigmas, )
+
+
 class SDTurboScheduler:
     @classmethod
     def INPUT_TYPES(s):
@@ -673,6 +694,7 @@ NODE_CLASS_MAPPINGS = {
     "KarrasScheduler": KarrasScheduler,
     "ExponentialScheduler": ExponentialScheduler,
     "PolyexponentialScheduler": PolyexponentialScheduler,
+    "LaplaceScheduler": LaplaceScheduler,
     "VPScheduler": VPScheduler,
     "BetaSamplingScheduler": BetaSamplingScheduler,
     "SDTurboScheduler": SDTurboScheduler,