Add sgm_uniform scheduler that acts like the default one in sgm.

comfy/samplers.py

@@ -347,6 +347,17 @@ def ddim_scheduler(model, steps):
     sigs += [0.0]
     return torch.FloatTensor(sigs)
 
+def sgm_scheduler(model, steps):
+    sigs = []
+    timesteps = torch.linspace(model.inner_model.inner_model.num_timesteps - 1, 0, steps + 1)[:-1].type(torch.int)
+    for x in range(len(timesteps)):
+        ts = timesteps[x]
+        if ts > 999:
+            ts = 999
+        sigs.append(model.t_to_sigma(torch.tensor(ts)))
+    sigs += [0.0]
+    return torch.FloatTensor(sigs)
+
 def blank_inpaint_image_like(latent_image):
     blank_image = torch.ones_like(latent_image)
     # these are the values for "zero" in pixel space translated to latent space
@@ -525,7 +536,7 @@ def encode_adm(model, conds, batch_size, width, height, device, prompt_type):
 
 
 class KSampler:
-    SCHEDULERS = ["normal", "karras", "exponential", "simple", "ddim_uniform"]
+    SCHEDULERS = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
     SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
                 "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
                 "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddim", "uni_pc", "uni_pc_bh2"]
@@ -570,6 +581,8 @@ class KSampler:
             sigmas = simple_scheduler(self.model_wrap, steps)
         elif self.scheduler == "ddim_uniform":
             sigmas = ddim_scheduler(self.model_wrap, steps)
+        elif self.scheduler == "sgm_uniform":
+            sigmas = sgm_scheduler(self.model_wrap, steps)
         else:
             print("error invalid scheduler", self.scheduler)