Add KSamplerAdvanced node.

This node exposes more sampling options and makes it possible for example
to sample the first few steps on the latent image, do some operations on it
 and then do the rest of the sampling steps. This can be achieved using the
start_at_step and end_at_step options.

comfy/samplers.py

@@ -168,15 +168,24 @@ class KSampler:
             self.sigmas = sigmas[-(steps + 1):]
 
 
-    def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None):
+    def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False):
         sigmas = self.sigmas
         sigma_min = self.sigma_min
 
-        if last_step is not None:
+        if last_step is not None and last_step < (len(sigmas) - 1):
             sigma_min = sigmas[last_step]
             sigmas = sigmas[:last_step + 1]
+            if force_full_denoise:
+                sigmas[-1] = 0
+
         if start_step is not None:
-            sigmas = sigmas[start_step:]
+            if start_step < (len(sigmas) - 1):
+                sigmas = sigmas[start_step:]
+            else:
+                if latent_image is not None:
+                    return latent_image
+                else:
+                    return torch.zeros_like(noise)
 
         noise *= sigmas[0]
         if latent_image is not None:

nodes.py

@@ -221,13 +221,50 @@ class LatentRotate:
 
         s = torch.rot90(samples, k=rotate_by, dims=[3, 2])
         return (s,)
+def common_ksampler(device, model, seed, 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):
+    if disable_noise:
+        noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
+    else:
+        noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=torch.manual_seed(seed), device="cpu")
+
+    model = model.to(device)
+    noise = noise.to(device)
+    latent_image = latent_image.to(device)
+
+    positive_copy = []
+    negative_copy = []
+
+    for p in positive:
+        t = p[0]
+        if t.shape[0] < noise.shape[0]:
+            t = torch.cat([t] * noise.shape[0])
+        t = t.to(device)
+        positive_copy += [[t] + p[1:]]
+    for n in negative:
+        t = n[0]
+        if t.shape[0] < noise.shape[0]:
+            t = torch.cat([t] * noise.shape[0])
+        t = t.to(device)
+        negative_copy += [[t] + n[1:]]
+
+    if sampler_name in comfy.samplers.KSampler.SAMPLERS:
+        sampler = comfy.samplers.KSampler(model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise)
+    else:
+        #other samplers
+        pass
+
+    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)
+    samples = samples.cpu()
+    model = model.cpu()
+    return (samples, )
+
 class KSampler:
     def __init__(self, device="cuda"):
         self.device = device
 
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": 
+        return {"required":
                     {"model": ("MODEL",),
                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
@@ -246,38 +283,43 @@ class KSampler:
     CATEGORY = "sampling"
 
     def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):
-        noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=torch.manual_seed(seed), device="cpu")
-        model = model.to(self.device)
-        noise = noise.to(self.device)
-        latent_image = latent_image.to(self.device)
-
-        positive_copy = []
-        negative_copy = []
-
-        for p in positive:
-            t = p[0]
-            if t.shape[0] < noise.shape[0]:
-                t = torch.cat([t] * noise.shape[0])
-            t = t.to(self.device)
-            positive_copy += [[t] + p[1:]]
-        for n in negative:
-            t = n[0]
-            if t.shape[0] < noise.shape[0]:
-                t = torch.cat([t] * noise.shape[0])
-            t = t.to(self.device)
-            negative_copy += [[t] + n[1:]]
-
-        if sampler_name in comfy.samplers.KSampler.SAMPLERS:
-            sampler = comfy.samplers.KSampler(model, steps=steps, device=self.device, sampler=sampler_name, scheduler=scheduler, denoise=denoise)
-        else:
-            #other samplers
-            pass
+        return common_ksampler(self.device, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
 
-        samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image)
-        samples = samples.cpu()
-        model = model.cpu()
-        return (samples, )
+class KSamplerAdvanced:
+    def __init__(self, device="cuda"):
+        self.device = device
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                    "add_noise": (["enable", "disable"], ),
+                    "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
+                    "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
+                    "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
+                    "positive": ("CONDITIONING", ),
+                    "negative": ("CONDITIONING", ),
+                    "latent_image": ("LATENT", ),
+                    "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
+                    "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
+                    "return_with_leftover_noise": (["disable", "enable"], ),
+                    }}
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "sample"
+
+    CATEGORY = "sampling"
 
+    def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0):
+        force_full_denoise = True
+        if return_with_leftover_noise == "enable":
+            force_full_denoise = False
+        disable_noise = False
+        if add_noise == "disable":
+            disable_noise = True
+        return common_ksampler(self.device, model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)
 
 class SaveImage:
     def __init__(self):
@@ -365,6 +407,7 @@ NODE_CLASS_MAPPINGS = {
     "LoadImage": LoadImage,
     "ConditioningCombine": ConditioningCombine,
     "ConditioningSetArea": ConditioningSetArea,
+    "KSamplerAdvanced": KSamplerAdvanced,
     "LatentRotate": LatentRotate,
 }