Add a SamplerLCMUpscale node.

This sampler is an LCM sampler that upscales the latent during sampling.

It can be used to generate at a higher resolution with an LCM model very
quickly.

To try it use it with a basic 5 step LCM workflow with scale_ratio 1.5 or
2.0

comfy_extras/nodes_advanced_samplers.py

+import comfy.samplers
+import comfy.utils
+import torch
+import numpy as np
+from tqdm.auto import trange, tqdm
+import math
+
+
+@torch.no_grad()
+def sample_lcm_upscale(model, x, sigmas, extra_args=None, callback=None, disable=None, total_upscale=2.0, upscale_method="bislerp", upscale_steps=None):
+    extra_args = {} if extra_args is None else extra_args
+
+    if upscale_steps is None:
+        upscale_steps = max(len(sigmas) // 2 + 1, 2)
+    else:
+        upscale_steps += 1
+        upscale_steps = min(upscale_steps, len(sigmas) + 1)
+
+    upscales = np.linspace(1.0, total_upscale, upscale_steps)[1:]
+
+    orig_shape = x.size()
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+        x = denoised
+        if i < len(upscales):
+            x = comfy.utils.common_upscale(x, round(orig_shape[-1] * upscales[i]), round(orig_shape[-2] * upscales[i]), upscale_method, "disabled")
+
+        if sigmas[i + 1] > 0:
+            x += sigmas[i + 1] * torch.randn_like(x)
+    return x
+
+
+class SamplerLCMUpscale:
+    upscale_methods = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"]
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"scale_ratio": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 20.0, "step": 0.01}),
+                     "scale_steps": ("INT", {"default": -1, "min": -1, "max": 1000, "step": 1}),
+                     "upscale_method": (s.upscale_methods,),
+                      }
+               }
+    RETURN_TYPES = ("SAMPLER",)
+    CATEGORY = "sampling/custom_sampling/samplers"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, scale_ratio, scale_steps, upscale_method):
+        if scale_steps < 0:
+            scale_steps = None
+        sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method})
+        return (sampler, )
+
+NODE_CLASS_MAPPINGS = {
+    "SamplerLCMUpscale": SamplerLCMUpscale,
+}

nodes.py

@@ -1945,6 +1945,7 @@ def init_custom_nodes():
         "nodes_pag.py",
         "nodes_align_your_steps.py",
         "nodes_attention_multiply.py",
+        "nodes_advanced_samplers.py",
     ]
 
     import_failed = []