Allow controlling downscale and upscale methods in PatchModelAddDownscale.

comfy/utils.py

@@ -318,7 +318,9 @@ def bislerp(samples, width, height):
         coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear")
         coords_2 = coords_2.to(torch.int64)
         return ratios, coords_1, coords_2
-    
+
+    orig_dtype = samples.dtype
+    samples = samples.float()
     n,c,h,w = samples.shape
     h_new, w_new = (height, width)
     
@@ -347,7 +349,7 @@ def bislerp(samples, width, height):
 
     result = slerp(pass_1, pass_2, ratios)
     result = result.reshape(n, h_new, w_new, c).movedim(-1, 1)
-    return result
+    return result.to(orig_dtype)
 
 def lanczos(samples, width, height):
     images = [Image.fromarray(np.clip(255. * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples]

comfy_extras/nodes_model_downscale.py

 import torch
+import comfy.utils
 
 class PatchModelAddDownscale:
+    upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"]
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
@@ -9,13 +11,15 @@ class PatchModelAddDownscale:
                               "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                               "end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}),
                               "downscale_after_skip": ("BOOLEAN", {"default": True}),
+                              "downscale_method": (s.upscale_methods,),
+                              "upscale_method": (s.upscale_methods,),
                               }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
     CATEGORY = "_for_testing"
 
-    def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip):
+    def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method):
         sigma_start = model.model.model_sampling.percent_to_sigma(start_percent)
         sigma_end = model.model.model_sampling.percent_to_sigma(end_percent)
 
@@ -23,12 +27,12 @@ class PatchModelAddDownscale:
             if transformer_options["block"][1] == block_number:
                 sigma = transformer_options["sigmas"][0].item()
                 if sigma <= sigma_start and sigma >= sigma_end:
-                    h = torch.nn.functional.interpolate(h, scale_factor=(1.0 / downscale_factor), mode="bicubic", align_corners=False)
+                    h = comfy.utils.common_upscale(h, round(h.shape[-1] * (1.0 / downscale_factor)), round(h.shape[-2] * (1.0 / downscale_factor)), downscale_method, "disabled")
             return h
 
         def output_block_patch(h, hsp, transformer_options):
             if h.shape[2] != hsp.shape[2]:
-                h = torch.nn.functional.interpolate(h, size=(hsp.shape[2], hsp.shape[3]), mode="bicubic", align_corners=False)
+                h = comfy.utils.common_upscale(h, hsp.shape[-1], hsp.shape[-2], upscale_method, "disabled")
             return h, hsp
 
         m = model.clone()