Take some code from chainner to implement ESRGAN and other upscale models.

comfy_extras/nodes_upscale_model.py

@@ -0,0 +1,52 @@
+import os
+from comfy_extras.chainner_models import model_loading
+from comfy.sd import load_torch_file
+import comfy.model_management
+from nodes import filter_files_extensions, recursive_search, supported_ckpt_extensions
+import torch
+
+class UpscaleModelLoader:
+    models_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.realpath(__file__))), "models")
+    upscale_model_dir = os.path.join(models_dir, "upscale_models")
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model_name": (filter_files_extensions(recursive_search(s.upscale_model_dir), supported_ckpt_extensions), ),
+                             }}
+    RETURN_TYPES = ("UPSCALE_MODEL",)
+    FUNCTION = "load_model"
+
+    CATEGORY = "loaders"
+
+    def load_model(self, model_name):
+        model_path = os.path.join(self.upscale_model_dir, model_name)
+        sd = load_torch_file(model_path)
+        out = model_loading.load_state_dict(sd).eval()
+        return (out, )
+
+
+class ImageUpscaleWithModel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "upscale_model": ("UPSCALE_MODEL",),
+                              "image": ("IMAGE",),
+                              }}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "upscale"
+
+    CATEGORY = "image"
+
+    def upscale(self, upscale_model, image):
+        device = comfy.model_management.get_torch_device()
+        upscale_model.to(device)
+        in_img = image.movedim(-1,-3).to(device)
+        with torch.inference_mode():
+            s = upscale_model(in_img).cpu()
+        upscale_model.cpu()
+        s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0)
+        return (s,)
+
+NODE_CLASS_MAPPINGS = {
+    "UpscaleModelLoader": UpscaleModelLoader,
+    "ImageUpscaleWithModel": ImageUpscaleWithModel
+}