feat(api): support more RealESRGAN-based models

api/onnx_web/convert/upscaling/resrgan.py

@@ -11,6 +11,39 @@
 
 TAG_X4_V3 = "real-esrgan-x4-v3"
 
+SPECIAL_KEYS = {
+    "model.0.bias": "conv_first.bias",
+    "model.0.weight": "conv_first.weight",
+    "model.1.sub.23.bias": "conv_body.bias",
+    "model.1.sub.23.weight": "conv_body.weight",
+    "model.3.bias": "conv_up1.bias",
+    "model.3.weight": "conv_up1.weight",
+    "model.6.bias": "conv_up2.bias",
+    "model.6.weight": "conv_up2.weight",
+    "model.8.bias": "conv_hr.bias",
+    "model.8.weight": "conv_hr.weight",
+    "model.10.bias": "conv_last.bias",
+    "model.10.weight": "conv_last.weight",
+}
+
+SUB_NAME = r"model\.1\.sub\.(\d)+\.RDB(\d)\.conv(\d)\.0\.(bias|weight)"
+
+
+def fix_resrgan_keys(model):
+    original_keys = list(model.keys())
+    for key in original_keys:
+        if key in SPECIAL_KEYS:
+            new_key = SPECIAL_KEYS[key]
+        else:
+            # convert RDBN keys
+            sub_index, rdb_index, conv_index, node_type = key.match(SUB_NAME)
+            new_key = f"model.1.sub.{sub_index}.rdb{rdb_index}.{conv_index}.{node_type}"
+
+        model[new_key] = model[key]
+        del model[key]
+
+    return model
+
 
 @torch.no_grad()
 def convert_upscale_resrgan(
@@ -54,8 +87,11 @@ def convert_upscale_resrgan(
     torch_model = torch.load(source, map_location=conversion.map_location)
     if "params_ema" in torch_model:
         model.load_state_dict(torch_model["params_ema"])
-    else:
+    elif "params" in torch_model:
         model.load_state_dict(torch_model["params"], strict=False)
+    else:
+        # keys need fixed up to match
+        model.load_state_dict(fix_resrgan_keys(torch_model), strict=False)
 
     model.to(conversion.training_device).train(False)
     model.eval()