feat(api): add an option for custom checkpoint config to extras file (f…

…ixes #130)

api/onnx_web/convert/diffusion_original.py

@@ -1110,7 +1110,9 @@ def get_config_path(
     return os.path.abspath(parts)
 
 
-def get_config_file(train_unfrozen=False, v2=False, prediction_type="epsilon"):
+def get_config_file(train_unfrozen=False, v2=False, prediction_type="epsilon", config_file=None):
+    if config_file is not None:
+        return config_file
 
     config_base_name = "training"
 
@@ -1142,6 +1144,7 @@ def extract_checkpoint(
     extract_ema=False,
     train_unfrozen=False,
     is_512=True,
+    config_file=None,
 ):
     """
 
@@ -1229,7 +1232,7 @@ def extract_checkpoint(
         else:
             prediction_type = "epsilon"
 
-        original_config_file = get_config_file(train_unfrozen, v2, prediction_type)
+        original_config_file = get_config_file(train_unfrozen, v2, prediction_type, config_file=config_file)
 
         logger.info(f"Pred and size are {prediction_type} and {image_size}, using config: {original_config_file}")
         db_config.resolution = image_size
@@ -1406,6 +1409,7 @@ def convert_diffusion_original(
     model: ModelDict,
     source: str,
 ):
+    config = model["config"]
     name = model["name"]
     source = source or model["source"]
 
@@ -1424,7 +1428,7 @@ def convert_diffusion_original(
         logger.info("Torch pipeline already exists, reusing: %s", torch_path)
     else:
         logger.info("Converting original Diffusers check to Torch model: %s -> %s", source, torch_path)
-        extract_checkpoint(ctx, torch_name, source)
+        extract_checkpoint(ctx, torch_name, source, config_file=config)
         logger.info("Converted original Diffusers checkpoint to Torch model.")
 
     convert_diffusion_stable(ctx, model, working_name)

api/schemas/extras.yaml

@@ -33,6 +33,10 @@ $defs:
   diffusion_model:
     allOf:
       - $ref: "#/$defs/base_model"
+      - type: object
+        properties:
+          config:
+            type: string
 
   upscaling_model:
     allOf: