make blending happen once after conversion

api/onnx_web/convert/__main__.py

@@ -223,19 +223,22 @@ def convert_models(ctx: ConversionContext, args, models: Models):
                         ctx, name, model["source"], format=model_format
                     )
 
+                    converted = False
                     if model_format in model_formats_original:
-                        convert_diffusion_original(
+                        converted, _dest = convert_diffusion_original(
                             ctx,
                             model,
                             source,
                         )
                     else:
-                        convert_diffusion_diffusers(
+                        converted, _dest = convert_diffusion_diffusers(
                             ctx,
                             model,
                             source,
                         )
 
+                    # make sure blending only happens once, not every run
+                    if converted:
                         # keep track of which models have been blended
                         blend_models = {}
 
@@ -284,7 +287,7 @@ def convert_models(ctx: ConversionContext, args, models: Models):
                             lora_source = fetch_model(
                                 ctx,
                                 f"{name}-lora-{lora_name}",
-                            lora_source
+                                lora_source,
                                 dest=lora_dest,
                             )
                             lora_weight = lora.get("weight", 1.0)

api/onnx_web/convert/diffusion/diffusers.py

@@ -88,7 +88,7 @@ def convert_diffusion_diffusers(
     ctx: ConversionContext,
     model: Dict,
     source: str,
-):
+) -> Tuple[bool, str]:
     """
     From https://github.com/huggingface/diffusers/blob/main/scripts/convert_stable_diffusion_checkpoint_to_onnx.py
     """
@@ -111,7 +111,7 @@ def convert_diffusion_diffusers(
 
     if path.exists(dest_path) and path.exists(model_index):
         logger.info("ONNX model already exists, skipping")
-        return
+        return (False, dest_path)
 
     pipeline = StableDiffusionPipeline.from_pretrained(
         source,
@@ -328,3 +328,5 @@ def convert_diffusion_diffusers(
         )
 
     logger.info("ONNX pipeline is loadable")
+
+    return (True, dest_path)

api/onnx_web/convert/diffusion/original.py

@@ -18,7 +18,7 @@ import os
 import re
 import shutil
 from logging import getLogger
-from typing import Dict, List
+from typing import Dict, List, Tuple
 
 import torch
 from diffusers import (
@@ -1658,7 +1658,7 @@ def convert_diffusion_original(
     ctx: ConversionContext,
     model: ModelDict,
     source: str,
-):
+) -> Tuple[bool, str]:
     name = model["name"]
     source = source or model["source"]
 
@@ -1670,7 +1670,7 @@ def convert_diffusion_original(
 
     if os.path.exists(dest_path) and os.path.exists(dest_index):
         logger.info("ONNX pipeline already exists, skipping")
-        return
+        return (False, dest_path)
 
     torch_name = name + "-torch"
     torch_path = os.path.join(ctx.cache_path, torch_name)
@@ -1698,10 +1698,12 @@ def convert_diffusion_original(
     if "vae" in model:
         del model["vae"]
 
-    convert_diffusion_diffusers(ctx, model, working_name)
+    result = convert_diffusion_diffusers(ctx, model, working_name)
 
     if "torch" in ctx.prune:
         logger.info("removing intermediate Torch models: %s", torch_path)
         shutil.rmtree(torch_path)
 
     logger.info("ONNX pipeline saved to %s", name)
+    return result
+