start wiring LoRAs into prompt

api/onnx_web/convert/diffusion/lora.py

@@ -8,7 +8,6 @@ import torch
 from onnx import ModelProto, load, numpy_helper
 from onnx.checker import check_model
 from onnx.external_data_helper import (
-    ExternalDataInfo,
     convert_model_to_external_data,
     set_external_data,
     write_external_data_tensors,
@@ -61,7 +60,6 @@ def fix_node_name(key: str):
 def merge_lora(
     base_name: str,
     lora_names: str,
-    dest_path: str,
     dest_type: Literal["text_encoder", "unet"],
     lora_weights: "np.NDArray[np.float64]" = None,
 ):
@@ -227,7 +225,7 @@ if __name__ == "__main__":
     args = parser.parse_args()
     logger.info("merging %s with %s with weights: %s", args.lora_models, args.base, args.lora_weights)
 
-    blend_model = merge_lora(args.base, args.lora_models, args.dest, args.type, args.lora_weights)
+    blend_model = merge_lora(args.base, args.lora_models, args.type, args.lora_weights)
     if args.dest is None or args.dest == "" or args.dest == "ort":
         # convert to external data and save to memory
         (bare_model, external_data) = buffer_external_data_tensors(blend_model)
@@ -247,3 +245,7 @@ if __name__ == "__main__":
             model_file.write(bare_model.SerializeToString())
 
         logger.info("successfully saved blended model: %s", dest_file)
+
+        check_model(dest_file)
+
+        logger.info("checked blended model")

api/onnx_web/diffusers/load.py

@@ -1,6 +1,6 @@
 from logging import getLogger
 from os import path
-from typing import Any, Optional, Tuple
+from typing import Any, List, Optional, Tuple
 
 import numpy as np
 from diffusers import (
@@ -106,6 +106,13 @@ def get_tile_latents(
     return full_latents[:, :, y:yt, x:xt]
 
 
+def get_loras_from_prompt(prompt: str) -> List[str]:
+    return [
+        "arch",
+        "glass",
+    ]
+
+
 def optimize_pipeline(
     server: ServerContext,
     pipe: StableDiffusionPipeline,
@@ -156,7 +163,9 @@ def load_pipeline(
     device: DeviceParams,
     lpw: bool,
     inversion: Optional[str],
+    loras: Optional[List[str]] = None,
 ):
+    loras = loras or []
     pipe_key = (
         pipeline.__name__,
         model,
@@ -164,6 +173,7 @@ def load_pipeline(
         device.provider,
         lpw,
         inversion,
+        loras,
     )
     scheduler_key = (scheduler_name, model)
     scheduler_type = get_pipeline_schedulers()[scheduler_name]
@@ -223,26 +233,36 @@ def load_pipeline(
             )
 
         # test LoRA blending
-        lora_models = [path.join(server.model_path, "lora", f"{i}.safetensors") for i in [
+        lora_models = [path.join(server.model_path, "lora", f"{i}.safetensors") for i in loras]
-            "arch",
+        logger.info("blending base model %s with LoRA models: %s", model, lora_models)
-            "glass",
-        ]]
 
-        logger.info("blending text encoder with LoRA models: %s", lora_models)
+        # blend and load text encoder
-        blended_text_encoder = merge_lora(path.join(server.model_path, "stable-diffusion-onnx-v1-5/text_encoder/model.onnx"), lora_models, None, "text_encoder")
+        blended_text_encoder = merge_lora(path.join(model, "text_encoder", "model.onnx"), lora_models, None, "text_encoder")
         (text_encoder_model, text_encoder_data) = buffer_external_data_tensors(blended_text_encoder)
         text_encoder_names, text_encoder_values = zip(*text_encoder_data)
         text_encoder_opts = SessionOptions()
         text_encoder_opts.add_external_initializers(list(text_encoder_names), list(text_encoder_values))
-        components["text_encoder"] = OnnxRuntimeModel(OnnxRuntimeModel.load_model(text_encoder_model.SerializeToString(), provider=device.ort_provider(), sess_options=text_encoder_opts))
+        components["text_encoder"] = OnnxRuntimeModel(
+            OnnxRuntimeModel.load_model(
+                text_encoder_model.SerializeToString(),
+                provider=device.ort_provider(),
+                sess_options=text_encoder_opts,
+            )
+        )
 
-        logger.info("blending unet with LoRA models: %s", lora_models)
+        # blend and load unet
-        blended_unet = merge_lora(path.join(server.model_path, "stable-diffusion-onnx-v1-5/unet/model.onnx"), lora_models, None, "unet")
+        blended_unet = merge_lora(path.join(model, "unet", "model.onnx"), lora_models, None, "unet")
         (unet_model, unet_data) = buffer_external_data_tensors(blended_unet)
         unet_names, unet_values = zip(*unet_data)
         unet_opts = SessionOptions()
         unet_opts.add_external_initializers(list(unet_names), list(unet_values))
-        components["unet"] = OnnxRuntimeModel(OnnxRuntimeModel.load_model(unet_model.SerializeToString(), provider=device.ort_provider(), sess_options=unet_opts))
+        components["unet"] = OnnxRuntimeModel(
+            OnnxRuntimeModel.load_model(
+                unet_model.SerializeToString(),
+                provider=device.ort_provider(),
+                sess_options=unet_opts,
+            )
+        )
 
         pipe = pipeline.from_pretrained(
             model,

api/onnx_web/diffusers/run.py

@@ -14,7 +14,7 @@ from ..server import ServerContext
 from ..upscale import run_upscale_correction
 from ..utils import run_gc
 from ..worker import WorkerContext
-from .load import get_latents_from_seed, load_pipeline
+from .load import get_latents_from_seed, get_loras_from_prompt, load_pipeline
 
 logger = getLogger(__name__)
 
@@ -28,6 +28,7 @@ def run_txt2img_pipeline(
     upscale: UpscaleParams,
 ) -> None:
     latents = get_latents_from_seed(params.seed, size, batch=params.batch)
+    loras = get_loras_from_prompt(params.prompt)
     pipe = load_pipeline(
         server,
         OnnxStableDiffusionPipeline,
@@ -36,6 +37,7 @@ def run_txt2img_pipeline(
         job.get_device(),
         params.lpw,
         params.inversion,
+        loras,
     )
     progress = job.get_progress_callback()
 
@@ -99,6 +101,7 @@ def run_img2img_pipeline(
     source: Image.Image,
     strength: float,
 ) -> None:
+    loras = get_loras_from_prompt(params.prompt)
     pipe = load_pipeline(
         server,
         OnnxStableDiffusionImg2ImgPipeline,
@@ -107,6 +110,7 @@ def run_img2img_pipeline(
         job.get_device(),
         params.lpw,
         params.inversion,
+        loras,
     )
     progress = job.get_progress_callback()
     if params.lpw: