apply lint

api/onnx_web/convert/diffusion/lora.py

@@ -15,6 +15,7 @@ from onnx.external_data_helper import (
 from onnxruntime import InferenceSession, OrtValue, SessionOptions
 from safetensors.torch import load_file
 
+from ...server.context import ServerContext
 from ..utils import ConversionContext
 
 logger = getLogger(__name__)
@@ -55,6 +56,7 @@ def fix_node_name(key: str):
 
 
 def blend_loras(
+    context: ServerContext,
     base_name: str,
     lora_names: List[str],
     dest_type: Literal["text_encoder", "unet"],
@@ -236,6 +238,7 @@ def blend_loras(
 
 
 if __name__ == "__main__":
+    context = ConversionContext.from_environ()
     parser = ArgumentParser()
     parser.add_argument("--base", type=str)
     parser.add_argument("--dest", type=str)
@@ -251,7 +254,9 @@ if __name__ == "__main__":
         args.lora_weights,
     )
 
-    blend_model = blend_loras(args.base, args.lora_models, args.type, args.lora_weights)
+    blend_model = blend_loras(
+        context, 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)

api/onnx_web/convert/diffusion/textual_inversion.py

@@ -6,7 +6,6 @@ import numpy as np
 import torch
 from huggingface_hub.file_download import hf_hub_download
 from onnx import ModelProto, load_model, numpy_helper, save_model
-from torch.onnx import export
 from transformers import CLIPTokenizer
 
 from ...server.context import ServerContext
@@ -25,11 +24,15 @@ def blend_textual_inversions(
     inversion_weights: Optional[List[float]] = None,
     base_tokens: Optional[List[str]] = None,
 ) -> Tuple[ModelProto, CLIPTokenizer]:
-    dtype = np.float # TODO: fixed type, which one?
-    # prev: text_encoder.get_input_embeddings().weight.dtype
+    dtype = np.float
     embeds = {}
 
-    for name, format, weight, base_token in zip(inversion_names, inversion_formats, inversion_weights, base_tokens or inversion_names):
+    for name, format, weight, base_token in zip(
+        inversion_names,
+        inversion_formats,
+        inversion_weights,
+        base_tokens or inversion_names,
+    ):
         logger.info("blending Textual Inversion %s with weight of %s", name, weight)
         if format == "concept":
             embeds_file = hf_hub_download(repo_id=name, filename="learned_embeds.bin")
@@ -74,7 +77,9 @@ def blend_textual_inversions(
             raise ValueError(f"unknown Textual Inversion format: {format}")
 
         # add the tokens to the tokenizer
-        logger.info("found embeddings for %s tokens: %s", len(embeds.keys()), embeds.keys())
+        logger.info(
+            "found embeddings for %s tokens: %s", len(embeds.keys()), embeds.keys()
+        )
         num_added_tokens = tokenizer.add_tokens(list(embeds.keys()))
         if num_added_tokens == 0:
             raise ValueError(
@@ -85,7 +90,11 @@ def blend_textual_inversions(
 
         # resize the token embeddings
         # text_encoder.resize_token_embeddings(len(tokenizer))
-        embedding_node = [n for n in text_encoder.graph.initializer if n.name == "text_model.embeddings.token_embedding.weight"][0]
+        embedding_node = [
+            n
+            for n in text_encoder.graph.initializer
+            if n.name == "text_model.embeddings.token_embedding.weight"
+        ][0]
         embedding_weights = numpy_helper.to_array(embedding_node)
 
         weights_dim = embedding_weights.shape[1]
@@ -94,15 +103,18 @@ def blend_textual_inversions(
 
         for token, weights in embeds.items():
             token_id = tokenizer.convert_tokens_to_ids(token)
-            logger.debug(
-                "embedding %s weights for token %s", weights.shape, token
-            )
+            logger.debug("embedding %s weights for token %s", weights.shape, token)
             embedding_weights[token_id] = weights
 
         # replace embedding_node
         for i in range(len(text_encoder.graph.initializer)):
-            if text_encoder.graph.initializer[i].name == "text_model.embeddings.token_embedding.weight":
-                new_initializer = numpy_helper.from_array(embedding_weights.astype(np.float32), embedding_node.name)
+            if (
+                text_encoder.graph.initializer[i].name
+                == "text_model.embeddings.token_embedding.weight"
+            ):
+                new_initializer = numpy_helper.from_array(
+                    embedding_weights.astype(np.float32), embedding_node.name
+                )
                 logger.debug("new initializer data type: %s", new_initializer.data_type)
                 del text_encoder.graph.initializer[i]
                 text_encoder.graph.initializer.insert(i, new_initializer)

api/onnx_web/diffusers/load.py

@@ -221,7 +221,10 @@ def load_pipeline(
             inversion_names, inversion_weights = zip(*inversions)
             logger.debug("blending Textual Inversions from %s", inversion_names)
 
-            inversion_models = [path.join(server.model_path, "inversion", f"{name}.ckpt") for name in inversion_names]
+            inversion_models = [
+                path.join(server.model_path, "inversion", f"{name}.ckpt")
+                for name in inversion_names
+            ]
             text_encoder = load_model(path.join(model, "text_encoder", "model.onnx"))
             tokenizer = CLIPTokenizer.from_pretrained(
                 model,
@@ -249,16 +252,33 @@ def load_pipeline(
         # test LoRA blending
         if loras is not None and len(loras) > 0:
             lora_names, lora_weights = zip(*loras)
-            lora_models = [path.join(server.model_path, "lora", f"{name}.safetensors") for name in lora_names]
-            logger.info("blending base model %s with LoRA models: %s", model, lora_models)
+            lora_models = [
+                path.join(server.model_path, "lora", f"{name}.safetensors")
+                for name in lora_names
+            ]
+            logger.info(
+                "blending base model %s with LoRA models: %s", model, lora_models
+            )
 
             # blend and load text encoder
-            text_encoder = text_encoder or path.join(model, "text_encoder", "model.onnx")
-            blended_text_encoder = blend_loras(text_encoder, lora_models, "text_encoder", lora_weights=lora_weights)
-            (text_encoder, text_encoder_data) = buffer_external_data_tensors(blended_text_encoder)
+            text_encoder = text_encoder or path.join(
+                model, "text_encoder", "model.onnx"
+            )
+            text_encoder = blend_loras(
+                server,
+                text_encoder,
+                lora_models,
+                "text_encoder",
+                lora_weights=lora_weights,
+            )
+            (text_encoder, text_encoder_data) = buffer_external_data_tensors(
+                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))
+            text_encoder_opts.add_external_initializers(
+                list(text_encoder_names), list(text_encoder_values)
+            )
             components["text_encoder"] = OnnxRuntimeModel(
                 OnnxRuntimeModel.load_model(
                     text_encoder.SerializeToString(),
@@ -268,7 +288,13 @@ def load_pipeline(
             )
 
             # blend and load unet
-            blended_unet = blend_loras(path.join(model, "unet", "model.onnx"), lora_models, "unet", lora_weights=lora_weights)
+            blended_unet = blend_loras(
+                server,
+                path.join(model, "unet", "model.onnx"),
+                lora_models,
+                "unet",
+                lora_weights=lora_weights,
+            )
             (unet_model, unet_data) = buffer_external_data_tensors(blended_unet)
             unet_names, unet_values = zip(*unet_data)
             unet_opts = SessionOptions()

api/onnx_web/diffusers/utils.py

@@ -1,6 +1,6 @@
 from logging import getLogger
 from math import ceil
-from re import compile, Pattern
+from re import Pattern, compile
 from typing import List, Optional, Tuple
 
 import numpy as np
@@ -132,7 +132,9 @@ def expand_prompt(
     return prompt_embeds
 
 
-def get_tokens_from_prompt(prompt: str, pattern: Pattern[str]) -> Tuple[str, List[Tuple[str, float]]]:
+def get_tokens_from_prompt(
+    prompt: str, pattern: Pattern[str]
+) -> Tuple[str, List[Tuple[str, float]]]:
     """
     TODO: replace with Arpeggio
     """
@@ -145,7 +147,10 @@ def get_tokens_from_prompt(prompt: str, pattern: Pattern[str]) -> Tuple[str, Lis
         name, weight = next_match.groups()
         tokens.append((name, float(weight)))
         # remove this match and look for another
-        remaining_prompt = remaining_prompt[:next_match.start()] + remaining_prompt[next_match.end():]
+        remaining_prompt = (
+            remaining_prompt[: next_match.start()]
+            + remaining_prompt[next_match.end() :]
+        )
         next_match = pattern.search(remaining_prompt)
 
     return (remaining_prompt, tokens)