clean up text encoder loading logic, deduplicate sessions
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3EED7B957D362AF1
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@ -294,7 +294,6 @@ def load_controlnet(server, device, params):
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def load_text_encoders(
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server, device, model: str, inversions, loras, torch_dtype, params
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):
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text_encoder = load_model(path.join(model, "text_encoder", ONNX_MODEL))
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tokenizer = CLIPTokenizer.from_pretrained(
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model,
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subfolder="tokenizer",
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@ -304,14 +303,23 @@ def load_text_encoders(
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components = {}
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components["tokenizer"] = tokenizer
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if inversions is not None and len(inversions) > 0:
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logger.debug("blending Textual Inversions from %s", inversions)
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inversion_names, inversion_weights = zip(*inversions)
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text_encoder = load_model(path.join(model, "text_encoder", ONNX_MODEL))
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text_encoder_2 = None
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if params.is_xl():
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text_encoder_2 = load_model(path.join(model, "text_encoder_2", ONNX_MODEL))
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# blend embeddings, if any
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if inversions is not None and len(inversions) > 0:
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inversion_names, inversion_weights = zip(*inversions)
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inversion_models = [
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path.join(server.model_path, "inversion", name) for name in inversion_names
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]
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logger.debug(
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"blending base model %s with embeddings from %s", model, inversion_models
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)
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# TODO: blend text_encoder_2 as well
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text_encoder, tokenizer = blend_textual_inversions(
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server,
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text_encoder,
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@ -326,45 +334,15 @@ def load_text_encoders(
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),
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)
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# should be pretty small and should not need external data
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if loras is None or len(loras) == 0:
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text_encoder = path.join(model, "text_encoder", ONNX_MODEL)
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if params.is_xl():
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text_encoder_opts = device.sess_options(cache=False)
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text_encoder_session = InferenceSession(
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text_encoder,
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providers=[device.ort_provider("text-encoder")],
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sess_options=text_encoder_opts,
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)
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text_encoder_session._model_path = path.join(model, "text_encoder")
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text_encoder_2 = path.join(model, "text_encoder_2", ONNX_MODEL)
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text_encoder_2_opts = device.sess_options(cache=False)
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text_encoder_2_session = InferenceSession(
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text_encoder_2,
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providers=[device.ort_provider("text-encoder")],
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sess_options=text_encoder_2_opts,
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)
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text_encoder_2_session._model_path = path.join(model, "text_encoder_2")
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else:
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components["text_encoder"] = OnnxRuntimeModel(
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OnnxRuntimeModel.load_model(
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text_encoder,
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provider=device.ort_provider("text-encoder"),
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sess_options=device.sess_options(),
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)
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)
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else:
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# blend and load text encoder
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# blend LoRAs, if any
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if loras is not None and len(loras) > 0:
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lora_names, lora_weights = zip(*loras)
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lora_models = [
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path.join(server.model_path, "lora", name) for name in lora_names
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]
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logger.info("blending base model %s with LoRA models: %s", model, lora_models)
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# blend and load text encoder
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text_encoder = blend_loras(
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server,
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text_encoder,
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@ -373,23 +351,8 @@ def load_text_encoders(
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1 if params.is_xl() else None,
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params.is_xl(),
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)
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(text_encoder, text_encoder_data) = buffer_external_data_tensors(text_encoder)
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text_encoder_names, text_encoder_values = zip(*text_encoder_data)
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text_encoder_opts = device.sess_options(cache=False)
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text_encoder_opts.add_external_initializers(
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list(text_encoder_names), list(text_encoder_values)
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)
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if params.is_xl():
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text_encoder_session = InferenceSession(
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text_encoder.SerializeToString(),
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providers=[device.ort_provider("text-encoder")],
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sess_options=text_encoder_opts,
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)
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text_encoder_session._model_path = path.join(model, "text_encoder")
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components["text_encoder_session"] = text_encoder_session
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text_encoder_2 = path.join(model, "text_encoder_2", ONNX_MODEL)
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text_encoder_2 = blend_loras(
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server,
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text_encoder_2,
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@ -398,37 +361,59 @@ def load_text_encoders(
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2,
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params.is_xl(),
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)
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(text_encoder_2, text_encoder_2_data) = buffer_external_data_tensors(
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text_encoder_2
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)
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text_encoder_2_names, text_encoder_2_values = zip(*text_encoder_2_data)
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text_encoder_2_opts = device.sess_options(cache=False)
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text_encoder_2_opts.add_external_initializers(
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list(text_encoder_2_names), list(text_encoder_2_values)
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)
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text_encoder_2_session = InferenceSession(
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text_encoder_2.SerializeToString(),
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providers=[device.ort_provider("text-encoder")],
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sess_options=text_encoder_2_opts,
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)
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text_encoder_2_session._model_path = path.join(model, "text_encoder_2")
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components["text_encoder_2_session"] = text_encoder_2_session
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else:
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components["text_encoder"] = OnnxRuntimeModel(
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OnnxRuntimeModel.load_model(
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text_encoder.SerializeToString(),
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provider=device.ort_provider("text-encoder"),
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sess_options=text_encoder_opts,
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)
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# prepare external data for sessions
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(text_encoder, text_encoder_data) = buffer_external_data_tensors(text_encoder)
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text_encoder_names, text_encoder_values = zip(*text_encoder_data)
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text_encoder_opts = device.sess_options(cache=False)
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text_encoder_opts.add_external_initializers(
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list(text_encoder_names), list(text_encoder_values)
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)
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if params.is_xl():
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# encoder 2 only exists in XL
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(text_encoder_2, text_encoder_2_data) = buffer_external_data_tensors(
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text_encoder_2
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)
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text_encoder_2_names, text_encoder_2_values = zip(*text_encoder_2_data)
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text_encoder_2_opts = device.sess_options(cache=False)
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text_encoder_2_opts.add_external_initializers(
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list(text_encoder_2_names), list(text_encoder_2_values)
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)
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# session for te1
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text_encoder_session = InferenceSession(
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text_encoder,
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providers=[device.ort_provider("text-encoder")],
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sess_options=text_encoder_opts,
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)
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text_encoder_session._model_path = path.join(model, "text_encoder")
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components["text_encoder_session"] = text_encoder_session
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# session for te2
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text_encoder_2_session = InferenceSession(
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text_encoder_2,
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providers=[device.ort_provider("text-encoder")],
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sess_options=text_encoder_2_opts,
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)
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text_encoder_2_session._model_path = path.join(model, "text_encoder_2")
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components["text_encoder_2_session"] = text_encoder_2_session
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else:
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# session for te
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components["text_encoder"] = OnnxRuntimeModel(
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OnnxRuntimeModel.load_model(
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text_encoder,
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provider=device.ort_provider("text-encoder"),
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sess_options=text_encoder_opts,
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)
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)
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return components
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def load_unet(server, device, model, loras, unet_type, params):
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components = {}
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unet = path.join(model, unet_type, ONNX_MODEL)
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unet = load_model(path.join(model, unet_type, ONNX_MODEL))
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# LoRA blending
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if loras is not None and len(loras) > 0:
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@ -446,37 +431,26 @@ def load_unet(server, device, model, loras, unet_type, params):
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"unet",
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xl=params.is_xl(),
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)
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(unet_model, unet_data) = buffer_external_data_tensors(blended_unet)
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unet_names, unet_values = zip(*unet_data)
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unet_opts = device.sess_options(cache=False)
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unet_opts.add_external_initializers(list(unet_names), list(unet_values))
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if params.is_xl():
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unet_session = InferenceSession(
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unet_model.SerializeToString(),
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providers=[device.ort_provider("unet")],
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sess_options=unet_opts,
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)
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unet_session._model_path = path.join(model, "unet")
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components["unet_session"] = unet_session
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else:
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components["unet"] = OnnxRuntimeModel(
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OnnxRuntimeModel.load_model(
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unet_model.SerializeToString(),
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provider=device.ort_provider("unet"),
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sess_options=unet_opts,
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)
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)
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(unet_model, unet_data) = buffer_external_data_tensors(blended_unet)
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unet_names, unet_values = zip(*unet_data)
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unet_opts = device.sess_options(cache=False)
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unet_opts.add_external_initializers(list(unet_names), list(unet_values))
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# make sure a UNet has been loaded
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if not params.is_xl() and "unet" not in components:
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unet = path.join(model, unet_type, ONNX_MODEL)
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logger.debug("loading UNet (%s) from %s", unet_type, unet)
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if params.is_xl():
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unet_session = InferenceSession(
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unet_model.SerializeToString(),
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providers=[device.ort_provider("unet")],
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sess_options=unet_opts,
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)
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unet_session._model_path = path.join(model, "unet")
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components["unet_session"] = unet_session
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else:
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components["unet"] = OnnxRuntimeModel(
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OnnxRuntimeModel.load_model(
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unet,
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unet_model.SerializeToString(),
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provider=device.ort_provider("unet"),
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sess_options=device.sess_options(),
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sess_options=unet_opts,
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)
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)
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