diff --git "a/vae_encoder/openvino_model.xml" "b/vae_encoder/openvino_model.xml"
new file mode 100644--- /dev/null
+++ "b/vae_encoder/openvino_model.xml"
@@ -0,0 +1,9558 @@
+<?xml version="1.0"?>
+<net name="Model6" version="11">
+	<layers>
+		<layer id="0" name="sample" type="Parameter" version="opset1">
+			<data shape="?,3,?,?" element_type="f32" />
+			<output>
+				<port id="0" precision="FP32" names="sample">
+					<dim>-1</dim>
+					<dim>3</dim>
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="1" name="self.encoder.conv_in.weight" type="Const" version="opset1">
+			<data element_type="u8" shape="128, 3, 3, 3" offset="0" size="3456" />
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+				<port id="0" precision="U8">
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="2" name="Convert_661821" type="Convert" version="opset1">
+			<data destination_type="f16" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP16">
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="3" name="self.encoder.conv_in.weight/zero_point" type="Const" version="opset1">
+			<data element_type="u8" shape="128, 1, 1, 1" offset="3456" size="128" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="4" name="Convert_661824" type="Convert" version="opset1">
+			<data destination_type="f16" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			<output>
+				<port id="1" precision="FP16">
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="5" name="self.encoder.conv_in.weight/zero_point/subtract" type="Subtract" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP16">
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="6" name="self.encoder.conv_in.weight/scale" type="Const" version="opset1">
+			<data element_type="f16" shape="128, 1, 1, 1" offset="3584" size="256" />
+			<output>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="7" name="self.encoder.conv_in.weight/fq_weights_1" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy" />
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+				<port id="0" precision="FP16">
+					<dim>128</dim>
+					<dim>3</dim>
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+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="8" name="self.encoder.conv_in.weight/fq_weights_1/convert" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="FP16">
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="9" name="__module.encoder.conv_in/aten::_convolution/Convolution" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>3</dim>
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+					<dim>128</dim>
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+		</layer>
+		<layer id="10" name="__module.encoder.conv_in/aten::_convolution/Reshape" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+		</layer>
+		<layer id="11" name="__module.encoder.conv_in/aten::_convolution/Add" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
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+					<dim>128</dim>
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+					<dim>1</dim>
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+					<dim>-1</dim>
+					<dim>128</dim>
+					<dim>-1</dim>
+					<dim>-1</dim>
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+		</layer>
+		<layer id="12" name="self.encoder.down_blocks.0.resnets.0.norm1.weight" type="Const" version="opset1">
+			<data element_type="f32" shape="128" offset="4352" size="512" />
+			<output>
+				<port id="0" precision="FP32" names="self.encoder.down_blocks.0.resnets.0.norm1.weight">
+					<dim>128</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="13" name="self.encoder.down_blocks.0.resnets.0.norm1.bias" type="Const" version="opset1">
+			<data element_type="f32" shape="128" offset="4864" size="512" />
+			<output>
+				<port id="0" precision="FP32" names="self.encoder.down_blocks.0.resnets.0.norm1.bias">
+					<dim>128</dim>
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+			</output>
+		</layer>
+		<layer id="14" name="__module.encoder.down_blocks.0.resnets.0.norm1/aten::group_norm/GroupNormalization" type="GroupNormalization" version="opset12">
+			<data num_groups="32" epsilon="9.9999999747524271e-07" />
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+		<conversion_parameters>
+			<framework value="pytorch" />
+			<is_python_object value="True" />
+		</conversion_parameters>
+		<nncf>
+			<friendly_names_were_updated value="True" />
+			<weight_compression>
+				<advanced_parameters value="{'statistics_path': None, 'awq_params': {'subset_size': 32, 'percent_to_apply': 0.002, 'alpha_min': 0.0, 'alpha_max': 1.0, 'steps': 100}, 'scale_estimation_params': {'subset_size': 64, 'initial_steps': 5, 'scale_steps': 5, 'weight_penalty': -1.0}, 'gptq_params': {'damp_percent': 0.1, 'block_size': 128, 'subset_size': 128}, 'lora_correction_params': {'adapter_rank': 8, 'num_iterations': 3, 'apply_regularization': True, 'subset_size': 128, 'use_int8_adapters': True}}" />
+				<all_layers value="False" />
+				<awq value="False" />
+				<backup_mode value="int8_asym" />
+				<gptq value="False" />
+				<group_size value="-1" />
+				<ignored_scope value="[]" />
+				<lora_correction value="False" />
+				<mode value="int8_asym" />
+				<ratio value="1" />
+				<scale_estimation value="False" />
+				<sensitivity_metric value="weight_quantization_error" />
+			</weight_compression>
+		</nncf>
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+			<nncf_version value="2.14.0" />
+			<optimum_intel_version value="1.18.2" />
+			<optimum_version value="1.21.4" />
+			<pytorch_version value="2.5.1" />
+			<transformers_version value="4.41.2" />
+		</optimum>
+	</rt_info>
+</net>