diff --git a/models/turbine_models/custom_models/pipeline_base.py b/models/turbine_models/custom_models/pipeline_base.py
new file mode 100644
index 000000000..990165bad
--- /dev/null
+++ b/models/turbine_models/custom_models/pipeline_base.py
@@ -0,0 +1,500 @@
+# Copyright 2024 Advanced Micro Devices, inc.
+#
+# Licensed under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import logging
+import torch
+
+import iree.runtime as ireert
+from turbine_models.custom_models.sd_inference import utils, schedulers
+from turbine_models.custom_models.sdxl_inference.pipeline_ir import (
+    get_pipeline_ir,
+)
+from turbine_models.utils.sdxl_benchmark import run_benchmark
+from turbine_models.model_runner import vmfbRunner
+
+from PIL import Image
+import gc
+import os
+import numpy as np
+import time
+import copy
+from datetime import datetime as dt
+
+
+def merge_arg_into_map(model_map, arg, arg_name):
+    if isinstance(arg, dict):
+        for key in arg.keys():
+            model_map[key][arg_name] = arg[key]
+    else:
+        for key in model_map.keys():
+            model_map[key][arg_name] = arg
+    return model_map
+
+
+class PipelineComponent:
+    """
+    Wraps a VMFB runner with attributes for embedded metadata, device info, utilities and
+    has methods for handling I/O or otherwise assisting in interfacing with their pipeline
+    and its other components.
+    This aims to make new pipelines and execution modes easier to write, manage, and debug.
+    """
+
+    def __init__(self, dest_type=ireert.DeviceArray, dest_dtype="float16"):
+        self.runner = None
+        self.module_name = None
+        self.device = None
+        self.metadata = None
+        self.benchmark = False
+        self.output_type = dest_type
+        self.output_dtype = dest_dtype
+
+    def load(
+        self,
+        rt_device: str,
+        vmfb_path: str,
+        module_name: str,
+        external_weight_path: str = None,
+        extra_plugin=None,
+    ):
+        self.module_name = module_name
+        self.runner = vmfbRunner(
+            rt_device, vmfb_path, external_weight_path, extra_plugin
+        )
+        self.device = self.runner.config.device
+        self.module = getattr(self.runner.ctx.modules, module_name)
+        if "get_metadata" in self.module.keys():
+            self.metadata = self.module["get_metadata"]()
+
+    def unload(self):
+        self.device = None
+        self.runner = None
+        gc.collect()
+
+    def _run(self, function_name, inputs: list):
+        return self.module[function_name](*inputs)
+
+    def _run_and_benchmark(self, function_name, inputs: list):
+        start_time = time.time()
+        output = self._run(function_name, inputs)
+        latency = time.time() - start_time
+        print(f"Latency for {self.module_name}['{function_name}']: {latency}sec")
+        return output
+
+    def __call__(self, function_name, inputs: list):
+        if not isinstance(inputs, list):
+            inputs = [inputs]
+        if self.benchmark:
+            output = self._run_and_benchmark(function_name, inputs)
+        else:
+            output = self._run(function_name, inputs)
+        if output.dtype() != self.output_dtype:
+            output = output.astype(self.output_dtype)
+
+        match self.output_type:
+            case ireert.DeviceArray:
+                return output
+            case torch.Tensor:
+                return torch.tensor(output.to_host())
+            case np.ndarray:
+                return output.to_host()
+
+
+class TurbinePipelineBase:
+    """
+    This class is a lightweight base for Stable Diffusion
+    inference API classes. It should provide methods for:
+
+    - Exporting and compiling a set (model map) of torch IR modules
+    - preparing weights for an inference job
+    - loading weights for an inference job
+    - utilities i.e. filenames, downloads
+
+    The general flow of an arbitrary child of this pipeline base is as follows:
+    1. Initialize a model map and class attributes.
+    2. Preparation: Check if all necessary files are present, and generate them if not. (prepare_all() / prepare_submodel())
+        - This is done by submodel, so that users can generate a new submodel with the same pipeline.
+        - If vmfb not found, first check turbine tank for matching .vmfb file.
+        - If vmfb not downloadable, try downloading .mlir.
+        - If neither on Azure, run the export function in model map to export to torch IR and compile with IREE.
+        - If weights not found, run the export function in model map with weights_only=True.
+            - Apps should populate the weights with custom weights by now so they can be managed and converted if needed here.
+    3. Load the pipeline: Load the prepared files onto devices as vmfbRunners. (load_pipeline() / load_submodel() / reload_submodel())
+    4. Run Inference:
+
+
+
+    Arguments:
+    model_map: dict
+        A dictionary mapping submodel names to their export functions and hf model ids. This is used throughout the pipeline.
+        It also should provide I/O information for the submodels.
+    height: int
+        The height of the image to be generated
+    width: int
+        The width of the image to be generated
+    precision: str
+        The precision of the image latents. This usually decides the precision of all models in the pipeline.
+    max_length: int
+        The maximum sequence length for text encoders and diffusion models.
+    batch_size: int
+        The number of images to generate from each inference batch. This changes the shapes in all submodels.
+    device: str | dict[str]
+        Either a string i.e. "rocm://0", or a dictionary of such with keys matching the submodels of a given pipeline.
+        If a string, a dictionary will be created based on the pipeline's model map and the same device will be used for all submodels.
+    iree_target_triple: str | dict[str]
+        Either a string i.e. "gfx1100", or a dictionary with keys matching the submodels of a given pipeline.
+    ireec_flags: str | dict[str]
+        A comma-separated string of flags to pass to the IREE compiler, or a dict of them with keys matching submodels of a given pipeline.
+    """
+
+    def __init__(
+        self,
+        model_map: dict,
+        batch_size: int,
+        device: str | dict[str],
+        iree_target_triple: str | dict[str],
+        ireec_flags: str | dict[str] = None,
+        precision: str | dict[str] = "fp16",
+        td_spec: str | dict[str] = None,
+        decomp_attn: bool | dict[bool] = False,
+        external_weights: str | dict[str] = "safetensors",
+        pipeline_dir: str = "./shark_vmfbs",
+        external_weights_dir: str = "./shark_weights",
+    ):
+        self.map = model_map
+        self.batch_size = batch_size
+        if isinstance(device, dict):
+            assert isinstance(
+                iree_target_triple, dict
+            ), "Device and target triple must be both dicts or both strings."
+            for submodel in self.map.keys():
+                assert submodel in device.keys(), f"Device for {submodel} not found."
+                assert (
+                    submodel in iree_target_triple.keys()
+                ), f"Target arch for {submodel} not found."
+                self.map[submodel]["device"] = (device[submodel],)
+                self.map[submodel]["driver"] = (
+                    utils.iree_device_map(device[submodel]),
+                )
+                self.map[submodel]["target"] = iree_target_triple[submodel]
+        else:
+            assert isinstance(
+                iree_target_triple, str
+            ), "Device and target triple must be both dicts or both strings."
+            for submodel in self.map.keys():
+                self.map[submodel]["device"] = (device[submodel],)
+                self.map[submodel]["driver"] = (
+                    utils.iree_device_map(device[submodel]),
+                )
+                self.map[submodel]["target"] = iree_target_triple[submodel]
+        map_arguments = {
+            "ireec_flags": ireec_flags,
+            "precision": precision,
+            "td_spec": td_spec,
+            "decomp_attn": decomp_attn,
+            "external_weights": external_weights,
+        }
+        for arg in map_arguments.keys():
+            self.map = merge_arg_into_map(self.map, map_arguments[arg], arg)
+        np_dtypes = {
+            "fp16": np.float16,
+            "fp32": np.float32,
+        }
+        torch_dtypes = {
+            "fp16": torch.float16,
+            "fp32": torch.float32,
+        }
+        for submodel in self.map.keys():
+            self.map = merge_arg_into_map(
+                self.map, np_dtypes[self.map[submodel]["precision"]], "np_dtype"
+            )
+            self.map = merge_arg_into_map(
+                self.map, torch_dtypes[self.map[submodel]["precision"]], "torch_dtype"
+            )
+        print(self.map)
+
+        self.pipeline_dir = pipeline_dir
+        self.external_weights_dir = external_weights_dir
+
+        # Disabled for now -- enable through option when turbine tank is ready.
+        self.download = False
+
+        # These arguments are set at run or load time.
+        self.compiled_pipeline = False
+        self.split_scheduler = False
+        self.cpu_scheduling = False
+
+        # TODO: set this based on user-inputted guidance scale and negative prompt.
+        self.do_classifier_free_guidance = True  # False if any(x in hf_model_name for x in ["turbo", "lightning"]) else True
+        self._interrupt = False
+
+    # FILE MANAGEMENT AND PIPELINE SETUP
+
+    def prepare_all(
+        self,
+        mlirs: dict,
+        vmfbs: dict,
+        weights: dict,
+        interactive: bool = True,
+    ):
+        ready = self.is_prepared(vmfbs, weights)
+        match ready:
+            case True:
+                print("All necessary files found.")
+                return
+            case False:
+                if interactive:
+                    do_continue = input(
+                        f"\nIt seems you are missing some necessary files. Would you like to generate them now? (y/n)"
+                    )
+                    if do_continue.lower() != "y":
+                        exit()
+                for submodel in self.map.keys():
+                    if not self.map[submodel].get("vmfb"):
+                        print("Fetching: ", submodel)
+                        self.export_submodel(submodel, input_mlir=mlirs)
+                        if not self.map[submodel]["external_weights"]:
+                            assert not self.map[submodel].get(
+                                "weights"
+                            ), f"External weights should not be used for a model with inlined params."
+                return self.prepare_all(mlirs, vmfbs, weights, interactive)
+
+    def is_prepared(self, vmfbs, weights):
+        missing = {}
+        pipeline_dir = self.pipeline_dir
+        for key in self.map:
+            missing[key] = []
+            # vmfb is already present in model map
+            if self.map[key].get("vmfb"):
+                continue
+            # vmfb is passed in to this function
+            elif vmfbs.get(key):
+                self.map[key]["vmfb"] = vmfbs[key]
+                continue
+            # search self.pipeline_dir for key-specific vmfb
+            keywords = self.map[key].get("keywords", [])
+            keywords.extend(
+                [
+                    self.map[key]["safe_name"],
+                    "vmfb",
+                    "bs" + str(self.batch_size),
+                    self.map[key]["target"],
+                    self.map[key]["precision"],
+                ]
+            )
+            avail_files = os.listdir(pipeline_dir)
+            candidates = []
+            for filename in avail_files:
+                if all(str(x) in filename for x in keywords):
+                    candidates.append(os.path.join(pipeline_dir, filename))
+            if len(candidates) == 1:
+                self.map[key]["vmfb"] = candidates[0]
+            elif len(candidates) > 1:
+                print(f"Multiple files found for {key}: {candidates}")
+                print(f"Choosing {candidates[0]} for {key}.")
+                self.map[key]["vmfb"] = candidates[0]
+            else:
+                # vmfb not found in pipeline_dir. Add to list of files to generate.
+                missing[key].append("vmfb")
+
+            # Make sure vmfb needs external weights, as they may be inlined.
+            if self.map[key].get("external_weights"):
+                if self.map[key].get("weights"):
+                    # weights already found in model map
+                    continue
+                elif weights.get(key):
+                    # weights passed in to this function
+                    self.map[key]["weights"] = weights[key]
+                    continue
+                # search self.external_weights_dir for key-specific weights
+                w_keywords = [
+                    self.map[key]["safe_name"],
+                    self.map[key]["precision"],
+                    self.map[key]["external_weights"],
+                ]
+                avail_files = os.listdir(self.external_weights_dir)
+                candidates = []
+                for filename in avail_files:
+                    if all(str(x) in filename for x in w_keywords):
+                        candidates.append(
+                            os.path.join(self.external_weights_dir, filename)
+                        )
+                if len(candidates) == 1:
+                    self.map[key]["weights"] = candidates[0]
+                elif len(candidates) > 1:
+                    print(f"Multiple weight files found for {key}: {candidates}")
+                    print(f"Choosing {candidates[0]} for {key}.")
+                    self.map[key][weights] = candidates[0]
+                else:
+                    # weights not found in external_weights_dir. Add to list of files to generate.
+                    missing[key].append("weights")
+            if any(missing[key].values()):
+                print(f"Missing files for {key}: ", missing[key])
+                ready = False
+            else:
+                ready = True
+        return ready
+
+    def get_mlir_from_turbine_tank(self, submodel, container_name):
+        from turbine_models.turbine_tank import downloadModelArtifacts
+
+        safe_name = utils.create_safe_name(
+            self.hf_model_name,
+            f"_{self.max_length}_{self.height}x{self.width}_{self.precision}_{submodel}.mlir",
+        )
+        mlir_path = downloadModelArtifacts(
+            safe_name,
+            container_name,
+        )
+        return mlir_path
+
+    # IMPORT / COMPILE PHASE
+
+    def export_submodel(
+        self,
+        submodel: str,
+        weights_only: bool = False,
+    ):
+        if not os.path.exists(self.pipeline_dir):
+            os.makedirs(self.pipeline_dir)
+
+        if self.map[submodel]["external_weights"] and self.external_weights_dir:
+            if not os.path.exists(self.external_weights_dir):
+                os.makedirs(self.external_weights_dir, exist_ok=False)
+
+            self.map[submodel]["weights"] = os.path.join(
+                self.external_weights_dir,
+                f"{submodel}_{self.map[submodel]['precision']}."
+                + self.map["submodel"]["external_weights"],
+            )
+
+        elif not self.map["submodel"]["external_weights"]:
+            print(
+                "No external weights type specified using --external_weights, weights for imported .mlir files will not be externalized."
+            )
+            self.map[submodel]["weights"] = None
+
+        if weights_only:
+            input_mlir = None
+        elif "mlir" in self.map[submodel].keys():
+            input_mlir = self.map[submodel]["mlir"]
+        elif self.download:
+            try:
+                input_mlir = self.get_mlir_from_turbine_tank(
+                    submodel, self.tank_container
+                )
+            except:
+                input_mlir = None
+        else:
+            input_mlir = None
+        self.map[submodel]["mlir"] = input_mlir
+
+        match submodel:
+            case "unetloop": #SDXL ONLY FOR NOW
+                pipeline_file = get_pipeline_ir(
+                    self.width,
+                    self.height,
+                    self.precision,
+                    self.batch_size,
+                    self.max_length,
+                    "unet_loop",
+                )
+                pipeline_keys = [
+                    utils.create_safe_name(self.hf_model_name.split("/")[-1], ""),
+                    "bs" + str(self.batch_size),
+                    f"{str(self.width)}x{str(self.height)}",
+                    self.precision,
+                    str(self.max_length),
+                    "unetloop",
+                ]
+                vmfb_path = utils.compile_to_vmfb(
+                    pipeline_file,
+                    self.map["unet"]["device"],
+                    self.map["unet"]["target"],
+                    self.ireec_flags["pipeline"],
+                    os.path.join(self.pipeline_dir, "_".join(pipeline_keys)),
+                    return_path=True,
+                    mlir_source="str",
+                )
+                self.map[submodel]["vmfb"] = vmfb_path
+                self.map[submodel]["weights"] = None
+            case "fullpipeline": #SDXL ONLY FOR NOW
+                pipeline_file = get_pipeline_ir(
+                    self.width,
+                    self.height,
+                    self.precision,
+                    self.batch_size,
+                    self.max_length,
+                    "tokens_to_image",
+                )
+                pipeline_keys = [
+                    utils.create_safe_name(self.hf_model_name.split("/")[-1], ""),
+                    "bs" + str(self.batch_size),
+                    f"{str(self.width)}x{str(self.height)}",
+                    self.precision,
+                    str(self.max_length),
+                    "fullpipeline",
+                ]
+                vmfb_path = utils.compile_to_vmfb(
+                    pipeline_file,
+                    self.map["unet"]["device"],
+                    self.map["unet"]["target"],
+                    self.ireec_flags["pipeline"],
+                    os.path.join(self.pipeline_dir, "_".join(pipeline_keys)),
+                    return_path=True,
+                    mlir_source="str",
+                )
+                self.map[submodel]["vmfb"] = vmfb_path
+                self.map[submodel]["weights"] = None
+            case _:
+                export_args = dict(**self.map[submodel]["export_args"])
+                export_args["input_mlir"] = self.map[submodel].get("mlir")
+                vmfb_path = self.map[submodel]["export"](*export_args)
+
+    # LOAD
+    def load_map(self):
+        for submodel in self.map.keys():
+            self.load_submodel(submodel)
+
+    def load_submodel(self, submodel):
+        if not self.map[submodel].get("vmfb"):
+            raise ValueError(f"VMFB not found for {submodel}.")
+        if not self.map[submodel].get("weights") and self.map[submodel].get(
+            "external_weights"
+        ):
+            raise ValueError(f"Weights not found for {submodel}.")
+        self.map[submodel]["runner"] = PipelineComponent()
+        self.map[submodel]["runner"].load(
+            self.map[submodel]["driver"],
+            self.map[submodel]["vmfb"],
+            self.map[submodel]["module_name"],
+            self.map[submodel].get("weights"),
+            self.map[submodel].get("extra_plugin"),
+        )
+        setattr(self, submodel, self.map[submodel]["runner"])
+
+    def unload_submodel(self, submodel):
+        self.map[submodel]["runner"].unload()
+        setattr(self, submodel, None)
+
+
+def numpy_to_pil_image(images):
+    """
+    Convert a numpy image or a batch of images to a PIL image.
+    """
+    if images.ndim == 3:
+        images = images[None, ...]
+    images = (images * 255).round().astype("uint8")
+    if images.shape[-1] == 1:
+        # special case for grayscale (single channel) images
+        pil_images = []
+        for batched_image in images:
+            for image in range(0, batched_image.size(dim=0)):
+                pil_images.append(Image.fromarray(image.squeeze(), mode="L"))
+    else:
+        pil_images = []
+        for image in images:
+            pil_images.append(Image.fromarray(image))
+    return pil_images
diff --git a/models/turbine_models/custom_models/sd3_inference/sd3_pipeline.py b/models/turbine_models/custom_models/sd3_inference/sd3_pipeline.py
index 256e4d21b..1068d6b6c 100644
--- a/models/turbine_models/custom_models/sd3_inference/sd3_pipeline.py
+++ b/models/turbine_models/custom_models/sd3_inference/sd3_pipeline.py
@@ -123,7 +123,7 @@ def __init__(
         self.external_weights_dir = external_weights_dir
         self.external_weights = external_weights
         self.vae_decomp_attn = vae_decomp_attn
-        self.custom_vae = custom_vae
+        self.custom_vae = None
         self.cpu_scheduling = cpu_scheduling
         self.torch_dtype = torch.float32 if self.precision == "fp32" else torch.float16
         self.vae_precision = vae_precision if vae_precision else self.precision