diff --git a/.github/workflows/test-studio.yml b/.github/workflows/test-studio.yml
index 765a6bf761..9b96bf270f 100644
--- a/.github/workflows/test-studio.yml
+++ b/.github/workflows/test-studio.yml
@@ -81,6 +81,4 @@ jobs:
         source shark.venv/bin/activate
         pip install -r requirements.txt --no-cache-dir
         pip install -e .
-        pip uninstall -y torch
-        pip install torch==2.1.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
         python apps/shark_studio/tests/api_test.py
diff --git a/.gitignore b/.gitignore
index f67152b007..7d6a0d4215 100644
--- a/.gitignore
+++ b/.gitignore
@@ -164,7 +164,7 @@ cython_debug/
 # vscode related
 .vscode
 
-# Shark related artefacts
+# Shark related artifacts
 *venv/
 shark_tmp/
 *.vmfb
@@ -172,6 +172,7 @@ shark_tmp/
 tank/dict_configs.py
 *.csv
 reproducers/
+apps/shark_studio/web/configs
 
 # ORT related artefacts
 cache_models/
@@ -188,6 +189,11 @@ variants.json
 # models folder
 apps/stable_diffusion/web/models/
 
+# model artifacts (SHARK)
+*.tempfile
+*.mlir
+*.vmfb
+
 # Stencil annotators.
 stencil_annotator/
 
diff --git a/apps/shark_studio/api/llm.py b/apps/shark_studio/api/llm.py
index 6ee80ae49e..217fb6784f 100644
--- a/apps/shark_studio/api/llm.py
+++ b/apps/shark_studio/api/llm.py
@@ -155,7 +155,9 @@ def __init__(
                 use_auth_token=hf_auth_token,
             )
         elif not os.path.exists(self.tempfile_name):
-            self.torch_ir, self.tokenizer = llm_model_map[model_name]["initializer"](
+            self.torch_ir, self.tokenizer = llm_model_map[self.hf_model_name][
+                "initializer"
+            ](
                 self.hf_model_name,
                 hf_auth_token,
                 compile_to="torch",
@@ -258,8 +260,7 @@ def format_out(results):
 
             history.append(format_out(token))
             while (
-                format_out(token)
-                != llm_model_map["meta-llama/Llama-2-7b-chat-hf"]["stop_token"]
+                format_out(token) != llm_model_map[self.hf_model_name]["stop_token"]
                 and len(history) < self.max_tokens
             ):
                 dec_time = time.time()
@@ -273,10 +274,7 @@ def format_out(results):
 
             self.prev_token_len = token_len + len(history)
 
-            if (
-                format_out(token)
-                == llm_model_map["meta-llama/Llama-2-7b-chat-hf"]["stop_token"]
-            ):
+            if format_out(token) == llm_model_map[self.hf_model_name]["stop_token"]:
                 break
 
         for i in range(len(history)):
@@ -310,7 +308,7 @@ def chat_hf(self, prompt):
                 self.first_input = False
 
             history.append(int(token))
-            while token != llm_model_map["meta-llama/Llama-2-7b-chat-hf"]["stop_token"]:
+            while token != llm_model_map[self.hf_model_name]["stop_token"]:
                 dec_time = time.time()
                 result = self.hf_mod(token.reshape([1, 1]), past_key_values=pkv)
                 history.append(int(token))
@@ -321,7 +319,7 @@ def chat_hf(self, prompt):
 
             self.prev_token_len = token_len + len(history)
 
-            if token == llm_model_map["meta-llama/Llama-2-7b-chat-hf"]["stop_token"]:
+            if token == llm_model_map[self.hf_model_name]["stop_token"]:
                 break
         for i in range(len(history)):
             if type(history[i]) != int:
diff --git a/apps/shark_studio/api/sd.py b/apps/shark_studio/api/sd.py
index b4f0f0ddc0..83574d294d 100644
--- a/apps/shark_studio/api/sd.py
+++ b/apps/shark_studio/api/sd.py
@@ -4,51 +4,59 @@
 import os
 import json
 import numpy as np
+import copy
 from tqdm.auto import tqdm
 
 from pathlib import Path
 from random import randint
-from turbine_models.custom_models.sd_inference import clip, unet, vae
+from turbine_models.custom_models.sd_inference.sd_pipeline import SharkSDPipeline
+from turbine_models.custom_models.sdxl_inference.sdxl_compiled_pipeline import (
+    SharkSDXLPipeline,
+)
+
+
 from apps.shark_studio.api.controlnet import control_adapter_map
+from apps.shark_studio.api.utils import parse_device
 from apps.shark_studio.web.utils.state import status_label
 from apps.shark_studio.web.utils.file_utils import (
     safe_name,
     get_resource_path,
     get_checkpoints_path,
 )
-from apps.shark_studio.modules.pipeline import SharkPipelineBase
-from apps.shark_studio.modules.schedulers import get_schedulers
-from apps.shark_studio.modules.prompt_encoding import (
-    get_weighted_text_embeddings,
-)
+
 from apps.shark_studio.modules.img_processing import (
-    resize_stencil,
     save_output_img,
-    resamplers,
-    resampler_list,
 )
 
 from apps.shark_studio.modules.ckpt_processing import (
     preprocessCKPT,
-    process_custom_pipe_weights,
+    save_irpa,
 )
-from transformers import CLIPTokenizer
-from diffusers.image_processor import VaeImageProcessor
-
-sd_model_map = {
-    "clip": {
-        "initializer": clip.export_clip_model,
-    },
-    "unet": {
-        "initializer": unet.export_unet_model,
-    },
-    "vae_decode": {
-        "initializer": vae.export_vae_model,
-    },
+
+EMPTY_SD_MAP = {
+    "clip": None,
+    "scheduler": None,
+    "unet": None,
+    "vae_decode": None,
+}
+
+EMPTY_SDXL_MAP = {
+    "prompt_encoder": None,
+    "scheduled_unet": None,
+    "vae_decode": None,
+    "pipeline": None,
+    "full_pipeline": None,
+}
+
+EMPTY_FLAGS = {
+    "clip": None,
+    "unet": None,
+    "vae": None,
+    "pipeline": None,
 }
 
 
-class StableDiffusion(SharkPipelineBase):
+class StableDiffusion:
     # This class is responsible for executing image generation and creating
     # /managing a set of compiled modules to run Stable Diffusion. The init
     # aims to be as general as possible, and the class will infer and compile
@@ -61,66 +69,36 @@ def __init__(
         height: int,
         width: int,
         batch_size: int,
+        steps: int,
+        scheduler: str,
         precision: str,
         device: str,
         custom_vae: str = None,
         num_loras: int = 0,
         import_ir: bool = True,
         is_controlled: bool = False,
-        hf_auth_token=None,
     ):
-        self.model_max_length = 77
-        self.batch_size = batch_size
         self.precision = precision
-        self.dtype = torch.float16 if precision == "fp16" else torch.float32
-        self.height = height
-        self.width = width
-        self.scheduler_obj = {}
-        static_kwargs = {
-            "pipe": {
-                "external_weights": "safetensors",
-            },
-            "clip": {"hf_model_name": base_model_id},
-            "unet": {
-                "hf_model_name": base_model_id,
-                "unet_model": unet.UnetModel(hf_model_name=base_model_id),
-                "batch_size": batch_size,
-                # "is_controlled": is_controlled,
-                # "num_loras": num_loras,
-                "height": height,
-                "width": width,
-                "precision": precision,
-                "max_length": self.model_max_length,
-            },
-            "vae_encode": {
-                "hf_model_name": base_model_id,
-                "vae_model": vae.VaeModel(
-                    hf_model_name=custom_vae if custom_vae else base_model_id,
-                ),
-                "batch_size": batch_size,
-                "height": height,
-                "width": width,
-                "precision": precision,
-            },
-            "vae_decode": {
-                "hf_model_name": base_model_id,
-                "vae_model": vae.VaeModel(
-                    hf_model_name=custom_vae if custom_vae else base_model_id,
-                ),
-                "batch_size": batch_size,
-                "height": height,
-                "width": width,
-                "precision": precision,
-            },
-        }
-        super().__init__(sd_model_map, base_model_id, static_kwargs, device, import_ir)
+        self.compiled_pipeline = False
+        self.base_model_id = base_model_id
+        self.custom_vae = custom_vae
+        self.is_sdxl = "xl" in self.base_model_id.lower()
+        if self.is_sdxl:
+            self.turbine_pipe = SharkSDXLPipeline
+            self.model_map = EMPTY_SDXL_MAP
+        else:
+            self.turbine_pipe = SharkSDPipeline
+            self.model_map = EMPTY_SD_MAP
+        external_weights = "safetensors"
+        max_length = 64
+        target_backend, self.rt_device, triple = parse_device(device)
         pipe_id_list = [
             safe_name(base_model_id),
             str(batch_size),
-            str(self.model_max_length),
+            str(max_length),
             f"{str(height)}x{str(width)}",
             precision,
-            self.device,
+            triple,
         ]
         if num_loras > 0:
             pipe_id_list.append(str(num_loras) + "lora")
@@ -129,227 +107,116 @@ def __init__(
         if custom_vae:
             pipe_id_list.append(custom_vae)
         self.pipe_id = "_".join(pipe_id_list)
+        self.pipeline_dir = Path(os.path.join(get_checkpoints_path(), self.pipe_id))
+        self.weights_path = Path(
+            os.path.join(
+                get_checkpoints_path(), safe_name(self.base_model_id + "_" + precision)
+            )
+        )
+        if not os.path.exists(self.weights_path):
+            os.mkdir(self.weights_path)
+
+        decomp_attn = True
+        attn_spec = None
+        if triple in ["gfx940", "gfx942", "gfx90a"]:
+            decomp_attn = False
+            attn_spec = "mfma"
+        elif triple in ["gfx1100", "gfx1103"]:
+            decomp_attn = False
+            attn_spec = "wmma"
+        elif target_backend == "llvm-cpu":
+            decomp_attn = False
+
+        self.sd_pipe = self.turbine_pipe(
+            hf_model_name=base_model_id,
+            scheduler_id=scheduler,
+            height=height,
+            width=width,
+            precision=precision,
+            max_length=max_length,
+            batch_size=batch_size,
+            num_inference_steps=steps,
+            device=target_backend,
+            iree_target_triple=triple,
+            ireec_flags=EMPTY_FLAGS,
+            attn_spec=attn_spec,
+            decomp_attn=decomp_attn,
+            pipeline_dir=self.pipeline_dir,
+            external_weights_dir=self.weights_path,
+            external_weights=external_weights,
+            custom_vae=custom_vae,
+        )
         print(f"\n[LOG] Pipeline initialized with pipe_id: {self.pipe_id}.")
-        del static_kwargs
         gc.collect()
 
     def prepare_pipe(self, custom_weights, adapters, embeddings, is_img2img):
         print(f"\n[LOG] Preparing pipeline...")
-        self.is_img2img = is_img2img
-        self.schedulers = get_schedulers(self.base_model_id)
-
-        self.weights_path = os.path.join(
-            get_checkpoints_path(), self.safe_name(self.base_model_id)
-        )
-        if not os.path.exists(self.weights_path):
-            os.mkdir(self.weights_path)
-
-        for model in adapters:
-            self.model_map[model] = adapters[model]
-
-        for submodel in self.static_kwargs:
-            if custom_weights:
-                custom_weights_params, _ = process_custom_pipe_weights(custom_weights)
-                if submodel not in ["clip", "clip2"]:
-                    self.static_kwargs[submodel][
-                        "external_weights"
-                    ] = custom_weights_params
-                else:
-                    self.static_kwargs[submodel]["external_weight_path"] = os.path.join(
-                        self.weights_path, submodel + ".safetensors"
+        self.is_img2img = False
+        mlirs = copy.deepcopy(self.model_map)
+        vmfbs = copy.deepcopy(self.model_map)
+        weights = copy.deepcopy(self.model_map)
+
+        if custom_weights:
+            custom_weights = os.path.join(
+                get_checkpoints_path("checkpoints"),
+                safe_name(self.base_model_id.split("/")[-1]),
+                custom_weights,
+            )
+            diffusers_weights_path = preprocessCKPT(custom_weights, self.precision)
+            for key in weights:
+                if key in ["scheduled_unet", "unet"]:
+                    unet_weights_path = os.path.join(
+                        diffusers_weights_path,
+                        "unet",
+                        "diffusion_pytorch_model.safetensors",
                     )
-            else:
-                self.static_kwargs[submodel]["external_weight_path"] = os.path.join(
-                    self.weights_path, submodel + ".safetensors"
-                )
-
-        self.get_compiled_map(pipe_id=self.pipe_id)
-        print("\n[LOG] Pipeline successfully prepared for runtime.")
-        return
+                    weights[key] = save_irpa(unet_weights_path, "unet.")
+
+                elif key in ["clip", "prompt_encoder"]:
+                    if not self.is_sdxl:
+                        sd1_path = os.path.join(
+                            diffusers_weights_path, "text_encoder", "model.safetensors"
+                        )
+                        weights[key] = save_irpa(sd1_path, "text_encoder_model.")
+                    else:
+                        clip_1_path = os.path.join(
+                            diffusers_weights_path, "text_encoder", "model.safetensors"
+                        )
+                        clip_2_path = os.path.join(
+                            diffusers_weights_path,
+                            "text_encoder_2",
+                            "model.safetensors",
+                        )
+                        weights[key] = [
+                            save_irpa(clip_1_path, "text_encoder_model_1."),
+                            save_irpa(clip_2_path, "text_encoder_model_2."),
+                        ]
+
+                elif key in ["vae_decode"] and weights[key] is None:
+                    vae_weights_path = os.path.join(
+                        diffusers_weights_path,
+                        "vae",
+                        "diffusion_pytorch_model.safetensors",
+                    )
+                    weights[key] = save_irpa(vae_weights_path, "vae.")
 
-    def encode_prompts_weight(
-        self,
-        prompt,
-        negative_prompt,
-        do_classifier_free_guidance=True,
-    ):
-        # Encodes the prompt into text encoder hidden states.
-        self.load_submodels(["clip"])
-        self.tokenizer = CLIPTokenizer.from_pretrained(
-            self.base_model_id,
-            subfolder="tokenizer",
+        vmfbs, weights = self.sd_pipe.check_prepared(
+            mlirs, vmfbs, weights, interactive=False
         )
-        clip_inf_start = time.time()
-
-        text_embeddings, uncond_embeddings = get_weighted_text_embeddings(
-            pipe=self,
-            prompt=prompt,
-            uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
+        print(f"\n[LOG] Loading pipeline to device {self.rt_device}.")
+        self.sd_pipe.load_pipeline(
+            vmfbs, weights, self.rt_device, self.compiled_pipeline
         )
-
-        if do_classifier_free_guidance:
-            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
-        pad = (0, 0) * (len(text_embeddings.shape) - 2)
-        pad = pad + (
-            0,
-            self.static_kwargs["unet"]["max_length"] - text_embeddings.shape[1],
+        print(
+            "\n[LOG] Pipeline successfully prepared for runtime. Generating images..."
         )
-        text_embeddings = torch.nn.functional.pad(text_embeddings, pad)
-
-        # SHARK: Report clip inference time
-        clip_inf_time = (time.time() - clip_inf_start) * 1000
-        if self.ondemand:
-            self.unload_submodels(["clip"])
-            gc.collect()
-        print(f"\n[LOG] Clip Inference time (ms) = {clip_inf_time:.3f}")
-
-        return text_embeddings.numpy().astype(np.float16)
-
-    def prepare_latents(
-        self,
-        generator,
-        num_inference_steps,
-        image,
-        strength,
-    ):
-        noise = torch.randn(
-            (
-                self.batch_size,
-                4,
-                self.height // 8,
-                self.width // 8,
-            ),
-            generator=generator,
-            dtype=self.dtype,
-        ).to("cpu")
-
-        self.scheduler.set_timesteps(num_inference_steps)
-        if self.is_img2img:
-            init_timestep = min(
-                int(num_inference_steps * strength), num_inference_steps
-            )
-            t_start = max(num_inference_steps - init_timestep, 0)
-            timesteps = self.scheduler.timesteps[t_start:]
-            latents = self.encode_image(image)
-            latents = self.scheduler.add_noise(latents, noise, timesteps[0].repeat(1))
-            return latents, [timesteps]
-        else:
-            self.scheduler.is_scale_input_called = True
-            latents = noise * self.scheduler.init_noise_sigma
-            return latents, self.scheduler.timesteps
-
-    def encode_image(self, input_image):
-        self.load_submodels(["vae_encode"])
-        vae_encode_start = time.time()
-        latents = self.run("vae_encode", input_image)
-        vae_inf_time = (time.time() - vae_encode_start) * 1000
-        if self.ondemand:
-            self.unload_submodels(["vae_encode"])
-        print(f"\n[LOG] VAE Encode Inference time (ms): {vae_inf_time:.3f}")
-
-        return latents
-
-    def produce_img_latents(
-        self,
-        latents,
-        text_embeddings,
-        guidance_scale,
-        total_timesteps,
-        cpu_scheduling,
-        mask=None,
-        masked_image_latents=None,
-        return_all_latents=False,
-    ):
-        # self.status = SD_STATE_IDLE
-        step_time_sum = 0
-        latent_history = [latents]
-        text_embeddings = torch.from_numpy(text_embeddings).to(self.dtype)
-        text_embeddings_numpy = text_embeddings.detach().numpy()
-        guidance_scale = torch.Tensor([guidance_scale]).to(self.dtype)
-        self.load_submodels(["unet"])
-        for i, t in tqdm(enumerate(total_timesteps)):
-            step_start_time = time.time()
-            timestep = torch.tensor([t]).to(self.dtype).detach().numpy()
-            latent_model_input = self.scheduler.scale_model_input(latents, t).to(
-                self.dtype
-            )
-            if mask is not None and masked_image_latents is not None:
-                latent_model_input = torch.cat(
-                    [
-                        torch.from_numpy(np.asarray(latent_model_input)).to(self.dtype),
-                        mask,
-                        masked_image_latents,
-                    ],
-                    dim=1,
-                ).to(self.dtype)
-            if cpu_scheduling:
-                latent_model_input = latent_model_input.detach().numpy()
-
-            # Profiling Unet.
-            # profile_device = start_profiling(file_path="unet.rdc")
-            noise_pred = self.run(
-                "unet",
-                [
-                    latent_model_input,
-                    timestep,
-                    text_embeddings_numpy,
-                    guidance_scale,
-                ],
-            )
-            # end_profiling(profile_device)
-
-            if cpu_scheduling:
-                noise_pred = torch.from_numpy(noise_pred.to_host())
-                latents = self.scheduler.step(noise_pred, t, latents).prev_sample
-            else:
-                latents = self.run("scheduler_step", (noise_pred, t, latents))
-
-            latent_history.append(latents)
-            step_time = (time.time() - step_start_time) * 1000
-            # print(
-            #     f"\n [LOG] step = {i} | timestep = {t} | time = {step_time:.2f}ms"
-            # )
-            step_time_sum += step_time
-
-            # if self.status == SD_STATE_CANCEL:
-            #    break
-
-        if self.ondemand:
-            self.unload_submodels(["unet"])
-            gc.collect()
-
-        avg_step_time = step_time_sum / len(total_timesteps)
-        print(f"\n[LOG] Average step time: {avg_step_time}ms/it")
-
-        if not return_all_latents:
-            return latents
-        all_latents = torch.cat(latent_history, dim=0)
-        return all_latents
-
-    def decode_latents(self, latents, cpu_scheduling=True):
-        latents_numpy = latents.to(self.dtype)
-        if cpu_scheduling:
-            latents_numpy = latents.detach().numpy()
-
-        # profile_device = start_profiling(file_path="vae.rdc")
-        vae_start = time.time()
-        images = self.run("vae_decode", latents_numpy).to_host()
-        vae_inf_time = (time.time() - vae_start) * 1000
-        # end_profiling(profile_device)
-        print(f"\n[LOG] VAE Inference time (ms): {vae_inf_time:.3f}")
-
-        images = torch.from_numpy(images).permute(0, 2, 3, 1).float().numpy()
-        pil_images = self.image_processor.numpy_to_pil(images)
-        return pil_images
+        return
 
     def generate_images(
         self,
         prompt,
         negative_prompt,
         image,
-        scheduler,
-        steps,
         strength,
         guidance_scale,
         seed,
@@ -359,69 +226,15 @@ def generate_images(
         control_mode,
         hints,
     ):
-        # TODO: Batched args
-        self.image_processor = VaeImageProcessor(do_convert_rgb=True)
-        self.scheduler = self.schedulers[scheduler]
-        self.ondemand = ondemand
-        if self.is_img2img:
-            image, _ = self.image_processor.preprocess(image, resample_type)
-        else:
-            image = None
-
-        print("\n[LOG] Generating images...")
-        batched_args = [
-            prompt,
-            negative_prompt,
-            image,
-        ]
-        for arg in batched_args:
-            if not isinstance(arg, list):
-                arg = [arg] * self.batch_size
-            if len(arg) < self.batch_size:
-                arg = arg * self.batch_size
-            else:
-                arg = [arg[i] for i in range(self.batch_size)]
-
-        text_embeddings = self.encode_prompts_weight(
+        img = self.sd_pipe.generate_images(
             prompt,
             negative_prompt,
+            1,
+            guidance_scale,
+            seed,
+            return_imgs=True,
         )
-
-        uint32_info = np.iinfo(np.uint32)
-        uint32_min, uint32_max = uint32_info.min, uint32_info.max
-        if seed < uint32_min or seed >= uint32_max:
-            seed = randint(uint32_min, uint32_max)
-
-        generator = torch.manual_seed(seed)
-
-        init_latents, final_timesteps = self.prepare_latents(
-            generator=generator,
-            num_inference_steps=steps,
-            image=image,
-            strength=strength,
-        )
-
-        latents = self.produce_img_latents(
-            latents=init_latents,
-            text_embeddings=text_embeddings,
-            guidance_scale=guidance_scale,
-            total_timesteps=final_timesteps,
-            cpu_scheduling=True,  # until we have schedulers through Turbine
-        )
-
-        # Img latents -> PIL images
-        all_imgs = []
-        self.load_submodels(["vae_decode"])
-        for i in tqdm(range(0, latents.shape[0], self.batch_size)):
-            imgs = self.decode_latents(
-                latents=latents[i : i + self.batch_size],
-                cpu_scheduling=True,
-            )
-            all_imgs.extend(imgs)
-        if self.ondemand:
-            self.unload_submodels(["vae_decode"])
-
-        return all_imgs
+        return img
 
 
 def shark_sd_fn_dict_input(
@@ -481,6 +294,7 @@ def shark_sd_fn(
     control_mode = None
     hints = []
     num_loras = 0
+    import_ir = True
     for i in embeddings:
         num_loras += 1 if embeddings[i] else 0
     if "model" in controlnets:
@@ -514,8 +328,10 @@ def shark_sd_fn(
         "device": device,
         "custom_vae": custom_vae,
         "num_loras": num_loras,
-        "import_ir": cmd_opts.import_mlir,
+        "import_ir": import_ir,
         "is_controlled": is_controlled,
+        "steps": steps,
+        "scheduler": scheduler,
     }
     submit_prep_kwargs = {
         "custom_weights": custom_weights,
@@ -527,8 +343,6 @@ def shark_sd_fn(
         "prompt": prompt,
         "negative_prompt": negative_prompt,
         "image": sd_init_image,
-        "steps": steps,
-        "scheduler": scheduler,
         "strength": strength,
         "guidance_scale": guidance_scale,
         "seed": seed,
@@ -566,9 +380,9 @@ def shark_sd_fn(
     for current_batch in range(batch_count):
         start_time = time.time()
         out_imgs = global_obj.get_sd_obj().generate_images(**submit_run_kwargs)
-        total_time = time.time() - start_time
-        text_output = f"Total image(s) generation time: {total_time:.4f}sec"
-        print(f"\n[LOG] {text_output}")
+        # total_time = time.time() - start_time
+        # text_output = f"Total image(s) generation time: {total_time:.4f}sec"
+        # print(f"\n[LOG] {text_output}")
         # if global_obj.get_sd_status() == SD_STATE_CANCEL:
         #     break
         # else:
@@ -596,6 +410,10 @@ def view_json_file(file_path):
     return content
 
 
+def safe_name(name):
+    return name.replace("/", "_").replace("\\", "_").replace(".", "_")
+
+
 if __name__ == "__main__":
     from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
     import apps.shark_studio.web.utils.globals as global_obj
diff --git a/apps/shark_studio/api/utils.py b/apps/shark_studio/api/utils.py
index e9268aa83b..0516255d2b 100644
--- a/apps/shark_studio/api/utils.py
+++ b/apps/shark_studio/api/utils.py
@@ -71,6 +71,8 @@ def get_devices_by_name(driver_name):
     available_devices.extend(cuda_devices)
     rocm_devices = get_devices_by_name("rocm")
     available_devices.extend(rocm_devices)
+    hip_devices = get_devices_by_name("hip")
+    available_devices.extend(hip_devices)
     cpu_device = get_devices_by_name("cpu-sync")
     available_devices.extend(cpu_device)
     cpu_device = get_devices_by_name("cpu-task")
@@ -127,6 +129,54 @@ def set_iree_runtime_flags():
     set_iree_vulkan_runtime_flags(flags=vulkan_runtime_flags)
 
 
+def parse_device(device_str):
+    from shark.iree_utils.compile_utils import (
+        clean_device_info,
+        get_iree_target_triple,
+        iree_target_map,
+    )
+
+    rt_driver, device_id = clean_device_info(device_str)
+    target_backend = iree_target_map(rt_driver)
+    if device_id:
+        rt_device = f"{rt_driver}://{device_id}"
+    else:
+        rt_device = rt_driver
+
+    match target_backend:
+        case "vulkan-spirv":
+            triple = get_iree_target_triple(device_str)
+            return target_backend, rt_device, triple
+        case "rocm":
+            triple = get_rocm_target_chip(device_str)
+            return target_backend, rt_device, triple
+        case "llvm-cpu":
+            return "llvm-cpu", "local-task", "x86_64-linux-gnu"
+
+
+def get_rocm_target_chip(device_str):
+    # TODO: Use a data file to map device_str to target chip.
+    rocm_chip_map = {
+        "6700": "gfx1031",
+        "6800": "gfx1030",
+        "6900": "gfx1030",
+        "7900": "gfx1100",
+        "MI300X": "gfx942",
+        "MI300A": "gfx940",
+        "MI210": "gfx90a",
+        "MI250": "gfx90a",
+        "MI100": "gfx908",
+        "MI50": "gfx906",
+        "MI60": "gfx906",
+    }
+    for key in rocm_chip_map:
+        if key in device_str:
+            return rocm_chip_map[key]
+    raise AssertionError(
+        f"Device {device_str} not recognized. Please file an issue at https://github.com/nod-ai/SHARK/issues."
+    )
+
+
 def get_all_devices(driver_name):
     """
     Inputs: driver_name
diff --git a/apps/shark_studio/modules/ckpt_processing.py b/apps/shark_studio/modules/ckpt_processing.py
index fc0bd3b7b8..433df13654 100644
--- a/apps/shark_studio/modules/ckpt_processing.py
+++ b/apps/shark_studio/modules/ckpt_processing.py
@@ -2,6 +2,11 @@
 import json
 import re
 import requests
+import torch
+import safetensors
+from shark_turbine.aot.params import (
+    ParameterArchiveBuilder,
+)
 from io import BytesIO
 from pathlib import Path
 from tqdm import tqdm
@@ -15,21 +20,21 @@
 )
 
 
-def get_path_to_diffusers_checkpoint(custom_weights):
+def get_path_to_diffusers_checkpoint(custom_weights, precision="fp16"):
     path = Path(custom_weights)
     diffusers_path = path.parent.absolute()
-    diffusers_directory_name = os.path.join("diffusers", path.stem)
+    diffusers_directory_name = os.path.join("diffusers", path.stem + f"_{precision}")
     complete_path_to_diffusers = diffusers_path / diffusers_directory_name
     complete_path_to_diffusers.mkdir(parents=True, exist_ok=True)
     path_to_diffusers = complete_path_to_diffusers.as_posix()
     return path_to_diffusers
 
 
-def preprocessCKPT(custom_weights, is_inpaint=False):
-    path_to_diffusers = get_path_to_diffusers_checkpoint(custom_weights)
+def preprocessCKPT(custom_weights, precision="fp16", is_inpaint=False):
+    path_to_diffusers = get_path_to_diffusers_checkpoint(custom_weights, precision)
     if next(Path(path_to_diffusers).iterdir(), None):
         print("Checkpoint already loaded at : ", path_to_diffusers)
-        return
+        return path_to_diffusers
     else:
         print(
             "Diffusers' checkpoint will be identified here : ",
@@ -51,8 +56,24 @@ def preprocessCKPT(custom_weights, is_inpaint=False):
         from_safetensors=from_safetensors,
         num_in_channels=num_in_channels,
     )
+    if precision == "fp16":
+        pipe.to(dtype=torch.float16)
     pipe.save_pretrained(path_to_diffusers)
+    del pipe
     print("Loading complete")
+    return path_to_diffusers
+
+
+def save_irpa(weights_path, prepend_str):
+    weights = safetensors.torch.load_file(weights_path)
+    archive = ParameterArchiveBuilder()
+    for key in weights.keys():
+        new_key = prepend_str + key
+        archive.add_tensor(new_key, weights[key])
+
+    irpa_file = weights_path.replace(".safetensors", ".irpa")
+    archive.save(irpa_file)
+    return irpa_file
 
 
 def convert_original_vae(vae_checkpoint):
diff --git a/apps/shark_studio/modules/schedulers.py b/apps/shark_studio/modules/schedulers.py
index 3e931b1c78..56df8973d0 100644
--- a/apps/shark_studio/modules/schedulers.py
+++ b/apps/shark_studio/modules/schedulers.py
@@ -101,11 +101,12 @@ def export_scheduler_model(model):
 
 
 scheduler_model_map = {
+    "PNDM": export_scheduler_model("PNDMScheduler"),
+    "DPMSolverSDE": export_scheduler_model("DpmSolverSDEScheduler"),
     "EulerDiscrete": export_scheduler_model("EulerDiscreteScheduler"),
     "EulerAncestralDiscrete": export_scheduler_model("EulerAncestralDiscreteScheduler"),
     "LCM": export_scheduler_model("LCMScheduler"),
     "LMSDiscrete": export_scheduler_model("LMSDiscreteScheduler"),
-    "PNDM": export_scheduler_model("PNDMScheduler"),
     "DDPM": export_scheduler_model("DDPMScheduler"),
     "DDIM": export_scheduler_model("DDIMScheduler"),
     "DPMSolverMultistep": export_scheduler_model("DPMSolverMultistepScheduler"),
diff --git a/apps/shark_studio/tests/api_test.py b/apps/shark_studio/tests/api_test.py
index 7bed2cb7b0..49f4482576 100644
--- a/apps/shark_studio/tests/api_test.py
+++ b/apps/shark_studio/tests/api_test.py
@@ -36,6 +36,7 @@ def test01_LLMSmall(self):
             device="cpu",
             precision="fp32",
             quantization="None",
+            streaming_llm=True,
         )
         count = 0
         label = "Turkishoure Turkish"
diff --git a/apps/shark_studio/web/ui/chat.py b/apps/shark_studio/web/ui/chat.py
index 418f087548..54ae4a139f 100644
--- a/apps/shark_studio/web/ui/chat.py
+++ b/apps/shark_studio/web/ui/chat.py
@@ -137,7 +137,7 @@ def view_json_file(file_obj):
             streaming_llm = gr.Checkbox(
                 label="Run in streaming mode (requires recompilation)",
                 value=True,
-                interactive=True,
+                interactive=False,
             )
             prompt_prefix = gr.Checkbox(
                 label="Add System Prompt",
diff --git a/apps/shark_studio/web/ui/sd.py b/apps/shark_studio/web/ui/sd.py
index fa2c1836fd..a4df173b1c 100644
--- a/apps/shark_studio/web/ui/sd.py
+++ b/apps/shark_studio/web/ui/sd.py
@@ -17,7 +17,6 @@
     write_default_sd_config,
 )
 from apps.shark_studio.api.sd import (
-    sd_model_map,
     shark_sd_fn_dict_input,
     cancel_sd,
 )
@@ -45,11 +44,10 @@
 import apps.shark_studio.web.utils.globals as global_obj
 
 sd_default_models = [
-    "CompVis/stable-diffusion-v1-4",
     "runwayml/stable-diffusion-v1-5",
     "stabilityai/stable-diffusion-2-1-base",
     "stabilityai/stable-diffusion-2-1",
-    "stabilityai/stable-diffusion-xl-1.0",
+    "stabilityai/stable-diffusion-xl-base-1.0",
     "stabilityai/sdxl-turbo",
 ]
 
@@ -231,14 +229,9 @@ def import_original(original_img, width, height):
 
 
 def base_model_changed(base_model_id):
-    ckpt_path = Path(
-        os.path.join(
-            cmd_opts.model_dir, "checkpoints", os.path.basename(str(base_model_id))
-        )
-    )
-    ckpt_path.mkdir(parents=True, exist_ok=True)
-
-    new_choices = get_checkpoints(ckpt_path) + get_checkpoints(model_type="checkpoints")
+    new_choices = get_checkpoints(
+        os.path.join("checkpoints", os.path.basename(str(base_model_id)))
+    ) + get_checkpoints(model_type="checkpoints")
 
     return gr.Dropdown(
         value=new_choices[0] if len(new_choices) > 0 else "None",
@@ -286,14 +279,14 @@ def base_model_changed(base_model_id):
                 with gr.Row():
                     height = gr.Slider(
                         384,
-                        768,
+                        1024,
                         value=cmd_opts.height,
                         step=8,
                         label="\U00002195\U0000FE0F Height",
                     )
                     width = gr.Slider(
                         384,
-                        768,
+                        1024,
                         value=cmd_opts.width,
                         step=8,
                         label="\U00002194\U0000FE0F Width",
@@ -586,6 +579,21 @@ def base_model_changed(base_model_id):
                                     object_fit="fit",
                                     preview=True,
                                 )
+                            with gr.Row():
+                                std_output = gr.Textbox(
+                                    value=f"{sd_model_info}\n"
+                                    f"Images will be saved at "
+                                    f"{get_generated_imgs_path()}",
+                                    lines=2,
+                                    elem_id="std_output",
+                                    show_label=True,
+                                    label="Log",
+                                    show_copy_button=True,
+                                )
+                                sd_element.load(
+                                    logger.read_sd_logs, None, std_output, every=1
+                                )
+                                sd_status = gr.Textbox(visible=False)
                             with gr.Row():
                                 batch_count = gr.Slider(
                                     1,
@@ -621,18 +629,19 @@ def base_model_changed(base_model_id):
                                 stop_batch = gr.Button("Stop")
                     with gr.Tab(label="Config", id=102) as sd_tab_config:
                         with gr.Column(elem_classes=["sd-right-panel"]):
-                            Path(get_configs_path()).mkdir(parents=True, exist_ok=True)
-                            default_config_file = os.path.join(
-                                get_configs_path(),
-                                "default_sd_config.json",
-                            )
-                            write_default_sd_config(default_config_file)
-                            sd_json = gr.JSON(
-                                label="SD Config",
-                                elem_classes=["fill"],
-                                value=view_json_file(default_config_file),
-                                render=False,
-                            )
+                            with gr.Row(elem_classes=["fill"]):
+                                Path(get_configs_path()).mkdir(
+                                    parents=True, exist_ok=True
+                                )
+                                default_config_file = os.path.join(
+                                    get_configs_path(),
+                                    "default_sd_config.json",
+                                )
+                                write_default_sd_config(default_config_file)
+                                sd_json = gr.JSON(
+                                    elem_classes=["fill"],
+                                    value=view_json_file(default_config_file),
+                                )
                             with gr.Row():
                                 with gr.Column(scale=3):
                                     load_sd_config = gr.FileExplorer(
@@ -695,30 +704,11 @@ def base_model_changed(base_model_id):
                                     inputs=[sd_json, sd_config_name],
                                     outputs=[sd_config_name],
                                 )
-                            with gr.Row(elem_classes=["fill"]):
-                                sd_json.render()
                         save_sd_config.click(
                             fn=save_sd_cfg,
                             inputs=[sd_json, sd_config_name],
                             outputs=[sd_config_name],
                         )
-                    with gr.Tab(label="Log", id=103) as sd_tab_log:
-                        with gr.Row():
-                            std_output = gr.Textbox(
-                                value=f"{sd_model_info}\n"
-                                f"Images will be saved at "
-                                f"{get_generated_imgs_path()}",
-                                elem_id="std_output",
-                                show_label=True,
-                                label="Log",
-                                show_copy_button=True,
-                            )
-                            sd_element.load(
-                                logger.read_sd_logs, None, std_output, every=1
-                            )
-                            sd_status = gr.Textbox(visible=False)
-                    with gr.Tab(label="Automation", id=104) as sd_tab_automation:
-                        pass
 
     pull_kwargs = dict(
         fn=pull_sd_configs,
diff --git a/apps/shark_studio/web/utils/file_utils.py b/apps/shark_studio/web/utils/file_utils.py
index 9617c16565..3619055676 100644
--- a/apps/shark_studio/web/utils/file_utils.py
+++ b/apps/shark_studio/web/utils/file_utils.py
@@ -47,7 +47,7 @@ def write_default_sd_config(path):
 
 
 def safe_name(name):
-    return name.replace("/", "_").replace("-", "_")
+    return name.split("/")[-1].replace("-", "_")
 
 
 def get_path_stem(path):
diff --git a/apps/shark_studio/web/utils/metadata/png_metadata.py b/apps/shark_studio/web/utils/metadata/png_metadata.py
index 72f663f246..d1cadc1e00 100644
--- a/apps/shark_studio/web/utils/metadata/png_metadata.py
+++ b/apps/shark_studio/web/utils/metadata/png_metadata.py
@@ -3,9 +3,8 @@
 from apps.shark_studio.web.utils.file_utils import (
     get_checkpoint_pathfile,
 )
-from apps.shark_studio.api.sd import (
-    sd_model_map,
-)
+from apps.shark_studio.api.sd import EMPTY_SD_MAP as sd_model_map
+
 from apps.shark_studio.modules.schedulers import (
     scheduler_model_map,
 )
diff --git a/apps/shark_studio/web/utils/tmp_configs.py b/apps/shark_studio/web/utils/tmp_configs.py
index 7f65120cbb..ebbc4ae6af 100644
--- a/apps/shark_studio/web/utils/tmp_configs.py
+++ b/apps/shark_studio/web/utils/tmp_configs.py
@@ -17,7 +17,7 @@ def clear_tmp_mlir():
         and filename.endswith(".mlir")
     ]
     for filename in mlir_files:
-        os.remove(shark_tmp + filename)
+        os.remove(os.path.join(shark_tmp, filename))
     print(f"Clearing .mlir temporary files took {time() - cleanup_start:.4f} seconds.")
 
 
diff --git a/requirements.txt b/requirements.txt
index 1ff2b685d7..bbb8adf6b1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,13 +1,14 @@
--f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
+-f https://download.pytorch.org/whl/nightly/cpu
 -f https://iree.dev/pip-release-links.html
 --pre
 
 setuptools
 wheel
 
-torch==2.3.0
+
+torch>=2.3.0
 shark-turbine @ git+https://github.com/iree-org/iree-turbine.git@main
-turbine-models @ git+https://github.com/nod-ai/SHARK-Turbine.git@main#subdirectory=models
+turbine-models @ git+https://github.com/nod-ai/SHARK-Turbine.git@ean-unify-sd#subdirectory=models
 
 # SHARK Runner
 tqdm
@@ -17,17 +18,14 @@ google-cloud-storage
 
 # Testing
 pytest
-pytest-xdist
-pytest-forked
 Pillow
 parameterized
 
 # Add transformers, diffusers and scipy since it most commonly used
 #accelerate is now required for diffusers import from ckpt.
 accelerate
-scipy
 ftfy
-gradio==4.19.2
+gradio==4.29.0
 altair
 omegaconf
 # 0.3.2 doesn't have binaries for arm64
diff --git a/setup_venv.ps1 b/setup_venv.ps1
index 749a7c4e6f..b6a6994449 100644
--- a/setup_venv.ps1
+++ b/setup_venv.ps1
@@ -88,5 +88,8 @@ else {python -m venv .\shark.venv\}
 .\shark.venv\Scripts\activate
 python -m pip install --upgrade pip
 pip install wheel
-pip install -r requirements.txt
+pip install --pre -r requirements.txt
+pip install -e .
+
+>>>>>>> 0c904eb7 (Shark Studio SDXL support, HIP driver support, simpler device info, small fixes)
 Write-Host "Source your venv with ./shark.venv/Scripts/activate"
diff --git a/setup_venv.sh b/setup_venv.sh
index 64f769d794..11fe79fd68 100755
--- a/setup_venv.sh
+++ b/setup_venv.sh
@@ -84,21 +84,7 @@ else
   PYTORCH_URL=https://download.pytorch.org/whl/nightly/cpu/
 fi
 
-$PYTHON -m pip install --no-warn-conflicts -e . -f https://llvm.github.io/torch-mlir/package-index/ -f ${RUNTIME} -f ${PYTORCH_URL}
-
-if [[ $(uname -s) = 'Linux' && ! -z "${IMPORTER}" ]]; then
-  T_VER=$($PYTHON -m pip show torch | grep Version)
-  T_VER_MIN=${T_VER:14:12}
-  TV_VER=$($PYTHON -m pip show torchvision | grep Version)
-  TV_VER_MAJ=${TV_VER:9:6}
-  $PYTHON -m pip uninstall -y torchvision
-  $PYTHON -m pip install torchvision==${TV_VER_MAJ}${T_VER_MIN} --no-deps -f https://download.pytorch.org/whl/nightly/cpu/torchvision/
-  if [ $? -eq 0 ];then
-    echo "Successfully Installed torch + cu118."
-  else
-    echo "Could not install torch + cu118." >&2
-  fi
-fi
+$PYTHON -m pip install --no-warn-conflicts -e . -f ${RUNTIME} -f ${PYTORCH_URL}
 
 if [[ -z "${NO_BREVITAS}" ]]; then
   $PYTHON -m pip install git+https://github.com/Xilinx/brevitas.git@dev
diff --git a/shark/iree_utils/_common.py b/shark/iree_utils/_common.py
index c58405b46e..1d022f67e4 100644
--- a/shark/iree_utils/_common.py
+++ b/shark/iree_utils/_common.py
@@ -76,6 +76,7 @@ def get_supported_device_list():
     "vulkan": "vulkan",
     "metal": "metal",
     "rocm": "rocm",
+    "hip": "hip",
     "intel-gpu": "level_zero",
 }
 
@@ -94,6 +95,7 @@ def iree_target_map(device):
     "vulkan": "vulkan-spirv",
     "metal": "metal",
     "rocm": "rocm",
+    "hip": "rocm",
     "intel-gpu": "opencl-spirv",
 }
 
diff --git a/shark/iree_utils/compile_utils.py b/shark/iree_utils/compile_utils.py
index 5fd1d4006a..f93c8fef2e 100644
--- a/shark/iree_utils/compile_utils.py
+++ b/shark/iree_utils/compile_utils.py
@@ -62,13 +62,16 @@ def get_iree_device_args(device, extra_args=[]):
         from shark.iree_utils.gpu_utils import get_iree_rocm_args
 
         return get_iree_rocm_args(device_num=device_num, extra_args=extra_args)
+    if device == "hip":
+        from shark.iree_utils.gpu_utils import get_iree_rocm_args
+        return get_iree_rocm_args(device_num=device_num, extra_args=extra_args, hip_driver=True)
     return []
 
 def get_iree_target_triple(device):
     args = get_iree_device_args(device)
     for flag in args:
-        if "triple" in flag.split("-"):
-            triple = flag.split("=")
+        if "triple" in flag:
+            triple = flag.split("=")[-1]
             return triple
     return ""
 
@@ -89,9 +92,9 @@ def clean_device_info(raw_device):
         if len(device_id) <= 2:
             device_id = int(device_id)
 
-    if device not in ["rocm", "vulkan"]:
+    if device not in ["hip", "rocm", "vulkan"]:
         device_id = None
-    if device in ["rocm", "vulkan"] and device_id == None:
+    if device in ["hip", "rocm", "vulkan"] and device_id == None:
         device_id = 0
     return device, device_id
 
diff --git a/shark/iree_utils/gpu_utils.py b/shark/iree_utils/gpu_utils.py
index 0eba67ff53..db6ef14e34 100644
--- a/shark/iree_utils/gpu_utils.py
+++ b/shark/iree_utils/gpu_utils.py
@@ -52,7 +52,7 @@ def check_rocm_device_arch_in_args(extra_args):
     return None
 
 
-def get_rocm_device_arch(device_num=0, extra_args=[]):
+def get_rocm_device_arch(device_num=0, extra_args=[], hip_driver=False):
     # ROCM Device Arch selection:
     # 1 : User given device arch using `--iree-rocm-target-chip` flag
     # 2 : Device arch from `iree-run-module --dump_devices=rocm` for device on index <device_num>
@@ -68,15 +68,23 @@ def get_rocm_device_arch(device_num=0, extra_args=[]):
     arch_in_device_dump = None
 
     # get rocm arch from iree dump devices
-    def get_devices_info_from_dump(dump):
+    def get_devices_info_from_dump(dump, driver):
         from os import linesep
-
-        dump_clean = list(
-            filter(
-                lambda s: "--device=rocm" in s or "gpu-arch-name:" in s,
-                dump.split(linesep),
+        
+        if driver == "hip":
+            dump_clean = list(
+                filter(
+                    lambda s: "AMD" in s,
+                    dump.split(linesep),
+                )
+            )
+        else:
+            dump_clean = list(
+                filter(
+                    lambda s: f"--device={driver}" in s or "gpu-arch-name:" in s,
+                    dump.split(linesep),
+                )
             )
-        )
         arch_pairs = [
             (
                 dump_clean[i].split("=")[1].strip(),
@@ -87,16 +95,17 @@ def get_devices_info_from_dump(dump):
         return arch_pairs
 
     dump_device_info = None
+    driver = "hip" if hip_driver else "rocm"
     try:
         dump_device_info = run_cmd(
-            "iree-run-module --dump_devices=rocm", raise_err=True
+            "iree-run-module --dump_devices=" + driver, raise_err=True
         )
     except Exception as e:
-        print("could not execute `iree-run-module --dump_devices=rocm`")
+        print("could not execute `iree-run-module --dump_devices=" + driver + "`")
 
     if dump_device_info is not None:
         device_num = 0 if device_num is None else device_num
-        device_arch_pairs = get_devices_info_from_dump(dump_device_info[0])
+        device_arch_pairs = get_devices_info_from_dump(dump_device_info[0], driver)
         if len(device_arch_pairs) > device_num:  # can find arch in the list
             arch_in_device_dump = device_arch_pairs[device_num][1]
 
@@ -107,24 +116,22 @@ def get_devices_info_from_dump(dump):
     default_rocm_arch = "gfx1100"
     print(
         "Did not find ROCm architecture from `--iree-rocm-target-chip` flag"
-        "\n or from `iree-run-module --dump_devices=rocm` command."
+        "\n or from `iree-run-module --dump_devices` command."
         f"\nUsing {default_rocm_arch} as ROCm arch for compilation."
     )
     return default_rocm_arch
 
 
 # Get the default gpu args given the architecture.
-def get_iree_rocm_args(device_num=0, extra_args=[]):
+def get_iree_rocm_args(device_num=0, extra_args=[], hip_driver=False):
     ireert.flags.FUNCTION_INPUT_VALIDATION = False
-    rocm_flags = ["--iree-rocm-link-bc=true"]
-
+    rocm_flags = []
     if check_rocm_device_arch_in_args(extra_args) is None:
-        rocm_arch = get_rocm_device_arch(device_num, extra_args)
+        rocm_arch = get_rocm_device_arch(device_num, extra_args, hip_driver=hip_driver)
         rocm_flags.append(f"--iree-rocm-target-chip={rocm_arch}")
 
     return rocm_flags
 
-
 # Some constants taken from cuda.h
 CUDA_SUCCESS = 0
 CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT = 16