From c2f717d6e95555fa390f38ea4c687866197778fb Mon Sep 17 00:00:00 2001
From: Ean Garvey <garveyej@gmail.com>
Date: Mon, 27 May 2024 00:02:18 -0500
Subject: [PATCH] Formatting

---
 apps/shark_studio/api/sd.py                  | 41 +++++++++++++++-----
 apps/shark_studio/api/utils.py               |  1 +
 apps/shark_studio/modules/ckpt_processing.py |  3 +-
 3 files changed, 34 insertions(+), 11 deletions(-)

diff --git a/apps/shark_studio/api/sd.py b/apps/shark_studio/api/sd.py
index a3c6f294e6..83574d294d 100644
--- a/apps/shark_studio/api/sd.py
+++ b/apps/shark_studio/api/sd.py
@@ -109,7 +109,9 @@ def __init__(
         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))
+            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)
@@ -155,29 +157,48 @@ def prepare_pipe(self, custom_weights, adapters, embeddings, is_img2img):
         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)
+            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")
+                    unet_weights_path = os.path.join(
+                        diffusers_weights_path,
+                        "unet",
+                        "diffusion_pytorch_model.safetensors",
+                    )
                     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")
+                        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")
+                        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.")
+                            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")
+                    vae_weights_path = os.path.join(
+                        diffusers_weights_path,
+                        "vae",
+                        "diffusion_pytorch_model.safetensors",
+                    )
                     weights[key] = save_irpa(vae_weights_path, "vae.")
-                
 
         vmfbs, weights = self.sd_pipe.check_prepared(
             mlirs, vmfbs, weights, interactive=False
diff --git a/apps/shark_studio/api/utils.py b/apps/shark_studio/api/utils.py
index d63da5fc1a..0516255d2b 100644
--- a/apps/shark_studio/api/utils.py
+++ b/apps/shark_studio/api/utils.py
@@ -135,6 +135,7 @@ def parse_device(device_str):
         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:
diff --git a/apps/shark_studio/modules/ckpt_processing.py b/apps/shark_studio/modules/ckpt_processing.py
index 523ca08b57..433df13654 100644
--- a/apps/shark_studio/modules/ckpt_processing.py
+++ b/apps/shark_studio/modules/ckpt_processing.py
@@ -30,7 +30,7 @@ def get_path_to_diffusers_checkpoint(custom_weights, precision="fp16"):
     return path_to_diffusers
 
 
-def preprocessCKPT(custom_weights, precision = "fp16", is_inpaint=False):
+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)
@@ -63,6 +63,7 @@ def preprocessCKPT(custom_weights, precision = "fp16", is_inpaint=False):
     print("Loading complete")
     return path_to_diffusers
 
+
 def save_irpa(weights_path, prepend_str):
     weights = safetensors.torch.load_file(weights_path)
     archive = ParameterArchiveBuilder()