Small cleanup

nod-ai · Feb 2, 2024 · 019ba70 · 019ba70
1 parent a43c559
commit 019ba70
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Showing 2 changed files with 44 additions and 40 deletions.
diff --git a/apps/shark_studio/api/sd.py b/apps/shark_studio/api/sd.py
@@ -374,46 +374,6 @@ def decode_latents(self, latents, cpu_scheduling=True):
         pil_images = self.image_processor.numpy_to_pil(images)
         return pil_images
 
-    # def process_sd_init_image(self, sd_init_image, resample_type):
-    #     if isinstance(sd_init_image, list):
-    #         images = []
-    #         for img in sd_init_image:
-    #             img, _ = self.process_sd_init_image(img, resample_type)
-    #             images.append(img)
-    #             is_img2img = True
-    #             return images, is_img2img
-    #     if isinstance(sd_init_image, str):
-    #         if os.path.isfile(sd_init_image):
-    #             sd_init_image = Image.open(sd_init_image, mode="r").convert("RGB")
-    #             image, is_img2img = self.process_sd_init_image(
-    #                 sd_init_image, resample_type
-    #             )
-    #         else:
-    #             image = None
-    #             is_img2img = False
-    #     elif isinstance(sd_init_image, Image.Image):
-    #         image = sd_init_image.convert("RGB")
-    #     elif sd_init_image:
-    #         image = sd_init_image["image"].convert("RGB")
-    #     else:
-    #         image = None
-    #         is_img2img = False
-    #     if image:
-    #         resample_type = (
-    #             resamplers[resample_type]
-    #             if resample_type in resampler_list
-    #             # Fallback to Lanczos
-    #             else Image.Resampling.LANCZOS
-    #         )
-    #         image = image.resize((self.width, self.height), resample=resample_type)
-    #         image_arr = np.stack([np.array(i) for i in (image,)], axis=0)
-    #         image_arr = image_arr / 255.0
-    #         image_arr = torch.from_numpy(image_arr).permute(0, 3, 1, 2).to(self.dtype)
-    #         image_arr = 2 * (image_arr - 0.5)
-    #         is_img2img = True
-    #         image = image_arr
-    #     return image, is_img2img
-
     def generate_images(
         self,
         prompt,

diff --git a/apps/shark_studio/modules/img_processing.py b/apps/shark_studio/modules/img_processing.py
@@ -1,13 +1,16 @@
 import os
 import re
 import json
+import torch
+import numpy as np
 
 from csv import DictWriter
 from PIL import Image, PngImagePlugin
 from pathlib import Path
 from datetime import datetime as dt
 from base64 import decode
 
+
 resamplers = {
     "Lanczos": Image.Resampling.LANCZOS,
     "Nearest Neighbor": Image.Resampling.NEAREST,
@@ -158,3 +161,44 @@ def resize_stencil(image: Image.Image, width, height, resampler_type=None):
         resampler = resamplers["Nearest Neighbor"]
     new_image = image.resize((n_width, n_height), resampler=resampler)
     return new_image, n_width, n_height
+
+
+def process_sd_init_image(self, sd_init_image, resample_type):
+    if isinstance(sd_init_image, list):
+        images = []
+        for img in sd_init_image:
+            img, _ = self.process_sd_init_image(img, resample_type)
+            images.append(img)
+            is_img2img = True
+            return images, is_img2img
+    if isinstance(sd_init_image, str):
+        if os.path.isfile(sd_init_image):
+            sd_init_image = Image.open(sd_init_image, mode="r").convert("RGB")
+            image, is_img2img = self.process_sd_init_image(
+                sd_init_image, resample_type
+            )
+        else:
+            image = None
+            is_img2img = False
+    elif isinstance(sd_init_image, Image.Image):
+        image = sd_init_image.convert("RGB")
+    elif sd_init_image:
+        image = sd_init_image["image"].convert("RGB")
+    else:
+        image = None
+        is_img2img = False
+    if image:
+        resample_type = (
+            resamplers[resample_type]
+            if resample_type in resampler_list
+            # Fallback to Lanczos
+            else Image.Resampling.LANCZOS
+        )
+        image = image.resize((self.width, self.height), resample=resample_type)
+        image_arr = np.stack([np.array(i) for i in (image,)], axis=0)
+        image_arr = image_arr / 255.0
+        image_arr = torch.from_numpy(image_arr).permute(0, 3, 1, 2).to(self.dtype)
+        image_arr = 2 * (image_arr - 0.5)
+        is_img2img = True
+        image = image_arr
+    return image, is_img2img