improve logging, provide rng within filter

api/onnx_web/image.py

@@ -7,7 +7,6 @@ import numpy as np
 from .utils import (
     Border,
     Point,
-    Size,
 )
 
 
@@ -197,16 +196,23 @@ def process_tiles(
     width, height = source.size
     image = Image.new('RGB', (width * scale, height * scale))
 
-    for x in range(width // tile):
-        for y in range(height // tile):
+    tiles_x = width // tile
+    tiles_y = height // tile
+    total = tiles_x * tiles_y
+
+    rng = random.RandomState(0)
+
+    for y in range(tiles_y):
+        for x in range(tiles_x):
+            idx = (y * tiles_x) + x
             left = x * tile
             top = y * tile
-            print('processing tile', x, y, left, top)
-            tile = source.crop((left, top, left + tile, top + tile))
+            print('processing tile %s of %s, %s.%s', idx, total, x, y)
+            tile_image = source.crop((left, top, left + tile, top + tile))
 
             for filter in filters:
-                tile = filter(tile)
+                tile_image = filter(tile_image)
 
-            image.paste(tile, (left * scale, top * scale))
+            image.paste(tile_image, (left * scale, top * scale))
 
     return image

api/onnx_web/upscale.py

@@ -147,8 +147,8 @@ def upscale_stable_diffusion(ctx: ServerContext, params: UpscaleParams, image: I
     # result = pipeline('', image=image)
 
     pipeline = StableDiffusionUpscalePipeline.from_pretrained('stabilityai/stable-diffusion-x4-upscaling')
-    upscale = lambda i: pipeline('an astronaut eating a hamburger', image=i).images[0]
-    result = process_tiles(image, 64, 4, [upscale])
+    upscale = lambda i: pipeline('an astronaut eating a hamburger', image=i, rng=np.random.RandomState(0)).images[0]
+    result = process_tiles(image, 128, 4, [upscale])
     return result