testing diffusers version

handler.py

@@ -4,7 +4,16 @@ from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
 from PIL import Image
 from skimage.exposure import match_histograms, adjust_gamma
 import numpy as np
+import diffusers
+def image_grid(imgs, rows, cols):
+    assert len(imgs) == rows*cols
 
+    w, h = imgs[0].size
+    grid = Image.new('RGB', size=(cols*w, rows*h))
+    
+    for i, img in enumerate(imgs):
+        grid.paste(img, box=(i%cols*w, i//cols*h))
+    return grid
 
 class EndpointHandler():
     def __init__(self, path=""):
@@ -39,23 +48,24 @@ class EndpointHandler():
         return ret
     
     def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
-        prompts = data["prompts"]
-        num_images_per_prompt = data.get("num_images_per_prompt", 1)
-        do_img2img = data.get("do_img2img", True)
-        match_histogram = data.get("match_histogram", True)
-        adjust_contrast = data.get("adjust_contrast", True)
-        num_inference_steps = data.get("num_inference_steps", 25)
-        guidance_scale = data.pop("guidance_scale", 7.5)    
-        all_stories = []
-        for _ in range(num_images_per_prompt):
-            story = self.generate_story(prompts, do_img2img, num_inference_steps, guidance_scale)
-            if match_histogram:
-                story = [match_colors(story[0], img) for img in story]
-            if adjust_contrast:
-                story = [add_contrast(img) for img in story]
-            all_stories.append(story)
-        all_stories = [self.generate_story(prompts, do_img2img, num_inference_steps, guidance_scale) for _ in range(num_images_per_prompt)]
-        return all_stories
+        return diffusers.__version__
+        # prompts = data["prompts"]
+        # num_images_per_prompt = data.get("num_images_per_prompt", 1)
+        # do_img2img = data.get("do_img2img", True)
+        # match_histogram = data.get("match_histogram", True)
+        # adjust_contrast = data.get("adjust_contrast", True)
+        # num_inference_steps = data.get("num_inference_steps", 25)
+        # guidance_scale = data.pop("guidance_scale", 7.5)    
+        # all_stories = []
+        # for _ in range(num_images_per_prompt):
+        #     story = self.generate_story(prompts, do_img2img, num_inference_steps, guidance_scale)
+        #     if match_histogram:
+        #         story = [match_colors(story[0], img) for img in story]
+        #     if adjust_contrast:
+        #         story = [add_contrast(img) for img in story]
+        #     all_stories.append(story)
+        # all_stories = [self.generate_story(prompts, do_img2img, num_inference_steps, guidance_scale) for _ in range(num_images_per_prompt)]
+        # return all_stories
                                       
 def match_colors(src,tar):
     ret = match_histograms(np.array(tar), np.array(src), multichannel=True)

requirements.txt

@@ -1 +1,2 @@
 scikit-image
+git+https://github.com/huggingface/diffusers@4fc7084875ac