tests

requirements.txt

@@ -2,4 +2,5 @@ lpips==0.1.4
 opencv-python==4.7.0.68
 ffmpeg-python
 diffusers["torch"]==0.23.0
-transformers==4.35.2
+transformers==4.35.2
+pytest

test_latentblending.py

@@ -0,0 +1,54 @@
+import unittest
+from latent_blending import LatentBlending
+from diffusers_holder import DiffusersHolder
+from diffusers import DiffusionPipeline
+import torch
+
+default_pipe = "stabilityai/stable-diffusion-xl-base-1.0"
+
+
+class TestDiffusersHolder(unittest.TestCase):
+
+    def test_load_diffusers_holder(self):
+        pipe = DiffusionPipeline.from_pretrained(default_pipe, torch_dtype=torch.float16).to('cuda')
+        dh = DiffusersHolder(pipe)
+        self.assertIsNotNone(dh, "Failed to load DiffusersHolder")
+
+
+class TestSingleImageGeneration(unittest.TestCase):
+
+    def test_single_image_generation(self):
+        pipe = DiffusionPipeline.from_pretrained(default_pipe, torch_dtype=torch.float16).to('cuda')
+        dh = DiffusersHolder(pipe)
+        dh.set_dimensions((1024, 704))
+        dh.set_num_inference_steps(40)
+        prompt = "Your prompt here"
+        text_embeddings = dh.get_text_embedding(prompt)
+        generator = torch.Generator(device=dh.device).manual_seed(int(420))
+        latents_start = dh.get_noise()
+        list_latents_1 = dh.run_diffusion(text_embeddings, latents_start)
+        img_orig = dh.latent2image(list_latents_1[-1])
+        self.assertIsNotNone(img_orig, "Failed to generate an image")
+
+
+class TestImageTransition(unittest.TestCase):
+
+    def test_image_transition(self):
+        pipe = DiffusionPipeline.from_pretrained(default_pipe, torch_dtype=torch.float16).to('cuda')
+        dh = DiffusersHolder(pipe)
+        lb = LatentBlending(dh)
+
+        lb.set_prompt1('photo of my first prompt1')
+        lb.set_prompt2('photo of my second prompt')
+        depth_strength = 0.6
+        t_compute_max_allowed = 10
+        num_inference_steps = 30
+        imgs_transition = lb.run_transition(
+            depth_strength=depth_strength,
+            num_inference_steps=num_inference_steps,
+            t_compute_max_allowed=t_compute_max_allowed)
+
+        self.assertTrue(len(imgs_transition) > 0, "No transition images generated")
+
+if __name__ == '__main__':
+    unittest.main()