app demo

app.py

@@ -80,8 +80,9 @@ class Pipeline:
         self.diffusion_sampler.guidance_interval_min = guidance_interval_min
         self.diffusion_sampler.guidance_interval_max = guidance_interval_max
         self.diffusion_sampler.timeshift = timeshift
-        generator = torch.Generator(device="cuda").manual_seed(seed)
-        xT = torch.randn((num_images, 4, self.resolution//8, self.resolution//8), device="cuda", dtype=torch.float32, generator=generator)
+        generator = torch.Generator(device="cpu").manual_seed(seed)
+        xT = torch.randn((num_images, 4, self.resolution//8, self.resolution//8), device="cpu", dtype=torch.float32, generator=generator)
+        xT = xT.to("cuda")
         with torch.no_grad():
             condition, uncondition = conditioner([self.classlabels2ids[y],]*num_images)
         # Sample images:
@@ -123,7 +124,7 @@ if __name__ == "__main__":
        w_scheduler=LinearScheduler(),
        guidance_fn=simple_guidance_fn,
        num_steps=50,
-       guidance=3.0,
+       guidance=4.0,
        state_refresh_rate=1,
        guidance_interval_min=0.3,
        guidance_interval_max=1.0,