example

app.py

@@ -226,6 +226,7 @@ class DDIMSampler(Sampler):
         steps = np.concatenate((steps, steps[-1:]), axis=0)
 
         x_t = torch.tile(noised_latents, (batch_size, 1, 1, 1)).to(self.device)  # 32, 32
+        print("sample", steps)
         for i in trange(len(steps) - 1):
             x_t = self.sample(model, x_t, steps[i], steps[i + 1], eta)
 
@@ -486,7 +487,7 @@ def init_webui(unet, vae, normal_t):
             looper = sampler.sample_loop(unet, vae.middle_c, batch_size, step_value, shape=img_size, eta=1.)
         else:
             input_image_value = Image.fromarray(input_image_value).resize((img_size[0] * 8, img_size[1] * 8),
-                                                                          Image.ANTIALIAS)
+                                                                          resample=Image.BILINEAR)
             input_image_value = np.array(input_image_value, dtype=np.float32) / 255.
             input_image_value = np.transpose(input_image_value, (2, 0, 1))
             input_image_value = torch.Tensor([input_image_value]).to(device)
@@ -537,7 +538,7 @@ def init_webui(unet, vae, normal_t):
                 inputs=[step_u, batch_size_u, sampler_name_u, img_size_u, ramdom_seed_u],
                 outputs=output_images_u,
                 fn=process_image_u,
-                # cache_examples=True,
+                cache_examples=False,
             )
         with gr.Tab(label="image to image"):
             with gr.Column():
@@ -565,7 +566,7 @@ def init_webui(unet, vae, normal_t):
                 inputs=[input_image, noise_step, step, batch_size, sampler_name, img_size, ramdom_seed],
                 outputs=output_images,
                 fn=process_image,
-                # cache_examples=True,
+                cache_examples=False,
             )
 
         start_button.click(process_image,