ddpm

app.py

@@ -258,8 +258,8 @@ def greet(input):
 
 	#samples, intermediate = sample_ddim_context(32, ctx, n=steps)
 
-	###samples, intermediate = sample_ddpm_context(image_count, steps, ctx)
-	samples, intermediate = sample_ddim_context(image_count, ctx, steps)
+	samples, intermediate = sample_ddpm_context(image_count, steps, ctx)
+#	#samples, intermediate = sample_ddim_context(image_count, ctx, steps)
 
 	#samples, intermediate = sample_ddim(32, n=steps)
 	#ctx = F.one_hot(torch.randint(0, 5, (32,)), 5).to(device=device).float()
@@ -269,16 +269,19 @@ def greet(input):
 	#response2 = transform2(transform(np.moveaxis(samples.detach().cpu().numpy(),1,3)[1]))
 	#response = im.fromarray(intermediate[24][0][1]).convert("RGB")
 
-	sx_gen_store = np.moveaxis(intermediate,2,4)
-	nsx_gen_store = norm_all(sx_gen_store, sx_gen_store.shape[0], image_count)
-
-	response = intermediate.shape;
-	response2 = transform2(transform(nsx_gen_store[-1][0]))
-
+#	# sx_gen_store = np.moveaxis(intermediate,2,4)
+	# nsx_gen_store = norm_all(sx_gen_store, sx_gen_store.shape[0], image_count)
+	#
+	# response = intermediate.shape;
+	# response2 = transform2(transform(nsx_gen_store[-1][0]))
+	#
 	sx_gen_store = np.moveaxis([samples],2,4)
 	nsx_gen_store = norm_all(sx_gen_store, sx_gen_store.shape[0], image_count)
 
 	response3 = transform2(transform(nsx_gen_store[-1][0]))
+
+
+
 	# response3 = transform2(transform(nsx_gen_store[-1][1]))
 	# response4 = transform2(transform(nsx_gen_store[-1][2]))
 	# response5 = transform2(transform(nsx_gen_store[-1][3]))