test multi adapter

app_haircolor_inpaint_adapter_15.py

@@ -27,6 +27,7 @@ from controlnet_aux import (
 )
 
 BASE_MODEL = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+# BASE_MODEL = "stable-diffusion-v1-5/stable-diffusion-inpainting"
 
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -42,26 +43,26 @@ lineart_detector = lineart_detector.to(DEVICE)
 pidiNet_detector = PidiNetDetector.from_pretrained('lllyasviel/Annotators')
 pidiNet_detector = pidiNet_detector.to(DEVICE)
 
-# adapters = MultiAdapter(
-#     [
-#         T2IAdapter.from_pretrained(
-#             "TencentARC/t2iadapter_canny_sd15v2",
-#             torch_dtype=torch.float16,
-#             varient="fp16",
-#         ),
-#         T2IAdapter.from_pretrained(
-#             "TencentARC/t2iadapter_sketch_sd15v2",
-#             torch_dtype=torch.float16,
-#             varient="fp16",
-#         ),
-#     ]
-# )
-# adapters = adapters.to(torch.float16)
-adapters = T2IAdapter.from_pretrained(
-    "TencentARC/t2iadapter_sketch_sd15v2",
-    torch_dtype=torch.float16,
-    varient="fp16",
+adapters = MultiAdapter(
+    [
+        T2IAdapter.from_pretrained(
+            "TencentARC/t2iadapter_canny_sd15v2",
+            torch_dtype=torch.float16,
+            varient="fp16",
+        ),
+        T2IAdapter.from_pretrained(
+            "TencentARC/t2iadapter_sketch_sd15v2",
+            torch_dtype=torch.float16,
+            varient="fp16",
+        ),
+    ]
 )
+adapters = adapters.to(torch.float16)
+# adapters = T2IAdapter.from_pretrained(
+#     "TencentARC/t2iadapter_sketch_sd15v2",
+#     torch_dtype=torch.float16,
+#     varient="fp16",
+# )
 
 basepipeline = DiffusionPipeline.from_pretrained(
     BASE_MODEL,
@@ -93,16 +94,16 @@ def image_to_image(
     time_cost_str = ''
     run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
     # canny_image = canny_detector(input_image, int(generate_size*1), generate_size)
-    # canny_image = custom_canny_detector(input_image)
+    canny_image = custom_canny_detector(input_image)
     # lineart_image = lineart_detector(input_image, int(generate_size*1), generate_size)
     # run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
     pidiNet_image = pidiNet_detector(input_image, int(generate_size*1), generate_size)
     pidiNet_image = pidiNet_image.convert("L")
-    # cond_image = [canny_image, pidiNet_image]
-    # cond_scale = [cond_scale1, cond_scale2]
+    cond_image = [canny_image, pidiNet_image]
+    cond_scale = [cond_scale1, cond_scale2]
     
-    cond_image = pidiNet_image
-    cond_scale = cond_scale1
+    # cond_image = pidiNet_image
+    # cond_scale = cond_scale1
 
     generator = torch.Generator(device=DEVICE).manual_seed(seed)
     generated_image = basepipeline(