test single adapter

app_haircolor_inpaint_adapter_15.py

@@ -41,21 +41,26 @@ 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_sd14v1",
-            torch_dtype=torch.float16,
-            varient="fp16",
-        ),
-        T2IAdapter.from_pretrained(
-            "TencentARC/t2iadapter_sketch_sd14v1",
-            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_canny_sd15v2",
+    torch_dtype=torch.float16,
+    varient="fp16",
 )
-adapters = adapters.to(torch.float16)
 
 basepipeline = DiffusionPipeline.from_pretrained(
     BASE_MODEL,
@@ -80,8 +85,8 @@ def image_to_image(
     num_steps: int,
     guidance_scale: float,
     generate_size: int,
-    cond_scale1: float = 0.8,
-    cond_scale2: float = 0.8,
+    cond_scale1: float = 1.2,
+    cond_scale2: float = 1.2,
 ):
     run_task_time = 0
     time_cost_str = ''
@@ -92,6 +97,9 @@ def image_to_image(
     pidiNet_image = pidiNet_detector(input_image, int(generate_size*1), generate_size)
     cond_image = [canny_image, pidiNet_image]
     cond_scale = [cond_scale1, cond_scale2]
+    
+    cond_image = canny_image
+    cond_scale = cond_scale1
 
     generator = torch.Generator(device=DEVICE).manual_seed(seed)
     generated_image = basepipeline(