fix adapter error

inversion_run_adapter.py

@@ -92,18 +92,12 @@ adapters = MultiAdapter(
 )
 adapters = adapters.to(torch.float16)
 
-adapters =  T2IAdapter.from_pretrained(
-    "TencentARC/t2i-adapter-lineart-sdxl-1.0",
-    torch_dtype=torch.float16,
-    varient="fp16",
-),
-
 pipeline = DiffusionPipeline.from_pretrained(
     BASE_MODEL,
     torch_dtype=torch.float16, 
     variant="fp16", 
     use_safetensors=True,
-    # vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16),
+    vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16),
     adapter=adapters,
     custom_pipeline="./pipelines/pipeline_sdxl_adapter_img2img.py",
 )
@@ -170,13 +164,12 @@ def run(
     print(f"-------->num_steps_inversion: {num_steps_inversion} num_steps_actual: {num_steps_actual} step_start: {config.step_start}")
     print(f"-------->timesteps len: {len(timesteps)} max_norm_zs len: {len(config.max_norm_zs)}")
     lineart_image = lineart_detector(input_image, detect_resolution=int(generate_size * lineart_detect), image_resolution=generate_size)
-    # canny_image = canndy_detector(input_image, detect_resolution=int(generate_size * canny_detect), image_resolution=generate_size)
+    canny_image = canndy_detector(input_image, detect_resolution=int(generate_size * canny_detect), image_resolution=generate_size)
     # pidinet_image = pidinet_detector(input_image, detect_resolution=512, image_resolution=generate_size, apply_filter=True)
     # depth_image = midas_detector(input_image, detect_resolution=512, image_resolution=generate_size)
-    # cond_image = [lineart_image, canny_image]
-    # conditioning_scale = [lineart_scale, canny_scale]
-    cond_image = lineart_image
-    conditioning_scale = lineart_scale
+    cond_image = [lineart_image, canny_image]
+    conditioning_scale = [lineart_scale, canny_scale]
+
     pipeline.__call__ = partial(
         pipeline.__call__,
         num_inference_steps=num_steps_inversion,