Update app.py

app.py

@@ -127,16 +127,16 @@ face_app = FaceAnalysis(name="buffalo_l", root=str(CACHE_ROOT), providers=provid
 face_app.prepare(ctx_id=(0 if torch.cuda.is_available() else -1), det_size=(640, 640))
 
 # ControlNet + SD パイプライン
-controlnet = ControlNetModel.from_pretrained(
-    "InstantX/InstantID", subfolder="ControlNetModel", torch_dtype=dtype
-)
+#controlnet = ControlNetModel.from_pretrained(
+#    "InstantX/InstantID", subfolder="ControlNetModel", torch_dtype=dtype
+#)
 pipe = StableDiffusionPipeline.from_single_file(
     BASE_CKPT, torch_dtype=dtype, safety_checker=None, use_safetensors=True, clip_skip=2
 )
 pipe.vae = AutoencoderKL.from_pretrained(
     "stabilityai/sd-vae-ft-mse", torch_dtype=dtype
 ).to(device)
-pipe.controlnet = controlnet
+#pipe.controlnet = controlnet
 pipe.scheduler = DPMSolverMultistepScheduler.from_config(
     pipe.scheduler.config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++"
 )
@@ -217,8 +217,8 @@ def generate(
         prompt=prompt,
         negative_prompt=neg,
         ip_adapter_image=img_in,
-        image=img_in,
-        controlnet_conditioning_scale=0.9,
+        #image=img_in,
+        #controlnet_conditioning_scale=0.9,
         num_inference_steps=int(steps) + 5,
         guidance_scale=cfg,
         width=int(w),
@@ -268,4 +268,4 @@ with gr.Blocks() as demo:
     )
 
 print("launching …")
-demo.queue().launch(show_error=True)
+demo.queue().launch(show_error=True)