Update app.py

app.py

@@ -153,7 +153,7 @@ def clamp_size_to_megapixels(w: int, h: int, max_mpx: float = 1.0) -> tuple[int,
     return max(64, int(w * r)) // 8 * 8, max(64, int(h * r)) // 8 * 8
 
 # ──────────────────────────────────────────────────────────────────────────────
-# Models (use dtype= and use_safetensors=True to avoid offload_state_dict issue)
+# Models (use torch_dtype= and use_safetensors=True)
 # ──────────────────────────────────────────────────────────────────────────────
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -168,17 +168,17 @@ scheduler = EulerAncestralDiscreteScheduler.from_pretrained(
 controlnet_scribble = ControlNetModel.from_pretrained(
     "xinsir/controlnet-scribble-sdxl-1.0",
     use_safetensors=True,
-    dtype=DTYPE,
+    torch_dtype=DTYPE,
 )
 controlnet_canny = ControlNetModel.from_pretrained(
     "xinsir/controlnet-canny-sdxl-1.0",
     use_safetensors=True,
-    dtype=DTYPE,
+    torch_dtype=DTYPE,
 )
 vae = AutoencoderKL.from_pretrained(
     "madebyollin/sdxl-vae-fp16-fix",
     use_safetensors=True,
-    dtype=DTYPE,
+    torch_dtype=DTYPE,
 )
 
 pipe_scribble = StableDiffusionXLControlNetPipeline.from_pretrained(
@@ -187,7 +187,7 @@ pipe_scribble = StableDiffusionXLControlNetPipeline.from_pretrained(
     vae=vae,
     scheduler=scheduler,
     use_safetensors=True,
-    dtype=DTYPE,
+    torch_dtype=DTYPE,
 )
 pipe_canny = StableDiffusionXLControlNetPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0",
@@ -195,7 +195,7 @@ pipe_canny = StableDiffusionXLControlNetPipeline.from_pretrained(
     vae=vae,
     scheduler=scheduler,
     use_safetensors=True,
-    dtype=DTYPE,
+    torch_dtype=DTYPE,
 )
 
 for p in (pipe_scribble, pipe_canny):