Update app.py

app.py

@@ -11,7 +11,7 @@ import sys
 from diffusers.utils import load_image
 from diffusers import EulerDiscreteScheduler, T2IAdapter
 
-from huggingface_hub import snapshot_download, hf_hub_download, login
+from huggingface_hub import snapshot_download, hf_hub_download, hf_hub_url, login
 import gradio as gr
 
 from pipeline_t2i_adapter import PhotoMakerStableDiffusionXLAdapterPipeline
@@ -25,16 +25,17 @@ HF_TOKEN = os.environ.get("HF_TOKEN", None)
 login(HF_TOKEN)
 # global variable
 
+# model_id = 'RunDiffusion/Juggernaut-XL-v9'
 # model_id = 'SG161222/RealVisXL_V5.0'
 # model_id = 'Lykon/dreamshaper-xl-lightning'
 # model_id = 'SG161222/RealVisXL_V5.0_Lightning'
+model_file = "https://huggingface.co/RunDiffusion/Juggernaut-XI-v11/blob/main/Juggernaut-XI-byRunDiffusion.safetensors"
 model_id = 'RunDiffusion/Juggernaut-XI-v11'
-base_model_path = Path(model_id)
-os.makedirs(base_model_path, exist_ok=True)
-snapshot_download(repo_id=model_id, local_dir=base_model_path)
+#base_model_path = Path(model_id)
+#os.makedirs(base_model_path, exist_ok=True)
+#snapshot_download(repo_id=model_id, local_dir=base_model_path)
+model_file = hf_hub_download(repo_id=model_id, filename="Juggernaut-XI-byRunDiffusion.safetensors", repo_type="model")
 
-# base_model_path = 
-# base_model_path = 
 face_detector = FaceAnalysis2(providers=['CPUExecutionProvider', 'CUDAExecutionProvider'], allowed_modules=['detection', 'recognition'])
 face_detector.prepare(ctx_id=0, det_size=(640, 640))
 
@@ -70,19 +71,34 @@ adapter = T2IAdapter.from_pretrained(
     "TencentARC/t2i-adapter-sketch-sdxl-1.0", torch_dtype=torch_dtype, variant="fp16"
 ).to(device)
 
-pipe = PhotoMakerStableDiffusionXLAdapterPipeline.from_pretrained(
+pipe = PhotoMakerStableDiffusionXLAdapterPipeline.from_single_file(
     base_model_path, 
     adapter=adapter, 
-    torch_dtype=torch_dtype,
-    use_safetensors=True, 
-    variant="fp16",
+    torch_dtype=torch.float16,
+    use_safetensors=True
 ).to(device)
 
+# pipe = PhotoMakerStableDiffusionXLAdapterPipeline.from_pretrained(
+#     base_model_path, 
+#     adapter=adapter, 
+#     torch_dtype=torch_dtype,
+#     use_safetensors=True, 
+#     variant="fp16",
+# ).to(device)
+
 pipe.unet = pipe.unet.to(device=device, dtype=torch_dtype)
+pipe.unet.to(memory_format=torch.channels_last)
+pipe.unet.eval()
+
 pipe.text_encoder = pipe.text_encoder.to(device=device, dtype=torch_dtype)
+pipe.text_encoder.eval()
+
 pipe.text_encoder_2 = pipe.text_encoder_2.to(device=device, dtype=torch_dtype)
-pipe.vae = pipe.vae.to(device=device, dtype=torch_dtype)
+pipe.text_encoder_2.eval()
 
+pipe.vae = pipe.vae.to(device=device, dtype=torch_dtype)
+pipe.vae.decode.to(memory_format=torch.channels_last)
+pipe.vae.eval()
 
 pipe.load_photomaker_adapter(
     os.path.dirname(photomaker_ckpt),