update

app.py

@@ -33,7 +33,7 @@ from pipeline_stable_diffusion_xl_instantid_img2img import StableDiffusionXLInst
 from controlnet_aux import ZoeDetector
 
 from compel import Compel, ReturnedEmbeddingsType
-import spaces
+#import spaces
 
 #from gradio_imageslider import ImageSlider
 
@@ -61,7 +61,17 @@ with open("defaults_data.json", "r") as file:
     lora_defaults = json.load(file)
     
 
-device = "cuda" 
+#device = "cuda" 
+if torch.cuda.is_available():
+    device = "cuda"
+    dtype = torch.float16
+elif torch.backends.mps.is_available():
+    device = "mps"
+    dtype = torch.float32
+else:
+    device = "cpu"
+    dtype = torch.float32
+
 
 state_dicts = {}
 
@@ -84,32 +94,32 @@ sdxl_loras_raw = [item for item in sdxl_loras_raw if item.get("new") != True]
 hf_hub_download(
     repo_id="InstantX/InstantID",
     filename="ControlNetModel/config.json",
-    local_dir="/data/checkpoints",
+    local_dir="data/checkpoints",
 )
 hf_hub_download(
     repo_id="InstantX/InstantID",
     filename="ControlNetModel/diffusion_pytorch_model.safetensors",
-    local_dir="/data/checkpoints",
+    local_dir="data/checkpoints",
 )
 hf_hub_download(
-    repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="/data/checkpoints"
+    repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="data/checkpoints"
 )
 hf_hub_download(
     repo_id="latent-consistency/lcm-lora-sdxl",
     filename="pytorch_lora_weights.safetensors",
-    local_dir="/data/checkpoints",
+    local_dir="data/checkpoints",
 )
-# download antelopev2
-if not os.path.exists("/data/antelopev2.zip"):
-    gdown.download(url="https://drive.google.com/file/d/18wEUfMNohBJ4K3Ly5wpTejPfDzp-8fI8/view?usp=sharing", output="/data/", quiet=False, fuzzy=True)
-    os.system("unzip /data/antelopev2.zip -d /data/models/")
+## download antelopev2
+#if not os.path.exists("data/antelopev2.zip"):
+#    gdown.download(url="https://drive.google.com/file/d/18wEUfMNohBJ4K3Ly5wpTejPfDzp-8fI8/view?usp=sharing", output="data/", quiet=False, fuzzy=True)
+#    os.system("unzip /data/antelopev2.zip -d /data/models/")
 
 app = FaceAnalysis(name='antelopev2', root='/data', providers=['CPUExecutionProvider'])
 app.prepare(ctx_id=0, det_size=(640, 640))
 
 # prepare models under ./checkpoints
-face_adapter = f'/data/checkpoints/ip-adapter.bin'
-controlnet_path = f'/data/checkpoints/ControlNetModel'
+face_adapter = f'data/checkpoints/ip-adapter.bin'
+controlnet_path = f'data/checkpoints/ControlNetModel'
 
 # load IdentityNet
 st = time.time()
@@ -226,7 +236,7 @@ def merge_incompatible_lora(full_path_lora, lora_scale):
                 )
                 del weights_sd
                 del lora_model
-@spaces.GPU
+#@spaces.GPU
 def generate_image(prompt, negative, face_emb, face_image, face_kps, image_strength, guidance_scale, face_strength, depth_control_scale, repo_name, loaded_state_dict, lora_scale, sdxl_loras, selected_state_index, st):
     print(loaded_state_dict)
     et = time.time()
@@ -487,7 +497,7 @@ def remove_custom_lora():
 with gr.Blocks(css="custom.css") as demo:
     gr_sdxl_loras = gr.State(value=sdxl_loras_raw)
     title = gr.HTML(
-        """<h1><img src="https://i.imgur.com/DVoGw04.png">
+        """<h1>
 <span>Face to All<br><small style="
     font-size: 13px;
     display: block;
@@ -597,4 +607,4 @@ with gr.Blocks(css="custom.css") as demo:
     share_button.click(None, [], [], js=share_js)
     demo.load(fn=classify_gallery, inputs=[gr_sdxl_loras], outputs=[gallery, gr_sdxl_loras], queue=False, js=js)
 demo.queue(max_size=20)
-demo.launch(share=True)
+demo.launch(share=True)

requirements.txt

@@ -6,7 +6,8 @@ insightface
 controlnet_aux
 timm==0.6.7
 gdown
-onnxruntime-gpu
+#onnxruntime-gpu
 peft
 compel
-gradio_imageslider
+gradio_imageslider
+gradio