safetensors

convert.py

@@ -0,0 +1,17 @@
+import torch
+import safetensors
+
+def load_safetensors(file_path: str):
+    return safetensors.load(file_path)
+
+def save_as_pytorch_bin(tensor_or_state_dict, file_path: str):
+    torch.save(tensor_or_state_dict, file_path)
+
+if __name__ == "__main__":
+    safetensors_file_path = "path/to/your/file.safetensors"
+    pytorch_bin_file_path = "path/to/save/pytorch_lora_weights.bin"
+
+    loaded_data = load_safetensors(safetensors_file_path)
+    save_as_pytorch_bin(loaded_data, pytorch_bin_file_path)
+
+load_safetensors('./LogoRedmond-LogoLoraForSDXL/LogoRedmond_LogoRedAF.safetensors')

handler.py

@@ -1,11 +1,10 @@
 from typing import Dict, List, Any
-from diffusers import DiffusionPipeline
+from diffusers import StableDiffusionPipeline
+
 
 class EndpointHandler():
     def __init__(self, path=""):
-        # Preload all the elements you are going to need at inference.
-        self.pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
-        self.pipeline.load_lora_weights("jonathanrstern/logo-generator")
+        self.pipeline = StableDiffusionPipeline.from_single_file("./safetensors.safetensors")
 
     def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
         """

requirements.txt

@@ -15,4 +15,6 @@ packaging
 zipp
 charset-normalizer
 urllib3
-certifi
+certifi
+torch
+torchvision

test.py

@@ -0,0 +1,13 @@
+from handler import EndpointHandler
+
+# init handler
+my_handler = EndpointHandler(path=".")
+
+# prepare sample payload
+payload = {"inputs": "geometric minimal, landscaping company, logo, line, simple"}
+
+# test the handler
+test=my_handler(payload)
+
+# show results
+print("test:", test)