Update app.py

app.py

@@ -10,6 +10,14 @@ schnell_model = "black-forest-labs/FLUX.1-schnell"
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
+repo_name = "ByteDance/Hyper-SD"
+ckpt_name = "Hyper-FLUX.1-dev-8steps-lora.safetensors"
+hyper_lora = hf_hub_download(repo_name, ckpt_name)
+
+repo_name = "alimama-creative/FLUX.1-Turbo-Alpha"
+ckpt_name = "diffusion_pytorch_model.safetensors"
+turbo_lora = hf_hub_download(repo_name, ckpt_name)
+
 pipe_dev = DiffusionPipeline.from_pretrained(dev_model, torch_dtype=torch.bfloat16)
 pipe_schnell = DiffusionPipeline.from_pretrained(
     schnell_model,
@@ -19,13 +27,12 @@ pipe_schnell = DiffusionPipeline.from_pretrained(
     tokenizer_2=pipe_dev.tokenizer_2,
     torch_dtype=torch.bfloat16
 )
+
 @spaces.GPU
 def run_dev_hyper(prompt):
     print("dev_hyper")
     pipe_dev.to("cuda")
-    repo_name = "ByteDance/Hyper-SD"
-    ckpt_name = "Hyper-FLUX.1-dev-8steps-lora.safetensors"
-    pipe_dev.load_lora_weights(hf_hub_download(repo_name, ckpt_name))
+    pipe_dev.load_lora_weights(hyper_lora)
     image = pipe_dev(prompt, num_inference_steps=8, joint_attention_kwargs={"scale": 0.125}).images[0]
     pipe_dev.unload_lora_weights()
     return image
@@ -34,9 +41,7 @@ def run_dev_hyper(prompt):
 def run_dev_turbo(prompt):
     print("dev_turbo")
     pipe_dev.to("cuda")
-    repo_name = "alimama-creative/FLUX.1-Turbo-Alpha"
-    ckpt_name = "diffusion_pytorch_model.safetensors"
-    pipe_dev.load_lora_weights(hf_hub_download(repo_name, ckpt_name))
+    pipe_dev.load_lora_weights(turbo_lora)
     image = pipe_dev(prompt, num_inference_steps=8).images[0]
     pipe_dev.unload_lora_weights()
     return image