Update app.py

app.py

@@ -17,11 +17,11 @@ controlnet = ControlNetModel.from_pretrained(
     "diffusers/controlnet-canny-sdxl-1.0",
     torch_dtype=torch.float16
 )
-vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+#vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
 pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0",
     controlnet=controlnet,
-    vae=vae,
+    #vae=vae,
     torch_dtype=torch.float16, 
     variant="fp16",
     use_safetensors=True
@@ -31,7 +31,7 @@ pipe.to("cuda")
 custom_model = "fffiloni/eugene_jour_general"
 
 # This is where you load your trained weights
-pipe.load_lora_weights(custom_model, use_auth_token=True)
+pipe.load_lora_weights(custom_model, weight_name="pytorch_lora_weights.safetensors", use_auth_token=True)
 
 #pipe.enable_model_cpu_offload()
 
@@ -55,7 +55,7 @@ def infer(image_in, prompt, controlnet_conditioning_scale, guidance_scale):
         prompt, 
         negative_prompt=negative_prompt, 
         image=image, 
-        #controlnet_conditioning_scale=controlnet_conditioning_scale,
+        controlnet_conditioning_scale=controlnet_conditioning_scale,
         guidance_scale = guidance_scale,
         num_inference_steps=50,
         cross_attention_kwargs={"scale": lora_scale}