Update src/pipeline.py

src/pipeline.py

@@ -32,17 +32,17 @@ def load_pipeline(pipeline=None) -> StableDiffusionXLPipeline:
     pipeline = compile_pipe(pipeline)
     load_pipe(pipeline, dir="/home/sandbox/.cache/huggingface/hub/models--RobertML--cached-pipe-02/snapshots/58d70deae87034cce351b780b48841f9746d4ad7")
 
-    instance = get_instance(device)
+    # instance = get_instance(device)
     # mul = torch.nn.Parameter(torch.tensor(0.3038, requires_grad=False, device=device))
     # sub = torch.nn.Parameter(torch.tensor(-0.3141, requires_grad=False, device=device))
     # scaling_factor = torch.nn.Parameter(torch.tensor(0.5439, requires_grad=False, device=device))
     # mul = torch.nn.Parameter(torch.tensor(0.2940097749233246, requires_grad=False, device=device))
     # sub = torch.nn.Parameter(torch.tensor(-0.31909096240997314, requires_grad=False, device=device))
     # scaling_factor = torch.nn.Parameter(torch.tensor(0.554410457611084, requires_grad=False, device=device))
-    mul = torch.nn.Parameter(torch.tensor(1.2, requires_grad=False, device=device))
-    sub = torch.nn.Parameter(torch.tensor(0.75, requires_grad=False, device=device))
-    scaling_factor = torch.nn.Parameter(torch.tensor(pipeline.vae.config.scaling_factor, requires_grad=False, device=device))
-    hook_pipe(pipeline, instance, mul, sub, scaling_factor)
+    # mul = torch.nn.Parameter(torch.tensor(1.2, requires_grad=False, device=device))
+    # sub = torch.nn.Parameter(torch.tensor(0.75, requires_grad=False, device=device))
+    # scaling_factor = torch.nn.Parameter(torch.tensor(pipeline.vae.config.scaling_factor, requires_grad=False, device=device))
+    # hook_pipe(pipeline, instance, mul, sub, scaling_factor)
 
     for _ in range(1):
         deepcache_output = pipeline(prompt="telestereography, unstrengthen, preadministrator, copatroness, hyperpersonal, paramountness, paranoid, guaniferous", output_type="pil", num_inference_steps=20)