DeepCache?

app.py

@@ -30,15 +30,21 @@ inpainting_pipeline = StableDiffusionInpaintPipeline.from_pretrained(
     safety_checker=None,
 ).to(preferred_device)
 
-inpainting_pipeline.unet = torch.compile(inpainting_pipeline.unet)
-inpainting_pipeline.vae = torch.compile(inpainting_pipeline.vae)
+from DeepCache import DeepCacheSDHelper
+helper = DeepCacheSDHelper(pipe=inpainting_pipeline)
+helper.set_params(cache_interval=3, cache_branch_id=0)
+helper.enable()
+
+if preferred_device == "cuda":
+    inpainting_pipeline.unet = torch.compile(inpainting_pipeline.unet)
+    inpainting_pipeline.vae = torch.compile(inpainting_pipeline.vae)
 
 # inpainting_pipeline.scheduler = LCMScheduler.from_config(inpainting_pipeline.scheduler.config)
 # inpainting_pipeline.load_lora_weights("latent-consistency/lcm-lora-sdv1-5", torch_dtype=preferred_dtype)
 # inpainting_pipeline.fuse_lora()
 
 seg_working_size = (seg_model_img_size, seg_model_img_size)
-repaint_working_size = (384, 384)
+repaint_working_size = (512, 512)
 
 default_inpainting_prompt = "award-winning photo of a leafy pedestrian mall full of people, with multiracial genderqueer joggers and bicyclists and wheelchair users talking and laughing"
 

requirements.txt

@@ -4,4 +4,4 @@ torch==2.2.1
 accelerate
 peft
 optimum
-
+DeepCache