Update app.py

app.py

@@ -14,7 +14,7 @@ from diffusers import EulerAncestralDiscreteScheduler, LCMScheduler, DDIMSchedul
 #EulerAncestralDiscreteScheduler 很不錯chatgpt推薦
 
 
-model_id = "hsuwill000/Koji_v2.1_diffusers-openvino"
+model_id = "hsuwill000/AbsoluteReality-openvino-8bit"
 
 #model_id = "spamsoms/LCM-anything-v5-openvino2"
 #adapter_id = "latent-consistency/lcm-lora-sdv1-5"
@@ -29,8 +29,7 @@ pipe = OVStableDiffusionPipeline.from_pretrained(
         model_id, 
         compile = False, 
         ov_config = {"CACHE_DIR":""},
-        #torch_dtype=torch.int8, #快
-        torch_dtype=torch.bfloat16, #中
+        torch_dtype=torch.uint8, 
         #variant="fp16", 
         #torch_dtype=torch.IntTensor, #慢,
         safety_checker=None,
@@ -38,7 +37,7 @@ pipe = OVStableDiffusionPipeline.from_pretrained(
         )
 print(pipe.scheduler.compatibles)
 #pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
-pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+#pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
 
 #pipe.load_lora_weights(adapter_id)
 #pipe.fuse_lora()
@@ -65,8 +64,8 @@ def infer(prompt,negative_prompt):
         negative_prompt = negative_prompt,
         width = WIDTH, 
         height = HIGH,
-        guidance_scale=7.5,
-        num_inference_steps=20,
+        guidance_scale=1.0,
+        num_inference_steps=6,
         num_images_per_prompt=1,
     ).images[0]