Use-GPU-for-diffusion-ping

app.py

@@ -111,6 +111,7 @@ DEFAULT_UI_BATCH_SIZE = 1
 MAX_BATCH_SIZE = 8
 PING_MODEL_ID = 50
 PING_SIZE = 64
+PING_STEPS = 8
 PING_SAMPLER = "euler"
 PING_SCHEDULER = "simple"
 STARTUP_ASSET_IDS = {
@@ -1230,6 +1231,7 @@ def infer_image(
         return image
 
 
+@spaces.GPU(duration=3)
 def infer_ping(model_name):
     with torch.inference_mode():
         model, clip = load_chain(model_name, [])
@@ -1239,21 +1241,22 @@ def infer_ping(model_name):
             height=PING_SIZE,
             batch_size=1,
         )[0]
-        conditioning = state["encode"].encode(clip=clip, text="")[0]
+        positive = state["encode"].encode(clip=clip, text="1girl")[0]
+        negative = state["encode"].encode(clip=clip, text="")[0]
         samples = run_sampler(
             model,
             secrets.randbits(64),
-            1,
+            PING_STEPS,
             1,
             PING_SAMPLER,
             PING_SCHEDULER,
-            conditioning,
-            conditioning,
+            positive,
+            negative,
             latent,
             1,
         )
         image = state["decode"].decode(vae=vae, samples=samples)[0]
-        return tensor_image(image).resize((1, 1), Image.Resampling.LANCZOS)
+        return tensor_image(image)
 
 
 def generate_gpu(request, regions, style_image, second_style_image):