updates some detail

app.py

@@ -53,10 +53,10 @@ DEFAULT_FORMAT = """<|special|>, <|characters|>, <|copyrights|>,
 <|quality|>, <|meta|>, <|rating|>
 """.strip()
 DEFAULT_TAGS = """
-1girl, 
-ningen mame, ciloranko, 
-solo, dragon girl,
-masterpiece, absurdres, safe, newest
+1girl, king halo (umamusume), umamusume,
+ningen mame, ciloranko, ogipote, misu kasumi,
+solo, leaning forward, sky,
+masterpiece, absurdres, sensitive, newest
 """.strip()
 DEFAULT_NL = """
 An illustration of a girl
@@ -228,7 +228,7 @@ TITPOP
                         target_length = gr.Dropdown(
                             label="Target Length",
                             choices=["very_short", "short", "long", "very_long"],
-                            value="short",
+                            value="long",
                         )
                         temp = gr.Slider(
                             label="Temp",

diff.py

@@ -1,7 +1,7 @@
 from functools import partial
 
 import torch
-from diffusers import StableDiffusionXLKDiffusionPipeline
+from diffusers import StableDiffusionXLKDiffusionPipeline, UNet2DConditionModel
 from k_diffusion.sampling import get_sigmas_polyexponential
 from k_diffusion.sampling import sample_dpmpp_2m_sde
 
@@ -37,6 +37,8 @@ def load_model(model_id="KBlueLeaf/Kohaku-XL-Zeta", device="cuda"):
     pipe = StableDiffusionXLKDiffusionPipeline.from_pretrained(
         model_id, torch_dtype=torch.float16
     ).to(device)
+    unet: UNet2DConditionModel = pipe.k_diffusion_model.inner_model.model
+    unet.enable_xformers_memory_efficient_attention()
     pipe.scheduler.set_timesteps = partial(
         set_timesteps_polyexponential, pipe.scheduler, pipe.scheduler.sigmas
     )