Update app.py

app.py

@@ -2,15 +2,30 @@
 import gradio as gr
 import numpy as np
 import random
-from diffusers import DiffusionPipeline
+#from diffusers import DiffusionPipeline
+from diffusers import StableDiffusionXLPipeline
 import torch
 import spaces
 
-pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-pipe = pipe.to("cuda")
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+MAX_IMAGE_SIZE = 1216
+
+#pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+#pipe = pipe.to("cuda")
+
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "yodayo-ai/holodayo-xl-2.1", 
+    torch_dtype=torch.float16, 
+    use_safetensors=True,
+    custom_pipeline="lpw_stable_diffusion_xl",
+    add_watermarker=False,
+    variant="fp16"
+)
+pipe.to('cuda')
+
+prompt = "1girl, solo, upper body, v, smile, looking at viewer, outdoors, night, masterpiece, best quality, very aesthetic, absurdres"
+negative_prompt = "nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn"
 
 @spaces.GPU
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
@@ -86,7 +101,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=512,
+                    value=832,
                 )
                 
                 height = gr.Slider(
@@ -94,7 +109,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=512,
+                    value=1216,
                 )
             
             with gr.Row():
@@ -102,17 +117,17 @@ with gr.Blocks(css=css) as demo:
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
-                    maximum=10.0,
+                    maximum=20.0,
                     step=0.1,
-                    value=0.0,
+                    value=7,
                 )
                 
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=12,
+                    maximum=28,
                     step=1,
-                    value=2,
+                    value=28,
                 )
 
     run_button.click(