Update app.py

app.py

@@ -48,11 +48,11 @@ with open('loras.json', 'r') as f:
 #pipe = diffusers.ZImagePipeline.from_pretrained("Disty0/Z-Image-Turbo-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
 #torch.cuda.empty_cache()
 #pipe = diffusers.ZImagePipeline.from_pretrained("dimitribarbot/Z-Image-Turbo-BF16", torch_dtype=torch.bfloat16)
-pipe = diffusers.ZImagePipeline.from_pretrained("AlekseyCalvin/Z-Image-Deturbo-Returbo-Base_Diffusers", torch_dtype=torch.bfloat16)
+pipe = diffusers.ZImagePipeline.from_pretrained("AlekseyCalvin/Z_Image_Deturbo_Diffusers", torch_dtype=torch.bfloat16)
 
 
 #pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.float16).to("cuda")
-pipe.vae = AutoencoderKL.from_pretrained("AlekseyCalvin/Custom_VAE-Z-image-FLUX.1-by-G-REPA", torch_dtype=torch.bfloat16, device_map="cuda")
+#pipe.vae = AutoencoderKL.from_pretrained("AlekseyCalvin/Custom_VAE-Z-image-FLUX.1-by-G-REPA", torch_dtype=torch.bfloat16, device_map="cuda")
 
 #custom_vae = AutoencoderKL.from_pretrained("AlekseyCalvin/AnimeVAE_by_Anzhc_for_Flux_ZiT", torch_dtype=torch.float32, ignore_mismatched_sizes=True, low_cpu_mem_usage=False, device_map=None)
 
@@ -223,7 +223,7 @@ with gr.Blocks(css=css) as app:
             with gr.Column():
                 with gr.Row():
                     cfg_scale = gr.Slider(label="CFG Scale", minimum=0, maximum=20, step=.1, value=1.0)
-                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=10)
+                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=18)
                 
                 with gr.Row():
                     width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)