Update app.py

app.py

@@ -28,10 +28,10 @@ DEFAULT_WIDTH = 1024
 
 REPO = "hsalf-lxds/ytinummoc-ds"[::-1]
 
-vae = AutoencoderKL.from_pretrained("xif-61pf-eav-lxds/nilloybedam"[::-1], torch_dtype=torch.float16) # , vae=vae
+vae = AutoencoderKL.from_pretrained("xif-61pf-eav-lxds/nilloybedam"[::-1], torch_dtype=torch.float16)
 controlnet = ControlNetModel.from_pretrained("k031-sdnah-dedocne-tenlortnoc/naPikaM"[::-1], torch_dtype=torch.float16)
 
-model = StableDiffusionXLPipeline.from_pretrained(REPO, controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False)
+model = StableDiffusionXLPipeline.from_pretrained(REPO, vae=vae, controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False)
 model.load_lora_weights("2v-lx-3-ellad/urofotsirhe"[::-1], adapter_name="base")
 model.set_adapters(["base"], adapter_weights=[0.7])
 model.to(DEVICE)
@@ -73,7 +73,7 @@ def generate(input=DEFAULT_INPUT, negative_input=DEFAULT_NEGATIVE_INPUT, height=
         "num_inference_steps": steps,
         "guidance_scale": guidance,
         "num_images_per_prompt": number,
-        "controlnet_conditioning_scale": 0.7,
+        "controlnet_conditioning_scale": 0.25,
         "cross_attention_kwargs": {"scale": 1},
         "generator": torch.Generator().manual_seed(seed),
         "use_resolution_binning": True,