Update app.py

app.py

@@ -16,8 +16,8 @@ import time
 import os
 from image_gen_aux import UpscaleWithModel
 from huggingface_hub import hf_hub_download
-from models.transformer_sd3 import SD3Transformer2DModel
-from pipeline_stable_diffusion_3_ipa import StableDiffusion3Pipeline
+#from models.transformer_sd3 import SD3Transformer2DModel
+#from pipeline_stable_diffusion_3_ipa import StableDiffusion3Pipeline
 from PIL import Image
 
 FTP_HOST = "1ink.us"
@@ -63,12 +63,6 @@ checkpoint = "microsoft/Phi-3.5-mini-instruct"
 vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16")
 #vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16")
 
-transformer = SD3Transformer2DModel.from_pretrained(
-    model_path, 
-    subfolder="transformer", 
-    torch_dtype=torch.bfloat16
-)
-
 pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-medium-bf16", transformer=transformer).to(device=torch.device("cuda:0"), dtype=torch.bfloat16)
 #pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-medium-bf16").to(torch.device("cuda:0"))
 #pipe = StableDiffusion3Pipeline.from_pretrained("ford442/RealVis_Medium_1.0b_bf16", torch_dtype=torch.bfloat16)
@@ -99,12 +93,6 @@ tokenizer.tokenizer_legacy=False
 model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map='balanced')
 #model = torch.compile(model)
 
-pipe.init_ipadapter(
-    ip_adapter_path=ipadapter_path, 
-    image_encoder_path=image_encoder_path, 
-    nb_token=64, 
-)
-
 upscaler_2 = UpscaleWithModel.from_pretrained("Kim2091/ClearRealityV1").to(torch.device("cuda:0"))
 
 def filter_text(text,phraseC):