Update app.py

app.py

@@ -8,26 +8,32 @@ import os
 from pipeline_flux_ipa import FluxPipeline
 from transformer_flux import FluxTransformer2DModel
 from attention_processor import IPAFluxAttnProcessor2_0
+
+from models.transformer_sd3 import SD3Transformer2DModel
+from pipeline_stable_diffusion_3_ipa import StableDiffusion3Pipeline
+
 from transformers import AutoProcessor, SiglipVisionModel
 from infer_flux_ipa_siglip import MLPProjModel, IPAdapter
 from huggingface_hub import hf_hub_download
 
+
 # Constants
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
+model_path = 'stabilityai/stable-diffusion-3.5-large'
 image_encoder_path = "google/siglip-so400m-patch14-384"
-ipadapter_path = hf_hub_download(repo_id="InstantX/FLUX.1-dev-IP-Adapter", filename="ip-adapter.bin")
+ipadapter_path = hf_hub_download(repo_id="InstantX/SD3.5-Large-IP-Adapter", filename="ip-adapter.bin")
 
-transformer = FluxTransformer2DModel.from_pretrained(
-    "black-forest-labs/FLUX.1-dev", 
+transformer = SD3Transformer2DModel.from_pretrained(
+    model_path, 
     subfolder="transformer", 
     torch_dtype=torch.bfloat16
 )
 
 pipe = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev", 
+    model_path, 
     transformer=transformer, 
     torch_dtype=torch.bfloat16
 )
@@ -88,7 +94,7 @@ css = """
 # Create the Gradio interface
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown("# InstantX's FLUX.1-dev-IP-Adapter")
+        gr.Markdown("# InstantX's SD3.5 IP Adapter")
         
         with gr.Row():
             with gr.Column():