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app.py

@@ -12,6 +12,7 @@ import torch
 from diffusers import StableDiffusion3ControlNetPipeline, SD3ControlNetModel
 from diffusers.utils import load_image
 from image_gen_aux import DepthPreprocessor
+from diffusers.models import SD3ControlNetModel, T2IAdapter
 
 # ----------------------------
 # Step 1: Download IP Adapter if not exists
@@ -51,18 +52,21 @@ image_encoder_path = "google/siglip-so400m-patch14-384"
 
 
 controlnet = SD3ControlNetModel.from_pretrained("stabilityai/stable-diffusion-3.5-large-controlnet-depth", torch_dtype=torch.float16)
-pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-3.5-large",
-    controlnet=controlnet,
-    torch_dtype=torch.float16,
-).to("cuda")
 
-pipe.load_ip_adapter(
+adapter = T2IAdapter.from_pretrained(
     ip_adapter_path=ip_adapter_path,
     image_encoder_path=image_encoder_path,
     nb_token=64,
+    torch_dtype=torch.float16
 )
 
+pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-3.5-large",
+    controlnet=controlnet,adapter=adapter,
+    torch_dtype=torch.float16,
+).to("cuda")
+
+
 
 
 # ----------------------------
@@ -77,6 +81,8 @@ def gui_generation(prompt,negative_prompt, ref_img, guidance_scale, ipadapter_sc
     control_image = depth_preprocessor(image, invert=True)[0].convert("RGB")
 
     generator = torch.Generator(device="cpu").manual_seed(0)
+    pipe.set_ip_adapter_scale(ipadapter_scale)  # Adjust the scale as needed
+
     image = pipe(
         width=1024,
         height=1024,
@@ -88,7 +94,6 @@ def gui_generation(prompt,negative_prompt, ref_img, guidance_scale, ipadapter_sc
         num_inference_steps=40,
         generator=generator,
         max_sequence_length=77,
-        ipadapter_scale=ipadapter_scale,
     ).images[0]
 
     return image