diff --git a/requirements.txt b/requirements.txt
index 466a932..9e15baa 100755
--- a/requirements.txt
+++ b/requirements.txt
@@ -28,6 +28,7 @@ insightface==0.7.3
 
 # IP-Adapter from GitHub
 git+https://github.com/tencent-ailab/IP-Adapter.git
+einops  # Required by IP-Adapter
 
 # Utilities
 pydantic==2.6.0
diff --git a/worker/generator.py b/worker/generator.py
index e87fc99..2fc3a0c 100755
--- a/worker/generator.py
+++ b/worker/generator.py
@@ -289,30 +289,52 @@ class SDXLGenerator:
                     )[0]
                 )
             else:
-                # Progress callback wrapper (only for standard pipeline)
-                def callback_wrapper(step: int, timestep: int, latents: torch.FloatTensor):
-                    if progress_callback:
-                        progress = int((step / num_inference_steps) * 100)
-                        try:
-                            asyncio.create_task(progress_callback(progress))
-                        except:
-                            pass
+                # Check if IP-Adapter is loaded - if so, we must use it with s_scale=0
+                # to avoid corrupted output from dangling adapter layers
+                if self.ip_adapter_loaded:
+                    logger.info("IP-Adapter loaded but no face requested, using s_scale=0")
+                    # Create zero embedding (512-dim for FaceID)
+                    zero_embed = torch.zeros((1, 512), device=self.device, dtype=torch.float16)
+                    
+                    image = await loop.run_in_executor(
+                        None,
+                        lambda: self.ip_model.generate(
+                            prompt=prompt,
+                            negative_prompt=negative_prompt,
+                            faceid_embeds=zero_embed,
+                            width=width,
+                            height=height,
+                            num_inference_steps=num_inference_steps,
+                            guidance_scale=guidance_scale,
+                            num_samples=1,
+                            seed=seed,
+                            s_scale=0.0,  # Disable face conditioning
+                        )[0]
+                    )
+                else:
+                    # Standard generation - IP-Adapter not loaded
+                    def callback_wrapper(step: int, timestep: int, latents: torch.FloatTensor):
+                        if progress_callback:
+                            progress = int((step / num_inference_steps) * 100)
+                            try:
+                                asyncio.create_task(progress_callback(progress))
+                            except:
+                                pass
 
-                # Standard generation without face lock
-                image = await loop.run_in_executor(
-                    None,
-                    lambda: self.pipeline(
-                        prompt=prompt,
-                        negative_prompt=negative_prompt,
-                        width=width,
-                        height=height,
-                        num_inference_steps=num_inference_steps,
-                        guidance_scale=guidance_scale,
-                        generator=generator,
-                        callback=callback_wrapper,
-                        callback_steps=1
-                    ).images[0]
-                )
+                    image = await loop.run_in_executor(
+                        None,
+                        lambda: self.pipeline(
+                            prompt=prompt,
+                            negative_prompt=negative_prompt,
+                            width=width,
+                            height=height,
+                            num_inference_steps=num_inference_steps,
+                            guidance_scale=guidance_scale,
+                            generator=generator,
+                            callback=callback_wrapper,
+                            callback_steps=1
+                        ).images[0]
+                    )
 
             logger.info("Image generated successfully")
             return image