Update app.py

app.py

@@ -291,21 +291,29 @@ def generate_image(prompt, negative, face_emb, face_image, face_kps, image_stren
     print('Prompt processing took: ', elapsed_time, 'seconds')
     print("Processing image...")
     st = time.time()
-    image = pipe(
-        prompt_embeds=conditioning,
-        pooled_prompt_embeds=pooled,
-        negative_prompt_embeds=negative_conditioning,
-        negative_pooled_prompt_embeds=negative_pooled,
-        width=1024,
-        height=1024,
-        image_embeds=face_emb,
-        image=face_image,
-        strength=1-image_strength,
-        control_image=images,
-        num_inference_steps=36,
-        guidance_scale = guidance_scale,
-        controlnet_conditioning_scale=[face_strength, depth_control_scale],
-    ).images[0]
+    
+    inputs = {
+        "prompt_embeds": conditioning,
+        "pooled_prompt_embeds": pooled,
+        "negative_prompt_embeds": negative_conditioning,
+        "negative_pooled_prompt_embeds": negative_pooled,
+        "width": 1024,
+        "height": 1024,
+        "image": face_image,
+        "strength": 1 - image_strength,
+        "guidance_scale": guidance_scale,
+    }
+    
+    if face_emb is not None:
+        inputs["image_embeds"] = face_emb
+        inputs["control_image"] = [face_kps, image_zoe.resize((height, width))]
+        inputs["controlnet_conditioning_scale"] = [face_strength, depth_control_scale]
+    else:
+        inputs["control_image"] = [image_zoe.resize((height, width))]
+        inputs["controlnet_conditioning_scale"] = [depth_control_scale]
+    
+    image = pipe(**inputs).images[0]
+
     et = time.time()
     elapsed_time = et - st
     print('Image processing took: ', elapsed_time, 'seconds')
@@ -320,11 +328,17 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
     # face_image = center_crop_image_as_square(face_image)
     try:
         face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))
-        face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face
+        face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] if face_info else None
+    except:
+        face_info = None
+    
+    if face_info:
         face_emb = face_info['embedding']
         face_kps = draw_kps(face_image, face_info['kps'])
-    except: 
-        raise gr.Error("No face found in your image. Only face images work here. Try again")
+    else:
+        face_emb = None
+        face_kps = Image.new("RGB", face_image.size, (0, 0, 0))  # empty placeholder for consistency
+
     et = time.time()
     elapsed_time = et - st
     print('Cropping and calculating face embeds took: ', elapsed_time, 'seconds')