makes GPU requirements default if CPU --use-cpu options is not used, remove faceswaplab_gpu

README.md

@@ -14,7 +14,7 @@ While FaceSwapLab is still under development, it has reached a good level of sta
 
 In short:
 
-+ **Ethical Guideline:** This extension should not be forked to create a public, easy way to bypass NSFW filtering. If you modify it for this purpose, keep it private, or you'll be banned.
++ **Ethical Guideline:**  NSFW is now configurable due to performance issue. Please don't use this to do harm.
 + **License:** This software is distributed under the terms of the GNU Affero General Public License (AGPL), version 3 or later.
 + **Model License:** This software uses InsightFace's pre-trained models, which are available for non-commercial research purposes only.
 

docs/index.markdown

@@ -20,7 +20,7 @@ While FaceSwapLab is still under development, it has reached a good level of sta
 
 In short:
 
-+ **Ethical Guideline:** This extension should not be forked to create a public, easy way to circumvent NSFW filtering.
++ **Ethical Guideline:** This extension is **not intended to facilitate the creation of not safe for work (NSFW) or non-consensual deepfake content**. Its purpose is to bring consistency to image creation, making it easier to repair existing images, or bring characters back to life.
 + **License:** This software is distributed under the terms of the GNU Affero General Public License (AGPL), version 3 or later.
 + **Model License:** This software uses InsightFace's pre-trained models, which are available for non-commercial research purposes only.
 
@@ -28,8 +28,6 @@ In short:
 
 This extension is **not intended to facilitate the creation of not safe for work (NSFW) or non-consensual deepfake content**. Its purpose is to bring consistency to image creation, making it easier to repair existing images, or bring characters back to life.
 
-While the code for this extension is licensed under the AGPL in compliance with models and other source materials, it's important to stress that **we strongly discourage any attempts to fork this project to create an uncensored version**. Any modifications to the code to enable the production of such content would be contrary to the ethical guidelines we advocate for.
-
 We will comply with European regulations regarding this type of software. As required by law, the code may include both visible and invisible watermarks. If your local laws prohibit the use of this extension, you should not use it.
 
 From an ethical perspective, the main goal of this extension is to generate consistent images by swapping faces. It's important to note that we've done our best to integrate censorship features. However, when users can access the source code, they might bypass these censorship measures. That's why we urge users to use this extension responsibly and avoid any malicious use. We emphasize the importance of respecting people's privacy and consent when swapping faces in images. We discourage any activities that could harm others, invade their privacy, or negatively affect their well-being.

install.py

@@ -7,15 +7,15 @@ from packaging.version import parse
 
 
 def check_install() -> None:
-    use_gpu = getattr(
-        shared.cmd_opts, "faceswaplab_gpu", False
-    ) or shared.opts.data.get("faceswaplab_use_gpu", False)
+    use_gpu = not getattr(shared.cmd_opts, "use-cpu", False)
 
     if use_gpu and sys.platform != "darwin":
+        print("Faceswaplab : Use GPU requirements")
         req_file = os.path.join(
             os.path.dirname(os.path.realpath(__file__)), "requirements-gpu.txt"
         )
     else:
+        print("Faceswaplab : Use CPU requirements")
         req_file = os.path.join(
             os.path.dirname(os.path.realpath(__file__)), "requirements.txt"
         )

scripts/faceswaplab_swapping/swapper.py

@@ -101,8 +101,9 @@ def cosine_similarity_face(face1: Face, face2: Face) -> float:
         non-negative similarity score.
     """
     # Reshape the face embeddings to have a shape of (1, -1)
-    vec1 = face1.embedding.reshape(1, -1)
-    vec2 = face2.embedding.reshape(1, -1)
+    assert face1.normed_embedding is not None and face2.normed_embedding is not None
+    vec1 = face1.normed_embedding.reshape(1, -1)
+    vec2 = face2.normed_embedding.reshape(1, -1)
 
     # Calculate the cosine similarity between the reshaped embeddings
     similarity = cosine_similarity(vec1, vec2)
@@ -312,7 +313,9 @@ def capture_stdout() -> Generator[StringIO, None, None]:
 
 @lru_cache(maxsize=3)
 def getAnalysisModel(
-    det_size: Tuple[int, int] = (640, 640), det_thresh: float = 0.5
+    det_size: Tuple[int, int] = (640, 640),
+    det_thresh: float = 0.5,
+    use_gpu: bool = False,
 ) -> insightface.app.FaceAnalysis:
     """
     Retrieves the analysis model for face analysis.
@@ -356,7 +359,9 @@ def getAnalysisModel(
 
 
 @lru_cache(maxsize=1)
-def getFaceSwapModel(model_path: str) -> upscaled_inswapper.UpscaledINSwapper:
+def getFaceSwapModel(
+    model_path: str, use_gpu: bool = False
+) -> upscaled_inswapper.UpscaledINSwapper:
     """
     Retrieves the face swap model and initializes it if necessary.
 
@@ -410,7 +415,9 @@ def get_faces(
         x = get_sd_option("faceswaplab_det_size", 640)
         det_size = (x, x)
 
-    face_analyser = getAnalysisModel(det_size, det_thresh)
+    face_analyser = getAnalysisModel(
+        det_size=det_size, det_thresh=det_thresh, use_gpu=not is_cpu_provider()
+    )
 
     # Get the detected faces from the image using the analysis model
     faces = face_analyser.get(img_data)
@@ -619,9 +626,9 @@ def swap_face(
         gender = source_face["gender"]
         logger.info("Source Gender %s", gender)
         if source_face is not None:
-            result = target_img_cv2
+            result: CV2ImgU8 = target_img_cv2
             model_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), model)
-            face_swapper = getFaceSwapModel(model_path)
+            face_swapper = getFaceSwapModel(model_path, use_gpu=not is_cpu_provider())
             logger.info("Target faces count : %s", len(target_faces))
 
             for i, swapped_face in enumerate(target_faces):