Update app.py

app.py

@@ -21,38 +21,7 @@ pipe = pipe.to(device)
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-# ---------------- SAFETY CHECKERS ---------------- #
-
-# 1. FalconsAI safety classifier (post-generation)
-from transformers import AutoProcessor, AutoModelForImageClassification
-
-falcon_processor = AutoProcessor.from_pretrained("Falconsai/nsfw_image_detection")
-falcon_model = AutoModelForImageClassification.from_pretrained("Falconsai/nsfw_image_detection").to(device)
-
-def falcon_check(image):
-    inputs = falcon_processor(images=image, return_tensors="pt").to(device)
-    with torch.no_grad():
-        outputs = falcon_model(**inputs)
-    logits = outputs.logits.softmax(dim=-1)[0]
-    unsafe_prob = float(logits[1])  # class index 1 = NSFW
-    return unsafe_prob
-   
-
-# 2. Secondary NSFW checker (example: CLIP-based)
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from transformers import AutoFeatureExtractor
-
-feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
-clip_safety = StableDiffusionSafetyChecker.from_pretrained(
-    "CompVis/stable-diffusion-safety-checker"
-).to(device)
-
-def clip_check(image):
-    # convert to PIL list format
-    images = [image]
-    safety_inputs = feature_extractor(images, return_tensors="pt").to(device)
-    checked_images, has_nsfw = clip_safety(images, safety_inputs.pixel_values)
-    return bool(has_nsfw[0])
+
 # @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
     prompt,
@@ -70,7 +39,6 @@ def infer(
 
     generator = torch.Generator().manual_seed(seed)
 
-    # generate image normally
     image = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
@@ -81,22 +49,6 @@ def infer(
         generator=generator,
     ).images[0]
 
-    # ---------------- SAFETY CHECK 1: FalconsAI ---------------- #
-    unsafe_prob = falcon_check(image)
-    if unsafe_prob > 0.35:  # adjust threshold if needed
-        return (
-            np.zeros((512, 512, 3), dtype=np.uint8),  # blank output
-            f"⚠️ Blocked by FalconsAI safety model | score={unsafe_prob:.2f}",
-        )
-
-    # ---------------- SAFETY CHECK 2: CLIP Checker ---------------- #
-    if clip_check(image):
-        return (
-            np.zeros((512, 512, 3), dtype=np.uint8),
-            "⛔ Blocked by CLIP NSFW detector",
-        )
-
-    # no flags — return real image
     return image, seed
 
 
@@ -135,8 +87,8 @@ with gr.Blocks(css=css) as demo:
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                value = "((nsfw, breasts, boobs, pussy, dick, sex, sexual, fetish:2))(low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn",
-                visible=False,
+                value = "(low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn",
+                visible=True,
             )
 
             seed = gr.Slider(