Update app.py

app.py

@@ -20,8 +20,11 @@ device="cpu"
 #prompt_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN)
 #prompt_pipe.to(device)
 
-img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_auth_token=YOUR_TOKEN)
-img_pipe.safety_checker = lambda images, clip_input: (images, False)
+img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    use_auth_token=YOUR_TOKEN,
+    safety_checker=None,             # ← disable safety checker
+)
 img_pipe.to(device)
 
 source_img = gr.Image(source="upload", type="filepath", label="init_img | 512*512 px")
@@ -38,32 +41,21 @@ def resize(value,img):
 
 
 def infer(source_img, prompt, guide, steps, seed, strength):
-    generator = torch.Generator('cpu').manual_seed(seed)
+    generator = torch.Generator("cpu").manual_seed(seed)
 
-    # Load and resize image (Gradio gives file path)
     source_image = Image.open(source_img).convert("RGB")
     source_image = source_image.resize((512, 512), Image.Resampling.LANCZOS)
 
-    # Run the img2img pipeline
     images_list = img_pipe(
-        [prompt],                          # prompt as list
-        image=source_image,                # fixed: image, not init_image
+        [prompt],
+        image=source_image,
         strength=strength,
         guidance_scale=guide,
         num_inference_steps=steps,
         generator=generator
     )
 
-    # Handle output (NSFW filter placeholder logic)
-    images = []
-    safe_image = Image.open("unsafe.png")
-    for i, image in enumerate(images_list["images"]):
-        if images_list["nsfw_content_detected"][i]:
-            images.append(image)  # replace with safe_image if NSFW
-        else:
-            images.append(image)
-
-    return images
+    return images_list["images"]
 
 print("Great sylvain ! Everything is working fine !")