Atualização

app.py

@@ -21,10 +21,10 @@ os.environ['REPLICATE_API_TOKEN'] = 'cbd8d6421f3037d482bd7d6ec8e7368350e3aaab'
 model = replicate.models.get("stability-ai/stable-diffusion-inpainting")
 version = model.versions.get("c28b92a7ecd66eee4aefcd8a94eb9e7f6c3805d5f06038165407fb5cb355ba67")
 
-sf_prompt_1 = "old bridge, mountain, grass"
-sf_neg_prompt_1 = ""
+sf_prompt_1 = "sunflowers, old bridge, mountain, grass"
+sf_neg_prompt_1 = "animal"
 
-sf_prompt_2 = "sunflowers, old bridge, mountain, grass"
+sf_prompt_2 = "fire, landscape"
 sf_neg_prompt_2 = "animal"
 
 template1 = Image.open("templates/template1.png").resize((512, 512))
@@ -33,6 +33,7 @@ template2 = Image.open("templates/template2.png").resize((512, 512))
 fontMain = ImageFont.truetype(font="fonts/arial.ttf", size=32)
 fontSecond = ImageFont.truetype(font="fonts/arial.ttf", size=18)
 
+
 def numpy_to_pil(images):
     if images.ndim == 3:
         images = images[None, ...]
@@ -110,9 +111,7 @@ async def predict(prompt, negative_prompt, image, mask_img):
 
     response = requests.get(output[0])
     img_final = Image.open(BytesIO(response.content))
-
     mask = mask_image.convert('L')
-
     PIL.Image.composite(img_final, image, mask)
     return (img_final)
 
@@ -157,7 +156,7 @@ def inference(obj2mask, image_numpy, main_text, second_text, only_test):
     generator = torch.Generator()
     generator.manual_seed(int(52362))
 
-    image = numpy_to_pil(image_numpy)[0].convert("RGB").resize((512, 512))
+    image = Image.fromarray(image_numpy).convert("RGB").resize((512, 512))
 
     mask_img = get_mask(obj2mask, image)