more changements

app.py

@@ -3,6 +3,10 @@ import gradio as gr
 import torch
 from diffusers import DiffusionPipeline, AutoencoderKL
 import rembg
+from io import BytesIO
+import PIL.Image as Image
+import cv2
+import numpy
 
 model_id = "dataautogpt3/OpenDalleV1.1"
 vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
@@ -18,9 +22,12 @@ pipe.to("cuda")
 @spaces.GPU
 def generate_image(prompt, neg_prompt):
     prompt += "no background, side view, minimalist shot"
-    image = pipe(prompt, negative_prompt=neg_prompt)
-    image2 = rembg.remove(image)
-    return image, image2
+    image_bytes = pipe(prompt, negative_prompt=neg_prompt)
+    pil_image = Image.open(BytesIO(image_bytes))
+    opencvImage = cv2.cvtColor(numpy.array(pil_image), cv2.COLOR_RGB2BGR)
+
+    cv2_image = rembg.remove(opencvImage)
+    return pil_image, cv2_image
 
 _TITLE = "Shoe Generator"
 with gr.Blocks(_TITLE) as ShoeGen:
@@ -30,10 +37,9 @@ with gr.Blocks(_TITLE) as ShoeGen:
             neg_prompt = gr.Textbox(label="Enter a negative prompt", value="low quality, watermark, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, body out of frame, blurry, bad anatomy, blurred, watermark, grainy, signature, cut off, draft, closed eyes, text, logo")
             button_gen = gr.Button("Generate Image")
         with gr.Column():
-            image = gr.Image(label="Generated Image", show_download_button=True)   
+            image = gr.Image(label="Generated Image")   
             image2 = gr.Image(label="Generated Image without background", show_download_button=True) 
     
-
     button_gen.click(generate_image, inputs=[prompt], outputs=[image, image2])
 
 ShoeGen.launch()

requirements.txt

@@ -4,4 +4,8 @@ diffusers==0.26.3
 transformers==4.38.1
 accelerate==0.27.2
 xformers
-rembg
+rembg
+Pillow
+Python-IO
+numpy
+opencv-python