Update app.py

app.py

@@ -1,30 +1,11 @@
 import gradio as gr
-#from diffusers import DiffusionPipeline
 from diffusers import LDMTextToImagePipeline
 import torch
 import numpy as np
 import PIL
 import cv2
 
-# -----------------
-#import os
-#print('\nDEBUG:   Cloning diffusers project')
-#os.system('git clone https://github.com/huggingface/diffusers')
-
-#print('\nDEBUG:   Pwd')
-#os.system('pwd')
-#os.system('ls -la')
-
-#print('\nDEBUG:   Install dependencies of diffusers')
-#os.system('cd diffusers && pip install -e .')
-
-#print('\nDEBUG:   Pip install from the build of diffusers')
-#os.system('pip install git+file:///home/user/app/diffusers')
-
-#from diffusers import DiffusionPipeline
-# -----------------
-
-print('\nDEBUG:   Version: 2')
+print('\nDEBUG:   Version: 3')
 
 #pipeline = LDMTextToImagePipeline.from_pretrained("fusing/latent-diffusion-text2im-large")
 pipeline = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
@@ -36,19 +17,23 @@ def greet(name):
     return "Hello " + name + "!!"
     
 def genimage(prompt, iterations):
-  image = pipeline([prompt], generator=generator, eta=0.3, guidance_scale=6.0, num_inference_steps=iterations)
+  image = pipeline([prompt], generator=generator, eta=0.3, guidance_scale=6.0, num_inference_steps=iterations)["sample"]
+  return image[0]
 
-  image_processed = image.cpu().permute(0, 2, 3, 1)
-  image_processed = image_processed  * 255.
-  image_processed = image_processed.numpy().astype(np.uint8)
-  image_pil = PIL.Image.fromarray(image_processed[0])
+  #image_processed = image.cpu().permute(0, 2, 3, 1)
+  #image_processed = image_processed  * 255.
+  #image_processed = image_processed.numpy().astype(np.uint8)
+  #image_pil = PIL.Image.fromarray(image_processed[0])
 
   # save image
-  file_name = "test.png"
-  image_pil.save(file_name)
-  img = cv2.imread(file_name)
-  #cv2_imshow(img)
-  return img
-
-iface = gr.Interface(fn=genimage, inputs=["text", "number"], outputs="image")
+  #file_name = "test.png"
+  #image_pil.save(file_name)
+  #img = cv2.imread(file_name)
+  ##cv2_imshow(img)
+  #return img
+
+iface = gr.Interface(
+  fn=genimage, 
+  inputs=["text", "number"], 
+  outputs=gr.Image(shape=[256,256], type="pil", elem_id="output_image"))
 iface.launch()