Update app.py

app.py

@@ -1,42 +1,57 @@
 import gradio as gr
-import torch
-import streamlit as st
+#import torch
+#from torch import autocast // only for GPU
+
 from PIL import Image
 import numpy as np
 from io import BytesIO
+import os
+MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
+
 from diffusers import StableDiffusionImg2ImgPipeline
 
+print("hello !")
+
+YOUR_TOKEN=MY_SECRET_TOKEN
+
 device="cuda"
 
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=st.secrets['USER_TOKEN'])
-pipe.to(device)
+#prompt_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN)
+#prompt_pipe.to(device)
+
+img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN)
+img_pipe.to(device)
+
+source_img = gr.Image(source="upload", type="filepath", label="init_img | 512*512 px")
+gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
 
 def resize(value,img):
+  #baseheight = value
   img = Image.open(img)
+  #hpercent = (baseheight/float(img.size[1]))
+  #wsize = int((float(img.size[0])*float(hpercent)))
+  #img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS)
   img = img.resize((value,value), Image.Resampling.LANCZOS)
   return img
 
-def infer(source_img, prompt, guide, steps, seed, Strength): 
-    generator = torch.Generator('cuda').manual_seed(seed)     
+
+def infer(prompt, source_img): 
+         
     source_image = resize(512, source_img)
     source_image.save('source.png')
-    image_list = pipe([prompt], init_image=source_image, strength=Strength, guidance_scale=guide, num_inference_steps=steps)
+    images_list = img_pipe([prompt] * 2, init_image=source_image, strength=0.75)
     images = []
     safe_image = Image.open(r"unsafe.png")
-    for i, image in enumerate(image_list["sample"]):
-        if(image_list["nsfw_content_detected"][i]):
+    for i, image in enumerate(images_list["sample"]):
+        if(images_list["nsfw_content_detected"][i]):
             images.append(safe_image)
         else:
             images.append(image)    
-    return image
-
-gr.Interface(fn=infer, inputs=[gr.Image(source="upload", type="filepath", label="Raw Image"), gr.Textbox(label = 'Prompt Input Text'),
-    gr.Slider(2, 15, value = 7, label = 'Guidence Scale'),
-    gr.Slider(10, 50, value = 25, step = 1, label = 'Number of Iterations'),
-    gr.Slider(
-        label = "Seed",
-        minimum = 0,
-        maximum = 2147483647,
-        step = 1,
-        randomize = True), gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5)
-    ], outputs='image').queue(max_size=10).launch(enable_queue=True)
+    return images
+
+print("Great ! Everything is working fine !")
+
+title="Img2Img Stable Diffusion"
+description="Img2Img Stable Diffusion example using CPU and HF token. <br />Warning: Slow process... ~5/10 min inference time. <b>NSFW filter enabled.</b>" 
+
+gr.Interface(fn=infer, inputs=["text", source_img], outputs=gallery,title=title,description=description).queue(max_size=100).launch(enable_queue=True)