Update app.py

app.py

@@ -1,37 +1,25 @@
 import gradio as gr
-import torch
+#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')
-#os.environ.get('HF_TOKEN_SD')
+MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
 
-#from diffusers import StableDiffusionPipeline
 from diffusers import StableDiffusionImg2ImgPipeline
 
-#from diffusers import DiffusionPipeline
-
-#pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+print("hello sylvain")
 
-print("hello! Jay shri Ram")
-
-YOUR_TOKEN = MY_SECRET_TOKEN
+YOUR_TOKEN=MY_SECRET_TOKEN
 
 device="cpu"
-model_id_or_path="CompVis/stable-diffusion-v1-4"
 
 #prompt_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN)
 #prompt_pipe.to(device)
 
-img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    model_id_or_path,
-    revision="fp16", 
-    torch_dtype=torch.float16,
-    use_auth_token=YOUR_TOKEN
-)
+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")
@@ -47,16 +35,13 @@ def resize(value,img):
   return img
 
 
-def infer(source_img, prompt, guide, steps, seed, strength): 
-    generator = torch.Generator('cpu').manual_seed(seed)
-    
+def infer(prompt, source_img): 
+         
     source_image = resize(512, source_img)
     source_image.save('source.png')
-    
-    images_list = img_pipe([prompt] * 2, 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(images_list["sample"]):
         if(images_list["nsfw_content_detected"][i]):
             images.append(safe_image)
@@ -64,19 +49,12 @@ def infer(source_img, prompt, guide, steps, seed, strength):
             images.append(image)    
     return images
 
-print("Great Buddy ! Everything is working fine !")
-
-title="Stable Diffusion(SD) Img2Img Experiment"
-description="<p style='text-align: center;'>Stable Diffusion Img2Img example using CUDA/CPU and HF token. <br />Warning: Slow process... ~5/10 min inference time. <b>NSFW filter enabled. <br /> <img id='visitor-badge' alt='visitor badge' src='https://visitor-badge.glitch.me/badge?page_id=gradio-blocks.DJ-stable-diffusion-img2img&left_color=#66ccff&right_color=#33bbdd' style='display: inline-block'/></b></p>" 
+print("Great sylvain ! Everything is working fine !")
 
-gr.Interface(fn=infer, inputs=[source_img,
-    "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 = .75)],
-    outputs=gallery,title=title,description=description, allow_flagging="manual", flagging_dir="flagged").queue(max_size=100).launch(enable_queue=True)
+title="Img2Img Stable Diffusion CPU"
+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)
 #from torch import autocast
 #import requests
 #import torch