test 5

app.py

@@ -1,18 +1,15 @@
+import torch
 import gradio as gr
-
+import numpy as np
 from torch import autocast
-import requests
-import torch
 from PIL import Image
-from io import BytesIO
-
 from diffusers import StableDiffusionImg2ImgPipeline
 
 # load the pipeline
 device = "cuda"
 model_id_or_path = "CompVis/stable-diffusion-v1-4"
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    model_id_or_path,
+model_id_or_path,
     revision="fp16", 
     torch_dtype=torch.float16,
     use_auth_token='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'
@@ -22,29 +19,25 @@ pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
 pipe = pipe.to(device)
 
 def diffuse(x, param):
-  print('in callback')
-  x = Image.fromarray(np.uint8(x))
-  init_image = x.resize((768, 512))
-  prompt = 'st petersburg logo'
-  if param == 'Эрмитаж':
-    prompt = "st petersburg logo winter palace image on background hermitage vector style"
-  elif param == 'Казанский собор':
-    prompt = "st petersburg logo kazansky sobor image on background"
-  elif param == 'Мосты':
-    prompt = 'st petersburg logo bridges over neva image on background beutiful high quality'
-
-  with autocast("cuda"):
-      images = pipe(prompt=prompt, init_image=init_image, strength=0.7, guidance_scale=7.5).images
-  return [images[0], param]
-
-import numpy as np
-import gradio as gr
+    print('in callback')
+    x = Image.fromarray(np.uint8(x))
+    init_image = x.resize((768, 512))
+    prompt = 'st petersburg logo'
+    if param == 'Эрмитаж':
+        prompt = "st petersburg logo winter palace image on background hermitage vector style"
+    elif param == 'Казанский собор':
+        prompt = "st petersburg logo kazansky sobor image on background"
+    elif param == 'Мосты':
+        prompt = 'st petersburg logo bridges over neva image on background beutiful high quality'
+
+    with autocast("cuda"):
+         images = pipe(prompt=prompt, init_image=init_image, strength=0.7, guidance_scale=7.5).images
+    return [images[0], param]
 
 def flip_image(x, param):
     return [np.fliplr(x), 'функция приняла на вход ' + param]
 
 with gr.Blocks() as demo:
-    
     gr.Markdown("Слово 'Санкт-Петербург'")
     with gr.Tab("Санкт-Петербург"):
         with gr.Row():

requirements.txt

@@ -1,2 +1,2 @@
 torch
-diffusers
+git+https://github.com/huggingface/diffusers