Update app.py

app.py

@@ -21,15 +21,34 @@ import requests
 from rudalle.pipelines import generate_images, show, super_resolution, cherry_pick_by_ruclip
 from rudalle import get_rudalle_model, get_tokenizer, get_vae, get_realesrgan
 from rudalle.utils import seed_everything
-from ruclip import load as get_ruclip
+#from ruclip import load as get_ruclip
 
 # prepare models:
 device = 'cuda'
+#device = torch.device("cuda:0" if torch.cuda.is_available() else "CPU")
 model = get_rudalle_model('Malevich', pretrained=True, fp16=True, device=device)
 tokenizer = get_tokenizer()
 vae = get_vae(dwt=True).to(device)
 
-#device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+# pipeline utils:
+#TEST--------
+realesrgan = get_realesrgan('x2', device=device)
+clip, processor = ruclip.load('ruclip-vit-base-patch32-384', device=device)
+clip_predictor = ruclip.Predictor(clip, processor, device, bs=8)
+text = 'радуга на фоне ночного города'
+
+seed_everything(42)
+pil_images = []
+scores = []
+for top_k, top_p, images_num in [
+    (2048, 0.995, 24),
+]:
+    _pil_images, _scores = generate_images(text, tokenizer, dalle, vae, top_k=top_k, images_num=images_num, bs=8, top_p=top_p)
+    pil_images += _pil_images
+    scores += _scores
+
+show(pil_images, 6)
+#TEST--------
 
 
 pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")