config bug fix

app.py

@@ -56,7 +56,7 @@ Please select a semantic concept word and a letter you wish to generate, it will
 
 DESCRIPTION="""This demo builds on the [Word-As-Image for Semantic Typography](https://wordasimage.github.io/Word-As-Image-Page/) work to support Arabic fonts and morphing whole words into semantic concepts."""
 
-DESCRIPTION += '\n<p>This demo is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"> Creative Commons Attribution-ShareAlike 4.0 International License</a>.</p>'
+# DESCRIPTION += '\n<p>This demo is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"> Creative Commons Attribution-ShareAlike 4.0 International License</a>.</p>'
 
 if (SPACE_ID := os.getenv('SPACE_ID')) is not None:
     DESCRIPTION += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
@@ -94,6 +94,7 @@ def set_config(semantic_concept, word, prompt, font_name, num_steps):
     cfg.font = font_name
     cfg.seed = 0
     cfg.num_iter = num_steps
+    cfg.batch_size = 1
     
     if ' ' in cfg.word:
         raise gr.Error(f'should be only one word')

code/config/base.yaml

@@ -45,6 +45,8 @@ conformal_0.5_dist_pixel_100_kernel201:
 
 demo:
   parent_config: baseline
+  batch_size: 1
+  token: false
   level_of_cc: 1
   num_iter: 500
   loss:

code/losses.py

@@ -7,11 +7,6 @@ from torch.nn import functional as nnf
 from easydict import EasyDict
 from shapely.geometry import Point
 from shapely.geometry.polygon import Polygon
-from torchvision import transforms
-from PIL import Image
-from transformers import CLIPProcessor, CLIPModel
-
-from diffusers import StableDiffusionPipeline
 
 class SDSLoss(nn.Module):
     def __init__(self, cfg, device, model):
@@ -19,7 +14,6 @@ class SDSLoss(nn.Module):
         self.cfg = cfg
         self.device = device
         self.pipe = model
-        self.pipe = self.pipe.to(self.device)
 
         # self.clip_model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14").to(self.device)
         # self.clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
@@ -47,8 +41,8 @@ class SDSLoss(nn.Module):
 
         self.text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
         self.text_embeddings = self.text_embeddings.repeat_interleave(self.cfg.batch_size, 0)
-        del self.pipe.tokenizer
-        del self.pipe.text_encoder
+        # del self.pipe.tokenizer
+        # del self.pipe.text_encoder
 
 
     def forward(self, x_aug):