Update app.py

app.py

@@ -77,7 +77,7 @@ iface = gr.Interface(
     ], 
     outputs=gallery,
     title="Generate images from text with V-Diffusion CC12M CFG",
-    description="<div>By typing a prompt and pressing submit you can generate images based on this prompt. <a href='https://github.com/crowsonkb/v-diffusion-pytorch' target='_blank'>V-Diffusion</a> is diffusion text-to-image model created by <a href='https://twitter.com/RiversHaveWings' target='_blank'>Katherine Crowson</a> and <a href='https://twitter.com/jd_pressman'>JDP</a>, trained on the <a href='https://github.com/google-research-datasets/conceptual-12m'>CC12M dataset</a>. CFG means it can generate images fast - and without CLIP Guidance<br>This UI to the model was assembled by <a style='color: rgb(245, 158, 11);font-weight:bold' href='https://twitter.com/multimodalart' target='_blank'>@multimodalart</a></div>",
+    description="<div>By typing a prompt and pressing submit you can generate images based on this prompt. <a href='https://github.com/crowsonkb/v-diffusion-pytorch' target='_blank'>V-Diffusion</a> is diffusion text-to-image model created by <a href='https://twitter.com/RiversHaveWings' target='_blank'>Katherine Crowson</a> and <a href='https://twitter.com/jd_pressman'>JDP</a>, trained on the <a href='https://github.com/google-research-datasets/conceptual-12m'>CC12M dataset</a>. CFG means it can generate images fast - and without CLIP Guidance<br>The UI to the model was assembled by <a style='color: rgb(99, 102, 241);font-weight:bold' href='https://twitter.com/multimodalart' target='_blank'>@multimodalart</a>, keep up with the <a style='color: rgb(99, 102, 241);' href='https://multimodal.art/news' target='_blank'>latest multimodal ai art news here</a> and consider <a style='color: rgb(99, 102, 241);' href='https://www.patreon.com/multimodalart' target='_blank'>supporting us on Patreon</a></div>",
     #article="<h4 style='font-size: 110%;margin-top:.5em'>Biases acknowledgment</h4><div>Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exarcbates societal biases. According to the <a href='https://arxiv.org/abs/2112.10752' target='_blank'>Latent Diffusion paper</a>:<i> \"Deep learning modules tend to reproduce or exacerbate biases that are already present in the data\"</i>. The model was trained on an unfiltered version the LAION-400M dataset, which scrapped non-curated image-text-pairs from the internet (the exception being the the removal of illegal content) and is meant to be used for research purposes, such as this one. <a href='https://laion.ai/laion-400-open-dataset/' target='_blank'>You can read more on LAION's website</a></div><h4 style='font-size: 110%;margin-top:1em'>Who owns the images produced by this demo?</h4><div>Definetly not me! Probably you do. I say probably because the Copyright discussion about AI generated art is ongoing. So <a href='https://www.theverge.com/2022/2/21/22944335/us-copyright-office-reject-ai-generated-art-recent-entrance-to-paradise' target='_blank'>it may be the case that everything produced here falls automatically into the public domain</a>. But in any case it is either yours or is in the public domain.</div>"
     )
-iface.launch()
+iface.launch(enable_queue=True)