description

app.py

@@ -69,17 +69,14 @@ def edict(x, source_text, edit_text,
 examples = [
  ['square_ims/american_gothic.jpg', 'A painting of two people frowning', 'A painting of two people smiling', 0.5, 3],
  ['square_ims/colloseum.jpg', 'An old ruined building', 'A new modern office building', 0.8, 3],
- ['square_ims/hf.png', 'Emoji', 'Emoji with a mustache', 0.8, 3],
  ]
 
-for dog_i in [1, 2]:
- for breed in ['Golden Retriever', 'Chihuahua', 'Dalmatian']:
- examples.append([f'square_ims/imagenet_dog_{dog_i}.jpg', 'A dog', f'A {breed}', 0.8, 3])
 
 examples.append(['square_ims/scream.jpg', 'A painting of someone screaming', 'A painting of an alien', 0.5, 3])
 examples.append(['square_ims/yosemite.jpg', 'Granite forest valley', 'Granite desert valley', 0.8, 3])
 examples.append(['square_ims/einstein.jpg', 'Mouth open', 'Mouth closed', 0.8, 3])
 examples.append(['square_ims/einstein.jpg', 'A man', 'A man in K.I.S.S. facepaint', 0.8, 3])
+"""
 examples.extend([
  ['square_ims/imagenet_cake_2.jpg', 'A cupcake', 'A Chinese New Year cupcake', 0.8, 3],
  ['square_ims/imagenet_cake_2.jpg', 'A cupcake', 'A Union Jack cupcake', 0.8, 3],
@@ -89,6 +86,40 @@ examples.extend([
  ['square_ims/imagenet_cake_2.jpg', 'A cupcake', 'A hedgehog cupcake', 0.8, 3],
  ['square_ims/imagenet_cake_2.jpg', 'A cupcake', 'A rose cupcake', 0.8, 3],
  ])
+"""
+
+for dog_i in [1, 2]:
+ for breed in ['Golden Retriever', 'Chihuahua', 'Dalmatian']:
+ examples.append([f'square_ims/imagenet_dog_{dog_i}.jpg', 'A dog', f'A {breed}', 0.8, 3])
+
+
+description = 'A gradio demo for [EDICT](https://arxiv.org/abs/2211.12446, CVPR23)'
+description = gr.Markdown(description)
+
+article = """
+
+### Prompting Style
+
+As with many text-to-image methods, the prompting style of EDICT can make a big difference. When in doubt, experiment! Some guidance that we've found:
+* Parallel the Original and Edit description construction as much as possible. Inserting/editing single words often is enough to affect a change while maintaining a lot of the original structure
+* Words that will affect the entire setting (e.g. "A photo of " vs. "A painting of") can make a big difference. Playing around with them can help a lot
+
+### Parameters
+Both `edit_strength` and `guidance_scale` have similar properties qualitatively: the higher the value the more the image will change. We suggest
+* Increasing/decreasing `edit_strength` first, particularly to alter/preserve more of the original structure/content
+* Then changing `guidance_scale` to make the change in the edited region more or less pronounced.
+
+Usually we find changing `edit_strength` to be enough, but feel free to play around (and report any interesting results)!
+
+
+Having difficulty coming up with a caption? Try [BLIP](https://huggingface.co/spaces/Salesforce/BLIP2) to automatically generate one!
+
+As with most StableDiffusion approaches, faces/text are often problematic to render, especially if they're small. Having these in the foreground will help keep them cleaner.
+
+A returned black image means that the [Safety Checker](https://huggingface.co/CompVis/stable-diffusion-safety-checker) triggered on the photo. This happens in odd cases sometimes (it often rejects
+the huggingface logo or variations), but we need to keep it in for obvious reasons.
+"""
+article = gr.Markdown(description)
 
 iface = gr.Interface(fn=edict, inputs=["image",
  gr.Textbox(label="Original Description"),
@@ -99,5 +130,8 @@ iface = gr.Interface(fn=edict, inputs=["image",
  gr.Slider(0, 10, value=3, step=0.5),
  ],
  examples = examples,
- outputs="image")
+ outputs="image",
+ description=description
+ article=article,
+ cache_examples=False)
 iface.launch()