Spaces:
doevent
/
Running on Zero

ohayonguy commited on
Commit
1de925c
1 Parent(s): 839dcf3

Improved description

Browse files
Files changed (1) hide show
  1. app.py +5 -12
app.py CHANGED
@@ -209,12 +209,12 @@ intro = """
209
  Gradio demo for the blind face image restoration version of [Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration](https://arxiv.org/abs/2410.00418).
210
  You may use this demo to enhance the quality of any image which contains faces.
211
 
212
- PMRF is a novel photo-realistic image restoration algorithm. It (provably) approximates the optimal estimator that minimizes the Mean Squared Error (MSE) under a perfect perceptual quality constraint. Please refer to our project's page for more details: https://pmrf-ml.github.io/.
213
 
214
  *Notes* :
215
 
216
- 1. Our model is designed to restore aligned face images, where there is *only one* face in the image, and the face is centered and aligned. Here, however, we incorporate mechanisms that allow restoring the quality of *any* image that contains *any* number of faces. Thus, the resulting quality of such general images is not guaranteed.
217
- 2. If the faces in your image are not aligned, make sure that the checkbox "The input is an aligned face image" in *not* marked.
218
  3. Too large images may result in out-of-memory error.
219
  """
220
 
@@ -244,19 +244,12 @@ This project is released under the <a rel="license" href="https://github.com/oha
244
  If you have any questions, please feel free to contact me at <b>guyoep@gmail.com</b>.
245
  """
246
 
247
- css = """
248
- #col-container {
249
- margin: 0 auto;
250
- max-width: 512px;
251
- }
252
- """
253
-
254
  demo = gr.Interface(
255
  inference,
256
  [
257
  gr.Image(label="Input", type="filepath", show_label=True),
258
  gr.Checkbox(label="Randomize seed", value=True),
259
- gr.Checkbox(label="The input is an aligned face image", value=False),
260
  gr.Slider(
261
  label="Scale factor (applicable to non-aligned face images)",
262
  minimum=1,
@@ -266,7 +259,7 @@ demo = gr.Interface(
266
  scale=1,
267
  ),
268
  gr.Slider(
269
- label="Number of Inference Steps",
270
  minimum=1,
271
  maximum=200,
272
  step=1,
 
209
  Gradio demo for the blind face image restoration version of [Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration](https://arxiv.org/abs/2410.00418).
210
  You may use this demo to enhance the quality of any image which contains faces.
211
 
212
+ PMRF is a novel photo-realistic image restoration algorithm. It (provably) approximates the optimal estimator that minimizes the Mean Squared Error (MSE) under a perfect perceptual quality constraint. Our model in this demo is specifically tailored for blind face image restoration. Please refer to our project's page for more details: https://pmrf-ml.github.io/.
213
 
214
  *Notes* :
215
 
216
+ 1. Our original model is designed to restore low-quality face images, where the image is square, there is *only one* face in the image, and the face is centered and aligned. In this demo, however, we incorporate mechanisms that allow restoring the quality of *any* image that contains *any* number of faces. Thus, the resulting quality of such general images is not guaranteed.
217
+ 2. If your image is not an aligned and square face image, make sure that the checkbox "The input is an aligned and square face image" in *not* marked.
218
  3. Too large images may result in out-of-memory error.
219
  """
220
 
 
244
  If you have any questions, please feel free to contact me at <b>guyoep@gmail.com</b>.
245
  """
246
 
 
 
 
 
 
 
 
247
  demo = gr.Interface(
248
  inference,
249
  [
250
  gr.Image(label="Input", type="filepath", show_label=True),
251
  gr.Checkbox(label="Randomize seed", value=True),
252
+ gr.Checkbox(label="The input is an aligned and square face image", value=False),
253
  gr.Slider(
254
  label="Scale factor (applicable to non-aligned face images)",
255
  minimum=1,
 
259
  scale=1,
260
  ),
261
  gr.Slider(
262
+ label="Number of inference steps (a larger number should lead to better image quality)",
263
  minimum=1,
264
  maximum=200,
265
  step=1,