Fabrice-TIERCELIN commited on
Commit
7e93de4
1 Parent(s): a14ce71

Make allocation work

Browse files
Files changed (1) hide show
  1. gradio_demo.py +51 -5
gradio_demo.py CHANGED
@@ -147,6 +147,8 @@ def stage2_process(
147
  output_format,
148
  allocation
149
  ):
 
 
150
  if allocation == 1:
151
  return restore_in_1min(
152
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
@@ -188,6 +190,7 @@ def stage2_process(
188
  def restore_in_1min(
189
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
190
  ):
 
191
  return restore(
192
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
193
  )
@@ -196,6 +199,7 @@ def restore_in_1min(
196
  def restore_in_2min(
197
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
198
  ):
 
199
  return restore(
200
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
201
  )
@@ -204,6 +208,7 @@ def restore_in_2min(
204
  def restore_in_3min(
205
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
206
  ):
 
207
  return restore(
208
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
209
  )
@@ -212,6 +217,7 @@ def restore_in_3min(
212
  def restore_in_4min(
213
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
214
  ):
 
215
  return restore(
216
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
217
  )
@@ -220,6 +226,7 @@ def restore_in_4min(
220
  def restore_in_5min(
221
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
222
  ):
 
223
  return restore(
224
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
225
  )
@@ -228,6 +235,7 @@ def restore_in_5min(
228
  def restore_in_6min(
229
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
230
  ):
 
231
  return restore(
232
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
233
  )
@@ -236,6 +244,7 @@ def restore_in_6min(
236
  def restore_in_7min(
237
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
238
  ):
 
239
  return restore(
240
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
241
  )
@@ -244,6 +253,7 @@ def restore_in_7min(
244
  def restore_in_8min(
245
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
246
  ):
 
247
  return restore(
248
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
249
  )
@@ -252,6 +262,7 @@ def restore_in_8min(
252
  def restore_in_9min(
253
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
254
  ):
 
255
  return restore(
256
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
257
  )
@@ -287,9 +298,36 @@ def restore(
287
  ):
288
  start = time.time()
289
  print('stage2_process ==>>')
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
290
  if torch.cuda.device_count() == 0:
291
  gr.Warning('Set this space to GPU config to make it work.')
292
- return None, None, None
293
  if output_format == "input":
294
  if noisy_image is None:
295
  output_format = "png"
@@ -412,6 +450,11 @@ def load_and_reset(param_setting):
412
  return edm_steps, s_cfg, s_stage2, s_stage1, s_churn, s_noise, a_prompt, n_prompt, color_fix_type, linear_CFG, \
413
  linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select
414
 
 
 
 
 
 
415
 
416
  def submit_feedback(event_id, fb_score, fb_text):
417
  if args.log_history:
@@ -433,7 +476,7 @@ title_html = """
433
 
434
  <p>This is an online demo of SUPIR, a practicing model scaling for photo-realistic image restoration.
435
  It is still a research project under tested and is not yet a stable commercial product.
436
- LlaVa is not integrated in this demo. The content added by SUPIR is imagination, not real-world information.
437
  The aim of SUPIR is the beauty and the illustration.
438
  Most of the processes only last few minutes.
439
  This demo can handle huge images but the process will be aborted if it lasts more than 9 min.
@@ -456,7 +499,7 @@ with gr.Blocks(title="SUPIR") as interface:
456
  if torch.cuda.device_count() == 0:
457
  with gr.Row():
458
  gr.HTML("""
459
- <p style="background-color: red;"><big><big><big><b>⚠️To use SUPIR, <a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/SUPIR?duplicate=true">Duplicate this space</a> and set a GPU with 30 GB VRAM.</b>
460
 
461
  You can't use SUPIR directly here because this space runs on a CPU, which is not enough for SUPIR. This is a template space. Please provide feedback if you have issues.
462
  </big></big></big></p>
@@ -508,6 +551,7 @@ with gr.Blocks(title="SUPIR") as interface:
508
  with gr.Column():
509
  color_fix_type = gr.Radio(["None", "AdaIn", "Wavelet"], label="Color-Fix Type", info="AdaIn=Improve following a style, Wavelet=For JPEG artifacts", value="Wavelet",
510
  interactive=True)
 
511
  s_cfg = gr.Slider(label="Text Guidance Scale", info="lower=follow the image, higher=follow the prompt", minimum=1.0, maximum=15.0,
512
  value=default_setting.s_cfg_Quality if torch.cuda.device_count() > 0 else 1.0, step=0.1)
513
  s_stage2 = gr.Slider(label="Restoring Guidance Strength", minimum=0., maximum=1., value=1., step=0.05)
@@ -529,7 +573,6 @@ with gr.Blocks(title="SUPIR") as interface:
529
  with gr.Column():
530
  ae_dtype = gr.Radio(['fp32', 'bf16'], label="Auto-Encoder Data Type", value="bf16",
531
  interactive=True)
532
- allocation = gr.Radio([["1 min", 1], ["2 min", 2], ["3 min", 3], ["4 min", 4], ["5 min", 5], ["6 min", 6], ["7 min", 7], ["8 min", 8], ["9 min", 9]], label="GPU allocation time", info="lower=May abort run, higher=Time penalty for next runs", value=6, interactive=True)
533
  randomize_seed = gr.Checkbox(label = "\U0001F3B2 Randomize seed", value = True, info = "If checked, result is always different")
534
  seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True)
535
  with gr.Group():
@@ -715,7 +758,8 @@ with gr.Blocks(title="SUPIR") as interface:
715
  spt_linear_CFG,
716
  spt_linear_s_stage2,
717
  model_select,
718
- output_format
 
719
  ], outputs = [
720
  result_slider,
721
  result_gallery,
@@ -723,6 +767,8 @@ with gr.Blocks(title="SUPIR") as interface:
723
  event_id
724
  ])
725
 
 
 
726
  restart_button.click(fn = load_and_reset, inputs = [
727
  param_setting
728
  ], outputs = [
 
147
  output_format,
148
  allocation
149
  ):
150
+ print('allocation')
151
+ print(allocation)
152
  if allocation == 1:
153
  return restore_in_1min(
154
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
 
190
  def restore_in_1min(
191
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
192
  ):
193
+ print('1 min')
194
  return restore(
195
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
196
  )
 
199
  def restore_in_2min(
200
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
201
  ):
202
+ print('2 min')
203
  return restore(
204
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
205
  )
 
208
  def restore_in_3min(
209
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
210
  ):
211
+ print('3 min')
212
  return restore(
213
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
214
  )
 
217
  def restore_in_4min(
218
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
219
  ):
220
+ print('4 min')
221
  return restore(
222
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
223
  )
 
226
  def restore_in_5min(
227
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
228
  ):
229
+ print('5 min')
230
  return restore(
231
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
232
  )
 
235
  def restore_in_6min(
236
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
237
  ):
238
+ print('6 min')
239
  return restore(
240
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
241
  )
 
244
  def restore_in_7min(
245
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
246
  ):
247
+ print('7 min')
248
  return restore(
249
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
250
  )
 
253
  def restore_in_8min(
254
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
255
  ):
256
+ print('8 min')
257
  return restore(
258
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
259
  )
 
262
  def restore_in_9min(
263
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
264
  ):
265
+ print('9 min')
266
  return restore(
267
  noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select, output_format
268
  )
 
298
  ):
299
  start = time.time()
300
  print('stage2_process ==>>')
301
+ print(noisy_image)
302
+ print(denoise_image)
303
+ print(prompt)
304
+ print(a_prompt)
305
+ print(n_prompt)
306
+ print(num_samples)
307
+ print(min_size)
308
+ print(downscale)
309
+ print(upscale)
310
+ print(edm_steps)
311
+ print(s_stage1)
312
+ print(s_stage2)
313
+ print(s_cfg)
314
+ print(randomize_seed)
315
+ print(seed)
316
+ print(s_churn)
317
+ print(s_noise)
318
+ print(color_fix_type)
319
+ print(diff_dtype)
320
+ print(ae_dtype)
321
+ print(gamma_correction)
322
+ print(linear_CFG)
323
+ print(linear_s_stage2)
324
+ print(spt_linear_CFG)
325
+ print(spt_linear_s_stage2)
326
+ print(model_select)
327
+ print(output_format)
328
  if torch.cuda.device_count() == 0:
329
  gr.Warning('Set this space to GPU config to make it work.')
330
+ return None, None, None, None
331
  if output_format == "input":
332
  if noisy_image is None:
333
  output_format = "png"
 
450
  return edm_steps, s_cfg, s_stage2, s_stage1, s_churn, s_noise, a_prompt, n_prompt, color_fix_type, linear_CFG, \
451
  linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select
452
 
453
+ def on_select_result(result_gallery, evt: gr.SelectData):
454
+ print('on_select_result')
455
+ print(result_gallery[0])
456
+ print(result_gallery[evt.index])
457
+ return [result_gallery[0][0], result_gallery[evt.index][0]]
458
 
459
  def submit_feedback(event_id, fb_score, fb_text):
460
  if args.log_history:
 
476
 
477
  <p>This is an online demo of SUPIR, a practicing model scaling for photo-realistic image restoration.
478
  It is still a research project under tested and is not yet a stable commercial product.
479
+ The content added by SUPIR is imagination, not real-world information.
480
  The aim of SUPIR is the beauty and the illustration.
481
  Most of the processes only last few minutes.
482
  This demo can handle huge images but the process will be aborted if it lasts more than 9 min.
 
499
  if torch.cuda.device_count() == 0:
500
  with gr.Row():
501
  gr.HTML("""
502
+ <p style="background-color: red;"><big><big><big><b>⚠️To use SUPIR, <a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/SUPIR?duplicate=true">duplicate this space</a> and set a GPU with 30 GB VRAM.</b>
503
 
504
  You can't use SUPIR directly here because this space runs on a CPU, which is not enough for SUPIR. This is a template space. Please provide feedback if you have issues.
505
  </big></big></big></p>
 
551
  with gr.Column():
552
  color_fix_type = gr.Radio(["None", "AdaIn", "Wavelet"], label="Color-Fix Type", info="AdaIn=Improve following a style, Wavelet=For JPEG artifacts", value="Wavelet",
553
  interactive=True)
554
+ allocation = gr.Radio([["1 min", 1], ["2 min", 2], ["3 min", 3], ["4 min", 4], ["5 min", 5], ["6 min", 6], ["7 min", 7], ["8 min", 8], ["9 min", 9]], label="GPU allocation time", info="lower=May abort run, higher=Time penalty for next runs", value=6, interactive=True)
555
  s_cfg = gr.Slider(label="Text Guidance Scale", info="lower=follow the image, higher=follow the prompt", minimum=1.0, maximum=15.0,
556
  value=default_setting.s_cfg_Quality if torch.cuda.device_count() > 0 else 1.0, step=0.1)
557
  s_stage2 = gr.Slider(label="Restoring Guidance Strength", minimum=0., maximum=1., value=1., step=0.05)
 
573
  with gr.Column():
574
  ae_dtype = gr.Radio(['fp32', 'bf16'], label="Auto-Encoder Data Type", value="bf16",
575
  interactive=True)
 
576
  randomize_seed = gr.Checkbox(label = "\U0001F3B2 Randomize seed", value = True, info = "If checked, result is always different")
577
  seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True)
578
  with gr.Group():
 
758
  spt_linear_CFG,
759
  spt_linear_s_stage2,
760
  model_select,
761
+ output_format,
762
+ allocation
763
  ], outputs = [
764
  result_slider,
765
  result_gallery,
 
767
  event_id
768
  ])
769
 
770
+ result_gallery.select(on_select_result, result_gallery, result_slider)
771
+
772
  restart_button.click(fn = load_and_reset, inputs = [
773
  param_setting
774
  ], outputs = [