ginipick commited on
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cb42b1e
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1 Parent(s): 3b53d1c

Update app.py

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  1. app.py +73 -86
app.py CHANGED
@@ -12,6 +12,7 @@ from diffusers.utils import load_image
12
  from diffusers.utils import export_to_video
13
  import random
14
  from transformers import pipeline
 
15
  # Translation model load
16
  translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
17
 
@@ -35,31 +36,29 @@ english_labels = {
35
  "Seed": "Seed"
36
  }
37
 
38
- # load pipelines
39
  base_model = "black-forest-labs/FLUX.1-schnell"
40
 
41
  taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to("cuda")
42
- pipe = FluxPipeline.from_pretrained(base_model,
43
- vae=taef1,
44
- torch_dtype=torch.bfloat16)
45
-
 
46
  pipe.transformer.to(memory_format=torch.channels_last)
47
  clip_slider = CLIPSliderFlux(pipe, device=torch.device("cuda"))
48
 
49
- MAX_SEED = 2**32-1
50
 
51
  def save_images_with_unique_filenames(image_list, save_directory):
52
  if not os.path.exists(save_directory):
53
  os.makedirs(save_directory)
54
-
55
  paths = []
56
  for image in image_list:
57
  unique_filename = f"{uuid.uuid4()}.png"
58
  file_path = os.path.join(save_directory, unique_filename)
59
-
60
  image.save(file_path)
61
  paths.append(file_path)
62
-
63
  return paths
64
 
65
  def convert_to_centered_scale(num):
@@ -91,8 +90,7 @@ def generate(prompt,
91
  x_concept_1="", x_concept_2="",
92
  avg_diff_x=None,
93
  total_images=[],
94
- gradio_progress=gr.Progress()
95
- ):
96
  # Translate prompt and concepts if Korean
97
  prompt = translate_if_korean(prompt)
98
  concept_1 = translate_if_korean(concept_1)
@@ -100,7 +98,7 @@ def generate(prompt,
100
 
101
  print(f"Prompt: {prompt}, ← {concept_2}, {concept_1} ➡️ . scale {scale}, interm steps {interm_steps}")
102
  slider_x = [concept_2, concept_1]
103
- # check if avg diff for directions need to be re-calculated
104
  if randomize_seed:
105
  seed = random.randint(0, MAX_SEED)
106
 
@@ -116,18 +114,22 @@ def generate(prompt,
116
  low_scale = -1 * scale
117
  for i in gradio_progress.tqdm(range(interm_steps), desc="Generating images"):
118
  cur_scale = low_scale + (high_scale - low_scale) * i / (interm_steps - 1)
119
- image = clip_slider.generate(prompt,
120
- width=768,
121
- height=768,
122
- guidance_scale=guidance_scale,
123
- scale=cur_scale, seed=seed, num_inference_steps=steps, avg_diff=avg_diff)
 
 
 
 
 
124
  images.append(image)
125
- canvas = Image.new('RGB', (256*interm_steps, 256))
126
  for i, im in enumerate(images):
127
- canvas.paste(im.resize((256,256)), (256 * i, 0))
128
 
129
  comma_concepts_x = f"{slider_x[1]}, {slider_x[0]}"
130
-
131
  scale_total = convert_to_centered_scale(interm_steps)
132
  scale_min = scale_total[0]
133
  scale_max = scale_total[-1]
@@ -140,9 +142,7 @@ def generate(prompt,
140
  return x_concept_1, x_concept_2, avg_diff_x, export_to_video(images, video_path, fps=5), canvas, images, images[scale_middle], post_generation_slider_update, seed
141
 
142
  def update_pre_generated_images(slider_value, total_images):
143
- number_images = 0
144
- if total_images:
145
- number_images = len(total_images)
146
  if number_images > 0:
147
  scale_tuple = convert_to_centered_scale(number_images)
148
  return total_images[scale_tuple.index(slider_value)][0]
@@ -152,41 +152,18 @@ def update_pre_generated_images(slider_value, total_images):
152
  def reset_recalc_directions():
153
  return True
154
 
155
- # =======================
156
- # 개선된 예제 프롬프트
157
- # =======================
158
  examples = [
159
- [
160
- "A serene mountain landscape with a gentle river flowing in the foreground under a bright blue sky.",
161
- "Early Spring",
162
- "Deep Winter",
163
- 1.5
164
- ],
165
- [
166
- "A bustling city street at dusk with neon lights reflecting on wet pavement, capturing the contrast between day and night.",
167
- "Daytime",
168
- "Nighttime",
169
- 2.5
170
- ],
171
- [
172
- "An abstract digital art composition featuring vibrant colors and dynamic shapes.",
173
- "Calm",
174
- "Energetic",
175
- 2.0
176
- ],
177
- [
178
- "여성의 미소와 함께하는 따뜻한 분위기의 인물 사진",
179
- "젊음",
180
- "노년",
181
- 2.5
182
- ]
183
  ]
184
 
185
- # =======================
186
- # 밝고 세련된 UI CSS
187
- # =======================
188
  css = """
189
- /* 배경 이미지와 밝은 적용 */
190
  body {
191
  background: #ffffff url('https://images.unsplash.com/photo-1506748686214-e9df14d4d9d0?ixlib=rb-1.2.1&auto=format&fit=crop&w=1600&q=80') no-repeat center center fixed;
192
  background-size: cover;
@@ -237,7 +214,10 @@ footer {
237
  }
238
  """
239
 
240
- with gr.Blocks(css=css) as demo:
 
 
 
241
  x_concept_1 = gr.State("")
242
  x_concept_2 = gr.State("")
243
  total_images = gr.State([])
@@ -261,15 +241,14 @@ with gr.Blocks(css=css) as demo:
261
  concept_1 = gr.Textbox(
262
  label=english_labels["1st direction to steer"],
263
  info="Initial state",
264
- placeholder="winter"
265
  )
266
  with gr.Column(scale=1):
267
  concept_2 = gr.Textbox(
268
  label=english_labels["2nd direction to steer"],
269
  info="Final state",
270
- placeholder="summer"
271
  )
272
-
273
  with gr.Row(elem_classes="slider-container"):
274
  x = gr.Slider(
275
  minimum=0,
@@ -279,7 +258,6 @@ with gr.Blocks(css=css) as demo:
279
  label=english_labels["Strength"],
280
  info="Maximum strength for each direction (above 2.5 may be unstable)"
281
  )
282
-
283
  submit = gr.Button(english_labels["Generate directions"], size="lg", variant="primary")
284
 
285
  # Advanced Options Panel
@@ -301,7 +279,6 @@ with gr.Blocks(css=css) as demo:
301
  step=0.1,
302
  value=3.5
303
  )
304
-
305
  with gr.Row():
306
  with gr.Column(scale=1):
307
  iterations = gr.Slider(
@@ -319,7 +296,6 @@ with gr.Blocks(css=css) as demo:
319
  maximum=4,
320
  step=1
321
  )
322
-
323
  with gr.Row():
324
  with gr.Column(scale=1):
325
  randomize_seed = gr.Checkbox(
@@ -337,32 +313,27 @@ with gr.Blocks(css=css) as demo:
337
  )
338
 
339
  # Right Column - Output
340
- with gr.Column(scale=6):
341
  with gr.Group(elem_classes="main-panel"):
342
  gr.Markdown("### Generated Results")
 
 
 
 
 
 
 
 
343
  with gr.Row():
344
- with gr.Column(scale=3):
345
- image_seq = gr.Image(
346
- label=english_labels["Strip"],
347
- elem_id="strip",
348
- height=100
349
- )
350
- with gr.Column(scale=2):
351
- output_image = gr.Video(
352
- label=english_labels["Looping video"],
353
- elem_id="video",
354
- loop=True,
355
- autoplay=True,
356
- height=100
357
- )
358
- with gr.Row(): # Moved this block to be after the video
359
- with gr.Column():
360
  post_generation_image = gr.Image(
361
  label=english_labels["Generated Images"],
362
  type="filepath",
363
  elem_id="interactive",
364
- elem_classes="image-display"
 
365
  )
 
366
  post_generation_slider = gr.Slider(
367
  minimum=-10,
368
  maximum=10,
@@ -376,19 +347,35 @@ with gr.Blocks(css=css) as demo:
376
  examples=examples,
377
  inputs=[prompt, concept_1, concept_2, x],
378
  fn=generate,
379
- outputs=[x_concept_1, x_concept_2, avg_diff_x, output_image, image_seq, total_images,
380
- post_generation_image, post_generation_slider, seed],
 
 
 
 
 
 
 
381
  cache_examples="lazy"
382
  )
383
 
384
  # Event Handlers
385
  submit.click(
386
  fn=generate,
387
- inputs=[prompt, concept_1, concept_2, x, randomize_seed, seed, recalc_directions,
388
- iterations, steps, interm_steps, guidance_scale, x_concept_1, x_concept_2,
389
- avg_diff_x, total_images],
390
- outputs=[x_concept_1, x_concept_2, avg_diff_x, output_image, image_seq, total_images,
391
- post_generation_image, post_generation_slider, seed]
 
 
 
 
 
 
 
 
 
392
  )
393
 
394
  iterations.change(fn=reset_recalc_directions, outputs=[recalc_directions])
 
12
  from diffusers.utils import export_to_video
13
  import random
14
  from transformers import pipeline
15
+
16
  # Translation model load
17
  translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
18
 
 
36
  "Seed": "Seed"
37
  }
38
 
39
+ # Load pipelines
40
  base_model = "black-forest-labs/FLUX.1-schnell"
41
 
42
  taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to("cuda")
43
+ pipe = FluxPipeline.from_pretrained(
44
+ base_model,
45
+ vae=taef1,
46
+ torch_dtype=torch.bfloat16
47
+ )
48
  pipe.transformer.to(memory_format=torch.channels_last)
49
  clip_slider = CLIPSliderFlux(pipe, device=torch.device("cuda"))
50
 
51
+ MAX_SEED = 2**32 - 1
52
 
53
  def save_images_with_unique_filenames(image_list, save_directory):
54
  if not os.path.exists(save_directory):
55
  os.makedirs(save_directory)
 
56
  paths = []
57
  for image in image_list:
58
  unique_filename = f"{uuid.uuid4()}.png"
59
  file_path = os.path.join(save_directory, unique_filename)
 
60
  image.save(file_path)
61
  paths.append(file_path)
 
62
  return paths
63
 
64
  def convert_to_centered_scale(num):
 
90
  x_concept_1="", x_concept_2="",
91
  avg_diff_x=None,
92
  total_images=[],
93
+ gradio_progress=gr.Progress()):
 
94
  # Translate prompt and concepts if Korean
95
  prompt = translate_if_korean(prompt)
96
  concept_1 = translate_if_korean(concept_1)
 
98
 
99
  print(f"Prompt: {prompt}, ← {concept_2}, {concept_1} ➡️ . scale {scale}, interm steps {interm_steps}")
100
  slider_x = [concept_2, concept_1]
101
+ # Re-calculate latent direction if needed
102
  if randomize_seed:
103
  seed = random.randint(0, MAX_SEED)
104
 
 
114
  low_scale = -1 * scale
115
  for i in gradio_progress.tqdm(range(interm_steps), desc="Generating images"):
116
  cur_scale = low_scale + (high_scale - low_scale) * i / (interm_steps - 1)
117
+ image = clip_slider.generate(
118
+ prompt,
119
+ width=768,
120
+ height=768,
121
+ guidance_scale=guidance_scale,
122
+ scale=cur_scale,
123
+ seed=seed,
124
+ num_inference_steps=steps,
125
+ avg_diff=avg_diff
126
+ )
127
  images.append(image)
128
+ canvas = Image.new('RGB', (256 * interm_steps, 256))
129
  for i, im in enumerate(images):
130
+ canvas.paste(im.resize((256, 256)), (256 * i, 0))
131
 
132
  comma_concepts_x = f"{slider_x[1]}, {slider_x[0]}"
 
133
  scale_total = convert_to_centered_scale(interm_steps)
134
  scale_min = scale_total[0]
135
  scale_max = scale_total[-1]
 
142
  return x_concept_1, x_concept_2, avg_diff_x, export_to_video(images, video_path, fps=5), canvas, images, images[scale_middle], post_generation_slider_update, seed
143
 
144
  def update_pre_generated_images(slider_value, total_images):
145
+ number_images = len(total_images) if total_images else 0
 
 
146
  if number_images > 0:
147
  scale_tuple = convert_to_centered_scale(number_images)
148
  return total_images[scale_tuple.index(slider_value)][0]
 
152
  def reset_recalc_directions():
153
  return True
154
 
155
+ # Five examples fitting the "Time Stream" theme (one Korean example included)
 
 
156
  examples = [
157
+ ["신선한 토마토가 부패한 토마토�� 변해가는 과정", "Fresh", "Rotten", 2.0],
158
+ ["A blooming flower gradually withers into decay", "Bloom", "Wither", 1.5],
159
+ ["A vibrant cityscape transforms into a derelict ruin over time", "Modern", "Ruined", 2.5],
160
+ ["A lively forest slowly changes into an autumnal landscape", "Spring", "Autumn", 2.0],
161
+ ["A calm ocean evolves into a stormy seascape as time passes", "Calm", "Stormy", 3.0]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
162
  ]
163
 
164
+ # CSS for a bright and modern UI with a background image
 
 
165
  css = """
166
+ /* Bright and modern UI with background image */
167
  body {
168
  background: #ffffff url('https://images.unsplash.com/photo-1506748686214-e9df14d4d9d0?ixlib=rb-1.2.1&auto=format&fit=crop&w=1600&q=80') no-repeat center center fixed;
169
  background-size: cover;
 
214
  }
215
  """
216
 
217
+ with gr.Blocks(css=css, title="타임 스트림") as demo:
218
+ # Title and Description
219
+ gr.Markdown("# 타임 스트림\nA creative journey through the transformation of images over time.")
220
+
221
  x_concept_1 = gr.State("")
222
  x_concept_2 = gr.State("")
223
  total_images = gr.State([])
 
241
  concept_1 = gr.Textbox(
242
  label=english_labels["1st direction to steer"],
243
  info="Initial state",
244
+ placeholder="Fresh"
245
  )
246
  with gr.Column(scale=1):
247
  concept_2 = gr.Textbox(
248
  label=english_labels["2nd direction to steer"],
249
  info="Final state",
250
+ placeholder="Rotten"
251
  )
 
252
  with gr.Row(elem_classes="slider-container"):
253
  x = gr.Slider(
254
  minimum=0,
 
258
  label=english_labels["Strength"],
259
  info="Maximum strength for each direction (above 2.5 may be unstable)"
260
  )
 
261
  submit = gr.Button(english_labels["Generate directions"], size="lg", variant="primary")
262
 
263
  # Advanced Options Panel
 
279
  step=0.1,
280
  value=3.5
281
  )
 
282
  with gr.Row():
283
  with gr.Column(scale=1):
284
  iterations = gr.Slider(
 
296
  maximum=4,
297
  step=1
298
  )
 
299
  with gr.Row():
300
  with gr.Column(scale=1):
301
  randomize_seed = gr.Checkbox(
 
313
  )
314
 
315
  # Right Column - Output
316
+ with gr.Column(scale=8):
317
  with gr.Group(elem_classes="main-panel"):
318
  gr.Markdown("### Generated Results")
319
+ # Video output on top (bigger) and image output below
320
+ output_video = gr.Video(
321
+ label=english_labels["Looping video"],
322
+ elem_id="video",
323
+ loop=True,
324
+ autoplay=True,
325
+ height=400
326
+ )
327
  with gr.Row():
328
+ with gr.Column(scale=1):
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
329
  post_generation_image = gr.Image(
330
  label=english_labels["Generated Images"],
331
  type="filepath",
332
  elem_id="interactive",
333
+ elem_classes="image-display",
334
+ height=200
335
  )
336
+ with gr.Column(scale=1):
337
  post_generation_slider = gr.Slider(
338
  minimum=-10,
339
  maximum=10,
 
347
  examples=examples,
348
  inputs=[prompt, concept_1, concept_2, x],
349
  fn=generate,
350
+ outputs=[
351
+ x_concept_1, x_concept_2, avg_diff_x,
352
+ output_video, # video output (larger)
353
+ canvas, # image strip (below video)
354
+ total_images,
355
+ post_generation_image,
356
+ post_generation_slider,
357
+ seed
358
+ ],
359
  cache_examples="lazy"
360
  )
361
 
362
  # Event Handlers
363
  submit.click(
364
  fn=generate,
365
+ inputs=[
366
+ prompt, concept_1, concept_2, x, randomize_seed, seed,
367
+ recalc_directions, iterations, steps, interm_steps,
368
+ guidance_scale, x_concept_1, x_concept_2, avg_diff_x, total_images
369
+ ],
370
+ outputs=[
371
+ x_concept_1, x_concept_2, avg_diff_x,
372
+ output_video, # video output (larger)
373
+ canvas, # image strip (below video)
374
+ total_images,
375
+ post_generation_image,
376
+ post_generation_slider,
377
+ seed
378
+ ]
379
  )
380
 
381
  iterations.change(fn=reset_recalc_directions, outputs=[recalc_directions])