Spaces:
Paused
Paused
Update app.py
Browse files
app.py
CHANGED
|
@@ -38,7 +38,6 @@ english_labels = {
|
|
| 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,
|
|
@@ -47,7 +46,6 @@ pipe = FluxPipeline.from_pretrained(
|
|
| 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):
|
|
@@ -95,20 +93,16 @@ def generate(prompt,
|
|
| 95 |
prompt = translate_if_korean(prompt)
|
| 96 |
concept_1 = translate_if_korean(concept_1)
|
| 97 |
concept_2 = translate_if_korean(concept_2)
|
| 98 |
-
|
| 99 |
print(f"Prompt: {prompt}, โ {concept_2}, {concept_1} โก๏ธ . 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 |
-
|
| 105 |
if not sorted(slider_x) == sorted([x_concept_1, x_concept_2]) or recalc_directions:
|
| 106 |
gradio_progress(0, desc="Calculating directions...")
|
| 107 |
avg_diff = clip_slider.find_latent_direction(slider_x[0], slider_x[1], num_iterations=iterations)
|
| 108 |
x_concept_1, x_concept_2 = slider_x[0], slider_x[1]
|
| 109 |
else:
|
| 110 |
avg_diff = avg_diff_x
|
| 111 |
-
|
| 112 |
images = []
|
| 113 |
high_scale = scale
|
| 114 |
low_scale = -1 * scale
|
|
@@ -128,7 +122,6 @@ def generate(prompt,
|
|
| 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]
|
|
@@ -136,7 +129,6 @@ def generate(prompt,
|
|
| 136 |
scale_middle = scale_total.index(0)
|
| 137 |
post_generation_slider_update = gr.update(label=comma_concepts_x, value=0, minimum=scale_min, maximum=scale_max, interactive=True)
|
| 138 |
avg_diff_x = avg_diff.cpu()
|
| 139 |
-
|
| 140 |
video_path = f"{uuid.uuid4()}.mp4"
|
| 141 |
print(video_path)
|
| 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
|
|
@@ -152,7 +144,7 @@ def update_pre_generated_images(slider_value, total_images):
|
|
| 152 |
def reset_recalc_directions():
|
| 153 |
return True
|
| 154 |
|
| 155 |
-
# Five
|
| 156 |
examples = [
|
| 157 |
["์ ์ ํ ํ ๋งํ ๊ฐ ๋ถํจํ ํ ๋งํ ๋ก ๋ณํด๊ฐ๋ ๊ณผ์ ", "Fresh", "Rotten", 2.0],
|
| 158 |
["A blooming flower gradually withers into decay", "Bloom", "Wither", 1.5],
|
|
@@ -170,17 +162,14 @@ body {
|
|
| 170 |
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
|
| 171 |
color: #333;
|
| 172 |
}
|
| 173 |
-
|
| 174 |
footer {
|
| 175 |
visibility: hidden;
|
| 176 |
}
|
| 177 |
-
|
| 178 |
.container {
|
| 179 |
max-width: 1200px;
|
| 180 |
margin: 20px auto;
|
| 181 |
padding: 0 10px;
|
| 182 |
}
|
| 183 |
-
|
| 184 |
.main-panel {
|
| 185 |
background-color: rgba(255, 255, 255, 0.9);
|
| 186 |
border-radius: 12px;
|
|
@@ -188,25 +177,21 @@ footer {
|
|
| 188 |
margin-bottom: 20px;
|
| 189 |
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
|
| 190 |
}
|
| 191 |
-
|
| 192 |
.controls-panel {
|
| 193 |
background-color: rgba(255, 255, 255, 0.85);
|
| 194 |
border-radius: 8px;
|
| 195 |
padding: 16px;
|
| 196 |
box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.05);
|
| 197 |
}
|
| 198 |
-
|
| 199 |
.image-display {
|
| 200 |
min-height: 400px;
|
| 201 |
display: flex;
|
| 202 |
flex-direction: column;
|
| 203 |
justify-content: center;
|
| 204 |
}
|
| 205 |
-
|
| 206 |
.slider-container {
|
| 207 |
padding: 10px 0;
|
| 208 |
}
|
| 209 |
-
|
| 210 |
.advanced-panel {
|
| 211 |
margin-top: 20px;
|
| 212 |
border-top: 1px solid #eaeaea;
|
|
@@ -215,8 +200,7 @@ footer {
|
|
| 215 |
"""
|
| 216 |
|
| 217 |
with gr.Blocks(css=css, title="ํ์ ์คํธ๋ฆผ") as demo:
|
| 218 |
-
#
|
| 219 |
-
gr.Markdown("# ํ์ ์คํธ๋ฆผ\nA creative journey through the transformation of images over time.")
|
| 220 |
|
| 221 |
x_concept_1 = gr.State("")
|
| 222 |
x_concept_2 = gr.State("")
|
|
@@ -316,31 +300,23 @@ with gr.Blocks(css=css, title="ํ์ ์คํธ๋ฆผ") as demo:
|
|
| 316 |
with gr.Column(scale=8):
|
| 317 |
with gr.Group(elem_classes="main-panel"):
|
| 318 |
gr.Markdown("### Generated Results")
|
| 319 |
-
#
|
| 320 |
-
|
| 321 |
-
|
| 322 |
-
elem_id="video",
|
| 323 |
-
loop=True,
|
| 324 |
-
autoplay=True,
|
| 325 |
-
height=400
|
| 326 |
-
)
|
| 327 |
with gr.Row():
|
| 328 |
-
|
| 329 |
-
|
| 330 |
-
|
| 331 |
-
|
| 332 |
-
|
| 333 |
-
|
| 334 |
-
|
| 335 |
-
|
| 336 |
-
|
| 337 |
-
|
| 338 |
-
|
| 339 |
-
|
| 340 |
-
|
| 341 |
-
step=1,
|
| 342 |
-
label=english_labels["From 1st to 2nd direction"]
|
| 343 |
-
)
|
| 344 |
|
| 345 |
# Examples Section
|
| 346 |
gr.Examples(
|
|
@@ -349,12 +325,12 @@ with gr.Blocks(css=css, title="ํ์ ์คํธ๋ฆผ") as demo:
|
|
| 349 |
fn=generate,
|
| 350 |
outputs=[
|
| 351 |
x_concept_1, x_concept_2, avg_diff_x,
|
| 352 |
-
output_video,
|
| 353 |
-
|
| 354 |
-
total_images,
|
| 355 |
-
post_generation_image,
|
| 356 |
-
post_generation_slider,
|
| 357 |
-
seed
|
| 358 |
],
|
| 359 |
cache_examples="lazy"
|
| 360 |
)
|
|
@@ -369,8 +345,8 @@ with gr.Blocks(css=css, title="ํ์ ์คํธ๋ฆผ") as demo:
|
|
| 369 |
],
|
| 370 |
outputs=[
|
| 371 |
x_concept_1, x_concept_2, avg_diff_x,
|
| 372 |
-
output_video,
|
| 373 |
-
|
| 374 |
total_images,
|
| 375 |
post_generation_image,
|
| 376 |
post_generation_slider,
|
|
|
|
| 38 |
|
| 39 |
# Load pipelines
|
| 40 |
base_model = "black-forest-labs/FLUX.1-schnell"
|
|
|
|
| 41 |
taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to("cuda")
|
| 42 |
pipe = FluxPipeline.from_pretrained(
|
| 43 |
base_model,
|
|
|
|
| 46 |
)
|
| 47 |
pipe.transformer.to(memory_format=torch.channels_last)
|
| 48 |
clip_slider = CLIPSliderFlux(pipe, device=torch.device("cuda"))
|
|
|
|
| 49 |
MAX_SEED = 2**32 - 1
|
| 50 |
|
| 51 |
def save_images_with_unique_filenames(image_list, save_directory):
|
|
|
|
| 93 |
prompt = translate_if_korean(prompt)
|
| 94 |
concept_1 = translate_if_korean(concept_1)
|
| 95 |
concept_2 = translate_if_korean(concept_2)
|
|
|
|
| 96 |
print(f"Prompt: {prompt}, โ {concept_2}, {concept_1} โก๏ธ . scale {scale}, interm steps {interm_steps}")
|
| 97 |
slider_x = [concept_2, concept_1]
|
|
|
|
| 98 |
if randomize_seed:
|
| 99 |
seed = random.randint(0, MAX_SEED)
|
|
|
|
| 100 |
if not sorted(slider_x) == sorted([x_concept_1, x_concept_2]) or recalc_directions:
|
| 101 |
gradio_progress(0, desc="Calculating directions...")
|
| 102 |
avg_diff = clip_slider.find_latent_direction(slider_x[0], slider_x[1], num_iterations=iterations)
|
| 103 |
x_concept_1, x_concept_2 = slider_x[0], slider_x[1]
|
| 104 |
else:
|
| 105 |
avg_diff = avg_diff_x
|
|
|
|
| 106 |
images = []
|
| 107 |
high_scale = scale
|
| 108 |
low_scale = -1 * scale
|
|
|
|
| 122 |
canvas = Image.new('RGB', (256 * interm_steps, 256))
|
| 123 |
for i, im in enumerate(images):
|
| 124 |
canvas.paste(im.resize((256, 256)), (256 * i, 0))
|
|
|
|
| 125 |
comma_concepts_x = f"{slider_x[1]}, {slider_x[0]}"
|
| 126 |
scale_total = convert_to_centered_scale(interm_steps)
|
| 127 |
scale_min = scale_total[0]
|
|
|
|
| 129 |
scale_middle = scale_total.index(0)
|
| 130 |
post_generation_slider_update = gr.update(label=comma_concepts_x, value=0, minimum=scale_min, maximum=scale_max, interactive=True)
|
| 131 |
avg_diff_x = avg_diff.cpu()
|
|
|
|
| 132 |
video_path = f"{uuid.uuid4()}.mp4"
|
| 133 |
print(video_path)
|
| 134 |
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
|
|
|
|
| 144 |
def reset_recalc_directions():
|
| 145 |
return True
|
| 146 |
|
| 147 |
+
# Five "Time Stream" themed examples (one Korean example included)
|
| 148 |
examples = [
|
| 149 |
["์ ์ ํ ํ ๋งํ ๊ฐ ๋ถํจํ ํ ๋งํ ๋ก ๋ณํด๊ฐ๋ ๊ณผ์ ", "Fresh", "Rotten", 2.0],
|
| 150 |
["A blooming flower gradually withers into decay", "Bloom", "Wither", 1.5],
|
|
|
|
| 162 |
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
|
| 163 |
color: #333;
|
| 164 |
}
|
|
|
|
| 165 |
footer {
|
| 166 |
visibility: hidden;
|
| 167 |
}
|
|
|
|
| 168 |
.container {
|
| 169 |
max-width: 1200px;
|
| 170 |
margin: 20px auto;
|
| 171 |
padding: 0 10px;
|
| 172 |
}
|
|
|
|
| 173 |
.main-panel {
|
| 174 |
background-color: rgba(255, 255, 255, 0.9);
|
| 175 |
border-radius: 12px;
|
|
|
|
| 177 |
margin-bottom: 20px;
|
| 178 |
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
|
| 179 |
}
|
|
|
|
| 180 |
.controls-panel {
|
| 181 |
background-color: rgba(255, 255, 255, 0.85);
|
| 182 |
border-radius: 8px;
|
| 183 |
padding: 16px;
|
| 184 |
box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.05);
|
| 185 |
}
|
|
|
|
| 186 |
.image-display {
|
| 187 |
min-height: 400px;
|
| 188 |
display: flex;
|
| 189 |
flex-direction: column;
|
| 190 |
justify-content: center;
|
| 191 |
}
|
|
|
|
| 192 |
.slider-container {
|
| 193 |
padding: 10px 0;
|
| 194 |
}
|
|
|
|
| 195 |
.advanced-panel {
|
| 196 |
margin-top: 20px;
|
| 197 |
border-top: 1px solid #eaeaea;
|
|
|
|
| 200 |
"""
|
| 201 |
|
| 202 |
with gr.Blocks(css=css, title="ํ์ ์คํธ๋ฆผ") as demo:
|
| 203 |
+
gr.Markdown("# ํ์ ์คํธ๋ฆผ\nA creative journey through the transformation of images over time.\nWhen you input text, a video transitioning from the past to the future is generated.")
|
|
|
|
| 204 |
|
| 205 |
x_concept_1 = gr.State("")
|
| 206 |
x_concept_2 = gr.State("")
|
|
|
|
| 300 |
with gr.Column(scale=8):
|
| 301 |
with gr.Group(elem_classes="main-panel"):
|
| 302 |
gr.Markdown("### Generated Results")
|
| 303 |
+
# Swapped order: Image strip on top, video below (video is larger)
|
| 304 |
+
image_strip = gr.Image(label="Image Strip", type="filepath", elem_id="strip", height=200)
|
| 305 |
+
output_video = gr.Video(label=english_labels["Looping video"], elem_id="video", loop=True, autoplay=True, height=600)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 306 |
with gr.Row():
|
| 307 |
+
post_generation_image = gr.Image(
|
| 308 |
+
label=english_labels["Generated Images"],
|
| 309 |
+
type="filepath",
|
| 310 |
+
elem_id="interactive",
|
| 311 |
+
elem_classes="image-display"
|
| 312 |
+
)
|
| 313 |
+
post_generation_slider = gr.Slider(
|
| 314 |
+
minimum=-10,
|
| 315 |
+
maximum=10,
|
| 316 |
+
value=0,
|
| 317 |
+
step=1,
|
| 318 |
+
label=english_labels["From 1st to 2nd direction"]
|
| 319 |
+
)
|
|
|
|
|
|
|
|
|
|
| 320 |
|
| 321 |
# Examples Section
|
| 322 |
gr.Examples(
|
|
|
|
| 325 |
fn=generate,
|
| 326 |
outputs=[
|
| 327 |
x_concept_1, x_concept_2, avg_diff_x,
|
| 328 |
+
output_video, # 4th output from generate goes to output_video
|
| 329 |
+
image_strip, # 5th output (canvas) goes to image_strip
|
| 330 |
+
total_images, # 6th output (list of images)
|
| 331 |
+
post_generation_image, # 7th output (interactive image)
|
| 332 |
+
post_generation_slider, # 8th output (slider update)
|
| 333 |
+
seed # 9th output (seed)
|
| 334 |
],
|
| 335 |
cache_examples="lazy"
|
| 336 |
)
|
|
|
|
| 345 |
],
|
| 346 |
outputs=[
|
| 347 |
x_concept_1, x_concept_2, avg_diff_x,
|
| 348 |
+
output_video, # video
|
| 349 |
+
image_strip, # canvas
|
| 350 |
total_images,
|
| 351 |
post_generation_image,
|
| 352 |
post_generation_slider,
|