Spaces:
Runtime error
Runtime error
Update
Browse files
app.py
CHANGED
@@ -67,13 +67,14 @@ def create_advanced_demo(model: Model) -> gr.Blocks:
|
|
67 |
step=1,
|
68 |
value=1234,
|
69 |
label='Seed')
|
70 |
-
superresolve = gr.Checkbox(value=False,
|
71 |
-
label='Superresolve')
|
72 |
run_button = gr.Button('Run')
|
73 |
with gr.Column():
|
74 |
with gr.Tabs():
|
75 |
-
with gr.TabItem('Result'):
|
76 |
result = gr.Image(show_label=False, elem_id='result')
|
|
|
|
|
|
|
77 |
with gr.TabItem('Denoising Process'):
|
78 |
result_video = gr.Video(show_label=False,
|
79 |
elem_id='result-video')
|
@@ -89,10 +90,10 @@ def create_advanced_demo(model: Model) -> gr.Blocks:
|
|
89 |
num_steps,
|
90 |
randomize_seed,
|
91 |
seed,
|
92 |
-
superresolve,
|
93 |
],
|
94 |
outputs=[
|
95 |
result,
|
|
|
96 |
seed,
|
97 |
result_video,
|
98 |
])
|
|
|
67 |
step=1,
|
68 |
value=1234,
|
69 |
label='Seed')
|
|
|
|
|
70 |
run_button = gr.Button('Run')
|
71 |
with gr.Column():
|
72 |
with gr.Tabs():
|
73 |
+
with gr.TabItem('Result (Superresolved)'):
|
74 |
result = gr.Image(show_label=False, elem_id='result')
|
75 |
+
with gr.TabItem('Result (Raw)'):
|
76 |
+
result_raw = gr.Image(show_label=False,
|
77 |
+
elem_id='result-raw')
|
78 |
with gr.TabItem('Denoising Process'):
|
79 |
result_video = gr.Video(show_label=False,
|
80 |
elem_id='result-video')
|
|
|
90 |
num_steps,
|
91 |
randomize_seed,
|
92 |
seed,
|
|
|
93 |
],
|
94 |
outputs=[
|
95 |
result,
|
96 |
+
result_raw,
|
97 |
seed,
|
98 |
result_video,
|
99 |
])
|
model.py
CHANGED
@@ -156,17 +156,16 @@ class Model:
|
|
156 |
return PIL.Image.open(out_file)
|
157 |
|
158 |
def run(self, model_name: str, scheduler_type: str, num_steps: int,
|
159 |
-
randomize_seed: bool,
|
160 |
-
|
161 |
self.set_pipeline(model_name, scheduler_type)
|
162 |
if scheduler_type == 'PNDM':
|
163 |
num_steps = max(4, min(num_steps, 100))
|
164 |
if randomize_seed:
|
165 |
seed = self.rng.randint(0, 100000)
|
166 |
res, filename = self.generate_with_video(seed, num_steps)
|
167 |
-
|
168 |
-
|
169 |
-
return res, seed, filename
|
170 |
|
171 |
@staticmethod
|
172 |
def to_grid(images: list[PIL.Image.Image],
|
|
|
156 |
return PIL.Image.open(out_file)
|
157 |
|
158 |
def run(self, model_name: str, scheduler_type: str, num_steps: int,
|
159 |
+
randomize_seed: bool,
|
160 |
+
seed: int) -> tuple[PIL.Image.Image, PIL.Image.Image, int, str]:
|
161 |
self.set_pipeline(model_name, scheduler_type)
|
162 |
if scheduler_type == 'PNDM':
|
163 |
num_steps = max(4, min(num_steps, 100))
|
164 |
if randomize_seed:
|
165 |
seed = self.rng.randint(0, 100000)
|
166 |
res, filename = self.generate_with_video(seed, num_steps)
|
167 |
+
superresolved = self.superresolve(res)
|
168 |
+
return superresolved, res, seed, filename
|
|
|
169 |
|
170 |
@staticmethod
|
171 |
def to_grid(images: list[PIL.Image.Image],
|
style.css
CHANGED
@@ -13,6 +13,10 @@ div#result {
|
|
13 |
max-width: 400px;
|
14 |
max-height: 400px;
|
15 |
}
|
|
|
|
|
|
|
|
|
16 |
div#result-video {
|
17 |
max-width: 400px;
|
18 |
max-height: 400px;
|
|
|
13 |
max-width: 400px;
|
14 |
max-height: 400px;
|
15 |
}
|
16 |
+
div#result-raw {
|
17 |
+
max-width: 400px;
|
18 |
+
max-height: 400px;
|
19 |
+
}
|
20 |
div#result-video {
|
21 |
max-width: 400px;
|
22 |
max-height: 400px;
|