None1145 commited on
Commit
d840e13
1 Parent(s): 7e47b3d

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +13 -34
app.py CHANGED
@@ -1,27 +1,15 @@
1
  import gradio as gr
2
  import numpy as np
3
  import random
4
-
5
- # import spaces #[uncomment to use ZeroGPU]
6
- from diffusers import DiffusionPipeline
7
  import torch
8
 
9
- device = "cuda" if torch.cuda.is_available() else "cpu"
10
- model_repo_id = "stabilityai/sdxl-turbo" # Replace to the model you would like to use
11
-
12
- if torch.cuda.is_available():
13
- torch_dtype = torch.float16
14
- else:
15
- torch_dtype = torch.float32
16
-
17
- pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
18
- pipe = pipe.to(device)
19
 
20
  MAX_SEED = np.iinfo(np.int32).max
21
- MAX_IMAGE_SIZE = 1024
22
 
23
-
24
- # @spaces.GPU #[uncomment to use ZeroGPU]
25
  def infer(
26
  prompt,
27
  negative_prompt,
@@ -57,17 +45,8 @@ examples = [
57
  "A delicious ceviche cheesecake slice",
58
  ]
59
 
60
- css = """
61
- #col-container {
62
- margin: 0 auto;
63
- max-width: 640px;
64
- }
65
- """
66
-
67
- with gr.Blocks(css=css) as demo:
68
  with gr.Column(elem_id="col-container"):
69
- gr.Markdown(" # Text-to-Image Gradio Template")
70
-
71
  with gr.Row():
72
  prompt = gr.Text(
73
  label="Prompt",
@@ -102,18 +81,18 @@ with gr.Blocks(css=css) as demo:
102
  with gr.Row():
103
  width = gr.Slider(
104
  label="Width",
105
- minimum=256,
106
  maximum=MAX_IMAGE_SIZE,
107
  step=32,
108
- value=1024, # Replace with defaults that work for your model
109
  )
110
 
111
  height = gr.Slider(
112
  label="Height",
113
- minimum=256,
114
  maximum=MAX_IMAGE_SIZE,
115
  step=32,
116
- value=1024, # Replace with defaults that work for your model
117
  )
118
 
119
  with gr.Row():
@@ -122,15 +101,15 @@ with gr.Blocks(css=css) as demo:
122
  minimum=0.0,
123
  maximum=10.0,
124
  step=0.1,
125
- value=0.0, # Replace with defaults that work for your model
126
  )
127
 
128
  num_inference_steps = gr.Slider(
129
  label="Number of inference steps",
130
  minimum=1,
131
- maximum=50,
132
  step=1,
133
- value=2, # Replace with defaults that work for your model
134
  )
135
 
136
  gr.Examples(examples=examples, inputs=[prompt])
@@ -151,4 +130,4 @@ with gr.Blocks(css=css) as demo:
151
  )
152
 
153
  if __name__ == "__main__":
154
- demo.launch()
 
1
  import gradio as gr
2
  import numpy as np
3
  import random
4
+ from optimum.intel import OVStableDiffusionXLPipeline
 
 
5
  import torch
6
 
7
+ model_id = "None1145/noobai-XL-Vpred-0.65s-openvino"
8
+ pipe = OVStableDiffusionXLPipeline.from_pretrained(model_id)
 
 
 
 
 
 
 
 
9
 
10
  MAX_SEED = np.iinfo(np.int32).max
11
+ MAX_IMAGE_SIZE = 2048
12
 
 
 
13
  def infer(
14
  prompt,
15
  negative_prompt,
 
45
  "A delicious ceviche cheesecake slice",
46
  ]
47
 
48
+ with gr.Blocks() as img:
 
 
 
 
 
 
 
49
  with gr.Column(elem_id="col-container"):
 
 
50
  with gr.Row():
51
  prompt = gr.Text(
52
  label="Prompt",
 
81
  with gr.Row():
82
  width = gr.Slider(
83
  label="Width",
84
+ minimum=512,
85
  maximum=MAX_IMAGE_SIZE,
86
  step=32,
87
+ value=832, # Replace with defaults that work for your model
88
  )
89
 
90
  height = gr.Slider(
91
  label="Height",
92
+ minimum=512,
93
  maximum=MAX_IMAGE_SIZE,
94
  step=32,
95
+ value=1216, # Replace with defaults that work for your model
96
  )
97
 
98
  with gr.Row():
 
101
  minimum=0.0,
102
  maximum=10.0,
103
  step=0.1,
104
+ value=5.0, # Replace with defaults that work for your model
105
  )
106
 
107
  num_inference_steps = gr.Slider(
108
  label="Number of inference steps",
109
  minimum=1,
110
+ maximum=60,
111
  step=1,
112
+ value=28, # Replace with defaults that work for your model
113
  )
114
 
115
  gr.Examples(examples=examples, inputs=[prompt])
 
130
  )
131
 
132
  if __name__ == "__main__":
133
+ img.launch()