Falln87 commited on
Commit
898a621
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1 Parent(s): 0acb520

Update app.py

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Files changed (1) hide show
  1. app.py +11 -26
app.py CHANGED
@@ -1,22 +1,22 @@
1
  import gradio as gr
2
  import numpy as np
3
  import random
4
- from diffusers import DiffusionPipeline
5
  import torch
 
6
 
7
  device = "cuda" if torch.cuda.is_available() else "cpu"
8
 
9
  if torch.cuda.is_available():
10
  torch.cuda.max_memory_allocated(device=device)
11
- pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
12
  pipe.enable_xformers_memory_efficient_attention()
13
  pipe = pipe.to(device)
14
  else:
15
- pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
16
  pipe = pipe.to(device)
17
 
18
  MAX_SEED = np.iinfo(np.int32).max
19
- MAX_IMAGE_SIZE = 1024
20
 
21
  def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
22
 
@@ -37,12 +37,6 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
37
 
38
  return image
39
 
40
- examples = [
41
- "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
42
- "An astronaut riding a green horse",
43
- "A delicious ceviche cheesecake slice",
44
- ]
45
-
46
  css="""
47
  #col-container {
48
  margin: 0 auto;
@@ -50,17 +44,12 @@ css="""
50
  }
51
  """
52
 
53
- if torch.cuda.is_available():
54
- power_device = "GPU"
55
- else:
56
- power_device = "CPU"
57
 
58
  with gr.Blocks(css=css) as demo:
59
 
60
  with gr.Column(elem_id="col-container"):
61
  gr.Markdown(f"""
62
- # Text-to-Image Gradio Template
63
- Currently running on {power_device}.
64
  """)
65
 
66
  with gr.Row():
@@ -68,7 +57,7 @@ with gr.Blocks(css=css) as demo:
68
  prompt = gr.Text(
69
  label="Prompt",
70
  show_label=False,
71
- max_lines=1,
72
  placeholder="Enter your prompt",
73
  container=False,
74
  )
@@ -103,7 +92,7 @@ with gr.Blocks(css=css) as demo:
103
  minimum=256,
104
  maximum=MAX_IMAGE_SIZE,
105
  step=32,
106
- value=512,
107
  )
108
 
109
  height = gr.Slider(
@@ -111,7 +100,7 @@ with gr.Blocks(css=css) as demo:
111
  minimum=256,
112
  maximum=MAX_IMAGE_SIZE,
113
  step=32,
114
- value=512,
115
  )
116
 
117
  with gr.Row():
@@ -121,7 +110,7 @@ with gr.Blocks(css=css) as demo:
121
  minimum=0.0,
122
  maximum=10.0,
123
  step=0.1,
124
- value=0.0,
125
  )
126
 
127
  num_inference_steps = gr.Slider(
@@ -129,14 +118,10 @@ with gr.Blocks(css=css) as demo:
129
  minimum=1,
130
  maximum=12,
131
  step=1,
132
- value=2,
133
  )
134
 
135
- gr.Examples(
136
- examples = examples,
137
- inputs = [prompt]
138
- )
139
-
140
  run_button.click(
141
  fn = infer,
142
  inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
 
1
  import gradio as gr
2
  import numpy as np
3
  import random
 
4
  import torch
5
+ from diffusers import StableDiffusion3Pipeline
6
 
7
  device = "cuda" if torch.cuda.is_available() else "cpu"
8
 
9
  if torch.cuda.is_available():
10
  torch.cuda.max_memory_allocated(device=device)
11
+ pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16)
12
  pipe.enable_xformers_memory_efficient_attention()
13
  pipe = pipe.to(device)
14
  else:
15
+ pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16)
16
  pipe = pipe.to(device)
17
 
18
  MAX_SEED = np.iinfo(np.int32).max
19
+ MAX_IMAGE_SIZE = 2048
20
 
21
  def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
22
 
 
37
 
38
  return image
39
 
 
 
 
 
 
 
40
  css="""
41
  #col-container {
42
  margin: 0 auto;
 
44
  }
45
  """
46
 
 
 
 
 
47
 
48
  with gr.Blocks(css=css) as demo:
49
 
50
  with gr.Column(elem_id="col-container"):
51
  gr.Markdown(f"""
52
+ # FallnAI Text2Image
 
53
  """)
54
 
55
  with gr.Row():
 
57
  prompt = gr.Text(
58
  label="Prompt",
59
  show_label=False,
60
+ max_lines=4,
61
  placeholder="Enter your prompt",
62
  container=False,
63
  )
 
92
  minimum=256,
93
  maximum=MAX_IMAGE_SIZE,
94
  step=32,
95
+ value=1024,
96
  )
97
 
98
  height = gr.Slider(
 
100
  minimum=256,
101
  maximum=MAX_IMAGE_SIZE,
102
  step=32,
103
+ value=1024,
104
  )
105
 
106
  with gr.Row():
 
110
  minimum=0.0,
111
  maximum=10.0,
112
  step=0.1,
113
+ value=2.0,
114
  )
115
 
116
  num_inference_steps = gr.Slider(
 
118
  minimum=1,
119
  maximum=12,
120
  step=1,
121
+ value=4,
122
  )
123
 
124
+
 
 
 
 
125
  run_button.click(
126
  fn = infer,
127
  inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],