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Running on Zero
Running on Zero
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e6aa373 b1c9d0d e6aa373 d191aa7 e6aa373 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import DiffusionPipeline
MODEL_ID = "sd2-community/stable-diffusion-2-1"
DEFAULT_WIDTH = 768
DEFAULT_HEIGHT = 768
DEFAULT_GUIDANCE = 7.5
DEFAULT_STEPS = 30
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained(
MODEL_ID, torch_dtype=dtype, variant="fp16", use_safetensors=True
).to(device)
pipe.safety_checker = None
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
@spaces.GPU(duration=65)
def infer(prompt, negative_prompt="", seed=42, randomize_seed=False, width=DEFAULT_WIDTH, height=DEFAULT_HEIGHT, guidance_scale=DEFAULT_GUIDANCE, num_inference_steps=DEFAULT_STEPS, progress=gr.Progress(track_tqdm=True)):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator(device=device).manual_seed(seed)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt or None,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images[0]
return image, seed
examples = [
"a tiny astronaut hatching from an egg on the moon",
"a cat holding a sign that says hello world",
"a photo of a beautiful mountain landscape at sunset",
]
css = """
#col-container {
margin: 0 auto;
max-width: 680px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("""# Stable Diffusion 2.1
865M-param latent text-to-image diffusion model, native 768×768.
[[model](https://huggingface.co/sd2-community/stable-diffusion-2-1)] [[license: CreativeML OpenRAIL++-M](https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/LICENSE-MODEL)]
""")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0)
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=DEFAULT_WIDTH,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=DEFAULT_HEIGHT,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=1,
maximum=15,
step=0.1,
value=DEFAULT_GUIDANCE,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=DEFAULT_STEPS,
)
gr.Examples(
examples=examples,
fn=infer,
inputs=[prompt],
outputs=[result, seed],
cache_examples=True,
cache_mode="lazy",
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs=[result, seed],
)
demo.launch()
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