Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,236 Bytes
223453a e1f65c8 223453a e1f65c8 223453a e1f65c8 223453a e1f65c8 223453a e1f65c8 223453a e1f65c8 223453a e1f65c8 223453a e1f65c8 223453a e1f65c8 223453a e1f65c8 223453a e1f65c8 223453a e1f65c8 223453a 0a34ffd |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 |
import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
import torch
from spaces import GPU # IMPORTANTE: Ativando suporte ao ZeroGPU
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
model_repo_id = "stabilityai/sdxl-turbo" # Pode trocar por outro modelo se quiser
pipe = None # O modelo só vai carregar DENTRO da função GPU
@GPU # 🚨 Essa é a função que o Hugging Face vai usar para alocar GPU temporária
def infer(
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
progress=gr.Progress(track_tqdm=True),
):
global pipe
if pipe is None:
# 🚨 Carregamento do modelo só quando o ZeroGPU te der acesso à GPU
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator(device="cuda" if torch.cuda.is_available() else "cpu").manual_seed(seed)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images[0]
return image, seed
examples = [
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"An astronaut riding a green horse",
"A delicious ceviche cheesecake slice",
]
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(" # Text-to-Image Gradio Template (ZeroGPU Ready ✅)")
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, variant="primary")
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",
visible=False,
)
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=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=0.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=2,
)
gr.Examples(examples=examples, inputs=[prompt])
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],
)
if __name__ == "__main__":
demo.launch(share=True)
|