Spaces:
Running
Running
File size: 4,691 Bytes
e82d5e9 1df8975 e82d5e9 1df8975 e82d5e9 1df8975 e82d5e9 a72c1a1 e82d5e9 a72c1a1 e82d5e9 65311af e82d5e9 a72c1a1 e82d5e9 a72c1a1 e82d5e9 1df8975 e82d5e9 1df8975 e82d5e9 13ce6ad |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 |
import gradio as gr
import numpy as np
import os
import random
import requests
from PIL import Image
from io import BytesIO
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
class APIClient:
def __init__(self, api_key=os.getenv("API_KEY"), base_url="inference.prodia.com"):
self.headers = {
"Content-Type": "application/json",
"Accept": "image/jpeg",
"Authorization": f"Bearer {api_key}"
}
self.base_url = f"https://{base_url}"
def _post(self, url, json=None):
r = requests.post(url, headers=self.headers, json=json)
r.raise_for_status()
return Image.open(BytesIO(r.content)).convert("RGBA")
def job(self, config):
body = {"type": "inference.flux.dev.txt2img.v1", "config": config}
return self._post(f"{self.base_url}/v2/job", json=body)
def infer(prompt, seed=42, randomize_seed=False, resolution="1024x1024", guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
width, height = resolution.split("x")
image = generative_api.job({
"prompt": prompt,
"width": int(width),
"height": int(height),
"seed": seed,
"num_inference_steps": num_inference_steps,
"guidance_scale": guidance_scale
})
return image, seed
generative_api = APIClient()
with open("header.html", "r") as file:
header = file.read()
examples = [
"a tiny astronaut hatching from an egg on the moon",
"a cat holding a sign that says hello world",
"an anime illustration of a wiener schnitzel",
]
css="""
#col-container {
margin: 0 auto;
max-width: 520px;
}
.image-container img {
max-width: 512px;
max-height: 512px;
margin: 0 auto;
border-radius: 0px;
}
.is-custom-button {
background-color: #3a4454;
border-color: #4b5563;
}
@media (prefers-color-scheme: light) {
.is-custom-button {
background-color: #f3f4f6;
border-color: #e5e7eb;
}
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""# Fast FLUX.1 [dev]
12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)
[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
""")
gr.HTML(header)
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt"
)
run_button = gr.Button("Run", scale=0)
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=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():
resolution = gr.Dropdown(
label="Resolution",
value="1024x1024",
choices=[
"1024x1024",
"1024x576",
"576x1024"
]
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=1,
maximum=15,
step=0.1,
value=3.5,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=28,
)
gr.Examples(
examples = examples,
fn = infer,
inputs = [prompt],
outputs = [result, seed],
cache_examples="lazy"
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn = infer,
inputs = [prompt, seed, randomize_seed, resolution, guidance_scale, num_inference_steps],
outputs = [result, seed]
)
demo.queue(default_concurrency_limit=4, max_size=5, api_open=False).launch(max_threads=256, show_api=False) |