Spaces:
Running
Running
File size: 5,062 Bytes
0cb616f 3a6953c 0cb616f 3a6953c c18a289 9b4fa71 3a6953c c18a289 3a6953c 9b4fa71 3a6953c 9b4fa71 3a6953c 0cb616f 3a6953c 0cb616f 3a6953c 0cb616f 3a6953c cd5f59c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 |
import spaces # type: ignore
import os
import uuid
from PIL import Image
import gradio as gr
import numpy as np
import random
import torch
from diffusers import FluxPipeline
from sd_embed.embedding_funcs import get_weighted_text_embeddings_flux1
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
torch_dtype=dtype,
)
pipe.to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
@spaces.GPU(duration=90)
def infer(
prompt: str,
seed=42,
randomize_seed=False,
width=1024,
height=1024,
guidance_scale=5.0,
num_inference_steps=28,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
prompt_embeds, pooled_prompt_embeds = get_weighted_text_embeddings_flux1(
pipe=pipe, prompt=prompt
)
image = pipe(
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
width=width,
height=height,
num_inference_steps=num_inference_steps,
generator=generator,
guidance_scale=guidance_scale,
).images[0]
assert isinstance(
image, Image.Image
), "The output is not an instance of Image.Image"
filepath = os.path.join("images", "{uuid}.png".format(uuid=str(uuid.uuid4().hex)))
image.save(filepath)
return (
image,
gr.DownloadButton(
label="Download PNG", value=filepath, size="sm", visible=True
),
seed,
)
examples = [
"a cat holding a sign that says flux.1 is great",
"an old man holding a sign that says Increase Zero-GPU Limit",
]
css = """
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("""# FLUX.1
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)]
""")
with gr.Row(equal_height=False):
with gr.Column():
prompt = gr.TextArea(
label="Prompt",
show_label=False,
lines=3,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", variant="primary", scale=0)
result = gr.Image(
format="webp",
type="pil",
label="Result",
show_label=False,
show_download_button=False,
show_share_button=False,
)
download = gr.DownloadButton(size="sm", visible=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():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=832,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1216,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=0,
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, download, seed],
cache_examples="lazy",
)
gr.on(
triggers=[run_button.click],
fn=lambda: gr.update(visible=False),
outputs=download,
api_name=False,
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=[result, download, seed],
)
if __name__ == "__main__":
os.makedirs("images", exist_ok=True)
demo.queue(api_open=True).launch()
|