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import spaces
import time
import os
import gradio as gr
import torch
from einops import rearrange
from PIL import Image
from flux.cli import SamplingOptions
from flux.sampling import denoise, get_noise, get_schedule, prepare, unpack
from flux.util import load_ae, load_clip, load_flow_model, load_t5
from pulid.pipeline_flux import PuLIDPipeline
from pulid.utils import resize_numpy_image_long
def get_models(name: str, device: torch.device, offload: bool):
t5 = load_t5(device, max_length=128)
clip = load_clip(device)
model = load_flow_model(name, device="cpu" if offload else device)
model.eval()
ae = load_ae(name, device="cpu" if offload else device)
return model, ae, t5, clip
class FluxGenerator:
def __init__(self):
self.device = torch.device('cuda')
self.offload = False
self.model_name = 'flux-dev'
self.model, self.ae, self.t5, self.clip = get_models(
self.model_name,
device=self.device,
offload=self.offload,
)
self.pulid_model = PuLIDPipeline(self.model, 'cuda', weight_dtype=torch.bfloat16)
self.pulid_model.load_pretrain()
flux_generator = FluxGenerator()
@spaces.GPU
@torch.inference_mode()
def generate_image(
width,
height,
num_steps,
start_step,
guidance,
seed,
prompt,
id_image=None,
id_weight=1.0,
neg_prompt="",
true_cfg=1.0,
timestep_to_start_cfg=1,
max_sequence_length=128,
):
flux_generator.t5.max_length = max_sequence_length
seed = int(seed)
if seed == -1:
seed = None
opts = SamplingOptions(
prompt=prompt,
width=width,
height=height,
num_steps=num_steps,
guidance=guidance,
seed=seed,
)
if opts.seed is None:
opts.seed = torch.Generator(device="cpu").seed()
t0 = time.perf_counter()
use_true_cfg = abs(true_cfg - 1.0) > 1e-2
if id_image is not None:
id_image = resize_numpy_image_long(id_image, 1024)
id_embeddings, uncond_id_embeddings = flux_generator.pulid_model.get_id_embedding(id_image, cal_uncond=use_true_cfg)
else:
id_embeddings = None
uncond_id_embeddings = None
# prepare input
x = get_noise(
1,
opts.height,
opts.width,
device=flux_generator.device,
dtype=torch.bfloat16,
seed=opts.seed,
)
timesteps = get_schedule(
opts.num_steps,
x.shape[-1] * x.shape[-2] // 4,
shift=True,
)
if flux_generator.offload:
flux_generator.t5, flux_generator.clip = flux_generator.t5.to(flux_generator.device), flux_generator.clip.to(flux_generator.device)
inp = prepare(t5=flux_generator.t5, clip=flux_generator.clip, img=x, prompt=opts.prompt)
inp_neg = prepare(t5=flux_generator.t5, clip=flux_generator.clip, img=x, prompt=neg_prompt) if use_true_cfg else None
# offload TEs to CPU, load model to gpu
if flux_generator.offload:
flux_generator.t5, flux_generator.clip = flux_generator.t5.cpu(), flux_generator.clip.cpu()
torch.cuda.empty_cache()
flux_generator.model = flux_generator.model.to(flux_generator.device)
# denoise initial noise
x = denoise(
flux_generator.model, **inp, timesteps=timesteps, guidance=opts.guidance, id=id_embeddings, id_weight=id_weight,
start_step=start_step, uncond_id=uncond_id_embeddings, true_cfg=true_cfg,
timestep_to_start_cfg=timestep_to_start_cfg,
neg_txt=inp_neg["txt"] if use_true_cfg else None,
neg_txt_ids=inp_neg["txt_ids"] if use_true_cfg else None,
neg_vec=inp_neg["vec"] if use_true_cfg else None,
)
# offload model, load autoencoder to gpu
if flux_generator.offload:
flux_generator.model.cpu()
torch.cuda.empty_cache()
flux_generator.ae.decoder.to(x.device)
# decode latents to pixel space
x = unpack(x.float(), opts.height, opts.width)
with torch.autocast(device_type=flux_generator.device.type, dtype=torch.bfloat16):
x = flux_generator.ae.decode(x)
if flux_generator.offload:
flux_generator.ae.decoder.cpu()
torch.cuda.empty_cache()
t1 = time.perf_counter()
# bring into PIL format
x = x.clamp(-1, 1)
x = rearrange(x[0], "c h w -> h w c")
img = Image.fromarray((127.5 * (x + 1.0)).cpu().byte().numpy())
return img, str(opts.seed), flux_generator.pulid_model.debug_img_list
css = """
footer {
visibility: hidden;
}
"""
def create_demo(args, model_name: str, device: str = "cuda" if torch.cuda.is_available() else "cpu",
offload: bool = False):
with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo:
gr.Markdown("### 'AI ํฌํ ์ง๋'์ด์ฉ ์๋ด: 1) '์คํ์ผ'์ค ํ๋๋ฅผ ์ ํ. 2) ์น์บ ์ ํด๋ฆญํ๊ณ ์ผ๊ตด์ด ๋ณด์ด๋ฉด ์นด๋ฉ๋ผ ๋ฒํผ ํด๋ฆญ. 3) '์์ฑ' ๋ฒํผ์ ํด๋ฆญํ๊ณ ๊ธฐ๋ค๋ฆฌ๋ฉด ๋ฉ๋๋ค.")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="ํ๋กฌํํธ", value="์ด์ํ, ์๊ฐ, ์ํ์ ")
id_image = gr.Image(label="ID ์ด๋ฏธ์ง", sources=["webcam", "upload"], type="numpy")
generate_btn = gr.Button("์์ฑ")
with gr.Column():
output_image = gr.Image(label="์์ฑ๋ ์ด๋ฏธ์ง")
with gr.Row():
with gr.Column():
gr.Markdown("### ์คํ์ผ")
all_examples = [
#์ฐ์ฃผ ์ฌํI ['I am an astronaut on a spacewalk. There is no helmet, and my face is visible. The background is Earth & starship as seen from space shuttle.', 'example_inputs/1.webp'],
#์ฐ์ฃผ ์ฌํII ['I am an astronaut on a spacewalk. There is no helmet, and my face is visible. The background is Earth & starship as seen from space shuttle.I am holding sign with glowing green text "I Love Mom"', 'example_inputs/2.webp'],
#๋ด๊ฐ ์ด๋ฅธ์ด ๋๋ฉด ['profile photo of a 40-year-old Adult Looking straight ahead, wear suite', 'example_inputs/3.webp'],
#์์ด์ธ๋งจ ๋ณ์ ['I am an "IRON MAN"', 'example_inputs/4.webp'],
#ํ์ฑ ํํ ['I am wearing a spacesuit and have become an astronaut walking on Mars. I'm not wearing a helmet. I'm looking straight ahead. The background is a desolate area of Mars, and a space rover and a space station can be seen.', 'example_inputs/5.webp'],
#์คํ์ด๋๋งจ ['I am an "spider MAN"', 'example_inputs/6.webp'],
#์ฐ์ฃผ์ ์กฐ์ข
['I am wearing a spacesuit and have become an astronaut. I am piloting a spacecraft. Through the spacecraft's window, I can see outer space.', 'example_inputs/7.webp'],
#๋งํ ์ฃผ์ธ๊ณต ['portrait, pixar style', 'example_inputs/8.webp'],
#์๋์ฐ๋จผ ['I am an "wonder woman"', 'example_inputs/9.webp'],
#์นด์ฐ๋ณด์ด ['Cowboy, american comics style', 'example_inputs/10.webp'],
]
example_images = [example[1] for example in all_examples]
example_captions = [example[0] for example in all_examples]
gallery = gr.Gallery(
value=list(zip(example_images, example_captions)),
label="์์ ๊ฐค๋ฌ๋ฆฌ",
show_label=False,
elem_id="gallery",
columns=5,
rows=2,
object_fit="contain",
height="auto"
)
def fill_example(evt: gr.SelectData):
return [all_examples[evt.index][i] for i in [0, 1]]
gallery.select(
fill_example,
None,
[prompt, id_image],
)
generate_btn.click(
fn=lambda *args: generate_image(*args)[0], # Only return the first item (the image)
inputs=[
gr.Slider(256, 1536, 896, step=16, visible=False), # width
gr.Slider(256, 1536, 1152, step=16, visible=False), # height
gr.Slider(1, 20, 20, step=1, visible=False), # num_steps
gr.Slider(0, 10, 0, step=1, visible=False), # start_step
gr.Slider(1.0, 10.0, 4, step=0.1, visible=False), # guidance
gr.Textbox(-1, visible=False), # seed
prompt,
id_image,
gr.Slider(0.0, 3.0, 1, step=0.05, visible=False), # id_weight
gr.Textbox("Low quality, worst quality, text, signature, watermark, extra limbs", visible=False), # neg_prompt
gr.Slider(1.0, 10.0, 1, step=0.1, visible=False), # true_cfg
gr.Slider(0, 20, 1, step=1, visible=False), # timestep_to_start_cfg
gr.Slider(128, 512, 128, step=128, visible=False), # max_sequence_length
],
outputs=[output_image],
)
return demo
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="PuLID for FLUX.1-dev")
parser.add_argument("--name", type=str, default="flux-dev", choices=list('flux-dev'),
help="ํ์ฌ๋ flux-dev๋ง ์ง์ํฉ๋๋ค")
parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu",
help="์ฌ์ฉํ ๋๋ฐ์ด์ค")
parser.add_argument("--offload", action="store_true", help="์ฌ์ฉํ์ง ์์ ๋ ๋ชจ๋ธ์ CPU๋ก ์ฎ๊น๋๋ค")
parser.add_argument("--port", type=int, default=8080, help="์ฌ์ฉํ ํฌํธ")
parser.add_argument("--dev", action='store_true', help="๊ฐ๋ฐ ๋ชจ๋")
parser.add_argument("--pretrained_model", type=str, help='๊ฐ๋ฐ์ฉ')
args = parser.parse_args()
import huggingface_hub
huggingface_hub.login(os.getenv('HF_TOKEN'))
demo = create_demo(args, args.name, args.device, args.offload)
demo.launch(share=True)
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