|
import gradio as gr |
|
import numpy as np |
|
import random |
|
import spaces |
|
import torch |
|
from diffusers import FluxPipeline |
|
|
|
dtype = torch.bfloat16 |
|
device = "cuda" if torch.cuda.is_available() else "cpu" |
|
|
|
pipe = FluxPipeline.from_pretrained("Shakker-Labs/AWPortrait-FL", torch_dtype=torch.bfloat16).to(device) |
|
|
|
MAX_SEED = np.iinfo(np.int32).max |
|
MAX_IMAGE_SIZE = 2048 |
|
|
|
@spaces.GPU(duration=190) |
|
def infer(prompt, seed=42, randomize_seed=False, width=768, height=1024, guidance_scale=3.5, num_inference_steps=24, progress=gr.Progress(track_tqdm=True)): |
|
if randomize_seed: |
|
seed = random.randint(0, MAX_SEED) |
|
generator = torch.Generator().manual_seed(seed) |
|
image = pipe( |
|
prompt=prompt, |
|
width=width, |
|
height=height, |
|
num_inference_steps=num_inference_steps, |
|
generator=generator, |
|
guidance_scale=guidance_scale |
|
).images[0] |
|
return image, seed |
|
|
|
examples = [ |
|
"close up portrait, Amidst the interplay of light and shadows in a photography studio,a soft spotlight traces the contours of a face,highlighting a figure clad in a sleek black turtleneck...", |
|
"a cinematic portrait of an elegant elderly man with a wise expression...", |
|
"a dramatic close-up shot of a warrior princess with fierce eyes..." |
|
] |
|
|
|
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(f"""# AWPortrait-FL |
|
12B param flow transformer guidance-distilled from [AWPortrait-FL](https://huggingface.co/Shakker-Labs/AWPortrait-FL) |
|
[[non-commercial license](https://huggingface.co/Shakker-Labs/AWPortrait-FL/blob/main/LICENSE.md)] |
|
""") |
|
|
|
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): |
|
|
|
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=768, |
|
) |
|
|
|
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=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=24, |
|
) |
|
|
|
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, width, height, guidance_scale, num_inference_steps], |
|
outputs=[result, seed] |
|
) |
|
|
|
demo.launch() |
|
|
