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import gradio as gr | |
import numpy as np | |
import random | |
import spaces | |
import torch | |
from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL | |
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast | |
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images | |
import os # Import os module to access environment variables | |
# Retrieve the token from the environment variable | |
hf_token = os.environ.get("HF_API_TOKEN") | |
dtype = torch.bfloat16 | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device) | |
good_vae = AutoencoderKL.from_pretrained( | |
"black-forest-labs/FLUX.1-dev", | |
subfolder="vae", | |
torch_dtype=dtype, | |
token=hf_token | |
).to(device) | |
pipe = DiffusionPipeline.from_pretrained( | |
"black-forest-labs/FLUX.1-dev", | |
torch_dtype=dtype, | |
vae=taef1, | |
token=hf_token | |
).to(device) | |
torch.cuda.empty_cache() | |
MAX_SEED = np.iinfo(np.int32).max | |
MAX_IMAGE_SIZE = 2048 | |
pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe) | |
def infer( | |
prompt, | |
seed=42, | |
randomize_seed=False, | |
width=1024, | |
height=1024, | |
guidance_scale=3.5, | |
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) | |
for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images( | |
prompt=prompt, | |
guidance_scale=guidance_scale, | |
num_inference_steps=num_inference_steps, | |
width=width, | |
height=height, | |
generator=generator, | |
output_type="pil", | |
good_vae=good_vae, | |
): | |
yield img, seed | |
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; | |
} | |
/* Optional: Additional dark mode customizations */ | |
body { | |
background-color: #1e1e1e; | |
color: #ffffff; | |
} | |
.gradio-container { | |
background-color: #2c2c2c; | |
} | |
.gr-button, .gr-slider, .gr-checkbox { | |
background-color: #3a3a3a; | |
color: #ffffff; | |
} | |
.markdown { | |
color: #ffffff; | |
} | |
.gr-accordion { | |
background-color: #3a3a3a; | |
color: #ffffff; | |
} | |
.gr-input, .gr-textbox, .gr-slider { | |
background-color: #3a3a3a; | |
color: #ffffff; | |
} | |
.gr-slider .gr-slider-track { | |
background-color: #555555; | |
} | |
.gr-slider .gr-slider-thumb { | |
background-color: #ffffff; | |
} | |
.gr-button:hover { | |
background-color: #555555; | |
color: #ffffff; | |
} | |
""" | |
# Define the dark theme using gr.themes.Dark() | |
dark_theme = gr.themes.Dark() | |
with gr.Blocks(theme=dark_theme, css=css) as demo: | |
with gr.Column(elem_id="col-container"): | |
gr.Markdown("""# 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)] | |
""") | |
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=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=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, width, height, guidance_scale, num_inference_steps], | |
outputs=[result, seed] | |
) | |
demo.launch() | |