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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
from gradio_imageslider import ImageSlider
from PIL import Image, ImageDraw, ImageFont
dtype = torch.bfloat16
#model_id = "black-forest-labs/FLUX.1-dev"
model_id = "camenduru/FLUX.1-dev-diffusers"
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(model_id, subfolder="vae", torch_dtype=dtype).to(device)
#pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=dtype, vae=taef1).to(device)
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=dtype, vae=good_vae).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 get_cmp_image(im1: Image.Image, im2: Image.Image, sigmas: float):
dst = Image.new('RGB', (im1.width + im2.width, im1.height))
dst.paste(im1.convert('RGB'), (0, 0))
dst.paste(im2.convert('RGB'), (im1.width, 0))
font = ImageFont.truetype('Roboto-Regular.ttf', 72, encoding='unic')
draw = ImageDraw.Draw(dst)
draw.text((64, im1.height - 128), 'Default Flux', 'red', font=font)
draw.text((im1.width + 64, im1.height - 128), f'Sigmas * factor {sigmas}', 'red', font=font)
return dst
# @spaces.GPU(duration=90)
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, mul_sigmas=0.95, is_cmp=True, progress=gr.Progress(track_tqdm=True)):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
sigmas = sigmas * mul_sigmas
image_sigmas = pipe(
prompt=prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
output_type="pil",
sigmas=sigmas
).images[0]
if is_cmp:
image_def = pipe(
prompt=prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
output_type="pil",
).images[0]
return [image_def, image_sigmas], get_cmp_image(image_def, image_sigmas, mul_sigmas), seed
else: return [image_sigmas, image_sigmas], None, 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;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""# FLUX.1 [dev] sigmas test
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)
result = ImageSlider(label="Result", show_label=False, type="pil", slider_color="pink")
result_cmp = gr.Image(label="Result (comparing)", show_label=False, type="pil", format="png", height=256, show_download_button=True, show_share_button=False)
with gr.Accordion("Advanced Settings", open=True):
with gr.Row():
sigmas = gr.Slider(
label="Sigmas",
minimum=0,
maximum=1.0,
step=0.01,
value=0.95,
)
is_cmp = gr.Checkbox(label="Compare images with/without sigmas", value=True)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=9119,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
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, result_cmp, 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, sigmas, is_cmp],
outputs = [result, result_cmp, seed]
)
demo.launch() |