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import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import FluxPipeline, FluxTransformer2DModel,FlowMatchEulerDiscreteScheduler, AutoencoderKL
from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
dtype = torch.bfloat16
device = "cuda"
sd3_repo = "stabilityai/stable-diffusion-3-medium-diffusers"
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained (sd3_repo, subfolder="scheduler")
text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype)
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype)
text_encoder_2 = T5EncoderModel.from_pretrained(sd3_repo, subfolder="text_encoder_3", torch_dtype=dtype)
tokenizer_2 = T5TokenizerFast.from_pretrained(sd3_repo, subfolder="tokenizer_3", torch_dtype=dtype)
vae = AutoencoderKL.from_pretrained("diffusers-internal-dev/FLUX.1-schnell", subfolder="vae", torch_dtype=dtype)
transformer = FluxTransformer2DModel.from_pretrained("diffusers-internal-dev/FLUX.1-schnell", subfolder="transformer", torch_dtype=dtype)
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = FluxPipeline(
scheduler=scheduler,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=text_encoder_2,
tokenizer_2=tokenizer_2,
vae=vae,
transformer=transformer,
).to("cuda")
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
@spaces.GPU()
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, 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=0.0
).images[0]
return image, 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 Schnell
12B param rectified flow transformer distilled from [FLUX.1 Pro](https://blackforestlabs.ai/) for 4 step generation
[[blog](https://blackforestlabs.ai/2024/07/31/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)]
""")
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():
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=4,
)
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, num_inference_steps],
outputs = [result, seed]
)
demo.launch() |