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import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import DiffusionPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load the model in FP16
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.float16)
# Move the pipeline to GPU if available
pipe = pipe.to(device)
# Convert text encoders to full precision
pipe.text_encoder = pipe.text_encoder.to(torch.float32)
if hasattr(pipe, 'text_encoder_2'):
pipe.text_encoder_2 = pipe.text_encoder_2.to(torch.float32)
# Enable memory efficient attention if available and on CUDA
if device == "cuda" and hasattr(pipe, 'enable_xformers_memory_efficient_attention'):
try:
pipe.enable_xformers_memory_efficient_attention()
print("xformers memory efficient attention enabled")
except Exception as e:
print(f"Could not enable memory efficient attention: {e}")
# Compile the UNet for potential speedups if on CUDA
if device == "cuda":
try:
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
print("UNet compiled for potential speedups")
except Exception as e:
print(f"Could not compile UNet: {e}")
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(device=device).manual_seed(seed)
# Use full precision for text encoding
with torch.no_grad():
text_inputs = pipe.tokenizer(prompt, return_tensors="pt").to(device)
text_embeddings = pipe.text_encoder(text_inputs.input_ids)[0]
# Use mixed precision for the rest of the pipeline
with torch.inference_mode(), torch.autocast(device_type='cuda', dtype=torch.float16):
image = pipe(
prompt_embeds=text_embeddings,
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/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() |