Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,054 Bytes
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import gradio as gr
from diffusers import DiffusionPipeline
import spaces
import torch
from concurrent.futures import ProcessPoolExecutor
from huggingface_hub import hf_hub_download
dev_model = "black-forest-labs/FLUX.1-dev"
schnell_model = "black-forest-labs/FLUX.1-schnell"
device = "cuda" if torch.cuda.is_available() else "cpu"
repo_name = "ByteDance/Hyper-SD"
ckpt_name = "Hyper-FLUX.1-dev-8steps-lora.safetensors"
hyper_lora = hf_hub_download(repo_name, ckpt_name)
repo_name = "alimama-creative/FLUX.1-Turbo-Alpha"
ckpt_name = "diffusion_pytorch_model.safetensors"
turbo_lora = hf_hub_download(repo_name, ckpt_name)
pipe_dev = DiffusionPipeline.from_pretrained(dev_model, torch_dtype=torch.bfloat16).to("cuda")
pipe_schnell = DiffusionPipeline.from_pretrained(
schnell_model,
text_encoder=pipe_dev.text_encoder,
text_encoder_2=pipe_dev.text_encoder_2,
tokenizer=pipe_dev.tokenizer,
tokenizer_2=pipe_dev.tokenizer_2,
torch_dtype=torch.bfloat16
)
@spaces.GPU(duration=75)
def run_parallel_models(prompt):
pipe_dev.load_lora_weights(hyper_lora)
image = pipe_dev(prompt, num_inference_steps=8, joint_attention_kwargs={"scale": 0.125}).images[0]
pipe_dev.unload_lora_weights()
yield image, gr.update(), gr.update()
pipe_dev.load_lora_weights(turbo_lora)
image = pipe_dev(prompt, num_inference_steps=8).images[0]
yield gr.update(), image, gr.update()
pipe_dev.unload_lora_weights()
pipe_dev.to("cpu")
pipe_schnell.to("cuda")
image = pipe_schnell(prompt, num_inference_steps=4).images[0]
yield gr.update(), gr.update(), image
#run_parallel_models.zerogpu = True
with gr.Blocks() as demo:
gr.Markdown("# Low Step Flux Comparison")
gr.Markdown("Compare the quality (not the speed) of FLUX Schnell (4 steps), FLUX.1[dev] HyperFLUX (8 steps), FLUX.1[dev]-Turbo-Alpha (8 steps). It runs a bit slow as it's inferencing the three models.")
with gr.Row():
with gr.Column(scale=2):
prompt = gr.Textbox(label="Prompt")
with gr.Column(scale=1, min_width=120):
submit = gr.Button("Run")
with gr.Row():
schnell = gr.Image(label="FLUX Schnell (4 steps)")
hyper = gr.Image(label="FLUX.1[dev] HyperFLUX (8 steps)")
turbo = gr.Image(label="FLUX.1[dev]-Turbo-Alpha (8 steps)")
gr.Examples(
examples=[
["the spirit of a Tamagotchi wandering in the city of Vienna"],
["a photo of a lavender cat"],
["a tiny astronaut hatching from an egg on the moon"],
["a delicious ceviche cheesecake slice"],
["an insect robot preparing a delicious meal"],
["a Charmander fine dining with a view to la Sagrada Família"]],
fn=run_parallel_models,
inputs=[prompt],
outputs=[schnell, hyper, turbo],
cache_examples="lazy"
)
gr.on(
triggers=[submit.click, prompt.submit],
fn=run_parallel_models,
inputs=[prompt],
outputs=[schnell, hyper, turbo]
)
demo.launch() |