Spaces:
Running
Running
File size: 4,328 Bytes
be26805 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
import spaces
import argparse
import os
import time
from os import path
from safetensors.torch import load_file
from huggingface_hub import hf_hub_download
cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
os.environ["TRANSFORMERS_CACHE"] = cache_path
os.environ["HF_HUB_CACHE"] = cache_path
os.environ["HF_HOME"] = cache_path
import gradio as gr
import torch
from diffusers import FluxPipeline
torch.backends.cuda.matmul.allow_tf32 = True
class timer:
def __init__(self, method_name="timed process"):
self.method = method_name
def __enter__(self):
self.start = time.time()
print(f"{self.method} starts")
def __exit__(self, exc_type, exc_val, exc_tb):
end = time.time()
print(f"{self.method} took {str(round(end - self.start, 2))}s")
if not path.exists(cache_path):
os.makedirs(cache_path, exist_ok=True)
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"))
pipe.fuse_lora(lora_scale=0.125)
pipe.to(device="cuda", dtype=torch.bfloat16)
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown(
"""
<div style="text-align: center; max-width: 650px; margin: 0 auto;">
<h1 style="font-size: 2.5rem; font-weight: 700; margin-bottom: 1rem; display: contents;">Hyper-FLUX-8steps-LoRA</h1>
<p style="font-size: 1rem; margin-bottom: 1.5rem;">AutoML team from ByteDance</p>
</div>
"""
)
with gr.Row():
with gr.Column(scale=3):
with gr.Group():
prompt = gr.Textbox(
label="Your Image Description",
placeholder="E.g., A serene landscape with mountains and a lake at sunset",
lines=3
)
with gr.Accordion("Advanced Settings", open=False):
with gr.Group():
with gr.Row():
height = gr.Slider(label="Height", minimum=256, maximum=1152, step=64, value=1024)
width = gr.Slider(label="Width", minimum=256, maximum=1152, step=64, value=1024)
with gr.Row():
steps = gr.Slider(label="Inference Steps", minimum=6, maximum=25, step=1, value=8)
scales = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=5.0, step=0.1, value=3.5)
seed = gr.Number(label="Seed (for reproducibility)", value=3413, precision=0)
generate_btn = gr.Button("Generate Image", variant="primary", scale=1)
with gr.Column(scale=4):
output = gr.Image(label="Your Generated Image")
gr.Markdown(
"""
<div style="max-width: 650px; margin: 2rem auto; padding: 1rem; border-radius: 10px; background-color: #f0f0f0;">
<h2 style="font-size: 1.5rem; margin-bottom: 1rem;">How to Use</h2>
<ol style="padding-left: 1.5rem;">
<li>Enter a detailed description of the image you want to create.</li>
<li>Adjust advanced settings if desired (tap to expand).</li>
<li>Tap "Generate Image" and wait for your creation!</li>
</ol>
<p style="margin-top: 1rem; font-style: italic;">Tip: Be specific in your description for best results!</p>
</div>
"""
)
@spaces.GPU
def process_image(height, width, steps, scales, prompt, seed):
global pipe
with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"):
return pipe(
prompt=[prompt],
generator=torch.Generator().manual_seed(int(seed)),
num_inference_steps=int(steps),
guidance_scale=float(scales),
height=int(height),
width=int(width),
max_sequence_length=256
).images[0]
generate_btn.click(
process_image,
inputs=[height, width, steps, scales, prompt, seed],
outputs=output
)
if __name__ == "__main__":
demo.launch()
|