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import os
import shutil
import subprocess
import gradio as gr
# Use /tmp in Spaces, or ./data locally if you set LOCAL_TRAIN=1
LOCAL = os.environ.get("LOCAL_TRAIN", "").lower() in ("1","true")
DATA_DIR = os.path.join(os.getcwd(), "data") if LOCAL else "/tmp/data"
os.makedirs(DATA_DIR, exist_ok=True)
def prepare_dataset(files):
# wipe and copy uploaded files
for f in os.listdir(DATA_DIR):
os.remove(os.path.join(DATA_DIR, f))
for file in files:
dst = os.path.join(DATA_DIR, os.path.basename(file.name))
shutil.copyfile(file.name, dst)
return f"✅ {len(files)} files uploaded to {DATA_DIR}"
def start_training(base_model, trigger_word, steps, r, alpha):
# pass args via environment to train.py
env = os.environ.copy()
env.update({
"BASE_MODEL": base_model,
"TRIGGER_WORD": trigger_word,
"NUM_STEPS": str(steps),
"LORA_R": str(r),
"LORA_ALPHA": str(alpha),
"LOCAL_TRAIN": os.environ.get("LOCAL_TRAIN","")
})
# run training and capture all output
proc = subprocess.run(
["python3","train.py"],
capture_output=True, text=True, env=env
)
return proc.stdout + ("\n" + proc.stderr if proc.stderr else "")
model_choices = [
"HiDream-ai/HiDream-I1-Dev",
"runwayml/stable-diffusion-v1-5",
"stabilityai/stable-diffusion-2-1"
]
with gr.Blocks() as demo:
gr.Markdown("# 🖌️ HiDream LoRA Trainer")
gr.Markdown(f"Running in **{'local' if LOCAL else 'Spaces'}** mode; data dir: `{DATA_DIR}`")
with gr.Row():
uploader = gr.File(file_types=["image",".txt"], file_count="multiple", label="Upload images + texts")
up_btn = gr.Button("📂 Upload")
up_status = gr.Textbox(label="Upload status")
mdl = gr.Dropdown(model_choices, value=model_choices[0], label="Base model")
tw = gr.Textbox(label="Trigger word", placeholder="e.g. rami-style")
st = gr.Slider(10,500,value=100,step=10,label="Training steps")
r_v = gr.Slider(4,128,value=16,step=4,label="LoRA rank (r)")
a_v = gr.Slider(4,128,value=16,step=4,label="LoRA alpha")
tr_btn = gr.Button("🚀 Train")
log_tb = gr.Textbox(label="Training log", lines=20)
up_btn.click(prepare_dataset, inputs=uploader, outputs=up_status)
tr_btn.click(start_training, inputs=[mdl,tw,st,r_v,a_v], outputs=log_tb)
demo.launch(server_name="0.0.0.0", server_port=7860)
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