Update app.py

app.py

@@ -1,154 +1,65 @@
+import os
+import shutil
+import subprocess
 import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    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,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
 
+# 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 "")
 
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+model_choices = [
+    "HiDream-ai/HiDream-I1-Dev",
+    "runwayml/stable-diffusion-v1-5",
+    "stabilityai/stable-diffusion-2-1"
 ]
 
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        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, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
+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.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
+    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")
 
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
+    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")
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+    tr_btn = gr.Button("🚀 Train")
+    log_tb  = gr.Textbox(label="Training log", lines=20)
 
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
+    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)
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch(server_name="0.0.0.0", server_port=7860)