:fire:Initialized

.gitattributes

@@ -33,3 +33,11 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+# LFS track binary media
+*.png filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.mov filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,13 @@
+# Python
+__pycache__/
+*.pyc
+.env
+venv/
+.venv/
+
+# model checkpoints & outputs
+ckpts/
+samples/
+outputs/
+.cache/
+hf_hub_download/

.vscode/settings.json

@@ -0,0 +1,7 @@
+{
+    "workbench.colorCustomizations": {
+        "activityBar.background": "#232D48",
+        "titleBar.activeBackground": "#303F65",
+        "titleBar.activeForeground": "#F8F9FC"
+    }
+}

app.py

@@ -0,0 +1,113 @@
+import gradio as gr
+import subprocess
+import os
+import glob
+import time
+from pathlib import Path
+from huggingface_hub import snapshot_download
+
+MODEL_REPO = "hpcai-tech/Open-Sora-v2"
+CKPT_DIR = Path("ckpts")
+SAMPLES_DIR = Path("samples")
+
+def ensure_ckpts():
+    if CKPT_DIR.exists() and any(CKPT_DIR.iterdir()):
+        print("Found existing checkpoints in", CKPT_DIR)
+        return True
+    hf_token = os.environ.get("HUGGINGFACE_HUB_TOKEN") or os.environ.get("HF_TOKEN")
+    if not hf_token:
+        print("No HF token found in env. Cannot auto-download. Please add HUGGINGFACE_HUB_TOKEN or download ckpts manually.")
+        return False
+    print("Downloading model weights from HF... (this will take several minutes)")
+    try:
+        snapshot_download(repo_id=MODEL_REPO, local_dir=str(CKPT_DIR), local_dir_use_symlinks=False)
+        print("Download complete.")
+        return True
+    except Exception as e:
+        print("Error downloading checkpoints:", e)
+        return False
+
+def find_latest_video():
+    SAMPLES_DIR.mkdir(exist_ok=True)
+    matches = list(SAMPLES_DIR.glob("*.mp4"))
+    if not matches:
+        return None
+    matches.sort(key=lambda p: p.stat().st_mtime, reverse=True)
+    return str(matches[0])
+
+def run_torch_inference(config, prompt, ref_image=None, aspect_ratio=None, num_frames=None, offload=False):
+    SAMPLES_DIR.mkdir(exist_ok=True)
+    cmd = [
+        "torchrun", "--nproc_per_node", "1", "--standalone",
+        "scripts/diffusion/inference.py",
+        f"configs/diffusion/inference/{config}.py",
+        "--save-dir", str(SAMPLES_DIR),
+        "--prompt", prompt
+    ]
+    if ref_image:
+        cmd += ["--cond_type", "i2v_head", "--ref", ref_image]
+    if aspect_ratio:
+        cmd += ["--aspect_ratio", aspect_ratio]
+    if num_frames:
+        cmd += ["--num_frames", str(num_frames)]
+    if offload:
+        cmd += ["--offload", "True"]
+    print("Running command:", " ".join(cmd))
+    try:
+        subprocess.run(cmd, check=True, env=os.environ)
+    except subprocess.CalledProcessError as e:
+        print("Inference failed:", e)
+        raise
+
+def generate_video(prompt, mode="t2i2v_256px", ref_image_path=None, aspect_ratio="16:9", num_frames=None, offload=False):
+    # mode options: t2i2v_256px, 256px (text2video direct), 768px...
+    ok = ensure_ckpts()
+    if not ok:
+        return "Model checkpoints not found and no HF token provided. Upload ckpts to ./ckpts or set HUGGINGFACE_HUB_TOKEN."
+
+    # Map UI selection to config file names
+    config_map = {
+        "256 (t2i2v)": "t2i2v_256px",
+        "256 (t2v)": "256px",
+        "768 (t2v)": "768px",
+        "768 (t2i2v)": "t2i2v_768px"
+    }
+    config = config_map.get(mode, "t2i2v_256px")
+    try:
+        run_torch_inference(config, prompt, ref_image=ref_image_path, aspect_ratio=aspect_ratio, num_frames=num_frames, offload=offload)
+        # wait for file to appear
+        for _ in range(120):
+            latest = find_latest_video()
+            if latest:
+                return latest
+            time.sleep(1)
+        return "No output video detected after inference."
+    except Exception as e:
+        return f"Error during generation: {str(e)}"
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎬 Open-Sora (Open-Sora-v2) — Text/Image to Video")
+    with gr.Row():
+        prompt = gr.Textbox(lines=3, label="Prompt", placeholder="A cinematic shot of ...")
+    with gr.Row():
+        mode = gr.Radio(["256 (t2i2v)", "256 (t2v)", "768 (t2v)", "768 (t2i2v)"], value="256 (t2i2v)", label="Generation Mode")
+        aspect_ratio = gr.Dropdown(["16:9","9:16","1:1","2.39:1"], value="16:9", label="Aspect Ratio")
+        num_frames = gr.Number(value=17, label="Frames (use 4k+1 rules)", precision=0)
+    with gr.Row():
+        ref_image = gr.Image(type="filepath", label="Reference image (optional, for I2V)")
+        offload = gr.Checkbox(label="Memory offload (slower but uses less GPU memory)", value=False)
+    generate_btn = gr.Button("Generate Video")
+    output_video = gr.Video(label="Generated Video")
+    status = gr.Textbox(label="Status/Logs", interactive=False)
+
+    def on_generate(prompt_text, mode_val, ar, nf, ref_img, off):
+        status_text = "Starting..."
+        status.update(status_text)
+        res = generate_video(prompt_text, mode_val, ref_image_path=ref_img, aspect_ratio=ar, num_frames=int(nf) if nf else None, offload=off)
+        return res, f"Completed: {res}"
+
+    generate_btn.click(on_generate, inputs=[prompt, mode, aspect_ratio, num_frames, ref_image, offload], outputs=[output_video, status])
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))

requirements.txt

@@ -0,0 +1,13 @@
+# core
+torch>=2.4.0
+transformers
+diffusers
+xformers==0.0.27.post2
+flash-attn
+huggingface_hub[cli]
+gradio
+accelerate
+numpy
+opencv-python-headless
+tqdm
+omegaconf

start.sh

@@ -0,0 +1,7 @@
+#!/usr/bin/env bash
+# create venv and install (if needed)
+python -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+# run app (Gradio)
+python app.py

utils/download_ckpts.py

@@ -0,0 +1,16 @@
+# Small utility to download model files using huggingface_hub snapshot_download
+from huggingface_hub import snapshot_download
+import os
+
+MODEL_REPO = "hpcai-tech/Open-Sora-v2"
+OUT_DIR = "ckpts"
+
+def download_ckpts():
+    if not os.path.exists(OUT_DIR):
+        os.makedirs(OUT_DIR, exist_ok=True)
+    print(f"Downloading {MODEL_REPO} to {OUT_DIR} (this may take a while)...")
+    snapshot_download(repo_id=MODEL_REPO, local_dir=OUT_DIR, local_dir_use_symlinks=False)
+    print("Download complete.")
+
+if __name__ == "__main__":
+    download_ckpts()