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app.py

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+import os
+import subprocess
+import sys
+
+# ---------------------------------------------------------------------------
+# LTX-2.4 prompt enhancer as a standalone ZeroGPU Space (Gemma-4 E2B). Kept
+# separate so the 5GB enhancer + torchvision live off the main video Space's
+# budget; the video Spaces call this over gradio_client. Replicates diffusers'
+# LTX2Pipeline.enhance_prompt exactly (system prompt by mode, greedy decoding).
+# The diffusers wheel is installed only for the LTX-2.4 system-prompt constants.
+# ---------------------------------------------------------------------------
+from huggingface_hub import hf_hub_download
+
+HF_TOKEN = os.environ.get("HF_TOKEN")
+_whl = hf_hub_download(
+    "diffusers-internal-dev/ltx24-wheels",
+    "canon/diffusers-0.40.0.dev0-py3-none-any.whl",
+    repo_type="dataset",
+    token=HF_TOKEN,
+)
+_TARGET = "/tmp/ltx24_diffusers"
+subprocess.run(
+    [sys.executable, "-m", "pip", "install", "--no-deps", "--target", _TARGET, _whl],
+    check=True,
+)
+sys.path.insert(0, _TARGET)
+
+import gradio as gr
+import spaces
+import torch
+from transformers import AutoModelForImageTextToText, AutoProcessor
+
+from diffusers.pipelines.ltx2.utils import (
+    GEMMA4_PROMPT_ENHANCEMENT_CONFIG,
+    LTX2_4_I2V_DEFAULT_SYSTEM_PROMPT,
+    LTX2_4_T2V_DEFAULT_SYSTEM_PROMPT,
+)
+
+ENHANCER_ID = "google/gemma-4-E2B-it"
+CFG = GEMMA4_PROMPT_ENHANCEMENT_CONFIG
+
+print("[VERSION] ltx-2.4-enhancer v1 — loading", flush=True)
+processor = AutoProcessor.from_pretrained(ENHANCER_ID, token=HF_TOKEN)
+model = AutoModelForImageTextToText.from_pretrained(
+    ENHANCER_ID, torch_dtype=torch.bfloat16, token=HF_TOKEN
+).to("cuda")
+model.eval()
+print("[VERSION] enhancer ready", flush=True)
+
+
+@spaces.GPU(duration=120)
+def enhance(prompt, image=None, progress=gr.Progress()):
+    if not prompt or not prompt.strip():
+        raise gr.Error("Please enter a prompt to enhance.")
+    # I2V (reference-image) system prompt when an image is supplied, else T2V.
+    system_prompt = LTX2_4_I2V_DEFAULT_SYSTEM_PROMPT if image is not None else LTX2_4_T2V_DEFAULT_SYSTEM_PROMPT
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": f"{CFG.user_prompt_prefix}: {prompt}"},
+    ]
+    template = processor.tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    model_inputs = processor(text=template, images=image, return_tensors="pt").to("cuda")
+    torch.manual_seed(10)  # greedy decoding is deterministic; seed is inert but matches diffusers
+    with torch.no_grad():
+        generated = model.generate(**model_inputs, max_new_tokens=512, **CFG.generation_kwargs)
+    generated_ids = [seq[len(model_inputs.input_ids[i]):] for i, seq in enumerate(generated)]
+    return processor.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+
+with gr.Blocks(title="LTX-2.4 Prompt Enhancer") as demo:
+    gr.Markdown(
+        "# ✨ LTX-2.4 Prompt Enhancer\n"
+        "Gemma-4 (`google/gemma-4-E2B-it`) prompt enhancer for LTX-2.4 — expands a short prompt into "
+        "a detailed audio-visual caption in the model's training-caption style. Add a first-frame "
+        "image to enhance for image-to-video. Called by the LTX-2.4 video Spaces over `gradio_client`."
+    )
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", lines=3, placeholder="e.g. a red fox in a snowy forest")
+            image = gr.Image(label="First frame (optional — for image-to-video)", type="pil")
+            btn = gr.Button("Enhance", variant="primary")
+        out = gr.Textbox(label="Enhanced prompt", lines=12, show_copy_button=True)
+    btn.click(enhance, inputs=[prompt, image], outputs=[out], api_name="enhance")
+
+if __name__ == "__main__":
+    demo.launch(show_error=True)