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| import os | |
| import random | |
| import tempfile | |
| import uuid | |
| import numpy as np | |
| import spaces | |
| import torch | |
| import gradio as gr | |
| from diffusers import LTXConditionPipeline | |
| from diffusers.utils import export_to_video | |
| # --------------------------------------------------------------------------- | |
| # Fix a real bug in gradio_client 1.5.2 (bundled with ZeroGPU's forced | |
| # gradio==5.9.1): its JSON-schema-to-python-type parser assumes every schema | |
| # node is a dict, but valid JSON Schema allows a bare boolean (e.g. | |
| # additionalProperties: true) — that crashes EVERY API call to this Space | |
| # with "TypeError: argument of type 'bool' is not iterable". Patched here, | |
| # not worked around with an old pydantic pin (which broke other packages' | |
| # serialization elsewhere in this account's other Spaces) — this is the | |
| # actual bug, proven across studio-chat and studio-media this session. | |
| import gradio_client.utils as _gc_utils | |
| _orig_json_schema_to_python_type = _gc_utils._json_schema_to_python_type | |
| def _patched_json_schema_to_python_type(schema, defs): | |
| if not isinstance(schema, dict): | |
| return "bool" if isinstance(schema, bool) else "Any" | |
| return _orig_json_schema_to_python_type(schema, defs) | |
| _gc_utils._json_schema_to_python_type = _patched_json_schema_to_python_type | |
| # --------------------------------------------------------------------------- | |
| # Model — loaded once at import time. Under ZeroGPU's patched torch, | |
| # .to("cuda") here is deferred (no real GPU touched yet); the ZeroGPU | |
| # "tensors packing" step prepares it for fast real materialization inside | |
| # the @spaces.GPU-decorated call. | |
| # --------------------------------------------------------------------------- | |
| MODEL_ID = "Lightricks/LTX-Video-0.9.7-distilled" | |
| RESOLUTIONS = { | |
| "704x480 (landscape, fastest)": (704, 480), | |
| "480x704 (portrait, fastest)": (480, 704), | |
| "832x480 (landscape)": (832, 480), | |
| "480x832 (portrait)": (480, 832), | |
| } | |
| pipe = LTXConditionPipeline.from_pretrained(MODEL_ID, torch_dtype=torch.bfloat16) | |
| pipe.to("cuda") | |
| pipe.vae.enable_tiling() | |
| def _video_duration(prompt, negative_prompt, seed, random_seed, num_frames, steps, resolution): | |
| # UNMEASURED estimate ahead of the first real call — the distilled | |
| # checkpoint's own model card recommends ~7 steps for fast generation, | |
| # so this starts conservative and gets recalibrated against real | |
| # wall-clock time once deployed, the same discipline used for | |
| # studio-media's FLUX.1-dev steps slider. | |
| return min(120, 25 + int(num_frames) // 8 * int(steps) * 1.5) | |
| def generate_video(prompt, negative_prompt, seed, random_seed, num_frames, steps, resolution): | |
| if not prompt or not prompt.strip(): | |
| raise gr.Error("Enter a prompt first.") | |
| width, height = RESOLUTIONS.get(resolution, (704, 480)) | |
| num_frames = int(num_frames) | |
| steps = int(steps) | |
| s = random.randint(0, 2**31 - 1) if random_seed else int(seed) | |
| generator = torch.Generator("cuda").manual_seed(s) | |
| use_negative = bool(negative_prompt and negative_prompt.strip()) | |
| result = pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt if use_negative else None, | |
| width=width, | |
| height=height, | |
| num_frames=num_frames, | |
| num_inference_steps=steps, | |
| generator=generator, | |
| output_type="pil", | |
| ) | |
| frames = result.frames[0] | |
| out_path = os.path.join(tempfile.gettempdir(), f"ltxvideo_{uuid.uuid4().hex}.mp4") | |
| export_to_video(frames, out_path, fps=24) | |
| return out_path, str(s) | |
| with gr.Blocks(title="Beexly Studio Video (ZeroGPU)") as demo: | |
| gr.Markdown( | |
| "# Beexly Studio Video (ZeroGPU)\n\n" | |
| "Personal video-generation tool. **Not connected to any Galaxy " | |
| "Sports Edge product, data, or prediction engine.**\n\n" | |
| "`LTX-Video-0.9.7-distilled` (Lightricks) under the LTX-Video Open " | |
| "Weights License — free for personal/non-commercial use; commercial " | |
| "use requires a separate paid agreement above a revenue threshold. " | |
| "This stays a personal tool, same posture as the non-commercial " | |
| "FLUX.1-dev model on studio-media: **never for anything shipped on " | |
| "galaxysportsedge.com.**" | |
| ) | |
| with gr.Tab("Text to Video"): | |
| v_prompt = gr.Textbox(label="Prompt", lines=2) | |
| v_negative = gr.Textbox(label="Negative prompt (optional)", value="") | |
| v_resolution = gr.Dropdown(list(RESOLUTIONS.keys()), value="704x480 (landscape, fastest)", label="Resolution") | |
| with gr.Row(): | |
| v_seed = gr.Textbox(label="Seed", value="0") | |
| v_random_seed = gr.Checkbox(label="Random seed", value=True) | |
| v_frames = gr.Slider(41, 121, value=65, step=8, label="Frames (num_frames must be 8n+1; ~24fps, so 65 frames ≈ 2.7s)") | |
| v_steps = gr.Slider(4, 12, value=7, step=1, label="Steps (7 is the distilled checkpoint's recommended default)") | |
| v_btn = gr.Button("Generate", variant="primary") | |
| v_out = gr.Video(label="Generated video") | |
| v_seed_used = gr.Textbox(label="Seed used", interactive=False) | |
| v_btn.click( | |
| generate_video, | |
| inputs=[v_prompt, v_negative, v_seed, v_random_seed, v_frames, v_steps, v_resolution], | |
| outputs=[v_out, v_seed_used], | |
| api_name="generate_video", | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |