app.py

from __future__ import annotations

import json
import tempfile
import os

os.environ.setdefault("HF_HOME", r"D:\hf-cache")
os.environ.setdefault("HUGGINGFACE_HUB_CACHE", r"D:\hf-cache\hub")

import spaces
import gradio as gr
import librosa
import numpy as np
import torch
import trimesh
from diffusers import AutoencoderKL
from diffusers.pipelines.deprecated.audio_diffusion.mel import Mel
from huggingface_hub import hf_hub_download
from PIL import Image
from safetensors.torch import load_file
from transformers import AutoTokenizer, CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
from transformers import ClapTextModelWithProjection

from audio_dit import AudioDiT
from pixel_dit import DiT
from voxel_dit import VoxelDiT
from mmdit import MMDiT

# All boot-time loading and cached "null" embeddings stay on CPU.
# ZeroGPU only grants a GPU for the duration of an @spaces.GPU-decorated
# call, and import-time code runs without one, so nothing here should
# touch "cuda" directly. Each sampling function below moves the specific
# models/tensors it needs onto the allocated GPU (or CPU fallback) itself.
DEV = "cpu"
SCALE = 0.18215
CLIP_ID = "openai/clip-vit-base-patch32"
MAX_TOKENS = 40

print("[boot] loading shared CLIP text encoder...")
tokenizer = CLIPTokenizer.from_pretrained(CLIP_ID)
text_encoder = CLIPTextModel.from_pretrained(CLIP_ID).to(DEV).eval()


@torch.no_grad()
def encode(strings: list[str]):
    t = tokenizer(strings, padding="max_length", max_length=MAX_TOKENS, truncation=True, return_tensors="pt").to(DEV)
    o = text_encoder(**t)
    return o.last_hidden_state.float(), o.pooler_output.float()


null_seq, null_pool = encode([""])

print("[boot] loading PixelModel v5...")
pm5_weights = hf_hub_download("bench-labs/PixelModel-v5", "model.safetensors")
pm5_state = load_file(pm5_weights)
pixel_model = DiT(dim=384, depth=12, heads=6).to(DEV).eval()
pixel_model.load_state_dict({k[len("dit."):]: v for k, v in pm5_state.items() if k.startswith("dit.")})
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse").to(DEV).eval()

print("[boot] loading VoxelModel v1...")
vm1_weights = hf_hub_download("bench-labs/VoxelModel-v1", "model.safetensors")
voxel_model = VoxelDiT().to(DEV).eval()
voxel_model.load_state_dict(load_file(vm1_weights))

print("[boot] loading AudioModel v1...")
audio_cfg = json.load(open(hf_hub_download("bench-labs/AudioModel-v1", "config.json")))
a_dit = audio_cfg["dit"]
audio_model = AudioDiT(x_res=a_dit["x_res"], y_res=a_dit["y_res"],
                       text_seq_dim=a_dit["text_seq_dim"], text_pool_dim=a_dit["text_pool_dim"]).to(DEV).eval()
audio_model.load_state_dict(load_file(hf_hub_download("bench-labs/AudioModel-v1", "model_best.safetensors")))
audio_mel = Mel(x_res=a_dit["x_res"], y_res=a_dit["y_res"],
                sample_rate=audio_cfg["mel"]["sample_rate"], n_fft=audio_cfg["mel"]["n_fft"],
                hop_length=audio_cfg["mel"]["hop_length"], top_db=audio_cfg["mel"]["top_db"])
# Fast mel->STFT pseudo-inverse (librosa's pre-0.10 behavior). librosa 0.10+ uses an
# NNLS/L-BFGS solver here that allocates ~2GB and takes minutes on CPU for this
# 384x256 spectrogram; the pinv gives the same Griffin-Lim output in about a second.
audio_mel_pinv = np.linalg.pinv(
    librosa.filters.mel(sr=audio_mel.sr, n_fft=audio_mel.n_fft, n_mels=audio_mel.y_res, dtype=np.float32)
)
# Text-only CLAP tower: runs the same text_model + text_projection the reference
# sample.py uses, but skips the unused HTSAT audio tower (~150M params of dead weight).
audio_tokenizer = AutoTokenizer.from_pretrained("laion/clap-htsat-unfused")
audio_clap = ClapTextModelWithProjection.from_pretrained("laion/clap-htsat-unfused").to(DEV).eval()
audio_null_seq = audio_null_pool = None

print("[boot] loading PixelModel v6...")
v6_weights = hf_hub_download("bench-labs/PixelModel-v6", "model.safetensors")
pixel_model_v6 = MMDiT().to(DEV).eval()
pixel_model_v6.load_state_dict(load_file(v6_weights), strict=False)
t5_tokenizer = T5TokenizerFast.from_pretrained("google/flan-t5-base")
t5_encoder = T5EncoderModel.from_pretrained("google/flan-t5-base").to(DEV).eval()
vae_v6 = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix").to(DEV).float().eval()
null_v6_seq, null_v6_mask, null_v6_pool = None, None, None

print("[boot] ready.")


def _gpu_device() -> str:
    return "cuda" if torch.cuda.is_available() else "cpu"


@spaces.GPU(duration=60)
@torch.no_grad()
def sample_image(prompt: str, steps: int, cfg: float, seed: int, progress=gr.Progress()):
    if not prompt.strip():
        raise gr.Error("Type a prompt first.")
    device = _gpu_device()
    pixel_model.to(device)
    vae.to(device)
    steps = int(steps)
    g = torch.Generator(device=device).manual_seed(int(seed))
    seq, pool = encode([prompt])
    seq, pool = seq.to(device), pool.to(device)
    nseq, npool = null_seq.to(device), null_pool.to(device)
    x = torch.randn(1, 4, 32, 32, device=device, generator=g)
    dt = 1.0 / steps
    for i in progress.tqdm(range(steps), desc="sampling"):
        t = torch.full((1,), i * dt, device=device)
        vc = pixel_model(x, t, seq, pool)
        vu = pixel_model(x, t, nseq, npool)
        x = x + (vu + cfg * (vc - vu)) * dt
    img = vae.decode((x / SCALE)).sample
    img = ((img.clamp(-1, 1) + 1) / 2).permute(0, 2, 3, 1).cpu().numpy()[0]
    return (img * 255).round().astype(np.uint8)


@spaces.GPU(duration=90)
@torch.no_grad()
def sample_voxel(prompt: str, steps: int, cfg: float, threshold: float, seed: int, progress=gr.Progress()):
    if not prompt.strip():
        raise gr.Error("Type a prompt first.")
    device = _gpu_device()
    voxel_model.to(device)
    steps = int(steps)
    g = torch.Generator(device=device).manual_seed(int(seed))
    seq, pool = encode([prompt])
    seq, pool = seq.to(device), pool.to(device)
    nseq, npool = null_seq.to(device), null_pool.to(device)
    x = torch.randn(1, 1, 32, 32, 32, device=device, generator=g)
    dt = 1.0 / steps
    for i in progress.tqdm(range(steps), desc="sampling"):
        t = torch.full((1,), i * dt, device=device)
        vc = voxel_model(x, t, seq, pool)
        vu = voxel_model(x, t, nseq, npool)
        x = x + (vu + cfg * (vc - vu)) * dt
    grid = (x[0, 0] > threshold).cpu().numpy()
    if not grid.any():
        raise gr.Error("Nothing came back above the occupancy threshold — try lowering it or re-rolling the seed.")
    return grid_to_glb(grid)


@spaces.GPU(duration=120)
@torch.no_grad()
def sample_image_v6(prompt: str, steps: int, cfg: float, seed: int, progress=gr.Progress()):
    global null_v6_seq, null_v6_mask, null_v6_pool
    if not prompt.strip():
        raise gr.Error("Type a prompt first.")
    device = _gpu_device()
    pixel_model_v6.to(device)
    t5_encoder.to(device)
    vae_v6.to(device)
    steps = int(steps)

    def encode_v6(strings):
        t = t5_tokenizer(strings, padding="max_length", max_length=32, truncation=True, return_tensors="pt").to(device)
        seq = t5_encoder(**t).last_hidden_state.float()
        _, pool = encode(strings)
        return seq, t["attention_mask"].float(), pool.to(device)

    seq, mask, pool = encode_v6([prompt])
    if null_v6_seq is None:
        null_v6_seq, null_v6_mask, null_v6_pool = encode_v6([""])
    else:
        null_v6_seq, null_v6_mask, null_v6_pool = (
            null_v6_seq.to(device), null_v6_mask.to(device), null_v6_pool.to(device)
        )
    g = torch.Generator(device=device).manual_seed(int(seed))
    x = torch.randn(1, 4, 32, 32, device=device, generator=g)
    dt = 1.0 / steps
    for i in progress.tqdm(range(steps), desc="sampling"):
        t = torch.full((1,), i * dt, device=device)
        vc = pixel_model_v6(x, t, seq, mask, pool)
        vu = pixel_model_v6(x, t, null_v6_seq, null_v6_mask, null_v6_pool)
        x = x + (vu + cfg * (vc - vu)) * dt
    img = vae_v6.decode(x / vae_v6.config.scaling_factor).sample
    return (((img.clamp(-1, 1) + 1) / 2).permute(0, 2, 3, 1).cpu().numpy()[0] * 255).round().astype(np.uint8)


AUDIO_MAX_TOKENS = 32


@torch.no_grad()
def encode_audio(strings, device="cpu"):
    t = audio_tokenizer(strings, padding="max_length", max_length=AUDIO_MAX_TOKENS,
                        truncation=True, return_tensors="pt").to(device)
    out = audio_clap(**t)
    return out.last_hidden_state.float(), out.text_embeds.float()


def spectrogram_to_audio(mel, img):
    """Invert a mel spectrogram image back to audio (Griffin-Lim, 32 iters).



    Same 0..255 -> dB mapping as diffusers' Mel.image_to_audio, with the fast

    pseudo-inverse mel->STFT step (see the boot comment on audio_mel_pinv).

    """
    log_S = (np.frombuffer(img.tobytes(), dtype="uint8").reshape((img.height, img.width)).astype(np.float32)
             * mel.top_db / 255 - mel.top_db)
    power = librosa.db_to_power(log_S)
    stft_mag = np.clip(audio_mel_pinv @ power, 0, None) ** 0.5  # power=2.0 -> magnitude
    audio = librosa.griffinlim(stft_mag, n_iter=mel.n_iter, hop_length=mel.hop_length,
                               n_fft=mel.n_fft, window="hann")
    peak = np.abs(audio).max()
    return audio if peak == 0 else audio / peak * 0.9


@spaces.GPU(duration=90)
@torch.no_grad()
def sample_audio(prompt: str, steps: int, cfg: float, seed: int, progress=gr.Progress()):
    global audio_null_seq, audio_null_pool
    if not prompt.strip():
        raise gr.Error("Type a prompt first.")
    device = _gpu_device()
    audio_model.to(device)
    audio_clap.to(device)
    steps = int(steps)
    g = torch.Generator(device=device).manual_seed(int(seed))
    seq, pool = encode_audio([prompt], device=device)
    if audio_null_seq is None:
        audio_null_seq, audio_null_pool = encode_audio([""], device=device)
    else:
        audio_null_seq, audio_null_pool = audio_null_seq.to(device), audio_null_pool.to(device)
    x = torch.randn(1, 1, audio_model.y_res, audio_model.x_res, device=device, generator=g)
    dt = 1.0 / steps
    for i in progress.tqdm(range(steps), desc="sampling"):
        t = torch.full((1,), i * dt, device=device)
        vc = audio_model(x, t, seq, pool)
        vu = audio_model(x, t, audio_null_seq, audio_null_pool)
        x = x + (vu + cfg * (vc - vu)) * dt
    row = x[0, 0].clamp(-1, 1).float().cpu().numpy()
    img = Image.fromarray(((row + 1) * 127.5 + 0.5).astype(np.uint8))
    audio = spectrogram_to_audio(audio_mel, img)
    return audio_mel.get_sample_rate(), audio


def grid_to_glb(grid: np.ndarray) -> str:
    voxel = trimesh.voxel.VoxelGrid(encoding=grid)
    mesh = voxel.as_boxes()
    mesh.visual.face_colors = [180, 180, 190, 255]
    path = tempfile.NamedTemporaryFile(suffix=".glb", delete=False).name
    mesh.export(path)
    return path


with gr.Blocks(title="BenchLabs Models") as demo:
    gr.Markdown(
        "# BenchLabs Models\n"
        "Three tiny diffusion models, running live with on-demand GPU (ZeroGPU). "
        "All are under 45M trained parameters — the whole model fits in a PNG image "
        "if you're curious — see the model pages linked below."
    )
    with gr.Tab("Text → Image (PixelModel v6)"):
        gr.Markdown("A larger MMDiT model conditioned by T5 and CLIP. 256x256 output.")
        with gr.Row():
            with gr.Column():
                v6_prompt = gr.Textbox(label="Prompt", placeholder="a red fox sitting in a snowy forest")
                v6_steps = gr.Slider(10, 100, value=50, step=5, label="Detail (sampling steps)")
                v6_cfg = gr.Slider(1.0, 10.0, value=3.0, step=0.5, label="Prompt strength (CFG)")
                v6_seed = gr.Number(value=0, precision=0, label="Seed")
                v6_btn = gr.Button("Generate image", variant="primary")
            with gr.Column():
                v6_out = gr.Image(label="Result", type="numpy")
        v6_btn.click(sample_image_v6, [v6_prompt, v6_steps, v6_cfg, v6_seed], v6_out)
        gr.Examples(
            [["a red fox sitting in a snowy forest", 50, 3.0, 0],
             ["a lighthouse on a cliff at sunset", 50, 3.0, 0],
             ["a city street at night with neon signs", 50, 3.0, 0]],
            [v6_prompt, v6_steps, v6_cfg, v6_seed],
        )

    with gr.Tab("Text → 3D (VoxelModel v1)"):
        gr.Markdown(
            "Good at bulky objects: chairs, tables, cars, mushrooms. "
            "Thin objects (swords, keys) don't survive 32³ voxelization, in the training "
            "data or the model, so expect a blob rather than a blade."
        )
        with gr.Row():
            with gr.Column():
                vox_prompt = gr.Textbox(label="Prompt", placeholder="a wooden chair")
                vox_steps = gr.Slider(10, 50, value=25, step=1, label="Detail (sampling steps)")
                vox_cfg = gr.Slider(1.0, 10.0, value=5.0, step=0.5, label="Prompt strength (CFG)")
                vox_thresh = gr.Slider(-1.0, 1.0, value=0.0, step=0.05, label="Occupancy threshold")
                vox_seed = gr.Number(value=0, precision=0, label="Seed")
                vox_btn = gr.Button("Generate 3D model", variant="primary")
            with gr.Column():
                vox_out = gr.Model3D(label="Result")
        vox_btn.click(sample_voxel, [vox_prompt, vox_steps, vox_cfg, vox_thresh, vox_seed], vox_out)
        gr.Examples(
            [["a wooden chair", 25, 5.0, 0.0, 0],
             ["a purple mushroom", 25, 5.0, 0.0, 0],
             ["a small boat", 25, 5.0, 0.0, 0]],
            [vox_prompt, vox_steps, vox_cfg, vox_thresh, vox_seed],
        )

    with gr.Tab("Text → Audio (AudioModel v1)"):
        gr.Markdown(
            "17.8 seconds of sound at 22 kHz from a text prompt. The same tiny DiT + "
            "rectified-flow recipe as the other tabs, applied to a mel spectrogram "
            "image instead of pixels. Conditioned by the CLAP text tower; audio comes "
            "back via Griffin-Lim (32 iterations), so expect lo-fi, slightly phasey "
            "sound — there's no learned vocoder in v1."
        )
        with gr.Row():
            with gr.Column():
                aud_prompt = gr.Textbox(label="Prompt", placeholder="a dog barking")
                aud_steps = gr.Slider(10, 100, value=50, step=5, label="Detail (sampling steps)")
                aud_cfg = gr.Slider(1.0, 10.0, value=4.0, step=0.5, label="Prompt strength (CFG)")
                aud_seed = gr.Number(value=0, precision=0, label="Seed")
                aud_btn = gr.Button("Generate audio", variant="primary")
            with gr.Column():
                aud_out = gr.Audio(label="Result")
        aud_btn.click(sample_audio, [aud_prompt, aud_steps, aud_cfg, aud_seed], aud_out)
        gr.Examples(
            [["a dog barking", 50, 4.0, 0],
             ["rain falling on a roof", 50, 4.0, 0],
             ["footsteps on gravel", 50, 4.0, 0]],
            [aud_prompt, aud_steps, aud_cfg, aud_seed],
        )

    with gr.Tab("Text → Image (PixelModel v5)"):
        gr.Markdown(
            "Good at material and light: food, landscapes, skies, interiors. "
            "Weak on faces, hands, and anything needing precise structure or text."
        )
        with gr.Row():
            with gr.Column():
                img_prompt = gr.Textbox(label="Prompt", placeholder="a bowl of ramen with a soft boiled egg")
                img_steps = gr.Slider(10, 50, value=25, step=1, label="Detail (sampling steps)")
                img_cfg = gr.Slider(1.0, 10.0, value=5.0, step=0.5, label="Prompt strength (CFG)")
                img_seed = gr.Number(value=0, precision=0, label="Seed")
                img_btn = gr.Button("Generate image", variant="primary")
            with gr.Column():
                img_out = gr.Image(label="Result", type="numpy")
        img_btn.click(sample_image, [img_prompt, img_steps, img_cfg, img_seed], img_out)
        gr.Examples(
            [["a bowl of ramen with a soft boiled egg", 25, 5.0, 0],
             ["a wet cobblestone street at night", 25, 5.0, 0],
             ["a library of wooden shelves", 25, 5.0, 0]],
            [img_prompt, img_steps, img_cfg, img_seed],
        )

    gr.Markdown(
        "Models: [PixelModel v5](https://huggingface.co/bench-labs/PixelModel-v5) · "
        "[PixelModel v6](https://huggingface.co/bench-labs/PixelModel-v6) · "
        "[VoxelModel v1](https://huggingface.co/bench-labs/VoxelModel-v1) · "
        "[AudioModel v1](https://huggingface.co/bench-labs/AudioModel-v1)"
    )

if __name__ == "__main__":
    demo.queue(max_size=20).launch(server_name="0.0.0.0")