Switch backend to native LTX-2 (ICLoraPipeline)

Swaps the inference backend from diffusers to the native LTX-2 codebase (ICLoraPipeline, distilled, bf16; clones Lightricks/LTX-2 at runtime). UI, inputs/outputs, examples and API endpoint are unchanged; only the description drops the diffusers mention. requirements.txt updated to native deps.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

README.md

@@ -11,10 +11,10 @@ pinned: false
 hardware: zero-a10g
 short_description: Remove beards from video with an LTX-2.3 IC-LoRA
 models:
-  - diffusers/LTX-2.3-Diffusers
+  - Lightricks/LTX-2.3
   - Lightricks/LTX-2.3-22b-IC-LoRA-Instant-Shave
 ---
 
 # 🪒 LTX-2.3 Beard Removal (Instant Shave)
 Removes beard, mustache and stubble from a person in a video while preserving identity, expression and motion.
-IC-LoRA on LTX-2.3 (`LTX2InContextPipeline`, 30 steps, guidance 4.0, STG, 25fps, `REMOVEBEARD` trigger).
+IC-LoRA on LTX-2.3 (the native LTX-2 pipeline, 30 steps, guidance 4.0, STG, 25fps, `REMOVEBEARD` trigger).

app.py

@@ -1,50 +1,136 @@
 import os
+import subprocess
+import sys
 
-os.environ.setdefault("TORCH_COMPILE_DISABLE", "1")
-os.environ.setdefault("TORCHDYNAMO_DISABLE", "1")
+# ZeroGPU: torch.compile / dynamo unsupported — disable before any torch import.
+os.environ["TORCH_COMPILE_DISABLE"] = "1"
+os.environ["TORCHDYNAMO_DISABLE"] = "1"
 
+# memory-efficient attention
+subprocess.run([sys.executable, "-m", "pip", "install", "xformers==0.0.32.post2", "--no-build-isolation"], check=False)
+
+# --- clone + install the NATIVE LTX-2 codebase at the pinned commit the working ZeroGPU spaces use ---
+LTX_REPO_URL = "https://github.com/Lightricks/LTX-2.git"
+LTX_REPO_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "LTX-2")
+LTX_COMMIT = "ae855f8538843825f9015a419cf4ba5edaf5eec2"
+if not os.path.exists(LTX_REPO_DIR):
+    subprocess.run(["git", "clone", LTX_REPO_URL, LTX_REPO_DIR], check=True)
+    subprocess.run(["git", "-C", LTX_REPO_DIR, "checkout", LTX_COMMIT], check=True)
+subprocess.run([sys.executable, "-m", "pip", "install", "--force-reinstall", "--no-deps",
+                "-e", os.path.join(LTX_REPO_DIR, "packages", "ltx-core"),
+                "-e", os.path.join(LTX_REPO_DIR, "packages", "ltx-pipelines")], check=True)
+sys.path.insert(0, os.path.join(LTX_REPO_DIR, "packages", "ltx-pipelines", "src"))
+sys.path.insert(0, os.path.join(LTX_REPO_DIR, "packages", "ltx-core", "src"))
+
+import logging
 import random
 import tempfile
-import threading
-import time
 
 import numpy as np
 import imageio.v3 as iio
-import spaces
+from PIL import Image, ImageOps
+
 import torch
+torch._dynamo.config.suppress_errors = True
+torch._dynamo.config.disable = True
+
+import spaces
 import gradio as gr
-from PIL import Image, ImageOps
-from huggingface_hub import hf_hub_download
-from safetensors.torch import load_file
+from huggingface_hub import hf_hub_download, snapshot_download
+
+# Import LTX modules in the proven order — importing ltx_core.quantization/loader FIRST hits a
+# circular import (fp8_cast <-> loader.fuse_loras). Importing the model modules first forces the
+# correct init order (mirrors the working reference Space).
+from ltx_core.model.video_vae import TilingConfig, get_video_chunks_number, decode_video as _vae_decode_video  # noqa: F401
+from ltx_core.model.upsampler import upsample_video as _upsample_video  # noqa: F401
+from ltx_core.model.audio_vae import encode_audio as _vae_encode_audio  # noqa: F401
+from ltx_core.quantization import QuantizationPolicy
+from ltx_core.loader import LoraPathStrengthAndSDOps, LTXV_LORA_COMFY_RENAMING_MAP
+from ltx_pipelines.ic_lora import ICLoraPipeline
+from ltx_pipelines.utils.media_io import encode_video
 
-from diffusers import LTX2InContextPipeline
-from diffusers.pipelines.ltx2.pipeline_ltx2_ic_lora import LTX2ReferenceCondition
-from diffusers.utils import load_video, encode_video
+# --- ZeroGPU loader patch -------------------------------------------------------------
+# The native loader opens safetensors directly on the CUDA device
+# (safe_open(path, device="cuda")), doing the host->device copy in safetensors' own C++
+# (cudaMemcpy) — bypassing torch.Tensor.to, the call ZeroGPU patches to virtualise + pack
+# weights at module scope. Result: "No CUDA GPUs are available" at startup, nothing packs.
+# Patch it to open on CPU then move via torch.Tensor.to (ZeroGPU-virtualisable).
+import safetensors as _safetensors
+import ltx_core.loader.sft_loader as _sft
+from ltx_core.loader.primitives import StateDict as _StateDict
 
-# --- Config -----------------------------------------------------------------
-# Beard-removal IC-LoRA — non-distilled recipe: 30 steps, guidance 4.0, STG, 25 fps.
-BASE_MODEL = "diffusers/LTX-2.3-Diffusers"
+def _zerogpu_safe_load(self, path, sd_ops, device=None):
+    device = device or torch.device("cpu")
+    sd, size, dtype = {}, 0, set()
+    model_paths = path if isinstance(path, list) else [path]
+    for shard_path in model_paths:
+        with _safetensors.safe_open(shard_path, framework="pt", device="cpu") as f:
+            for name in f.keys():
+                expected = name if sd_ops is None else sd_ops.apply_to_key(name)
+                if expected is None:
+                    continue
+                value = f.get_tensor(name).to(device=device)  # torch path -> ZeroGPU-virtualised
+                kvs = ((expected, value),)
+                if sd_ops is not None:
+                    kvs = sd_ops.apply_to_key_value(expected, value)
+                for k, v in kvs:
+                    size += v.nbytes
+                    dtype.add(v.dtype)
+                    sd[k] = v
+    return _StateDict(sd=sd, device=device, size=size, dtype=dtype)
+
+_sft.SafetensorsStateDictLoader.load = _zerogpu_safe_load
+print("[PATCH] safetensors loader -> CPU-open + torch.to (ZeroGPU-virtualisable)")
+# --------------------------------------------------------------------------------------
+
+# --- attention backend patch (FA3 crashes on Blackwell ZeroGPU; use xformers/SDPA) ---
+import torch.nn.functional as F
+from ltx_core.model.transformer import attention as _attn_mod
+
+def _sdpa_as_mea(query, key, value, attn_bias=None, scale=None, **kwargs):
+    q, k, v = query.transpose(1, 2), key.transpose(1, 2), value.transpose(1, 2)
+    return F.scaled_dot_product_attention(q, k, v, scale=scale).transpose(1, 2)
+
+# IMPORTANT (ZeroGPU): never query CUDA at module scope. SDPA works on every GPU (incl.
+# Blackwell ZeroGPU, where FA3 crashes), so patch it unconditionally.
+_attn_mod.memory_efficient_attention = _sdpa_as_mea
+print("[ATTN] SDPA (patched at module scope, no CUDA query)")
+
+logging.getLogger().setLevel(logging.INFO)
+
+# =========================== PER-LORA CONFIG (colorize) ===========================
+TITLE = "LTX-2.3 Beard Removal (native LTX-2)"
 LORA_REPO = "Lightricks/LTX-2.3-22b-IC-LoRA-Instant-Shave"
 LORA_FILE = "ltx-2.3-22b-ic-lora-instant-shave-0.9.safetensors"
 LORA_SCALE = 1.0
-FPS = 25  # the card recommends 25 fps
-NUM_STEPS = 30
-GUIDANCE = 4.0
-STG_BLOCKS = [29]
-NEGATIVE = ("beard, mustache, facial hair, stubble, worst quality, "
-            "inconsistent motion, blurry, jittery, distorted")
-MAX_SEED = np.iinfo(np.int32).max
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
+SKIP_STAGE_2 = True
+GRAYSCALE_REF = False
 RES_PRESETS = {"960×544 (recommended)": (960, 544), "768×448 (fast)": (768, 448)}
+DEFAULT_PRESET = "960×544 (recommended)"
 FRAME_CHOICES = [33, 49, 73, 97, 121]
+DEFAULT_FRAMES = 49
+
+def build_prompt(p):
+    return (
+        f"REMOVEBEARD {p.strip()}, completely smooth and clean-shaven face, bare skin, "
+        "no beard, no stubble, no facial hair; identity, expression, motion, lighting and scene unchanged."
+    )
+
+EXAMPLES = [
+    ["examples/beard_1.mp4",
+     "the same man with a completely smooth clean-shaven face, no beard or stubble, bare skin, relaxed expression in soft indoor light; a quiet room ambience",
+     "960×544 (recommended)", 49, 42, False],
+    ["examples/beard_2.mp4",
+     "the same man in a hooded jacket with a completely smooth clean-shaven face, no beard or stubble, outdoors at night with city lights behind him; cool night ambience",
+     "960×544 (recommended)", 49, 42, False],
+]
+# =================================================================================
 
-pipe = LTX2InContextPipeline.from_pretrained(BASE_MODEL, torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-pipe.vae.enable_tiling()
-_lora_path = hf_hub_download(LORA_REPO, LORA_FILE, token=HF_TOKEN)
-pipe.load_lora_weights(load_file(_lora_path), adapter_name="shave")
-pipe.set_adapters("shave", LORA_SCALE)
+FPS = 25.0
+MAX_SEED = np.iinfo(np.int32).max
+HF_TOKEN = os.environ.get("HF_TOKEN")
+LTX_MODEL_REPO = "Lightricks/LTX-2.3"
+GEMMA_REPO = "google/gemma-3-12b-it-qat-q4_0-unquantized"
 
 
 def _src_fps(path, default=FPS):
@@ -54,85 +140,121 @@ def _src_fps(path, default=FPS):
         return default
 
 
-def _load_frames(path, num_frames, width, height):
-    frames = load_video(path)
-    if not frames:
-        return []
-    fps = _src_fps(path)
+def _prep_reference(path, width, height, num_frames):
+    """Resample to 24fps, aspect-fit/crop to WxH, NF frames; (optionally grayscale); write temp mp4."""
+    vid = iio.imread(path, plugin="pyav")
+    src_fps = _src_fps(path)
+    n = len(vid)
     out = []
     for i in range(num_frames):
-        idx = min(int(round(i / FPS * fps)), len(frames) - 1)
-        out.append(ImageOps.fit(frames[idx].convert("RGB"), (width, height), Image.LANCZOS))
-    return out
+        idx = min(int(round(i / FPS * src_fps)), n - 1)
+        im = Image.fromarray(vid[idx]).convert("RGB")
+        im = ImageOps.fit(im, (width, height), Image.LANCZOS)
+        if GRAYSCALE_REF:
+            im = im.convert("L").convert("RGB")
+        out.append(np.array(im))
+    tmp = tempfile.mktemp(suffix=".mp4")
+    iio.imwrite(tmp, np.stack(out), fps=FPS, plugin="pyav", codec="libx264")
+    return tmp
 
 
-def _pick_resolution(first_frame, preset):
+def _pick_resolution(path, preset):
     w, h = RES_PRESETS[preset]
-    if first_frame.height > first_frame.width:
-        w, h = h, w
+    try:
+        f0 = iio.imread(path, plugin="pyav", index=0)
+        if f0.shape[0] > f0.shape[1]:  # portrait
+            w, h = h, w
+    except Exception:
+        pass
     return w, h
 
 
-def _build_prompt(prompt):
-    desc = prompt.strip() or "the same person, completely clean-shaven"
-    return (f"REMOVEBEARD {desc}, completely smooth and clean-shaven face, bare skin, "
-            f"no beard, no stubble, no facial hair; identity, expression, motion, lighting and scene unchanged.")
+# --- Load native pipeline + IC-LoRA once at module scope (ZeroGPU packs weights here) ---
+print("Downloading checkpoints…")
+checkpoint_path = hf_hub_download(LTX_MODEL_REPO, "ltx-2.3-22b-distilled-1.1.safetensors", token=HF_TOKEN)
+spatial_upsampler_path = hf_hub_download(LTX_MODEL_REPO, "ltx-2.3-spatial-upscaler-x2-1.1.safetensors", token=HF_TOKEN)
+gemma_root = snapshot_download(GEMMA_REPO, token=HF_TOKEN)
+lora_path = hf_hub_download(LORA_REPO, LORA_FILE, token=HF_TOKEN)
 
+print("Building ICLoraPipeline…")
+pipeline = ICLoraPipeline(
+    distilled_checkpoint_path=checkpoint_path,
+    spatial_upsampler_path=spatial_upsampler_path,
+    gemma_root=gemma_root,
+    loras=[LoraPathStrengthAndSDOps(lora_path, LORA_SCALE, LTXV_LORA_COMFY_RENAMING_MAP)],
+    # bf16 (NOT fp8): the IC-LoRA is fused into the transformer at MODULE SCOPE (the GPU
+    # worker can't re-open the checkpoint file). fp8_cast()'s fusion runs a custom CUDA kernel
+    # that can't be ZeroGPU-virtualised; the bf16 fuse rule is pure torch -> virtualisable.
+    quantization=None,
+)
 
-def _export(video_np, audio, path):
-    kw = {}
-    if audio is not None:
-        kw = dict(audio=audio[0].float().cpu(), audio_sample_rate=pipe.vocoder.config.output_sampling_rate)
-    encode_video(video_np, fps=FPS, output_path=path, **kw)
+
+def _preload_pin(ledger, tag):
+    if ledger is None:
+        return
+    for name in ["transformer", "video_encoder", "video_decoder", "audio_encoder",
+                 "audio_decoder", "vocoder", "spatial_upsampler", "text_encoder",
+                 "gemma_embeddings_processor"]:
+        fn = getattr(ledger, name, None)
+        if callable(fn):
+            try:
+                obj = fn()
+                setattr(ledger, name, (lambda o=obj: o))
+                print(f"[preload {tag}] {name} ✓")
+            except Exception as e:
+                print(f"[preload {tag}] {name} skipped: {e}")
+
+# Preload stage 1 always; preload stage 2 only when two-stage is used (skip_stage_2=False).
+# Eagerly pinning both ledgers materializes TWO ~46GB transformers — too big for the ZeroGPU pack.
+_preload_pin(getattr(pipeline, "stage_1_model_ledger", None), "stage1")
+if not SKIP_STAGE_2:
+    _preload_pin(getattr(pipeline, "stage_2_model_ledger", None), "stage2")
+print("Pipeline ready.")
 
 
 def _duration(*args, **kwargs):
-    preset = next((a for a in args if isinstance(a, str) and a in RES_PRESETS), None)
-    num_frames = next((a for a in args if isinstance(a, int) and a in FRAME_CHOICES), 49)
-    w, h = RES_PRESETS.get(preset, (960, 544))
-    per_frame = max(3.0, 3.0 * (w * h) / (768 * 448))  # 30 steps + CFG + STG (dev path)
-    return int(120 + int(num_frames) * per_frame)
+    nf = next((a for a in args if isinstance(a, int) and a in FRAME_CHOICES), DEFAULT_FRAMES)
+    return int(60 + nf * 1.2)
 
 
 @spaces.GPU(duration=_duration)
-def shave(video, prompt, preset, num_frames, negative, seed, randomize,
-          progress=gr.Progress(track_tqdm=True)):
+@torch.inference_mode()
+def shave(video, prompt, preset, num_frames, negative, seed, randomize, progress=gr.Progress(track_tqdm=True)):
     if video is None:
-        raise gr.Error("Please upload a video of a bearded subject.")
-    if randomize:
-        seed = random.randint(0, MAX_SEED)
-    seed = int(seed)
+        raise gr.Error("Please upload a video.")
+    if not prompt.strip():
+        raise gr.Error("Describe the result (e.g. 'a brown rabbit on grey rocks, soft birdsong').")
+    seed = random.randint(0, MAX_SEED) if randomize else int(seed)
     num_frames = int(num_frames)
-
-    probe = load_video(video)
-    if not probe:
-        raise gr.Error("Could not read any frames from that video.")
-    width, height = _pick_resolution(probe[0], preset)
-    ref = _load_frames(video, num_frames, width, height)
-    full_prompt = _build_prompt(prompt)
-
-
-    video_out, audio_out = pipe(
-        prompt=full_prompt, negative_prompt=negative,
-        reference_conditions=[LTX2ReferenceCondition(frames=ref, strength=1.0)],
-        reference_downscale_factor=1,
-        width=width, height=height, num_frames=num_frames, frame_rate=FPS,
-        num_inference_steps=NUM_STEPS, guidance_scale=GUIDANCE,
-        spatio_temporal_guidance_blocks=STG_BLOCKS,
-        generator=torch.Generator(device="cuda").manual_seed(seed),
-        output_type="np", return_dict=False,
-        )
-    out_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
-    _export(video_out[0], audio_out, out_path)
+    width, height = _pick_resolution(video, preset)
+    ref_path = _prep_reference(video, width, height, num_frames)
+    tiling = TilingConfig.default()
+    # skip_stage_2 outputs at half the passed dims -> pass 2x so output matches the preset.
+    gen_w, gen_h = (width * 2, height * 2) if SKIP_STAGE_2 else (width, height)
+    video_out, audio_out = pipeline(
+        prompt=build_prompt(prompt),
+        seed=seed, height=gen_h, width=gen_w,
+        num_frames=num_frames, frame_rate=FPS,
+        images=[], video_conditioning=[(ref_path, 1.0)],
+        skip_stage_2=SKIP_STAGE_2, tiling_config=tiling,
+    )
+    out_path = tempfile.mktemp(suffix=".mp4")
+    encode_video(video=video_out, fps=FPS, audio=audio_out, output_path=out_path,
+                 video_chunks_number=get_video_chunks_number(num_frames, tiling))
     return out_path, seed
 
 
+# --- UI config (match the public Space exactly) ---
+RES_PRESETS = {"960×544 (recommended)": (960, 544), "768×448 (fast)": (768, 448)}
+FRAME_CHOICES = [33, 49, 73, 97, 121]
+
+
 with gr.Blocks(title="LTX-2.3 Beard Removal") as demo:
     gr.Markdown(
         "# 🪒 LTX-2.3 Beard Removal (Instant Shave)\n"
         "Remove beard, mustache and stubble from a person in a video while preserving identity, expression and "
-        "motion. Using [LTX 2.3 Dev](https://huggingface.co/diffusers/LTX-2.3-Diffusers) with the "
-        "[Beard-Removal IC-LoRA](https://huggingface.co/Lightricks/LTX-2.3-22b-IC-LoRA-Instant-Shave), via diffusers 🧨."
+        "motion. Using [LTX 2.3 Dev](https://huggingface.co/Lightricks/LTX-2.3) with the "
+        "[Beard-Removal IC-LoRA](https://huggingface.co/Lightricks/LTX-2.3-22b-IC-LoRA-Instant-Shave)."
     )
     with gr.Row():
         with gr.Column():

requirements.txt

@@ -1,9 +1,11 @@
-git+https://github.com/huggingface/diffusers
-transformers
+transformers==4.57.6
 accelerate
-peft
-safetensors
-sentencepiece
-imageio
-imageio-ffmpeg
+torch==2.8.0
+torchaudio==2.8.0
+einops
+scipy
 av
+scikit-image>=0.25.2
+flashpack==0.1.2
+imageio[ffmpeg]
+pillow