Source: examples/inference/ltx2_3

LTX-2.3 distilled inference configs¶

Ready-to-run fastvideo generate run configs for the LTX-2.3 distilled model (FastVideo/LTX-2.3-Distilled-Diffusers), covering both workloads (t2v / i2v), both two-stage step schedules (5+2, 8+3 = denoise + refine), and four resolutions.

fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_1280x832.yaml

Each config is self-contained (no preset registry needed): the two-stage refine is wired via generator.pipeline.preset_overrides.refine, and the base sampling knobs live under request.sampling. The refine upsampler auto-resolves from the model's spatial_upscaler.

Configs¶

workload	schedule	resolution (HxW)	file
t2v	5+2	1280x832	`t2v_5s2_1280x832.yaml`
t2v	5+2	1024x1536	`t2v_5s2_1024x1536.yaml`
t2v	5+2	768x1280	`t2v_5s2_768x1280.yaml`
t2v	5+2	512x768	`t2v_5s2_512x768.yaml`
t2v	8+3	1280x832	`t2v_8s3_1280x832.yaml`
t2v	8+3	1024x1536	`t2v_8s3_1024x1536.yaml`
t2v	8+3	768x1280	`t2v_8s3_768x1280.yaml`
t2v	8+3	512x768	`t2v_8s3_512x768.yaml`
i2v	5+2	1280x832	`i2v_5s2_1280x832.yaml`
i2v	5+2	1024x1536	`i2v_5s2_1024x1536.yaml`
i2v	5+2	768x1280	`i2v_5s2_768x1280.yaml`
i2v	5+2	512x768	`i2v_5s2_512x768.yaml`
i2v	8+3	1280x832	`i2v_8s3_1280x832.yaml`
i2v	8+3	1024x1536	`i2v_8s3_1024x1536.yaml`
i2v	8+3	768x1280	`i2v_8s3_768x1280.yaml`
i2v	8+3	512x768	`i2v_8s3_512x768.yaml`

Overriding without editing a file¶

Dotted overrides (prefixes generator. / request.) let you tweak any field:

# swap prompt
fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_1280x832.yaml \
  --request.prompt "a red fox running through fresh snow"

# change output path / gpu count
fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_512x768.yaml \
  --request.output.output_path outputs/preview.mp4 \
  --generator.engine.num_gpus 4

i2v¶

The i2v_* configs take a first-frame image via request.extensions.ltx2_images ([[path, frame_offset, weight]]). Edit the path in the file, or override it:

fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_1280x832.yaml \
  --request.extensions.ltx2_images '[["/data/portrait.jpg", 0, 1.0]]'

Schedules¶

5+2 is the fast preview schedule; 8+3 is the higher-quality distilled recipe. Refine (preset_overrides.refine.num_inference_steps) only accepts 2 or 3 steps.

Additional Files¶

i2v_5s2_1024x1536.yaml

# LTX-2.3 distilled i2v — 5+2 two-stage at 1024x1536.
#
# Run:
#   fastvideo generate --config examples/inference/ltx2_3/i2v_5s2_1024x1536.yaml
#
# Stage 1 denoises for 5 steps at half resolution; the latents are then
# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
generator:
  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
  engine:
    num_gpus: 1
  pipeline:
    workload_type: i2v
    preset_overrides:
      refine:
        enabled: true
        num_inference_steps: 2
        guidance_scale: 1.0
        add_noise: true
request:
  prompt: >-
    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
  sampling:
    height: 1024
    width: 1536
    num_frames: 121
    fps: 24
    guidance_scale: 1.0
    num_inference_steps: 5
  # i2v conditioning — replace the path with your own first-frame image.
  extensions:
    ltx2_images:
      - ["/path/to/your/first_frame.jpg", 0, 1.0]
    ltx2_image_crf: 0.0
  output:
    output_path: outputs/ltx2_3_i2v_5s2_1024x1536.mp4
    save_video: true

i2v_5s2_1280x832.yaml

# LTX-2.3 distilled i2v — 5+2 two-stage at 1280x832.
#
# Run:
#   fastvideo generate --config examples/inference/ltx2_3/i2v_5s2_1280x832.yaml
#
# Stage 1 denoises for 5 steps at half resolution; the latents are then
# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
generator:
  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
  engine:
    num_gpus: 1
  pipeline:
    workload_type: i2v
    preset_overrides:
      refine:
        enabled: true
        num_inference_steps: 2
        guidance_scale: 1.0
        add_noise: true
request:
  prompt: >-
    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
  sampling:
    height: 1280
    width: 832
    num_frames: 121
    fps: 24
    guidance_scale: 1.0
    num_inference_steps: 5
  # i2v conditioning — replace the path with your own first-frame image.
  extensions:
    ltx2_images:
      - ["/path/to/your/first_frame.jpg", 0, 1.0]
    ltx2_image_crf: 0.0
  output:
    output_path: outputs/ltx2_3_i2v_5s2_1280x832.mp4
    save_video: true

i2v_5s2_512x768.yaml

# LTX-2.3 distilled i2v — 5+2 two-stage at 512x768.
#
# Run:
#   fastvideo generate --config examples/inference/ltx2_3/i2v_5s2_512x768.yaml
#
# Stage 1 denoises for 5 steps at half resolution; the latents are then
# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
generator:
  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
  engine:
    num_gpus: 1
  pipeline:
    workload_type: i2v
    preset_overrides:
      refine:
        enabled: true
        num_inference_steps: 2
        guidance_scale: 1.0
        add_noise: true
request:
  prompt: >-
    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
  sampling:
    height: 512
    width: 768
    num_frames: 121
    fps: 24
    guidance_scale: 1.0
    num_inference_steps: 5
  # i2v conditioning — replace the path with your own first-frame image.
  extensions:
    ltx2_images:
      - ["/path/to/your/first_frame.jpg", 0, 1.0]
    ltx2_image_crf: 0.0
  output:
    output_path: outputs/ltx2_3_i2v_5s2_512x768.mp4
    save_video: true

i2v_5s2_768x1280.yaml

# LTX-2.3 distilled i2v — 5+2 two-stage at 768x1280.
#
# Run:
#   fastvideo generate --config examples/inference/ltx2_3/i2v_5s2_768x1280.yaml
#
# Stage 1 denoises for 5 steps at half resolution; the latents are then
# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
generator:
  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
  engine:
    num_gpus: 1
  pipeline:
    workload_type: i2v
    preset_overrides:
      refine:
        enabled: true
        num_inference_steps: 2
        guidance_scale: 1.0
        add_noise: true
request:
  prompt: >-
    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
  sampling:
    height: 768
    width: 1280
    num_frames: 121
    fps: 24
    guidance_scale: 1.0
    num_inference_steps: 5
  # i2v conditioning — replace the path with your own first-frame image.
  extensions:
    ltx2_images:
      - ["/path/to/your/first_frame.jpg", 0, 1.0]
    ltx2_image_crf: 0.0
  output:
    output_path: outputs/ltx2_3_i2v_5s2_768x1280.mp4
    save_video: true

i2v_8s3_1024x1536.yaml

# LTX-2.3 distilled i2v — 8+3 two-stage at 1024x1536.
#
# Run:
#   fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_1024x1536.yaml
#
# Stage 1 denoises for 8 steps at half resolution; the latents are then
# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
generator:
  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
  engine:
    num_gpus: 1
  pipeline:
    workload_type: i2v
    preset_overrides:
      refine:
        enabled: true
        num_inference_steps: 3
        guidance_scale: 1.0
        add_noise: true
request:
  prompt: >-
    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
  sampling:
    height: 1024
    width: 1536
    num_frames: 121
    fps: 24
    guidance_scale: 1.0
    num_inference_steps: 8
  # i2v conditioning — replace the path with your own first-frame image.
  extensions:
    ltx2_images:
      - ["/path/to/your/first_frame.jpg", 0, 1.0]
    ltx2_image_crf: 0.0
  output:
    output_path: outputs/ltx2_3_i2v_8s3_1024x1536.mp4
    save_video: true

i2v_8s3_1280x832.yaml

# LTX-2.3 distilled i2v — 8+3 two-stage at 1280x832.
#
# Run:
#   fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_1280x832.yaml
#
# Stage 1 denoises for 8 steps at half resolution; the latents are then
# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
generator:
  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
  engine:
    num_gpus: 1
  pipeline:
    workload_type: i2v
    preset_overrides:
      refine:
        enabled: true
        num_inference_steps: 3
        guidance_scale: 1.0
        add_noise: true
request:
  prompt: >-
    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
  sampling:
    height: 1280
    width: 832
    num_frames: 121
    fps: 24
    guidance_scale: 1.0
    num_inference_steps: 8
  # i2v conditioning — replace the path with your own first-frame image.
  extensions:
    ltx2_images:
      - ["/path/to/your/first_frame.jpg", 0, 1.0]
    ltx2_image_crf: 0.0
  output:
    output_path: outputs/ltx2_3_i2v_8s3_1280x832.mp4
    save_video: true

i2v_8s3_512x768.yaml

# LTX-2.3 distilled i2v — 8+3 two-stage at 512x768.
#
# Run:
#   fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_512x768.yaml
#
# Stage 1 denoises for 8 steps at half resolution; the latents are then
# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
generator:
  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
  engine:
    num_gpus: 1
  pipeline:
    workload_type: i2v
    preset_overrides:
      refine:
        enabled: true
        num_inference_steps: 3
        guidance_scale: 1.0
        add_noise: true
request:
  prompt: >-
    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
  sampling:
    height: 512
    width: 768
    num_frames: 121
    fps: 24
    guidance_scale: 1.0
    num_inference_steps: 8
  # i2v conditioning — replace the path with your own first-frame image.
  extensions:
    ltx2_images:
      - ["/path/to/your/first_frame.jpg", 0, 1.0]
    ltx2_image_crf: 0.0
  output:
    output_path: outputs/ltx2_3_i2v_8s3_512x768.mp4
    save_video: true

i2v_8s3_768x1280.yaml

# LTX-2.3 distilled i2v — 8+3 two-stage at 768x1280.
#
# Run:
#   fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_768x1280.yaml
#
# Stage 1 denoises for 8 steps at half resolution; the latents are then
# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
generator:
  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
  engine:
    num_gpus: 1
  pipeline:
    workload_type: i2v
    preset_overrides:
      refine:
        enabled: true
        num_inference_steps: 3
        guidance_scale: 1.0
        add_noise: true
request:
  prompt: >-
    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
  sampling:
    height: 768
    width: 1280
    num_frames: 121
    fps: 24
    guidance_scale: 1.0
    num_inference_steps: 8
  # i2v conditioning — replace the path with your own first-frame image.
  extensions:
    ltx2_images:
      - ["/path/to/your/first_frame.jpg", 0, 1.0]
    ltx2_image_crf: 0.0
  output:
    output_path: outputs/ltx2_3_i2v_8s3_768x1280.mp4
    save_video: true

optimized_nvfp4_t2v.py

# SPDX-License-Identifier: Apache-2.0
"""LTX-2.3 distilled text-to-video with the optimized NVFP4 inference stack.

Runs `FastVideo/LTX-2.3-Distilled-Diffusers` on a single GPU with the full
validated optimization stack:

* NVFP4 block-scaled linear layers (per-16 E2M1 weights + E4M3 scale factors),
* ATTN_QAT_INFER FP4 attention (arch-resolved kernel, receipt logged),
* torch.compile (fullgraph) over the DiT, text encoder, and VAE,
* single-stage 8-step distilled sampling at guidance 1.0.

Quick start
-----------
    # On GB200-class ARM hosts, unset LD_LIBRARY_PATH (see Hardware notes):
    env -u LD_LIBRARY_PATH python examples/inference/ltx2_3/optimized_nvfp4_t2v.py

    # Optional overrides:
    #   export LTX23_MODEL_PATH=/path/to/local/snapshot
    #   export LTX23_T2V_PROMPT="a red fox running through fresh snow"
    #   export LTX23_OUTPUT_DIR=outputs_video/ltx2_3_nvfp4_t2v

Hardware notes
--------------
- On GB200 / Blackwell, run with `env -u LD_LIBRARY_PATH ...` to avoid a
  system-cuBLAS / torch-cuBLAS mismatch that fails every GEMM (some ARM
  container images ship an LD_LIBRARY_PATH that breaks torch.compile's
  toolchain discovery). The `_inductor.shape_padding = False` line below
  also avoids a pad_mm landmine on the same generation of cards.
"""
from __future__ import annotations

import os
import time
from pathlib import Path

import torch._inductor.config as _inductor

from fastvideo import VideoGenerator
from fastvideo.configs.pipelines.base import PipelineConfig
from fastvideo.layers.quantization.nvfp4_config import NVFP4Config
from fastvideo.utils import maybe_download_model

# ATTN_QAT_INFER is the FP4 attention half of the NVFP4 deploy contract. It
# resolves per arch (CUTLASS SageAttention3-FP4 on sm_120a/sm_121a, FP4 FA4
# on sm_100a/sm_103a) and logs a one-line receipt of what actually bound.
os.environ.setdefault("FASTVIDEO_ATTENTION_BACKEND", "ATTN_QAT_INFER")
os.environ.setdefault("FASTVIDEO_STAGE_LOGGING", "1")

# Inductor knobs. The first one (shape_padding=False) is mandatory on
# Blackwell to avoid a cuBLAS INVALID_VALUE crash inside pad_mm. The rest
# are the same matmul-friendliness flags the sibling LTX-2 examples use.
_inductor.shape_padding = False
_inductor.conv_1x1_as_mm = True  # treat 1x1 convolutions as matrix muls
_inductor.coordinate_descent_tuning = True
_inductor.coordinate_descent_check_all_directions = True
_inductor.epilogue_fusion = False  # do not fuse pointwise ops into matmuls

MODEL_ID = os.path.expandvars(os.path.expanduser(os.getenv("LTX23_MODEL_PATH",
                                                           "FastVideo/LTX-2.3-Distilled-Diffusers")))
OUTPUT_DIR = Path(os.getenv("LTX23_OUTPUT_DIR", "outputs_video/ltx2_3_nvfp4_t2v"))
DEFAULT_PROMPT = ("A fashion model takes a slow step forward and shifts her weight, "
                  "the soft fabric of her clothing swaying and rippling with the "
                  "motion, her hair shifting gently, soft even studio lighting on a "
                  "clean light background, elegant slow-motion runway feel.")
PROMPT = os.getenv("LTX23_T2V_PROMPT", DEFAULT_PROMPT)


def main() -> None:
    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
    model_root = maybe_download_model(MODEL_ID)
    print(f"Model:      {model_root}")
    print(f"Output dir: {OUTPUT_DIR.resolve()}")

    # Loading the pipeline config *with model_path* binds model-specific
    # tuning (notably VAE precision/decoder defaults) into the config.
    pipeline_config = PipelineConfig.from_pretrained(model_root)

    # NVFP4 linear layers on the DiT. The default purges the original BF16
    # weights of the always-FP4 linears right after conversion — a large
    # peak-memory reduction. Refine-only layers (the cross-modal AV
    # projections) always keep theirs: the base stage profile runs them
    # dense by deployment contract. retain_original_weights=True keeps
    # everything (debugging).
    pipeline_config.dit_config.quant_config = NVFP4Config()

    # fullgraph=True is supported on the NVFP4 path: the FP4 quantize step
    # is a registered custom op (fastvideo::nvfp4_quantize_fa4), so dynamo
    # traces through it without graph breaks. mode="default" — the two
    # CUDAGraph modes ("reduce-overhead" / "max-autotune") are a separate
    # opt-in, not part of this validated preset.
    torch_compile_kwargs = {
        "backend": "inductor",
        "fullgraph": True,
        "mode": "default",
        "dynamic": False,
    }

    generator = VideoGenerator.from_pretrained(
        model_root,
        num_gpus=1,
        pipeline_config=pipeline_config,
        # Compile the DiT, text encoder, and VAE — all three stages benefit,
        # and the VAE's codec submodules compile cleanly under fullgraph.
        enable_torch_compile=True,
        enable_torch_compile_text_encoder=True,
        enable_torch_compile_vae=True,
        torch_compile_kwargs=torch_compile_kwargs,
        torch_compile_kwargs_vae=torch_compile_kwargs,
        # Keep everything resident — no CPU offload for serving-style runs.
        dit_cpu_offload=False,
        text_encoder_cpu_offload=False,
        vae_cpu_offload=False,
        ltx2_vae_tiling=False,
    )

    common_kwargs = dict(
        prompt=PROMPT,
        negative_prompt="",  # distilled is CFG-free; no negative needed
        guidance_scale=1.0,  # CFG=1 for distilled
        height=1280,
        width=832,  # portrait runway aspect
        num_frames=121,
        fps=24,  # ~5s clip
        # Single-stage 8-step distilled sampling — the validated preset for
        # this checkpoint (no two-stage refine; the NVFP4 deploy contract
        # runs the distilled single-stage recipe).
        num_inference_steps=8,
        save_video=True,
    )

    try:
        # Warmup: pays cold compile + first-shape guard work, untimed.
        print("\n[warmup] compiling + generating…")
        generator.generate_video(
            output_path=str(OUTPUT_DIR / "_warmup.mp4"),
            seed=7,
            **common_kwargs,
        )
        (OUTPUT_DIR / "_warmup.mp4").unlink(missing_ok=True)

        # Measured run.
        out_path = OUTPUT_DIR / "output_ltx2_3_nvfp4_t2v.mp4"
        print(f"\n[measured] generating: {out_path}")
        t0 = time.perf_counter()
        result = generator.generate_video(
            output_path=str(out_path),
            seed=2002,
            **common_kwargs,
        )
        wall = time.perf_counter() - t0
        e2e = (result.get("e2e_latency") if isinstance(result, dict) else None) or wall
        print(f"[measured] e2e={e2e:.2f}s wall={wall:.2f}s")
    finally:
        generator.shutdown()

    # Expected receipts — verify these two lines in your own run's log:
    #
    # 1. ATTN_QAT_INFER routing receipt (logged at backend resolution; on a
    #    GB200-class part it reads):
    #
    #      ATTN_QAT_INFER resolved: arch=sm_100 kernel=flash-attention-fp4 \
    #        qk_mode=nvfp4(per-16-e4m3-sf) pv_mode=bf16 train_sim_mismatch=measured
    #
    # 2. NVFP4 weight purge receipt (logged after model conversion; N/M/X
    #    depend on the checkpoint):
    #
    #      NVFP4 weight purge receipt: purged N original bf16 weight tensors \
    #        (X.XX GiB freed); retained M (refine-only dense fallback or \
    #        retain_original_weights).


if __name__ == "__main__":
    main()

t2v_5s2_1024x1536.yaml

# LTX-2.3 distilled t2v — 5+2 two-stage at 1024x1536.
#
# Run:
#   fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_1024x1536.yaml
#
# Stage 1 denoises for 5 steps at half resolution; the latents are then
# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
generator:
  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
  engine:
    num_gpus: 1
  pipeline:
    workload_type: t2v
    preset_overrides:
      refine:
        enabled: true
        num_inference_steps: 2
        guidance_scale: 1.0
        add_noise: true
request:
  prompt: >-
    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
  sampling:
    height: 1024
    width: 1536
    num_frames: 121
    fps: 24
    guidance_scale: 1.0
    num_inference_steps: 5
  output:
    output_path: outputs/ltx2_3_t2v_5s2_1024x1536.mp4
    save_video: true

t2v_5s2_1280x832.yaml

# LTX-2.3 distilled t2v — 5+2 two-stage at 1280x832.
#
# Run:
#   fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_1280x832.yaml
#
# Stage 1 denoises for 5 steps at half resolution; the latents are then
# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
generator:
  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
  engine:
    num_gpus: 1
  pipeline:
    workload_type: t2v
    preset_overrides:
      refine:
        enabled: true
        num_inference_steps: 2
        guidance_scale: 1.0
        add_noise: true
request:
  prompt: >-
    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
  sampling:
    height: 1280
    width: 832
    num_frames: 121
    fps: 24
    guidance_scale: 1.0
    num_inference_steps: 5
  output:
    output_path: outputs/ltx2_3_t2v_5s2_1280x832.mp4
    save_video: true

t2v_5s2_512x768.yaml

# LTX-2.3 distilled t2v — 5+2 two-stage at 512x768.
#
# Run:
#   fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_512x768.yaml
#
# Stage 1 denoises for 5 steps at half resolution; the latents are then
# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
generator:
  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
  engine:
    num_gpus: 1
  pipeline:
    workload_type: t2v
    preset_overrides:
      refine:
        enabled: true
        num_inference_steps: 2
        guidance_scale: 1.0
        add_noise: true
request:
  prompt: >-
    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
  sampling:
    height: 512
    width: 768
    num_frames: 121
    fps: 24
    guidance_scale: 1.0
    num_inference_steps: 5
  output:
    output_path: outputs/ltx2_3_t2v_5s2_512x768.mp4
    save_video: true

t2v_5s2_768x1280.yaml

# LTX-2.3 distilled t2v — 5+2 two-stage at 768x1280.
#
# Run:
#   fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_768x1280.yaml
#
# Stage 1 denoises for 5 steps at half resolution; the latents are then
# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
generator:
  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
  engine:
    num_gpus: 1
  pipeline:
    workload_type: t2v
    preset_overrides:
      refine:
        enabled: true
        num_inference_steps: 2
        guidance_scale: 1.0
        add_noise: true
request:
  prompt: >-
    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
  sampling:
    height: 768
    width: 1280
    num_frames: 121
    fps: 24
    guidance_scale: 1.0
    num_inference_steps: 5
  output:
    output_path: outputs/ltx2_3_t2v_5s2_768x1280.mp4
    save_video: true

t2v_8s3_1024x1536.yaml

# LTX-2.3 distilled t2v — 8+3 two-stage at 1024x1536.
#
# Run:
#   fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_1024x1536.yaml
#
# Stage 1 denoises for 8 steps at half resolution; the latents are then
# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
generator:
  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
  engine:
    num_gpus: 1
  pipeline:
    workload_type: t2v
    preset_overrides:
      refine:
        enabled: true
        num_inference_steps: 3
        guidance_scale: 1.0
        add_noise: true
request:
  prompt: >-
    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
  sampling:
    height: 1024
    width: 1536
    num_frames: 121
    fps: 24
    guidance_scale: 1.0
    num_inference_steps: 8
  output:
    output_path: outputs/ltx2_3_t2v_8s3_1024x1536.mp4
    save_video: true

t2v_8s3_1280x832.yaml

# LTX-2.3 distilled t2v — 8+3 two-stage at 1280x832.
#
# Run:
#   fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_1280x832.yaml
#
# Stage 1 denoises for 8 steps at half resolution; the latents are then
# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
generator:
  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
  engine:
    num_gpus: 1
  pipeline:
    workload_type: t2v
    preset_overrides:
      refine:
        enabled: true
        num_inference_steps: 3
        guidance_scale: 1.0
        add_noise: true
request:
  prompt: >-
    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
  sampling:
    height: 1280
    width: 832
    num_frames: 121
    fps: 24
    guidance_scale: 1.0
    num_inference_steps: 8
  output:
    output_path: outputs/ltx2_3_t2v_8s3_1280x832.mp4
    save_video: true

t2v_8s3_512x768.yaml

# LTX-2.3 distilled t2v — 8+3 two-stage at 512x768.
#
# Run:
#   fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_512x768.yaml
#
# Stage 1 denoises for 8 steps at half resolution; the latents are then
# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
generator:
  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
  engine:
    num_gpus: 1
  pipeline:
    workload_type: t2v
    preset_overrides:
      refine:
        enabled: true
        num_inference_steps: 3
        guidance_scale: 1.0
        add_noise: true
request:
  prompt: >-
    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
  sampling:
    height: 512
    width: 768
    num_frames: 121
    fps: 24
    guidance_scale: 1.0
    num_inference_steps: 8
  output:
    output_path: outputs/ltx2_3_t2v_8s3_512x768.mp4
    save_video: true

t2v_8s3_768x1280.yaml

# LTX-2.3 distilled t2v — 8+3 two-stage at 768x1280.
#
# Run:
#   fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_768x1280.yaml
#
# Stage 1 denoises for 8 steps at half resolution; the latents are then
# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
generator:
  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
  engine:
    num_gpus: 1
  pipeline:
    workload_type: t2v
    preset_overrides:
      refine:
        enabled: true
        num_inference_steps: 3
        guidance_scale: 1.0
        add_noise: true
request:
  prompt: >-
    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
  sampling:
    height: 768
    width: 1280
    num_frames: 121
    fps: 24
    guidance_scale: 1.0
    num_inference_steps: 8
  output:
    output_path: outputs/ltx2_3_t2v_8s3_768x1280.mp4
    save_video: true