Ltxv2 Video

LTX-2 / LTX-2.3 Video Workflows

Version naming (read this first)

There is no "LTX 3.2" or "LTX2.3" as separate products — the user's shorthand refers to Lightricks LTX-2.3, a point release of the LTX-2 family. The lineage is:

• LTX-Video (2024) — first text-to-video model from Lightricks.
• LTX-2 / LTX-V2 (Oct 2025) — 19B-class DiT audio-video foundation model. Bundled checkpoint ltx-2-19b-distilled.safetensors, Gemma 3 12B text encoder.
• LTX-2.3 (released ~March 2026) — 22B-parameter DiT update. Rebuilt VAE (sharper textures/faces/hair/text), ~4x larger text connector (text projection) for prompt adherence, native 9:16 portrait, LoRA support, HiFi-GAN vocoder for cleaner synchronized audio, up to 4K@50fps / ~20s clips. Apache 2.0. Distributed primarily as GGUF UNets (community quants) plus separate VAE / text-encoder / text-projection files — NOT a single bundled checkpoint like LTX-2.

When the user says "LTX3.2" / "LTX2.3", treat it as LTX-2.3. This skill covers both LTX-2 (bundled checkpoint path) and LTX-2.3 (GGUF UNet path).

⭐ Render-verified correct setup (read this FIRST — 2026-06-19)

The GGUF-UNet + DualCLIPLoader + gemma_3_12B_it_fp4_mixed path documented later in this skill (the Aitrepreneur installer path) produces soft/mushy video with inaccurate faces and eyes. It runs, but it is NOT the quality path. The setup below is the official Comfy-Org template, render-proven sharp (1280×704, accurate faces, synchronized 48 kHz stereo audio).

Models (exact, render-verified)

Node stack (the right one)

• LTXAVTextEncoderLoader (CORE, comfy_extras/nodes_lt_audio.py) — loads gemma + the full checkpoint together via comfy.sd.load_clip([gemma, ckpt], type=LTXV). This is the audio-video encoder driving both video and audio/voice. Do NOT use DualCLIPLoader(type=ltxv) + a separate ltx-2.3_text_projection file — that is the legacy video-only path and yields mush.
• Gemma abliterated LoRA via a LoraLoader (CLIP LoRA) on the encoder output → CLIPTextEncode.
• Two-stage: base sample (~768×512) → LTXVLatentUpsampler (×2 spatial, uses the upscaler model + the checkpoint VAE) → refine sample → 1280×704 output. The upscale is the sharpness. A single-stage graph is visibly softer.
• Guider: the Comfy-Org template uses plain CFGGuider cfg=1 (distilled); the LTXVideo repo example uses MultimodalGuider + GuiderParameters (separate AUDIO/VIDEO) + ClownSampler_Beta (RES4LYF). Both produce sharp output — the LoRAs + two-stage matter more than the guider.
• ffmpeg is required for the final mux: <comfy-venv>/python -m pip install imageio-ffmpeg, then reboot. CreateVideo/SaveVideo/VHS_VideoCombine fail with ffmpeg ... could not be found otherwise.

Custom nodes

ComfyUI-LTXVideo (LTXV* nodes, MultimodalGuider, GuiderParameters, LTXVPreprocess, LTXVTiledVAEDecode, GemmaAPITextEncode, LTXFloatToInt) + RES4LYF (ClownSampler_Beta, only for the repo-example sampler). LTXAVTextEncoderLoader, ResizeImageMaskNode, CreateVideo, SaveVideo, ManualSigmas, LTXVScheduler, the Primitive* nodes are all CORE ComfyUI.

Quality troubleshooting (symptom → cause → fix)

• Mushy/garbage, no clear subject → empty positive prompt, or DualCLIPLoader+projection text encoder. Fix: set a prompt; use LTXAVTextEncoderLoader.
• Coherent but soft/blurry, faces & eyes slightly wrong → no two-stage upscale and/or missing the gemma abliterated LoRA and/or the old distilled LoRA. Fix: full two-stage template + both LoRAs above.
• status: success but no video file / outputs only has a math or text node → the output node (SaveVideo/VHS) failed validation and was silently dropped; the graph short-circuited. Check the ComfyUI log for Failed to validate prompt for output N and fix that node (missing ffmpeg, a broken connection, a model-not-in-list).
• DualCLIPLoader reshape [15360,1920] invalid for input 27582328 → wrong/truncated gemma → use gemma_3_12B_it_fp8_scaled.
• LatentUpscaleModelLoader: ...x2-1.0 not in list → reference ...x2-1.1.
• SaveVideo writes to a subfolder (video/<prefix>_NNNNN.mp4) — its history outputs entry isn't under images/videos/gifs, so a naive "find the video" check misses it. Look on disk under output/video/.

MCP UI→API converter gotchas (src/services/workflow-converter.ts)

The official template exercised several convertUiToApi gaps (all now fixed — keep in mind if a template still mis-converts):

• V3 dynamic combos (COMFY_DYNAMICCOMBO_V3, e.g. ResizeImageMaskNode.resize_type): each selected option's nested input must be keyed <combo>.<nested> (e.g. resize_type.longer_size, resize_type.width), NOT flat — ComfyUI rebuilds the nested dict via dynamic_paths/finalize_prefix. A flat key is rejected required_input_missing.
• Reroute is virtual — its connections must be passed through (consumer resolves to the Reroute's input), else everything downstream dangles and the graph short-circuits.
• VHS_VideoCombine stores widgets_values as a name→value object, not a positional array.
• Typed Primitive* nodes (PrimitiveInt/Float/Boolean/StringMultiline) are real executable nodes — keep them as link sources, don't bake their values into a consumer's widgets_values by index (mis-positions V3 nested inputs).

Pack

packs/ltx-2.3-txt2vid (and the i2v/flf/extender variants) should be built on this official two-stage template. For a no-input-file T2V pack, set the template's bypass_i2v / "Switch to Text to Video?" boolean true and feed the I2V image input a blank EmptyImage (discarded at runtime but still validates).

Source note: the install scripts below pull LTX-2.3 files from a third-party mirror repo huggingface.co/Aitrepreneur/FLX, not the official Lightricks/LTX-2.3 repo. The official weights live at huggingface.co/Lightricks/LTX-2.3. Filenames/quants match what those scripts download.

Overview

LTX-2 is a DiT-based video foundation model from Lightricks. It uses a Gemma 3 12B text encoder and supports both text-to-video (T2V) and image-to-video (I2V). Key features:

• Distilled model for fast 8-step generation; dev model for higher quality (~20+ steps)
• Two-stage pipeline: Generate at low res, then 2x spatial upscale in latent space
• Camera control LoRAs for cinematic movements
• Synchronized audio-video generation in a single pass (LTX-2.3 audio VAE + HiFi-GAN vocoder)
• GGUF quantization (LTX-2.3) for low-VRAM local inference via ComfyUI-GGUF

Models

LTX-2 (bundled checkpoint path)

Loading note (LTX-2): The bundled checkpoint contains the VAE internally. The Gemma 3 text encoder loads separately via CLIPLoader with type: "ltxv" pointing at text_encoders/.

LTX-2.3 (GGUF UNet path — current install)

LTX-2.3 ships as a separate GGUF UNet + standalone VAE + text encoder + text projection, not a single bundled checkpoint. The install scripts (see below) place files like this:

Loading note (LTX-2.3): Because the UNet is a bare GGUF, the VAE no longer comes "for free" with a checkpoint — load LTX23_video_vae_bf16.safetensors explicitly with VAELoader. Place GGUF UNets in models/unet/ and use the GGUF Unet loader. Some community 2.3 workflows pair gemma_3_12B_it.safetensors (full) instead of the FP4 mixed file; the installer uses the FP4 mixed one.

Install scripts (Step-by-step source of truth)

Three installers (by "Aitrepreneur") were used; they all download from HF = https://huggingface.co/Aitrepreneur/FLX/resolve/main:

• LTX-2-3-MODELS-NODES_INSTALL-V2.bat — run from ...\ComfyUI_windows_portable\ComfyUI\. Locks the current pip env into a constraints file, sanitizes each node's requirements.txt (strips torch/file-wheels/extra-index lines), clones nodes, downloads models. Flags: /update, /force, /dryrun, /restore.
• LTX-2-3-ULTRA-COMFYUI-MANAGER_AUTO_INSTALL-V2.bat — full one-click: downloads ComfyUI portable v0.22.0, installs 7-Zip/Git if missing, clones the same nodes, downloads the same models, then launches ComfyUI.
• LTX-2-3-AUTO_INSTALL-RUNPOD-V2.sh — Linux/RunPod. Recreates a clean venv, pins torch 2.4.0 / torchvision 0.19.0 / torchaudio 2.4.0 / xformers 0.0.27.post2 on cu121, transformers 4.51.3, tokenizers >=0.21,<0.22, timm 1.0.15. Pins ComfyUI-LTXVideo to commit cd5d371518afb07d6b3641be8012f644f25269fc for workflow compatibility, and verifies the LTXVideo import at the end.

Exact model download URLs (all ?download=true from the FLX mirror), grouped by target folder:

models/text_encoders/ltx-2.3_text_projection_bf16.safetensors
models/text_encoders/gemma_3_12B_it_fp4_mixed.safetensors
models/vae/LTX23_video_vae_bf16.safetensors
models/vae/LTX23_audio_vae_bf16.safetensors
models/unet/ltx-2.3-22b-dev-<Q4_K_S|Q5_K_S|Q8_0>.gguf
models/latent_upscale_models/ltx-2.3-spatial-upscaler-x2-1.1.safetensors
models/loras/ltx-2.3-22b-distilled-lora-384-1.1.safetensors
models/loras/ltx-2-19b-ic-lora-detailer.safetensors

Custom nodes cloned by all three scripts:

LoRAs (Installed)

Concept/Style LoRAs (Installed)

Located in loras/LTXV2/:

• style/PLORAV7_LTX_000010500.safetensors
• concept/head_swap_v1_13500_first_frame.safetensors
• concept/LTX-2 - Better Female Nudity.safetensors
• action/LTX2-i2v-OralSuite.safetensors
• action/LTX2-i2v-SexThrust.safetensors
• And more in concept/ and action/ subfolders

Key Nodes

LTXVConditioning

Binds text conditioning with frame rate information:

{
  "class_type": "LTXVConditioning",
  "inputs": {
    "positive": ["<clip_text_encode>", 0],
    "negative": ["<clip_text_encode_neg>", 0],
    "frame_rate": 25
  }
}

EmptyLTXVLatentVideo

Creates the initial video latent (for T2V):

{
  "class_type": "EmptyLTXVLatentVideo",
  "inputs": {
    "width": 768,
    "height": 512,
    "length": 97,
    "batch_size": 1
  }
}

Frame count constraint: Must be 8n + 1 (9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, 97, 105, 113, 121).

LTXVScheduler

Dedicated sigma schedule for LTX-V2 latent space:

{
  "class_type": "LTXVScheduler",
  "inputs": {
    "steps": 8,
    "max_shift": 2.05,
    "base_shift": 0.95,
    "stretch": true,
    "terminal": 0.1
  }
}

Connect the optional latent input for latent-aware shift scaling.

Feeding a prior stage's output into I2V (e.g. Krea2 image → LTX video). The LoadImage that feeds LTXVImgToVideo.image needs the source frame registered as a ComfyUI INPUT. When that frame is an OUTPUT from an earlier stage, call stage_output_as_input with its { filename, subfolder?, type? } and drop the returned input filename into LoadImage. (For a file already on local disk, upload_image.) NEVER copy the output file into, or guess, a filesystem input/ path — ComfyUI's input/output dirs may be CUSTOM (--input-directory / --output-directory), so a guessed path makes LoadImage reject the file (Invalid image file) and wastes the render. stage_output_as_input goes through the server API (/view → /upload/image) and resolves the real dirs correctly.

VERIFY A VIDEO RENDER VIA THE FILESYSTEM, NOT /history. VHS_VideoCombine (and similar video nodes) write the .mp4 but frequently do NOT register the output in ComfyUI's /history — the prompt shows done with an empty outputs map and no error. Do NOT conclude the render "silently dropped" from get_history / get_job_status alone. Confirm the file with list_output_images (it now lists videos too, with kind: "video") — match the filename_prefix (e.g. ltxv2_…​.mp4) and check the mtime is fresh — then chain it into the next stage with stage_output_as_input.

LTXVImgToVideo (For I2V)

All-in-one node that encodes image, creates latent, and wraps conditioning:

{
  "class_type": "LTXVImgToVideo",
  "inputs": {
    "positive": ["<conditioning>", 0],
    "negative": ["<conditioning>", 0],
    "vae": ["<checkpoint>", 2],
    "image": ["<load_image>", 0],
    "width": 768,
    "height": 512,
    "length": 97,
    "batch_size": 1,
    "strength": 0.6
  }
}

Gotcha — strength controls motion; DON'T set it to 1.0. LTXVImgToVideo.strength is how strongly the output adheres to the start image: higher = more adherence = LESS motion. Setting it to 1.0 pins every frame to the start image → a FROZEN i2v with ZERO motion (the storyboard frames come out basically identical). Keep the verified value ~0.6 (as in the example above) for proper motion. If a generated i2v clip shows little/no motion, the FIRST thing to check is that strength wasn't bumped toward 1.0.

LTXVLatentUpsampler (For Two-Stage Upscale)

{
  "class_type": "LTXVLatentUpsampler",
  "inputs": {
    "latent": ["<sampler_output>", 0],
    "upscale_model": ["<upscale_loader>", 0]
  }
}

Requires LatentUpscaleModelLoader. Use ltx-2.3-spatial-upscaler-x2-1.1.safetensors for LTX-2.3 (or ltx-2-spatial-upscaler-x2-1.0.safetensors for LTX-2).

Sampler Settings

Distilled Model (Installed)

Uses SamplerCustomAdvanced with manual sigmas, NOT standard KSampler:

Stage 1 sigmas (via LTXVScheduler): max_shift=2.05, base_shift=0.95, stretch=true, terminal=0.1

Stage 2 sigmas (manual, for upscale refinement): 0.909375, 0.725, 0.421875, 0.0

Base Model (If Using Distilled LoRA on Base)

Resolution and Frame Count

Resolutions (Must be multiples of 32)

Start at lower resolution for Stage 1 to manage VRAM, then upscale.

Frame Count (8n + 1)

Frame Rate

Standard: 25 fps (conditioned via LTXVConditioning). 24 and 30 fps also supported.

Pipeline Flow: T2V Distilled

CheckpointLoaderSimple → MODEL + VAE
CLIPLoader (ltxv, gemma_3_12B_it_fp4_mixed) → CLIP
  ├─ CLIPTextEncode (positive) → CONDITIONING
  └─ CLIPTextEncode (negative) → CONDITIONING

LTXVConditioning (positive, negative, frame_rate=25) → pos/neg CONDITIONING
EmptyLTXVLatentVideo (768x512, 121 frames) → LATENT
LTXVScheduler (steps=8, max_shift=2.05, base_shift=0.95) → SIGMAS

SamplerCustomAdvanced (model, sigmas, positive, negative, latent)
  → Stage 1 LATENT

[Optional: LTXVLatentUpsampler → 2x LATENT → SamplerCustomAdvanced Stage 2]

VAEDecode (or LTXVSpatioTemporalTiledVAEDecode for VRAM savings) → IMAGE
VHS_VideoCombine (or CreateVideo + SaveVideo) → MP4

Complete Workflow: T2V Distilled (8-Step)

{
  "1": { "class_type": "CheckpointLoaderSimple", "inputs": { "ckpt_name": "ltx-2-19b-distilled.safetensors" }},
  "2": { "class_type": "CLIPLoader", "inputs": { "clip_name": "gemma_3_12B_it_fp4_mixed.safetensors", "type": "ltxv" }},
  "3": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["2", 0], "text": "<positive prompt>" }},
  "4": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["2", 0], "text": "" }},
  "5": { "class_type": "LTXVConditioning", "inputs": {
    "positive": ["3", 0], "negative": ["4", 0], "frame_rate": 25
  }},
  "6": { "class_type": "EmptyLTXVLatentVideo", "inputs": {
    "width": 768, "height": 512, "length": 121, "batch_size": 1
  }},
  "7": { "class_type": "LTXVScheduler", "inputs": {
    "steps": 8, "max_shift": 2.05, "base_shift": 0.95,
    "stretch": true, "terminal": 0.1, "latent": ["6", 0]
  }},
  "8": { "class_type": "KSamplerSelect", "inputs": { "sampler_name": "euler" }},
  "9": { "class_type": "SamplerCustomAdvanced", "inputs": {
    "model": ["1", 0],
    "positive": ["5", 0],
    "negative": ["5", 1],
    "sigmas": ["7", 0],
    "latent_image": ["6", 0],
    "noise": ["10", 0],
    "sampler": ["8", 0],
    "guider": ["11", 0]
  }},
  "10": { "class_type": "RandomNoise", "inputs": { "noise_seed": 42 }},
  "11": { "class_type": "CFGGuider", "inputs": {
    "model": ["1", 0],
    "positive": ["5", 0],
    "negative": ["5", 1],
    "cfg": 1.0
  }},
  "12": { "class_type": "VAEDecode", "inputs": { "samples": ["9", 0], "vae": ["1", 2] }},
  "13": { "class_type": "VHS_VideoCombine", "inputs": {
    "images": ["12", 0], "frame_rate": 25, "loop_count": 0,
    "filename_prefix": "ltxv2", "format": "video/h264-mp4",
    "pingpong": false, "save_output": true,
    "pix_fmt": "yuv420p", "crf": 19, "save_metadata": true, "trim_to_audio": false
  }}
}

Alternative simple output (built-in nodes instead of VHS):

{
  "12": { "class_type": "VAEDecode", "inputs": { "samples": ["9", 0], "vae": ["1", 2] }},
  "13": { "class_type": "CreateVideo", "inputs": { "images": ["12", 0], "fps": 25 }},
  "14": { "class_type": "SaveVideo", "inputs": { "video": ["13", 0], "filename_prefix": "video/ltxv2", "format": "auto", "codec": "auto" }}
}

Complete Workflow: LTX-2.3 GGUF (dev, T2V)

The LTX-2.3 path differs from LTX-2 in three places: the model is a GGUF UNet loaded with UnetLoaderGGUF (no CheckpointLoaderSimple), the VAE is loaded separately with VAELoader, and the dev model wants more steps (~20+) at low CFG. Everything downstream (LTXVConditioning, EmptyLTXVLatentVideo, LTXVScheduler, SamplerCustomAdvanced) is the same.

{
  "1": { "class_type": "UnetLoaderGGUF", "inputs": { "unet_name": "ltx-2.3-22b-dev-Q8_0.gguf" }},
  "2": { "class_type": "VAELoader", "inputs": { "vae_name": "LTX23_video_vae_bf16.safetensors" }},
  "3": { "class_type": "CLIPLoader", "inputs": { "clip_name": "gemma_3_12B_it_fp4_mixed.safetensors", "type": "ltxv" }},
  "4": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["3", 0], "text": "<positive prompt>" }},
  "5": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["3", 0], "text": "" }},
  "6": { "class_type": "LTXVConditioning", "inputs": {
    "positive": ["4", 0], "negative": ["5", 0], "frame_rate": 25
  }},
  "7": { "class_type": "EmptyLTXVLatentVideo", "inputs": {
    "width": 768, "height": 512, "length": 121, "batch_size": 1
  }},
  "8": { "class_type": "LTXVScheduler", "inputs": {
    "steps": 20, "max_shift": 2.05, "base_shift": 0.95,
    "stretch": true, "terminal": 0.1, "latent": ["7", 0]
  }},
  "9": { "class_type": "KSamplerSelect", "inputs": { "sampler_name": "euler" }},
  "10": { "class_type": "RandomNoise", "inputs": { "noise_seed": 42 }},
  "11": { "class_type": "CFGGuider", "inputs": {
    "model": ["1", 0], "positive": ["6", 0], "negative": ["6", 1], "cfg": 3.0
  }},
  "12": { "class_type": "SamplerCustomAdvanced", "inputs": {
    "model": ["1", 0], "positive": ["6", 0], "negative": ["6", 1],
    "sigmas": ["8", 0], "latent_image": ["7", 0],
    "noise": ["10", 0], "sampler": ["9", 0], "guider": ["11", 0]
  }},
  "13": { "class_type": "VAEDecode", "inputs": { "samples": ["12", 0], "vae": ["2", 0] }},
  "14": { "class_type": "CreateVideo", "inputs": { "images": ["13", 0], "fps": 25 }},
  "15": { "class_type": "SaveVideo", "inputs": { "video": ["14", 0], "filename_prefix": "video/ltxv23", "format": "auto", "codec": "auto" }}
}

For the distilled 2.3 path, apply ltx-2.3-22b-distilled-lora-384-1.1.safetensors to the GGUF UNet with LoraLoaderModelOnly and drop steps to 8, cfg 1.0 (same distilled settings as LTX-2). Note the VAE comes from node ["2", 0] (the separate VAELoader), not from the model loader.

Camera Control LoRAs

Seven official camera control LoRAs from Lightricks:

Usage: Apply with LoraLoaderModelOnly at strength 1.0. Do NOT describe camera movement in your prompt — the LoRA handles it.

{
  "class_type": "LoraLoaderModelOnly",
  "inputs": {
    "model": ["<checkpoint>", 0],
    "lora_name": "ltx-2-19b-lora-camera-control-dolly-left.safetensors",
    "strength_model": 1.0
  }
}

Cannot combine camera control LoRA with IC-LoRA (canny/depth/pose) in the same generation.

Concept/Style LoRAs

Apply with LoraLoaderModelOnly. Typical strength: 0.5–1.0.

{
  "class_type": "LoraLoaderModelOnly",
  "inputs": {
    "model": ["<checkpoint_or_camera_lora>", 0],
    "lora_name": "LTXV2\\concept\\LTX-2 - Better Female Nudity.safetensors",
    "strength_model": 0.8
  }
}

Concept/style LoRAs CAN be stacked with camera control LoRAs.

VRAM Considerations

VRAM warnings from MEMORY.md: "LTXV2 can OOM on 24GB — suggest FP8 quantized models or --lowvram"

Tips for 24GB GPUs

1. Use VAEDecodeTiled or LTXVSpatioTemporalTiledVAEDecode instead of standard VAEDecode
2. Start at 768x512 resolution, upscale in Stage 2
3. Use FP4 Gemma text encoder (installed)
4. For LTX-2.3, pick the GGUF quant to match VRAM: Q4_K_S (<12GB), Q5_K_S (12–16GB), Q8_0 (24GB+). The dev GGUF needs ~20+ steps; the distilled LoRA path runs ~8 steps
5. Always clear_vram before switching to LTX-V2 from another model family
6. Reduce frame count to 81 or 49 if OOM persists

Prompt Style

Natural language descriptions. Be specific about motion, camera angles, and temporal progression:

Good: "A woman with flowing auburn hair walks through a sun-dappled forest, leaves falling gently around her, soft golden hour lighting, cinematic depth of field"
Bad: "woman, forest, walking"

Describe the entire scene progression, not just a single moment. Include lighting, mood, and motion cues.

Two-Stage Upscale Pattern

For production quality, generate at low resolution then upscale:

1. Stage 1: Generate at 768x512, 121 frames, 8 steps (distilled)
2. Upscale: LTXVLatentUpsampler (2x spatial) → 1536x1024
3. Stage 2: Resample the upscaled latent with 3-4 steps at CFG 1.0
4. Decode: Use tiled VAE decode for the larger resolution

This requires the spatial upscaler model in models/latent_upscale_models/: ltx-2.3-spatial-upscaler-x2-1.1.safetensors (LTX-2.3) or ltx-2-spatial-upscaler-x2-1.0.safetensors (LTX-2).

Using alternate / GGUF base models (incl. the "sulphur" model)

You can swap the LTX UNet for any LTX-2.3-compatible base model. The most-asked-about one is Sulphur 2 (the user's "sulphur2Base_dev.safetensors" — see name note below).

What Sulphur 2 actually is (verified June 2026)

• It exists and is real. Sulphur 2 is an uncensored, realism-leaning finetune/derivative of LTX-2.3 (22B DiT), marketed as a drop-in replacement inside existing LTX-2.3 ComfyUI graphs (T2V + I2V + the other 2.3 formats). It is NOT its own architecture and is not LTX-2 (19B) compatible — it targets the LTX-2.3 stack (2.3 VAE + Gemma 3 text encoder + 2.3 text projection).
• Filename caveat: there is no file literally named sulphur2Base_dev.safetensors. The real base checkpoints are sulphur_dev_bf16.safetensors (46 GB) and sulphur_dev_fp8mixed.safetensors (29 GB). There is also a distilled variant (sulphur_distil_bf16.safetensors) and a LoRA (sulphur_lora_rank_768.safetensors). Treat "sulphur2Base_dev" as the user's shorthand for the Sulphur 2 base dev checkpoint.
• GGUF version: confirmed. vantagewithai/Sulphur-2-Base-GGUF hosts sulphur_dev-<quant>.gguf for Q3_K_S/M, Q4_0/1/K_S/K_M, Q5_0/1/K_S/K_M, Q6_K, Q8_0 (~10–23 GB). There is also a Civitai/Sulphur-2-distilled-fp8 and Civitai listings ("Sulphur 2 Base", "Rebels Sulphur 2 GGUF").
• Hosting: HF SulphurAI/Sulphur-2-base (safetensors + a bundled Qwen-based prompt-enhancer GGUF), HF vantagewithai/Sulphur-2-Base-GGUF (the GGUF quants), and Civitai mirrors. Uncensored open weights — in scope to document; nothing here is fabricated, but verify the exact repo/license yourself before downloading.

How to load it (it slots straight into the LTX-2.3 GGUF workflow above)

The GGUF quant is just a different UNet — load it with the same UnetLoaderGGUF node, keep the rest of the 2.3 graph identical:

1. Put sulphur_dev-Q8_0.gguf (or your chosen quant) in models/unet/.
2. In the LTX-2.3 GGUF workflow above, change node "1":

   "1": { "class_type": "UnetLoaderGGUF", "inputs": { "unet_name": "sulphur_dev-Q8_0.gguf" }}
   

3. Keep the same LTX-2.3 companions: VAELoader → LTX23_video_vae_bf16.safetensors, CLIPLoader (type=ltxv) → gemma_3_12B_it_fp4_mixed.safetensors, plus ltx-2.3_text_projection_bf16.safetensors. These must match the LTX-2.3 architecture — do not pair it with LTX-2 (19B) VAE/encoder.
4. For the bf16/fp8 safetensors (non-GGUF) variants, load with the LTX checkpoint/diffusion-model loader the workflow uses for the safetensors path (Lightricks recommends the native LTX Video nodes documented at docs.ltx.video, not the auto-generated Diffusers snippet) rather than UnetLoaderGGUF.
5. Obey the same constraints as any LTX-2.3 gen: frame count 8n+1, resolution multiples of 32, LTXVConditioning frame_rate, dev model ~20+ steps / distilled ~8 steps.

General rule for ANY alternate LTX base model

To verify a third-party model is usable before wiring it up:

• Confirm the architecture/version it was trained on (LTX-2 19B vs LTX-2.3 22B). Mixing a 2.3 UNet with a 2.0 VAE/encoder will fail or produce garbage.
• For GGUF: requires the ComfyUI-GGUF custom node (installed by the scripts), file in models/unet/, loaded via UnetLoaderGGUF. Match the correct VAE + text encoder + text projection for that LTX version.
• For safetensors finetunes: load like the matching official checkpoint, keep the official VAE/encoder of the same version.
• If you only have a LoRA (e.g. sulphur_lora_rank_768.safetensors), apply it to the matching base UNet with LoraLoaderModelOnly instead of swapping the whole model.

Troubleshooting

LTXVideo "kornia" import error (pad ImportError)

Symptom: ComfyUI-LTXVideo fails to load with an ImportError from kornia.geometry.transform.pyramid — pad can no longer be imported. This happens with kornia 0.8.3+, which stopped exporting pad from that module.

What the fix does (FIX-LTXVIDEO-KORNIA.bat, run from the ComfyUI_windows_portable folder): it patches ComfyUI/custom_nodes/ComfyUI-LTXVideo/pyramid_blending.py:

1. Backs the file up to pyramid_blending.py.bak_kornia_fix.
2. Removes the broken pad, line from the from kornia.geometry.transform.pyramid import ( ... ) block.
3. Inserts a compatibility shim right after import torch.nn.functional as F:

   # Compatibility fix for Kornia 0.8.3+ where pad is no longer exported here
   pad = F.pad
   

4. Verifies pad = F.pad is present and the broken import is gone.

Manual equivalent if you don't run the .bat — edit pyramid_blending.py: delete pad, from the kornia import list and add pad = F.pad after the import torch.nn.functional as F line, then restart ComfyUI. (Alternatively, pin kornia to a pre-0.8.3 release, but the patch is the lighter-touch fix and is what the install set ships.)

LTXVideo version / workflow mismatch

The RunPod installer pins ComfyUI-LTXVideo to commit cd5d371518afb07d6b3641be8012f644f25269fc for workflow compatibility. If 2.3 workflows error on the latest LTXVideo, check out that commit. Torch is pinned to 2.4.0 + cu121; do not let a node's requirements.txt upgrade torch (the installers sanitize requirements to prevent this).