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LTX 2.5 distilled FP8+NVFP4 on RTX PRO 6000 — cinematic T2V, I2V, and keyframe interpolation with 8-step inference, synchronized audio, and GPU prompt enhancement.","readme":"## LTX 2.5 Video\n\n**Lightricks LTX 2.5 distilled FP8+NVFP4** on **RTX PRO 6000** (`pro_6000`, 96 GB VRAM).\nSupports text-to-video (T2V), image-to-video (I2V), and multi-keyframe interpolation via\n`DistilledPipeline` (two-stage: 8-step stage 1 + 3-step stage 2 refinement).\n\n| Capability | Supported |\n|------------|-----------|\n| Text-to-video (T2V) | Yes |\n| Image-to-video (I2V) | Yes — `input_image_b64` (single image) |\n| Keyframe interpolation | Yes — `keyframes` list with **2+** images |\n| Synchronized audio | Yes — `generate_audio: true` (default) |\n| Prompt enhancement | Optional — Llama 3.2 3B (SGLang) + Florence-2 for I2V/keyframe |\n| Auto duration | Yes — `auto_duration: true` lets the duration head pick the length |\n| Generated keyframes | Yes — `generated_keyframes` plants continuity anchors mid-clip |\n| Inference steps | **8 + 3** by default; override via `stage_1_sigmas` / `stage_2_sigmas` |\n| Max resolution | Up to **3840x2160** |\n| Max duration | **20 s** to 1080p, **13 s** at 1440p, **6 s** at 4K |\n\nLonger requests are not rejected — the frame count is clamped down to the tier's\nbudget, which is set so a generation finishes inside ~6 minutes.\n\n**Runtime checkpoints (split layout):**\n- **Transformer:** NVFP4 from [vonkaiser/LTX-2.5-FP8-NVFP4](https://huggingface.co/vonkaiser/LTX-2.5-FP8-NVFP4) (~21 GB)\n- **Text Encoder:** Gemma 4 12B + projections, NVFP4 torchao (~7.4 GB)\n- **VAEs, spatial upscaler, duration head:** bf16, mirrored from [Lightricks/LTX-2.5](https://huggingface.co/Lightricks/LTX-2.5)\n- **DiT keyframe embedding patch:** 8 KB weight the NVFP4 DiT declares but omits, injected at load\n\n`DistilledPipeline` uses no classifier-free guidance, so there is no `negative_prompt`,\n`guidance_scale`, or `num_inference_steps` field — the sigma schedules are the real control.\n\n**License:** [LTX-2 Community License](https://huggingface.co/Lightricks/LTX-2/blob/main/LICENSE)\n\n---\n\n### `POST /generate`\n\n| Field | Type | Default | Notes |\n|-------|------|---------|-------|\n| `prompt` | string | — | Required. Max 16 384 chars |\n| `width` | int | 768 | Snapped to **multiple of 32** |\n| `height` | int | 512 | Snapped to **multiple of 32** |\n| `num_frames` | int | 97 | Snapped to **N*8+1** |\n| `frames` | int | — | Alias for `num_frames` |\n| `duration` | float | — | Target seconds; combine with `fps` or `num_frames` |\n| `fps` | float | 24 | Output frame rate; **8-50** |\n| `auto_duration` | bool | false | Duration head picks the length; excludes the three fields above |\n| `auto_duration_min_seconds` | float | 1.0 | Lower clamp for `auto_duration` |\n| `auto_duration_max_seconds` | float | 20.0 | Upper clamp for `auto_duration` |\n| `generated_keyframes` | int \\| list | — | Continuity anchor slots; int = evenly spaced, list = frame indices |\n| `stage_1_sigmas` | list[float] | distilled 8-step | Strictly decreasing, in [0,1], ending at 0 |\n| `stage_2_sigmas` | list[float] | distilled 3-step | Same rules as `stage_1_sigmas` |\n| `seed` | int | random | 0-2^32-1 |\n| `use_random_seed` | bool | true | Set `false` with `seed` for reproducibility |\n| `input_image_b64` | string | — | Base64 PNG/JPEG for **I2V** |\n| `keyframes` | list | — | `{image_b64, frame_idx, strength?, crf?}` — **2+** entries |\n| `enhance_prompt` | bool | false | Llama 3.2 3B (+ Florence-2 for I2V/keyframe) |\n| `generate_audio` | bool | true | Synchronized audio in MP4 |\n| `return_json` | bool | false | JSON + `video_b64` instead of raw bytes |\n","public":true,"version":"04e63030-a51a-598c-98c6-0695866d38e3","tool_description":null,"slug":"vonkaiser-ltx-25-video","standard_template":null,"cords":null,"jobs":[],"cord_ref_id":"1bd2c0e7-fd53-5ddb-ba61-fdf07d55869a","created_at":"2026-09-16T17:40:36.435368Z","updated_at":"2026-09-16T17:40:36.435368Z","image":{"image_id":"12209e07-f961-52d5-98d3-f568d35f1978","name":"ltx25videogen","readme":"Lightricks LTX 2.5 distilled FP8+NVFP4 (T2V/I2V/keyframe, DistilledPipeline) + SGLang nightly (Llama 3.2 3B prompt enhancer on GPU) + Florence-2 (captioner). Weights (~35 GB) load from /cache/ltx25_checkpoints on first boot. Inference steps: 8 stage-1 + 3 stage-2 refinement (pre-distilled model).","tag":"0.5.0","public":true,"status":"built and pushed","created_at":"2026-08-13T05:31:33.653317Z","build_started_at":"2026-08-13T05:31:41.211181Z","build_completed_at":"2026-08-13T05:53:35.795968Z","user":{"username":"vonkaiser","user_id":"c2e8a78e-ba27-52d9-a2dc-3d55d96dcc69","logo_id":"907bf599-3324-4eae-b722-edf206ffaa98","created_at":"2025-02-11T17:19:50.028828Z","logo":"https://logos.chutes.ai/logos/907bf599-3324-4eae-b722-edf206ffaa98.webp"},"logo_id":null,"patch_version":"initial","chutes_version":"0.6.11.rc2","logo":null},"user":{"username":"vonkaiser","user_id":"c2e8a78e-ba27-52d9-a2dc-3d55d96dcc69","logo_id":"907bf599-3324-4eae-b722-edf206ffaa98","created_at":"2025-02-11T17:19:50.028828Z","logo":"https://logos.chutes.ai/logos/907bf599-3324-4eae-b722-edf206ffaa98.webp"},"supported_gpus":["pro_6000"],"node_selector":{"dynamic":false,"exclude":null,"include":["pro_6000"],"gpu_count":1,"min_vram_gb_per_gpu":80,"max_hourly_price_per_gpu":null,"compute_multiplier":0.4,"supported_gpus":["pro_6000"]},"invocation_count":344,"current_estimated_price":{"usd":{"hour":1.8,"second":0.0005},"tao":{"hour":0.006474638481366351,"second":1.7985106892684308e-6}},"instances":[{"instance_id":"6afdb032-05ac-42cf-8bee-3fa5835e5a21","region":"n/a","active":true,"verified":true,"last_verified_at":"2026-10-11T20:19:18.332265Z"}],"logo_id":"fd4cdfe1-d3d9-4a94-834d-63d50874bad4","openrouter":false,"chutes_version":"0.6.11.rc2","revision":null,"concurrency":3,"boost":1.0,"max_instances":null,"scaling_threshold":null,"shutdown_after_seconds":null,"preemptible":true,"allow_external_egress":true,"tee":true,"lock_modules":false,"effective_compute_multiplier":0.9,"compute_multiplier_factors":{"base":0.4,"urgency_boost":1.0,"tee":2.25},"bounty":null,"logo":"https://logos.chutes.ai/logos/fd4cdfe1-d3d9-4a94-834d-63d50874bad4.webp","hot":true},{"chute_id":"700fee6b-2cf1-519f-9222-634e8b41601a","name":"Qwen-Image-2512","tagline":"","readme":"Qwen/Qwen-Image-2512","public":true,"version":"ad2e5c60-e7ca-5642-bd61-f604967c8c26","tool_description":null,"slug":"vonkaiser-qwen-image-2512","standard_template":null,"cords":null,"jobs":[],"cord_ref_id":"595cae49-0013-5ad9-9628-a3a458b32474","created_at":"2026-05-18T19:40:54.197456Z","updated_at":"2026-05-18T20:52:45.414200Z","image":{"image_id":"76660506-8cde-5c79-ba17-a241099f7458","name":"qwen-image-2512","readme":"Runtime image for Qwen-Image-2512 (sm120/Blackwell compatible) - eager fallback for restricted Triton/ldconfig environments","tag":"0.0.2","public":false,"status":"built and pushed","created_at":"2026-05-18T20:32:43.266827Z","build_started_at":"2026-05-18T20:32:47.427318Z","build_completed_at":"2026-05-18T20:47:25.066900Z","user":{"username":"vonkaiser","user_id":"c2e8a78e-ba27-52d9-a2dc-3d55d96dcc69","logo_id":"907bf599-3324-4eae-b722-edf206ffaa98","created_at":"2025-02-11T17:19:50.028828Z","logo":"https://logos.chutes.ai/logos/907bf599-3324-4eae-b722-edf206ffaa98.webp"},"logo_id":null,"patch_version":"initial","chutes_version":"0.6.5","logo":null},"user":{"username":"vonkaiser","user_id":"c2e8a78e-ba27-52d9-a2dc-3d55d96dcc69","logo_id":"907bf599-3324-4eae-b722-edf206ffaa98","created_at":"2025-02-11T17:19:50.028828Z","logo":"https://logos.chutes.ai/logos/907bf599-3324-4eae-b722-edf206ffaa98.webp"},"supported_gpus":["pro_6000"],"node_selector":{"dynamic":false,"exclude":["mi300x","b200"],"include":["pro_6000"],"gpu_count":1,"min_vram_gb_per_gpu":80,"max_hourly_price_per_gpu":null,"compute_multiplier":0.4,"supported_gpus":["pro_6000"]},"invocation_count":311,"current_estimated_price":{"usd":{"hour":1.8,"second":0.0005},"tao":{"hour":0.006474638481366351,"second":1.7985106892684308e-6}},"instances":[{"instance_id":"08e30e02-dddf-456b-806f-e59e896f7772","region":"n/a","active":true,"verified":true,"last_verified_at":"2026-09-09T09:07:38.925872Z"}],"logo_id":null,"openrouter":false,"chutes_version":"0.6.5","revision":null,"concurrency":4,"boost":1.0,"max_instances":1,"scaling_threshold":0.75,"shutdown_after_seconds":300,"preemptible":true,"allow_external_egress":true,"tee":true,"lock_modules":false,"effective_compute_multiplier":0.9,"compute_multiplier_factors":{"base":0.4,"urgency_boost":1.0,"tee":2.25},"bounty":null,"logo":null,"hot":true},{"chute_id":"d6c66e0a-3e04-595b-825c-f3a65e50b2c7","name":"AudioDojo","tagline":"One chute, 12 models, 13 endpoints — covering text-to-speech, voice cloning, voice design, transcription, denoising, source separation, speaker verification, VAD, and language detection. Including Kokoro-82M, Qwen3-TTS 1.7B, Whisper large-v3-turbo, NVIDIA Canary-Qwen 2.5B, and NVIDIA Parakeet TDT.","readme":"# AudioDojo\n\nA full-featured audio AI toolkit running 12 models behind 13 REST endpoints — all on a single chute. Send a request to any endpoint and get results back instantly; every model is loaded in GPU memory at all times, so there are no cold-start delays between calls.\n\n---\n\n## Text-to-Speech (TTS)\n\n| Endpoint | Model | What it does |\n|----------|-------|--------------|\n| `/speak` | Kokoro-82M | **Legacy alias** — same request/response contract as the original `chutes/kokoro` chute (`text`, `voice`, `speed` → `audio/wav`). Use this path if you are migrating from the standalone Kokoro deployment without changing client code. |\n| `/tts/kokoro` | Kokoro-82M | Same Kokoro synthesis as `/speak`; preferred path for new integrations. Ultra-fast TTS with **54 built-in voices** across 9 languages. Sub-second latency. |\n| `/tts/qwen3clone` | Qwen3-TTS 1.7B | **Zero-shot voice cloning** — provide 3–6 seconds of any voice as a reference clip and the model speaks new text in that voice. |\n| `/tts/qwen3custom` | Qwen3-TTS 1.7B | **Preset speakers + emotion control** — choose from 9 speaker identities and steer delivery with free-text instructions like \"excited\", \"whispering\", \"calm and slow\". |\n| `/tts/qwen3design` | Qwen3-TTS 1.7B | **Voice design from text** — describe the voice you want in plain English (e.g. \"a warm confident male narrator with a slight British accent\") and the model creates it on the fly. No reference audio needed. |\n\n## Speech-to-Text (STT)\n\n| Endpoint | Model | What it does |\n|----------|-------|--------------|\n| `/stt/whisper` | Whisper large-v3-turbo | Fast multilingual transcription — **99 languages**, optional word-level timestamps. |\n| `/stt/canary` | NVIDIA Canary-Qwen 2.5B | High-accuracy English ASR via speech-augmented language model. Best for complex or domain-specific audio. |\n| `/stt/parakeet` | NVIDIA Parakeet TDT 0.6B v3 | Real-time English transcription with word and segment timestamps in a single pass. |\n\n## Audio Processing\n\n| Endpoint | Model | What it does |\n|----------|-------|--------------|\n| `/audio/vad` | Silero VAD | **Voice activity detection** — returns precise start/end timestamps of speech segments. |\n| `/audio/denoise` | MetricGAN+ | **Speech enhancement** — removes background noise from recordings. Returns cleaned audio. |\n| `/audio/separate` | SepFormer | **Source separation** — isolates speech from music/noise. Returns the separated vocal track. |\n| `/audio/verifyspeaker` | ECAPA-TDNN | **Speaker verification** — compares two audio clips and returns a similarity score + same-speaker decision. |\n| `/audio/detectlanguage` | Whisper | **Language detection** — identifies the spoken language and returns a confidence score. |\n| `/audio/diarize` | — | Speaker diarization (identifies who spoke when). |\n\n---\n\n## Legacy Kokoro (`POST /speak`)\n\nDrop-in replacement for clients that called the old **kokoro** chute:\n\n- **Method/path:** `POST /speak`\n- **Body:** `{\"text\": \"...\", \"voice\": \"af_heart\", \"speed\": 1.0}` — `voice` and `speed` are optional (defaults: `af_heart`, `1.0`)\n- **Response:** `audio/wav` (24 kHz), same as before\n- **Implementation:** identical Kokoro-82M pipeline as `POST /tts/kokoro`; only the URL differs\n\n```bash\n# Legacy kokoro clients — no code changes except the chute base URL\ncurl -X POST https://YOUR_CHUTE_BASE/speak \\\n  -H 'Authorization: Bearer YOUR_API_KEY' \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"text\": \"Hello world!\", \"voice\": \"af_heart\", \"speed\": 1.0}' \\\n  -o output.wav\n```\n\n## Quick Start\n\nTTS endpoints accept JSON (`text`, optional `voice`/`speed`) and return `audio/wav`. STT and utility endpoints use base64-encoded audio and return JSON.\n\n```bash\n# Text-to-Speech via /tts/kokoro (returns audio/wav)\ncurl -X POST https://YOUR_CHUTE_BASE/tts/kokoro \\\n  -H 'Authorization: Bearer YOUR_API_KEY' \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"text\": \"Hello world!\", \"voice\": \"af_heart\", \"speed\": 1.0}' \\\n  -o output.wav\n\n# Speech-to-Text (returns JSON)\ncurl -X POST https://ttsstt-audio-megachute/stt/whisper \\\n  -H 'Authorization: Bearer YOUR_API_KEY' \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"audio_b64\": \"<base64-encoded-audio>\", \"return_timestamps\": true}'\n```\n\n## Available Kokoro Voices\n\n54 voices with naming convention `<language><gender>_<name>`:\n\n- **American English** — af_heart, af_bella, af_jessica, af_nicole, af_nova, af_river, af_sarah, af_sky, am_adam, am_echo, am_eric, am_liam, am_michael, am_onyx, am_puck, am_santa, and more\n- **British English** — bf_alice, bf_emma, bf_isabella, bf_lily, bm_daniel, bm_fable, bm_george, bm_lewis\n- **Spanish** — ef_dora, em_alex, em_santa\n- **French** — ff_siwis\n- **Hindi** — hf_alpha, hf_beta, hm_omega, hm_psi\n- **Italian** — if_sara, im_nicola\n- **Japanese** — jf_alpha, jf_gongitsune, jf_nezumi, jf_tebukuro, jm_kumo\n- **Portuguese** — pf_dora, pm_alex, pm_santa\n- **Chinese** — zf_xiaobei, zf_xiaoni, zf_xiaoxiao, zf_xiaoyi, zm_yunjian, zm_yunxi, zm_yunyang\n\n## Qwen3 Custom Speakers\n\nVivian, Serena, Uncle_Fu, Dylan, Eric, Ryan, Aiden, Ono_Anna, Sohee\n","public":true,"version":"35d475ec-e9ae-5e3f-8a5c-edc710918eba","tool_description":null,"slug":"vonkaiser-audiodojo","standard_template":null,"cords":null,"jobs":[],"cord_ref_id":"b8ea6406-4f36-5c3f-a10e-8fc05a606243","created_at":"2026-08-19T18:11:01.099631Z","updated_at":"2026-08-24T15:45:21.608738Z","image":{"image_id":"820439bb-78f2-5bc4-9ede-fdc1dc4fc0b9","name":"ttssttaudiostack","readme":"## Multi-model audio stack (12 models)\n\nTTS: Kokoro-82M, Qwen3-TTS 1.7B Clone/Custom/Design (CUDA-graph accelerated)\nSTT: Whisper-large-v3-turbo, Canary-Qwen 2.5B, Parakeet TDT 0.6B v3\nUtility: Silero VAD, ECAPA-TDNN, MetricGAN+, SepFormer, Lang-ID 107\n\nAll models loaded into GPU memory at startup. Requests queue and execute sequentially.","tag":"0.1.10","public":true,"status":"built and 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