LTX2.3 10Eros GGUF
GGUF Version of LTX2.3-10Eros for ComfyUI
Source du modèle
- Type
- LoRA, Text model
- Taille du fichier
- 9,63–21,2 GB
- Modèle de base
- TenStrip/LTX2.3-10Eros
- Version
- vRevision 177b526
- Mis à jour
- 4 oct. 2026
- Éditeur
- @vantagewithai
- Téléchargements
- 12,1 k
- Tâches
- Image en vidéo
- Source
- Hugging Face
Description de la source
GGUF Version of LTX2.3-10Eros for ComfyUI
Original model Link: https://huggingface.co/TenStrip/LTX2.3-10Eros
Watch us at Youtube: @VantageWithAI
- For v1.2 follow this link https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.2-GGUF
- For v1.3 follow this link https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.3-GGUF
- For v1.4 follow this link https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.4-GGUF
- For v1.5 follow this link https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.5-GGUF 10 Eros
Nodes: https://github.com/TenStrip/10S-Comfy-nodes
Reliant on https://huggingface.co/SulphurAI/Sulphur-2-base This is a different merge attempt for ideal I2V use. It uses layer scaled merges of different steps, it's not a straight weight merge. It behaves much nicer than lora load and respects prompt. Prompt should be enhanced, LTX has very little self reasoning and input when it is conditioned, first frame and all following motions, evolutions, and audio must be commanded-you will get nothing if you don't ask it.
!!! Larger distilled Loras will harm the model's fine tune, try the cond_safe ones: https://huggingface.co/TenStrip/LTX2.3_Distilled_Lora_1.1_Experiments/tree/main
For prompt enhancement, try this foreword in Grok or Uncensored LLM:
Generate a video scene script with a description based on the attached image for an LLM that has a tokenizer that uses interleaved attention to support long-context understanding that is fed into a multimodal video model. Strict specification, follow up to the word: No timestamps. No unnecessary embellishment. Output only plain English text and make it a copy box.
First, describe the image initial scene in concise natural language; subject(s), subject(s) appearance, subject(s) composition and pose, background, and context.
Next, formulate a naturally evolving scenario that would take place describing every moving body part, composition change, and manipulation from the uploaded initial frame that would be reflected in the video models post-latent evolution output. If the image is explicit or sexual in nature, use full anatomical terminology and spice it up slightly with visually representable erotic themes.
Center the prompt around this basic idea: [ concept ]
interweave this dialogue or sound concept into the scene with descriptions of voice tone followed by the lines delivered in quotations, in a temporal sequence between or during motions. Dialogue should be concise and non-rambling as it will take away from video quality: [ dialogue ]
Inside that prompt describe only notable audio and audio queues, both normal and explicit; background noise as well as foley and natural sounds. In a temporal sequence paired with coinciding motions. In the case of absent dialogue or soundscapes and only if background music is fitting; describe a fitting genre and melodic tone with matching mood.
Output only text following above instruction. Follow-up suggestions should be on the topic of expanding or changing motion or dialogue from the output text.
Sources
1 source
Sources
1 sourceVérifié 4 oct.
Artefacts du modèle
12 artefacts
Artefacts du modèle
12 artefactsExtraits de sources
3 extraits
Extraits de sources
3 extraits--- base_model: - TenStrip/LTX2.3-10Eros library_name: diffusers pipeline_tag: image-to-video tags: - image-to-video - ltx-2-3 - ltx-video - sulphur-2 - 10Eros --- **GGUF Version of LTX2.3-10Eros for ComfyUI** **Original model Link:** [https://huggingface.co/TenStrip/LTX2.3-10Eros](https://huggingface.co/TenStrip/LTX2.3-10Eros) **Watch us at Youtube:** [@VantageWithAI](https://www.youtube.com/@vantagewithai) - *For v1.2 follow this link* [https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.2-GGUF](https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.2-GGUF) - *For v1.3 follow this link* [https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.3-GGUF](https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.3-GGUF) - *For v1.4 follow this link* [https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.4-GGUF](https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.4-GGUF) - *For v1.5 follow this link* [https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.5-GGUF](https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.5-GGUF) **10 Eros** Nodes: https://github.com/TenStrip/10S-Comfy-nodes Reliant on https://huggingface.co/SulphurAI/Sulphur-2-base This is a different merge attempt for ideal I2V use. It uses layer scaled merges of different steps, it's not a straight weight merge. It behaves much nicer than lora load and respects prompt. Prompt should be enhanced, LTX has very little self reasoning and input when it is conditioned, first frame and all following motions, evolutions, and audio must be commanded-you will get nothing if you don't ask it. !!! Larger distilled Loras will harm the model's fine tune, try the cond_safe ones: https://huggingface.co/TenStrip/LTX2.3_Distilled_Lora_1.1_Experiments/tree/main For prompt enhancement, try this foreword in Grok or Uncensored LLM: Generate a video scene script with a description based on the attached image for an LLM that has a tokenizer that uses interleaved attention to support long-context understanding that is fed into a multimodal video model. Strict specification, follow up to the word: No timestamps. No unnecessary embellishment. Output only plain English text and make it a copy box. First, describe the image initial scene in concise natural language; subject(s), subject(s) appearance, subject(s) composition and pose, background, and context. Next, formulate a naturally evolving scenario that would take place...
--- base_model: - TenStrip/LTX2.3-10Eros library_name: diffusers pipeline_tag: image-to-video tags: - image-to-video - ltx-2-3 - ltx-video - sulphur-2 - 10Eros --- **GGUF Version of LTX2.3-10Eros for ComfyUI** **Original model Link:** [https://huggingface.co/TenStrip/LTX2.3-10Eros](https://huggingface.co/TenStrip/LTX2.3-10Eros) **Watch us at Youtube:** [@VantageWithAI](https://www.youtube.com/@vantagewithai) - *For v1.2 follow this link* [https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.2-GGUF](https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.2-GGUF) - *For v1.3 follow this link* [https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.3-GGUF](https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.3-GGUF) - *For v1.4 follow this link* [https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.4-GGUF](https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.4-GGUF) **10 Eros** Nodes: https://github.com/TenStrip/10S-Comfy-nodes Reliant on https://huggingface.co/SulphurAI/Sulphur-2-base This is a different merge attempt for ideal I2V use. It uses layer scaled merges of different steps, it's not a straight weight merge. It behaves much nicer than lora load and respects prompt. Prompt should be enhanced, LTX has very little self reasoning and input when it is conditioned, first frame and all following motions, evolutions, and audio must be commanded-you will get nothing if you don't ask it. !!! Larger distilled Loras will harm the model's fine tune, try the cond_safe ones: https://huggingface.co/TenStrip/LTX2.3_Distilled_Lora_1.1_Experiments/tree/main For prompt enhancement, try this foreword in Grok or Uncensored LLM: Generate a video scene script with a description based on the attached image for an LLM that has a tokenizer that uses interleaved attention to support long-context understanding that is fed into a multimodal video model. Strict specification, follow up to the word: No timestamps. No unnecessary embellishment. Output only plain English text and make it a copy box. First, describe the image initial scene in concise natural language; subject(s), subject(s) appearance, subject(s) composition and pose, background, and context. Next, formulate a naturally evolving scenario that would take place describing every moving body part, composition change, and manipulation from the uploaded initial frame that would be reflected in the video models pos...
--- base_model: - TenStrip/LTX2.3-10Eros library_name: diffusers pipeline_tag: image-to-video tags: - image-to-video - ltx-2-3 - ltx-video - sulphur-2 - 10Eros --- **GGUF Version of LTX2.3-10Eros for ComfyUI** **Original model Link:** [https://huggingface.co/TenStrip/LTX2.3-10Eros](https://huggingface.co/TenStrip/LTX2.3-10Eros) **Watch us at Youtube:** [@VantageWithAI](https://www.youtube.com/@vantagewithai) - *For v1.2 follow this link* [https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.2-GGUF](https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.2-GGUF) - *For v1.3 follow this link* [https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.3-GGUF](https://huggingface.co/vantagewithai/LTX2.3-10Eros-1.3-GGUF) **10 Eros** Nodes: https://github.com/TenStrip/10S-Comfy-nodes Reliant on https://huggingface.co/SulphurAI/Sulphur-2-base This is a different merge attempt for ideal I2V use. It uses layer scaled merges of different steps, it's not a straight weight merge. It behaves much nicer than lora load and respects prompt. Prompt should be enhanced, LTX has very little self reasoning and input when it is conditioned, first frame and all following motions, evolutions, and audio must be commanded-you will get nothing if you don't ask it. !!! Larger distilled Loras will harm the model's fine tune, try the cond_safe ones: https://huggingface.co/TenStrip/LTX2.3_Distilled_Lora_1.1_Experiments/tree/main For prompt enhancement, try this foreword in Grok or Uncensored LLM: Generate a video scene script with a description based on the attached image for an LLM that has a tokenizer that uses interleaved attention to support long-context understanding that is fed into a multimodal video model. Strict specification, follow up to the word: No timestamps. No unnecessary embellishment. Output only plain English text and make it a copy box. First, describe the image initial scene in concise natural language; subject(s), subject(s) appearance, subject(s) composition and pose, background, and context. Next, formulate a naturally evolving scenario that would take place describing every moving body part, composition change, and manipulation from the uploaded initial frame that would be reflected in the video models post-latent evolution output. If the image is explicit or sexual in nature, use full anatomical terminology and spice it up slightly with visually represent...
