v1.2 Changelog: Leveraged tuned connector data to reduce face drift and aid long prompts/director. Also using sulphur EXP weights on top of v1 to hone the most explicit motions. All common issues like mistaken extra anatomy, subtitles, unexpected transitions, etc all still present from v1. 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. 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.
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v1.5 Changelog: 1.4 and DMD only struggled with one thing, explicit motion and anatomy that was easy to access in version 1.2 and sulphur. All that data is still inside the model; versions 1.2, 1.3, 1.4, and 1.5 are almost all the same model. The difference is how the attn_2 and attn_1 gates are handled. 1.5 is 1.4 with a linear ramp across the gates to open up those explicit details from sulphur like they are on version 1.2's blocks. It still mostly maintains 1.4's base model style prompting and more stable outputs and structure. Because it is a hybrid of a model made for DMD and a model made for LTX distilled lora use, it goes best with a hybrid lora to make use of the motion vareity. Currently the hybrid block DMD lora here seems to work nicely: https://huggingface.co/TenStrip/LTX2.3_DMD_Lora/blob/main/LTX2.3_DMD_hybrid_v2.safetensors or these ones: https://huggingface.co/maximsobolev275/LTX2.3-DMD-Distill-Lora-Hybrids v1.4 Changelog: Built off 1.3 and bringing back explicit prompting and motion hopefully without any kind of anatomy redraw or negative tendency. Still requires intense prompt refinement. This version is set up to be trained on to fix it into a real base, it doesn't depict anatomy well but it also isn't confused by it which is priority for the first lora passes I'll do. Use lora stacks to improve it if needed. v1.3 Changelog: Only designed to work with DMD lora on a workflow like my V5 DMD. https://huggingface.co/TenStrip/LTX2.3-10Eros_Workflows/blob/main/10Eros_10SNodes_I2V_Basic_DMD_V5.json
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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. 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.
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Highly realistic skin texture and ultra-high resolution, suitable for generating Portrait and Real-world scenes. rich details and supports direct upscale to 8M pixels using TTP technology. This is pure base model, you can add all kind of NSFW / SFW LoRAs according to you like.
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MiniMax H3 is a general-purpose, omni-modal generative system. It supports unified understanding of multimodal contexts composed of text, images, video, and audio, and can generate video with native stereo audio at resolutions up to 2K and durations of up to 15 seconds. Thanks to its task-generalization-oriented system design, H3 already possesses broad multimodal context understanding and generation capabilities at the pre-training stage, enabling outstanding performance in following complex multimodal instructions. The complete H3 system consists of the following three modules: - H3-Context-IR: As inputs become increasingly complex, we build a dedicated system to deeply understand and refine the input multimodal instructions, then convert them into a form that H3 can readily understand—the Context Intermediate Representation—for generation. **H3-Context-IR is critical to the quality of the final output, so we strongly recommend incorporating it into your generation pipeline or following the “Prompting Guidance” to build your own context-processing system.** - H3-Base: Generates audio and video based on the H3-Context-IR output, producing results at 768p resolution. - H3-Regenerate-2K: Feeds the 768p result together with the original context back into H3 to regenerate the output at 2K resolution. This process leverages both H3’s powerful generative capabilities and the rich information contained in the original context, enabling it to produce high-resolution outputs with more accurate details and greater visual fidelity. H3\-Context\-IR is a hosted preprocessing and orchestration system designed for free\-form multimodal inputs\.
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💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    🖥️ Demo    |   💬 WeChat (微信)    |   🫨 Discord   |    Github    We are excited to introduce Qwen-Image-Edit-2511, an enhanced version over Qwen-Image-Edit-2509, featuring multiple improvements—including notably better consistency. To try out the latest model, please visit Qwen Chat and select the Image Editing feature.
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SCAIL-2 is an open-source model for **end-to-end controlled character animation**. It animates a reference character with a driving video, and also supports character replacement and multi-character scenarios without relying on intermediate pose representations. Prior approaches to character animation depend heavily on intermediate representations such as skeleton maps or inpainting masks. These intermediates are ambiguous under complex motion, restrict driving sources to human movements, and limit the reach of replacement and multi-character animation. SCAIL-2 removes this dependence and achieve **End-to-end Driving**. Using several off-the-shelf models (SCAIL-Preview, Wan-Animate, MoCha), 60K motion pairs were synthesized and trained through a Unified Motion Transfer Interface with dedicated masking channels and RoPE design. The reverse driving training recipe with the unification lets the model learn capabilities beyond its teacher models, yielding emergent abilities such as:
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This repository contains the quantized GGUF model weights for **SCAIL (Studio-Grade Character Animation via In-Context Learning)**, a framework that enables high-fidelity character animation under diverse and challenging conditions, including large motion variations, stylized characters, and multi-character interactions. The model was presented in the paper SCAIL: Towards Studio-Grade Character Animation via In-Context Learning of 3D-Consistent Pose Representations. Check our model architecture design, our video demo, as well as more comparisons against other baselines at this link, more creative examples will be added to the gallery soon.
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**Original model Link:** https://huggingface.co/SulphurAI/Sulphur-2-base **Watch us at Youtube:** @VantageWithAI An uncensored video generation model based on LTX 2.3 supporting both t2v and i2v natively, as well as all of the other ltx 2.3 formats.
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**Wan-Move: Motion-controllable Video Generation via Latent Trajectory Guidance [Paper]** Ruihang Chu, Yefei He, Zhekai Chen, Shiwei Zhang, Xiaogang Xu, Bin Xia, Dingdong Wang, Hongwei Yi, Xihui Liu, Hengshuang Zhao, Yu Liu, Yingya Zhang, Yujiu Yang Wan-Move spports diverse motion control applications in image-to-video generation. The generated samples (832×480p, 5s) exhibits high visual fidelity and accurate motion. The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generated contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the license.
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Z-Image is a powerful and highly efficient image generation model with **6B** parameters. Currently there are three variants: 📸 **Photorealistic Quality**: **Z-Image-Turbo** delivers strong photorealistic image generation while maintaining excellent aesthetic quality. 📖 **Accurate Bilingual Text Rendering**: **Z-Image-Turbo** excels at accurately rendering complex Chinese and English text.
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