Video LoRA Training for one-eye Calico Cat

@MuhammadAmmar74

Hi, Creating captivating videos of your one-eyed calico cat opens up exciting possibilities. I’ll deliver a specially tailored WAN 2.2 video LoRA. This will generate short clips showing her unique behaviors from various angles. My experience with similar projects ensures smooth execution and quality outputs. I understand the importance of a reproducible pipeline for future updates. Let’s chat about your vision and how I can help bring it to life. — Ammar

@ZeeeEditor1

Hey! Your project to create a unique video LoRA for your calico cat sounds fun and engaging. I will provide a tailored video dataset, ensuring natural behaviors and capturing her charm from several angles. With extensive experience in video production and machine learning, I ensure your project meets its creative vision. Let's collaborate and easily update the LoRA as you gather more clips! — Babar

@amirrmusavifr

Hi, I’m an AI expert with professional experience in computer vision, with a proven track record of working on complex image processing and AI/ML model development. With skill sets: • Algorithm Development: Strong understanding of computer vision algorithms and techniques, including convolutional neural networks (CNNs), object detection, image segmentation and feature extraction. • Model Training & fine-tuning: Develop and train machine learning models tailored for image analysis and visual data interpretation. I have worked on some well-known models like YOLO, RCNN, U-Net, Deeplab, ViT etc. • AI Integration: Implement and integrate AI models into existing software and hardware systems, ensuring high performance and scalability. • Data Analysis: Analyze and process large datasets of images and video feeds to identify patterns, trends, and insights. • Data Handling: Experience in handling and processing large datasets, including image and video data. Familiarity with data augmentation techniques and synthetic data generation. • Performance Optimization: Optimize algorithms and models for real-time processing and ensure they can handle large-scale data efficiently. • Programming Skills: Proficient in programming languages such as Python. Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras. • Tools & Libraries: Proficiency with OpenCV, scikit-image, and other relevant libraries. Experience with version control systems like Git.

@Sharminrubab

Hello! I am a skilled video editor and motion graphics designer with more than 7 years of experience creating polished, modern, engaging videos that align with your goals. I can bring your content to life with fantastic video editing, combining technical precision, creativity, and storytelling. I have experience with commercial video, cinematic, dynamic, and YouTube videos, documentaries, real estate, promotions and montage courses and tutorials, Green Screen, music videos, podcasts, fitness coaching, TikTok, Facebook, Instagram, travel vlogs, corporate, highlight reels, gaming, and short films, and more. I'm professionally trained in Adobe Premiere Pro, Adobe After Effects, DaVinci Resolve, and Adobe Audition. My approach includes professional video editing, adding footage, cutting & trimming, zooming in & out, smooth transitions, motion graphics, on-screen text, color correction & grading, cinematic & dynamic editing, subtitles, match audio & video, professional voice-over & music, SFX, audio enhancement, high-resolution (1080p or higher), and social media formats with commercial rights. I’d be happy to discuss your project further and align on the timeline and next steps. Looking forward to working together. Best regards, Rubab

@Internative123

Hello there, We have the expertise to create a WAN 2.2 video LoRA of your one‑eyed calico cat using 100–200 natural‑behaviour clips with clean temporal captions. We build a reproducible pipeline with stable frame extraction, resizing and action‑based clip captions for future updates. You receive a consistent LoRA ready for stylised video generation. We are professional video editor with over 5 years of experience delivering high-quality, engaging videos. We specialize in clean cuts, smooth transitions, color correction, audio syncing, and creating content optimized for social media and online platforms. We ensure fast turnaround, clear communication, and unlimited revisions so the final video perfectly matches your vision. Please visit our profile for portfolio; https://www.freelancer.com/u/Internative123/graphics Best regards, Internative Labs.

@Zawiya3

Hello, I am excited to apply for your Video LoRA Training project for a one-eye Calico cat. I have experience preparing datasets, training custom LoRA models, and optimizing AI workflows for consistent character generation across images and videos. I understand the importance of preserving unique visual traits, especially distinctive features such as the cat’s calico coat pattern and single-eye appearance, ensuring the trained model accurately captures its identity in a wide range of scenes and animations. My workflow includes dataset review and curation, frame extraction and preprocessing, caption generation, training parameter optimization, and thorough testing of the final LoRA model. I can work with your existing video footage to create a high-quality dataset that maximizes model accuracy while minimizing overfitting. The result will be a robust LoRA capable of generating realistic and consistent outputs for image and video generation platforms. I am committed to delivering a well-trained, production-ready model along with documentation on recommended prompts, settings, and usage guidelines. Throughout the project, I will provide regular updates, sample outputs, and opportunities for feedback to ensure the final LoRA meets your expectations. I look forward to helping bring your unique one-eye Calico cat into AI-generated content with exceptional quality and consistency.

@Vsion2

Greetings! I am ready to start the project immediately and provide high-quality work. I have 8 years of professional experience in video editing and creation, and I have completed 350+ similar projects. You can check an example of one of those projects in my portfolio here: https://www.freelancer.com/u/Vsion2 I'm interested in discussing your project, If you have any questions or special requirements, please don’t hesitate to message me. I'd be pleased to have the chance to assist you further with your project Best Regards Alema Akter

@zahidhasta

Hi, This is exactly the type of workflow I can help with. I have experience training LoRAs, building reproducible dataset pipelines, and working with image/video generation workflows on local GPU systems. Since you already have a ZIT image LoRA for identity consistency, I would keep the WAN 2.2 Video LoRA focused on motion, behavior, and temporal consistency rather than relearning appearance. My recommended pipeline: 1. Video preprocessing - Scene detection and clip segmentation - Resolution normalization and aspect-ratio preservation - Automatic quality filtering (blur/shake removal) - Consistent frame extraction strategy 2. Temporal-aware captioning - Generate captions at clip level rather than frame-only captions - Include action descriptions (walking, grooming, stretching, jumping, playing) - Track temporal relationships between beginning, middle, and end of clips - Gemma-based captioning can be integrated since you already achieved strong results compared to JoyCaption 3. Dataset preparation - Motion bucketing - Resolution bucketing - Metadata generation - Fully reproducible update pipeline so new footage can be added later without rebuilding from scratch I would be happy to review your current dataset and recommend the exact captioning and training configuration before training begins. Best regards, Zahid Hassan

@izan27

Hi, I have a strong background in video editing and machine learning, and I understand your requirement for creating a WAN 2.2 video LoRA for your one-eyed calico cat. I will utilize the ZIT image LoRA to ensure better consistency with the WAN 2.2 base model. By working with the dataset of 100-200 raw clips showcasing natural behaviors from various angles in daylight, I will focus on frame extraction, resizing, tagging, and captioning to generate short, stylized clips. For video captioning, I would recommend a combination of techniques considering the temporal relationship in the footage. I will ensure a reproducible pipeline for easy updates to the WAN 2.2 video LoRA with new content. I can also share my portfolio to showcase relevant projects. Please start a chat so we can discuss your project requirements further. Best regards, Izan Abbas

@Foraxis

It sounds like you've put a lot of thought into this project and want to get it done right. I recently worked on a project creating stylized video clips for a unique pet, similar to your one-eyed calico cat. My goal aligns perfectly with yours - to produce engaging and high-quality content that showcases your cat's natural behaviors effectively. I noticed your preference for WAN 2.2 and the need for a reproducible pipeline, ensuring future updates are seamless. With my expertise in video editing, focusing on performance, security, and user experience, I am confident in delivering outstanding results for your project. Would it be a ridiculous idea for you to send me a message and see whether we're the right fit for this project? Kind regards, Curtley

@muhammads867

Hello , I checked your project about Video LoRA Training for one-eye Calico Cat and I can help you with this. I have experience working on similar projects and can deliver a clean, modern and high quality result based on your requirements. You can check my previous work here: https://www.freelancer.com/u/muhammads867 I understand your requirements clearly. I focus on delivering high-quality, professional results that match your vision. Quick question: Do you have any reference design or style in mind? Looking forward to working with you Best Regards, M. Sajid

@mssaini1996

Hello! I'm Manish from PrimePixel and I must say, your project caught my eye. As a creative design and animation team specializing in 2D and 3D visual production, we have the expertise to bring your one-eyed calico cat's unique fur pattern to life using the WAN 2.2 video LoRA model. Drawing from our extensive experience in video editing, processing, and production, we can skillfully handle the 100-200 raw clips you have with behaviors of your feline friend - walking, jumping, grooming, stretching, and playing included. We'll leverage the power of the WAN2.2 I2V model to generate short yet beautifully stylized clips that showcase your cat's charming personality from multiple angles in natural daylight.

@omard211

So you need a reproducible WAN 2.2 video LoRA pipeline for your one eyed calico, updatable later. Since you already have a solid ZIT image LoRA, I2V is the right call for consistency. On captioning, video needs motion described, not just appearance. Static tags lose the temporal thread. I would caption each clip around the action. Walking, stretching, grooming, plus angle and lighting. Your SoloTagger and Gemma setup works. I would keep it for stills, then add motion captions on top. For frames, I extract at a steady rate, resize to the bucket WAN expects, and keep clips clean. The whole pipeline stays scripted, so dropping in new footage later just reruns it.

@abhiramukk

Hi, This is AB from United Kingdom. For the Video LoRA Training project for your one-eyed calico cat, I understand the need to create WAN 2.2 video LoRA files with stylized clips. To achieve this, I would recommend focusing on frame extraction, resizing, and tagging for accurate captioning. Maintaining a reproducible pipeline is crucial for future updates to the WAN 2.2 video LoRA with new content. As for video captioning, considering the temporal relationship, I suggest exploring options like solotagger with Gemma 4 26B A4B for optimal results. I have experience in similar projects and can provide you with relevant samples from my portfolio upon request. Looking forward to potentially collaborating on this unique project.

@joshuak485

Hi there, Your WAN 2.2 video LoRA goal is clear, and I can help you build a repeatable pipeline for a one-eyed calico cat with strong identity and motion consistency. I have worked with fine tuning workflows where the real challenge is not just the subject, but keeping pose, coat pattern, and temporal behavior stable across clips. You want a video LoRA for short stylised generations from I2V, and the key risk is inconsistent captioning across time, which can make the model learn motion noise instead of cat behavior. For this kind of dataset, I would treat clip selection, frame sampling, and temporal tags as one system, not separate steps. My approach would be to standardize your clips, extract frames at a controlled rate, and caption them with a reproducible schema that includes subject identity, action, camera angle, and scene context. For the captioning stack, I would test a hybrid flow with Solotagger plus Gemma 4 26B A4B for identity and attribute tags, then add structured temporal labels for motion and phase changes. That keeps the pipeline easy to update later when you add new clips. I can work with your time zone and stay aligned with your review process. Q: Do you want the captions to be shot-level only, or should they also describe motion changes within each clip? If you want, I can review your current LoRA pipeline on a quick call and suggest the best update path.

@jemelito10

Hello there! This project sounds fun in the best way. A one-eyed calico cat with a unique fur mix is exactly the kind of subject where clean data and a steady pipeline matter a lot. I’ve worked with LoRA-style training workflows and captioning setups before, so I’d keep this reproducible and easy to refresh later. 1. I’d organize the clips first: remove shaky or low-value shots, then standardize frame rate, crop, and resolution. 2. For captioning, I’d use short, consistent video captions with scene + action + identity cues, plus temporal hints only where they help. 3. I’d tag key traits separately: one-eyed, calico pattern, fur colors, pose, motion, lighting, and camera angle. 4. I’d build the dataset in a versioned structure so new clips can be added and the WAN 2.2 LoRA can be updated cleanly later. The big success factors are consistency in the cat’s identity, clean motion clips, and captions that stay structured instead of wordy. I can also share a portfolio of similar work if helpful. Jemelito

@nimran54

Hey , I am a US based Freelancer, I just finished reading the job description and I see you are looking for someone experienced in Model Monitoring, Video Services, After Effects. This is something I can do. For your WAN 2.2 video LoRA, I’d design a reproducible pipeline for frame extraction, clip filtering, temporal-aware captioning, and consistent tagging so the model can be updated later with new cat footage without retraining chaos. I’d focus on captions that preserve action, scene context, angle, and motion continuity rather than over-describing single frames, which is important for temporal relationship quality in video LoRA training. I can also recommend a clean dataset structure, versioned metadata, and a refresh workflow so future updates stay stable. Drop me a message before placing an order price can be discussed after discussion.

@demivision

Hi there, Employer, Thank you for sharing the details of your unique project. At Demivision LLC, we are a team passionate about AI-driven media and have extensive experience with video LoRA training, dataset curation, and machine learning pipelines for creative applications. We are excited about the opportunity to help you develop a WAN 2.2 video LoRA for your one-eyed calico cat and appreciate your clear emphasis on reproducibility and futureproofing. We understand that your primary focus is obtaining a consistent, stylised model that captures the cat’s unique features and natural behaviors across various scenes. For the crucial first step of video captioning with temporal consideration, we recommend a multi-stage, reproducible pipeline: - **Frame Extraction & Preprocessing:** Automated extraction at a set frame rate (e.g., 2-3 fps), followed by resizing and normalization, ensuring consistent input for both current and future data additions. - **Temporal-aware Video Captioning:** Since you’ve had success with solotagger + Gemma 4 26B A4B for images, we propose leveraging video captioning models that can recognize temporal context—such as BLIP-2 or VideoChat Caption, fine-tuned to highlight actions and appearance over short clips. This will generate richer, time-aware tags such as “grooming left side,” “jumping from sofa,” etc., which can be mapped back to your footage for LoRA training. - **Tagging Consistency:** We’ll ensure all captions use a standardized vocabulary, enabling seamless dataset extension in the future. Our team has delivered similar pipelines for animal modeling, stylized video LoRAs, and AI-powered video editing. Please find our portfolio attached, showcasing relevant work in AI video generation, LoRA training, and dataset automation. Looking forward to discussing the next steps and customizing the workflow to fit your vision! Best regards, Demivision LLC

@isabellam117

Hello, I have experience with AI model training, LoRA workflows, dataset preparation, and video generation pipelines. For your WAN 2.2 Video LoRA, I can build a fully reproducible training workflow that supports future dataset updates and retraining. My approach includes: • Video preprocessing, frame extraction, and resizing • Temporal-aware video captioning/tagging • Consistent character labeling for your unique one-eyed calico cat • Dataset organization for incremental updates • WAN 2.2 Video LoRA training and validation • Documentation of the complete pipeline I am familiar with modern captioning workflows and can recommend the most effective approach for maintaining identity consistency while capturing motion and temporal relationships. I can also provide examples of similar AI training and dataset engineering work. Ready to discuss the dataset and training requirements in detail. Best regards