svjack

Special thanks to mobeimunan for their contributions to the development of this model. The model currently supports the following 73 characters from Genshin Impact: To use this model, you need to install the following dependencies: Here's an example of how to generate an image of Zhongli using this model: Using Canny ControlNet to Restore 2D Images from 3D Toy Photos Here's an example of how to use Canny ControlNet to restore 2D images from 3D toy photos: You can also create a grid image from a list of PIL Image objects: This will create a grid image showing the original, Canny edge detection, and transformed images side by side. Below image list in : (Genshin Impact Toy/ Canny Image / Gemshin Impact Restore 2D Image) Generating an Animation of Zhongli Here's an example of how to generate an animation of Zhongli using the `AnimateDiffSDXLPipeline`: Use `AutoPipelineForImage2Image` to enhance output: Enhancing Animation with RIFE To enhance the animation using RIFE (Real-Time Intermediate Flow Estimation): Merging Videos Horizontally You can merge two videos horizontally using the following function: bash git clone https://github.com/svjack/RPG-DiffusionMaster cd RPG-DiffusionMaster bash conda create -n RPG python==3.9 conda activate RPG bash pip install ipykernel python -m ipykernel install --user --name RPG --display-name "RPG" bash pip install -r requirements.txt bash git clone https://github.com/huggingface/diffusers python from RegionalDiffusionbase import RegionalDiffusionPipeline from RegionalDiffusionxl import RegionalDiffusionXLPipeline from diffusers.schedulers import KarrasDiffusionSchedulers, DPMSolverMultistepScheduler from mllm import localllm, GPT4, DeepSeek import torch python pipe = RegionalDiffusionXLPipeline.fromsinglefile( "https://huggingface.co/svjack/GenshinImpactXLBase/blob/main/sdxlBasev10.safetensors", torchdtype=torch.float16, usesafetensors=True, variant="fp16" ) pipe.to("cuda") pipe.scheduler = DPMSolverMultistepScheduler.fromconfig(pipe.scheduler.config, usekarrassigmas=True) pipe.enablexformersmemoryefficientattention() python User input prompt prompt = 'ZHONGLI(genshin impact) with NING GUANG(genshin impact) in red cheongsam in the bar.' Process the prompt using DeepSeek MLLM paradict = DeepSeek(prompt) Extract parameters for image generation splitratio = paradict['Final split ratio'] regionalprompt = paradict['Regional Prompt'] negativeprompt = "" # Optional negative prompt python images = pipe( prompt=regionalprompt, splitratio=splitratio, # The ratio of the regional prompt batchsize=1, # Batch size baseratio=0.5, # The ratio of the base prompt baseprompt=prompt, numinferencesteps=20, # Sampling steps height=1024, negativeprompt=negativeprompt, # Negative prompt width=1024, seed=0, # Random seed guidancescale=7.0 ).images[0] Save the generated image images.save("testzhongning.png") "solo,ZHONGLI(genshin impact),1boy,highres," -> "solo,ZHONGLI drink tea use chinese cup (genshin impact),1boy,highres," "solo,KAMISATO AYATO(genshin impact),1boy,highres," -> "solo,KAMISATO AYATO smiling (genshin impact),1boy,highres," bash git clone https://huggingface.co/spaces/svjack/perturbed-attention-guidance-genshinimpactxl bash cd perturbed-attention-guidance-genshinimpactxl bash pip install -r requirements.txt bash python app.py ``` Left Use BreadcrumbsPerturbed-Attention-Guidance , Right Original Genshin Impact XL Left Seems more pretty

Radiant Youth Dynamism Synthesis Base Model: `flux.1-dev-SRPO-bf16.safetensors` Fine-tuned Adapters: - `Chineseidolflex2lorav1000004500.safetensors` (weight: 0.3) - `FLUX-dev-lora-adddetails.safetensors` (weight: 0.5) - `FluxXianglora000005250.safetensors` (weight: 1.0 or 1.1) Key Strengths: - Vibrant Realism: Expertly captures the lively essence of East Asian youth with a focus on natural, everyday scenes. - Dynamic Lighting: Renders sunlight, indoor lighting, and environmental reflections with nuanced subtlety. - Detailed Textures: Precisely depicts fabric details (e.g., hoodies, jeans, casual clothing), skin gloss, and environmental elements. - Emotional Authenticity: Conveys a spectrum of moods, from joyful exuberance to tranquil relaxation, with expressive facial features and body language. - Photorealistic Aesthetics: Optimized for high-quality photorealistic rendering, blending realism with vibrant artistic expression. - Black Hair Version: Real hair feature of Wang Xiang is Very fine, thick black hair. Provide thick black hair version images, This version is closer to the real and beautiful Wang Xiang. - Examples: Huggingface dataset https://huggingface.co/datasets/svjack/XiangHandsomeFluxSRPOPics are images generated by these models. Adapters: - `Chineseidolflex2lorav1000004500.safetensors` (0.3) - `FLUX-dev-lora-adddetails.safetensors` (0.5) - `FluxXianglora000005250.safetensors` (1.0) Key Features: - Stark contrast between overexposed window and dark clothing - Detailed fabric textures of hoodie and jeans - Natural body posture and hand gestures - Soft, grainy texture enhancing photorealistic quality Adapters: - `Chineseidolflex2lorav1000004500.safetensors` (0.3) - `FLUX-dev-lora-adddetails.safetensors` (0.5) - `FluxXianglora000005250.safetensors` (1.0) Key Features: - Soft, neutral expression with detailed facial features - Authentic dormitory setting with typical elements - Detailed fabric textures of clothing - Soft lighting with grainy texture for photorealistic effect Adapters: - `Chineseidolflex2lorav1000004500.safetensors` (0.3) - `FLUX-dev-lora-adddetails.safetensors` (0.5) - `FluxXianglora000005250.safetensors` (1.0) Key Features: - Contrast between bare skin and dark jeans - Soft natural lighting from window - Detailed depiction of dormitory environment - Intimate and contemplative atmosphere Example 6: Frontal lens head swap using Qwen Image Edit 2509 (Enhance appearance consistency) Technical Parameters | Setting | Recommendation | Notes | |------------------|--------------------|----------------------------------------| | CFG Scale | 1 (Fixed) | FLUX.1 architecture requirement | | Sampler | unipc | Optimal for photorealistic rendering | | Steps | 20 | Balances detail and speed | | Resolution | 1024x1024 | Maintains VRAM efficiency | | Motion Factor| 1-5 | Adjust for scene dynamics: lower for stillness, higher for action | Performance Profile - VRAM Usage: ~20GB at 1014x1024 (RTX 4090) - Troubleshooting: - Overexposed Skin: Add `softlight, subtleshadow` to negative prompts - Stiff Gestures: Increase motion factor + `naturalmovement` token - Texture Artifacts: Apply `detailrefiner` node at 0.3 strength License CC-BY-NC-SA 4.0 (Non-commercial, share-alike) Community Hub: https://huggingface.co/svjack/FluxXianglora/discussions

hakoniwaanimewan21models - drived from https://civitai.com/models/1626197

LoRA Model Card: `svjack/QwenImageSethosLora` Character: Sethos (Genshin Impact's Desert Traveler) Fine-tuned Adapter: `qwenimagesethoslorav1000008250.safetensors` Core Strengths: - Character Consistency: Maintains iconic features (long black hair with gray highlights, bright green eyes, ornate white-gold attire) across diverse settings. - Dynamic Composition: Balances anime-style aesthetics with symbolic cultural elements through layered backgrounds and dynamic perspectives. Key Features: - Cultural Fusion: "王家苇箭术" (Royal Reed Archery) references Sethos's combat lore while modern neon ties to urbanity . - Motion Dynamics: Arrow light trail creates visual flow between text elements and Sethos's pose . Key Features: - Contrast Design: Ice-blue text offsets warm beach tones, emphasizing Sethos's desert-to-coastal adaptability . - Anatomical Precision: Muscle definition and posture reflect his athletic lore as a desert traveler . Technical Parameters | Setting | Recommendation | Notes | |------------------|------------------|----------------------------------------| | Resolution | 1280×768 | Ideal for anime detail & text clarity | | CFG Scale | 7.5 | Balances abstract creativity & realism | | Sampler | DPM++ 2M Karras | Preserves fabric textures & hair flow | | Steps | 25 | Ensures fidelity to character design | | LoRA Weight | 1.0 | Maintains canonical features | Performance Profile - VRAM Consumption: ~24GB at 1280×768 - Thematic Flexibility: Supports desert, urban, and coastal narratives with consistent character rendering. License CC-BY-NC-SA 4.0 (Non-commercial, share-alike) Community Hub: https://huggingface.co/svjack/QwenImageSethosLora/discussions --- > ⚠️ Usage Tip: For desert scenes, integrate sand particle effects via negative prompts (e.g., `-lowdetaildunes`) to enhance Sethos's silhouette clarity .

LoRA Model Card: `svjack/PrinceCielPhantomhiveWan2214Blora` Elegant Victorian Noble Synthesis Base Model: `Wan2.2T2VA14B` Fine-tuned Adapters: - `PrinceCielPhantomhiveWan2214Blora000001750highnoise.safetensors` (weight: 1.0) - `PrinceCielPhantomhiveWan2214Blora000001750lownoise.safetensors` (weight: 1.0) Key Strengths: - Gothic Aesthetics: Excellently renders Victorian-era clothing, intricate accessories (eyepatch, pocket watches), and atmospheric settings (fog, rain, candlelight). - Emotional Depth: Captures a spectrum of emotions, from cold isolation to subtle vulnerability, often through solitary poses and expressive gestures. - Detailed Environments: Rich in atmospheric effects like rain puddles, fog, moonlight, and candlelight, enhancing the narrative quality of each scene. - Dynamic Lighting: Mastery in contrasting cool (moonlight, rain) and warm (candlelight, neon) light sources to highlight mood and texture. Adapters: - `PrinceCielPhantomhiveWan2214Blora000001750highnoise.safetensors` (1.0) - `PrinceCielPhantomhiveWan2214Blora000001750lownoise.safetensors` (1.0) Key Features: - Thick atmospheric fog and moonlight diffusion - Detailed lace and fabric texturing on clothing - Dynamic pose suggesting tension and readiness Adapters: - `PrinceCielPhantomhiveWan2214Blora000001750highnoise.safetensors` (1.0) - `PrinceCielPhantomhiveWan2214Blora000001750lownoise.safetensors` (1.0) Key Features: - Bioluminescent firefly effects and soft glow - Delicate plant details (roses, wisteria) - Contrast between natural serenity and character’s usual rigidity Adapters: - `PrinceCielPhantomhiveWan2214Blora000001750highnoise.safetensors` (1.0) - `PrinceCielPhantomhiveWan2214Blora000001750lownoise.safetensors` (1.0) Key Features: - Warm candlelight glow and shadow play on bookshelves - Detailed texturing on velvet and gilded book surfaces - Peaceful yet introspective atmosphere Technical Parameters | Setting | Recommendation | Notes | |------------------|--------------------|----------------------------------------| | CFG Scale | 1 (Fixed) | Wan2.2 architecture requirement | | Sampler | unipc | Optimal for atmospheric effects | | Steps | 4 | Balances detail and speed | | Resolution | 1280x768 | Maintains VRAM efficiency | | Motion Factor| 2-4 | Subtle for gestures, higher for dynamics | Performance Profile - VRAM Usage: ~23GB at 1280x768 (RTX 4090) - Render Speed: 35-50 sec/frame - Troubleshooting: - Overdark Shadows: Add `soft ambient light` to negative prompts - Stiff Fabric Rendering: Increase motion factor + `dynamiccloth` token - Eyepatch Detail Loss: Apply `detailenhancer` node at 0.3 strength License CC-BY-NC-SA 4.0 (Non-commercial, share-alike) Community Hub: https://huggingface.co/svjack/PrinceCielPhantomhiveWan2214Blora/discussions

LoRA Fine-tuned Qwen Model for Style Reference Image Edit Repository: https://huggingface.co/svjack/QwenImageEdit2509SrefLora This repository provides a LoRA (Low-Rank Adaptation) fine-tuned variant of the Qwen image model, specialized in generating new images that match the artistic style of reference images while allowing for content transformation. The model combines Qwen3-VL's ability to analyze and describe artistic styles with LoRA's efficient adaptation for style transfer. Style Analysis Pipeline 1. Qwen3-VL Analysis: The model first uses Qwen3-VL to analyze reference images and generate detailed style description prompts 2. LoRA Adaptation: The fine-tuned LoRA parameters specialize in style transfer and adaptation 3. Content-Style Fusion: Combines target content descriptions with extracted style characteristics for final image generation Method Comparison This approach differs from traditional style transfer methods by leveraging advanced vision-language understanding for more accurate style characterization and more flexible content-style separation. Reference Style Analysis: Red Parrot transformed from a red man with glasses | Reference Style | Style-Transformed Output | |-----------------|--------------------------| | | | Example 1: Cat Playing with Yarn Ball (Stamp Style) Reference Style Analysis: Vintage stamp design with perforated borders, teal background, and retro texture effects. | Reference Style | Style-Transformed Output | |-----------------|--------------------------| | | | Reference Style Analysis: 3D miniature landscape with precise lighting, material textures, and spatial composition. | Reference Style | Style-Transformed Output | |-----------------|--------------------------| | | | Reference Style Analysis: Dreamy soft-focus photography with bokeh effects, ethereal lighting, and translucent elements. | Reference Style | Style-Transformed Output | |-----------------|--------------------------| | | | Reference Style Analysis: Surreal portrait style with blurred edges, chromatic transitions, and fluorescent color effects. | Reference Style | Style-Transformed Output | |-----------------|--------------------------| | | | Example 5: Arab Knight in Constantinople (Comic Style) Reference Style Analysis: Bold comic art style with sharp edges, flat color areas, and strong contrast. | Reference Style | Style-Transformed Output | |-----------------|--------------------------| | | | - refer on workflow in - https://huggingface.co/svjack/QwenImageEdit2509SrefLora/resolve/main/qwenimageedit2509vlsref.json - Base Model: Qwen Image Edit 2509 Model - LoRA Rank: 32 - Training Data: 2,000+ style-content pairs across diverse artistic genres - Style Analysis: Qwen3-VL for automated style characterization and prompt generation - Specialized Capabilities: - Accurate style element extraction and replication - Content-style disentanglement and recombination - Multi-style blending and interpolation - Style strength modulation Optimized for: Single-GPU inference (NVIDIA 20GB+ recommended) with batch processing support for style-consistent image series generation. 1. Reference Image Quality - Use high-quality, stylistically consistent reference images - Ensure reference images have clear stylistic characteristics - Avoid references with conflicting or ambiguous styles 2. Prompt Formulation - Clearly separate style and content instructions - Use the model's specialized style vocabulary - Specify style strength parameters based on desired outcome This model is particularly valuable for creators, designers, and artists looking to maintain stylistic consistency across different subjects or explore creative variations within established visual styles. --- Note: This model is designed for creative style transfer applications. All copyrights for reference images and generated content belong to their respective owners.

LoRA Model Card: `svjack/QwenImageInfiniteFutureLora` Fractal-Inspired (Sci-Fi) Landscape Generation with Quantum Aesthetics Base Model: Qwen Image Generation Model Fine-tuned Adapter: `qwenimageinfinitefuturelorav1000002750.safetensors` Fractal Architecture Showcase (qwenimageinfinitefuturelorav1000002750.safetensors LoRA weight: 1.7) | Text Prompt | Output Image | |-------------|--------------| | ​​In the style of Infinite future landscape​​, this digitally crafted, hyper-realistic CGI image captures a sweeping aerial perspective of the ancient Athenian sanctuary complex. Dominating the foreground, the ​​Parthenon​​ rises with its forest of towering Doric columns—46 fluted marble pillars reaching 10.3 meters high like stone giants piercing the heavens, their rhythmic alignment stretching into the horizon to form an architectural labyrinth of sacred geometry. Beyond it, the ​​Erechtheion’s Caryatid Porch​​ emerges amidst a sea of temple spires, its six sculpted maiden columns frozen in eternal support beneath intricate Ionic scrollwork, while the ​​Temple of Athena Nike​​ perches atop southern cliffs like a crystalline jewel. The distant ​​Propylaea gateway​​ anchors the monumental composition, its massive colonnades dissolving into shadowed archways that frame the metropolis beyond—where countless terraced temples and altars cascade down the slopes, their marble facades shimmering like fractured quartz under the Mediterranean sun. Above this stone symphony, the sky unfolds in infinite layers: a dome of ​​vibrant cerulean​​ bleeds into zenithal ultramarine, scattered with cumulus clouds so volumetrically rendered they seem sculpted from ivory light. The sun blazes at frame-right, casting elongated silhouettes that stripe the sacred plateau with geometric precision, while golden-hour rays ignite gilded statue pinnacles into fleeting constellations against the deepening blue. Dominant hues of ​​Olympian white​​ and ​​aegean turquoise​​ harmonize with the terrain’s ochre undertones, while shadowed porticos breathe cool indigo into the composition—a chromatic dialogue between human grandeur and cosmic serenity. Every pixel echoes the paradox of timelessness: ancient stones whisper of mythic epochs, yet the vertiginous perspective and hyperreal cloudscape propel the vision into an eternal, uncharted future. | | | In the style of Infinite future landscape​​, this hyper-realistic digital panorama unveils a reimagined Istanbul where cobalt energy flows through the veins of history. Dominating the foreground, the ​​reinvented Dardanelles Strait​​ transforms into a luminous quantum corridor—its 61-kilometer length now pulsing with bioluminescent tides in gradients of cerulean and phthalo, while hexagonal energy harvesters rise from the waves like crystalline obelisks, converting tidal motion into azure plasma streams that illuminate the Marmara Sea. Above the strait, ​​AI-navigated skyships​​ trace holographic trade routes mirroring the 46,000-vessel annual traffic of the past, their cobalt vapor trails dissolving into nebular clouds. At the strait's Asian shore, ​​Çanakkale's resurrected fortresses​​ evolve into terraced cyber-ecosystems: Ottoman-era stone walls now draped with quantum moss that absorbs carbon emissions, while laser grid lighthouses project real-time maritime data onto the mist—a fusion of 16th-century military architecture and 22nd-century sustainability tech. On the European coast, the ​​Gallipoli Peninsula​​ morphs into a vertical memorial forest, where augmented reality poppies bloom in electric indigo, their roots interwoven with nano-sensors preserving WWI battlefield memories. Beyond the waters, ​​Hagia Sophia's quantum rebirth​​ dominates the skyline: its 1500-year-old dome now reinforced with floating graphene scaffolds, refracting sunlight into geometric constellations that dance across the Bosporus. The four minarets transmute into plasma conduits channeling zero-point energy, while the ancient nave's gold mosaics synchronize with holographic Quranic verses—a chromatic dialogue between Byzantine radiance and Ottoman cobalt. Adjacent to it, the ​​Topkapı Palace complex​​ becomes a neuro-archival hub, its courtyards emitting data streams that materialize as 3D Ottoman calligraphy in the air. The ​​Golden Horn estuary​​ evolves into a living bioremediation engine: genetically modified coral reefs thrive along its banks, emitting bioluminescent warnings when detecting water pollutants, their turquoise glow contrasting with chrome-alloy pedestrian bridges where citizens stroll amidst floating data orbs. At the horizon, the ​​Black Sea confluence​​ erupts in cosmic spectacle: neutrino-powered cargo submarines rise through vortex gates, trailing sapphire phosphorescence as they navigate toward Mediterranean trade hubs. The sky orchestrates ​​blue symphonics​​: malachite-tinted stratocumulus clouds filter light from orbital solar mirrors, casting elongated shadows that stripe the city with liquid topology. At frame-right, a reconstructed ​​Leander's Tower​​ serves as a climate regulator, projecting plasma shields that deflect heatwaves—its beam intersecting with Hagia Sophia's energy matrix to form a protective azure dome over the metropolis. | | | In the style of Infinite future landscape​​, the transfigured ​​Justinian Hologram​​ levitates in Hagia Sophia's quantum nave, his iconic purple chlamys woven from bio-engineered murex proteins that shift between Tyrian crimson (#66023C) and Byzantine violet (#702963) under cosmic radiation. The emperor's stemma crown pulses with zero-point energy coronas (#002FA7), while his outstretched hands cradle a self-diagnosing hologram of Hagia Sophia—its dome projecting real-time structural data in Greek uncials: "Your church, O Christ, holds empire and priesthood" . The original mosaic's gold tesserae now manifest as photonic nano-prisms orbiting his figure, refracting sunlight into 532nm sanctified wavelengths that trace his 548 CE coronation path across the omphalion . | | Anime-Infinite Fusion (qwenimageinfinitefuturelorav1000002750.safetensors LoRA weight: 1.0 + qwenimageanimelandscapelorav1000002000.safetensors LoRA weight: 1.5) | Text Prompt | Output Image | |-------------|--------------| | Anime style In the style of Infinite future landscape​​, high-quality nature scene with emerald green as the dominant color. A red panda balances on a bamboo stem while a bird perches on its head, with a cascading waterfall in the background. Sunlight filters through cherry blossom canopies, casting dappled shadows on a cobblestone path below. The sky transitions from teal to golden, with scattered clouds glowing under radiant sunlight. Style is anime, ratio is 16:9 | | | Anime style In the style of Infinite future landscape​​, Sunlight filters through clouds onto a cherry blossom-filled meadow, where a blue-haired youth in an indigo coat stands by a vermilion bridge, holding a book as petals drift past his smile. The scene blends dynamic nature with human stillness, using pink, red and blue hues to create harmony, while symbolic elements like blossoms and books add depth. | | | Anime style In the style of Infinite future landscape​​, This digital illustration in a vibrant, anime-inspired style depicts a vintage green tram with "P.R. 13" displayed on its front, traveling down a wet, reflective railway track during a stunning sunset. The sky is ablaze with vivid orange, pink, and purple clouds, casting a warm glow over the scene. | | | Anime style In the style of Infinite future landscape​​, This digital illustration in a breathtaking, high-detail anime-inspired style showcases a dazzling sunlit landscape where golden rays pierce through a canopy of cherry blossoms, casting intricate shadows on a cobblestone path below. The sky is a radiant gradient of azure and gold, with scattered clouds glowing under the sun's brilliance. | | - Compare with https://huggingface.co/svjack/QwenImageAnimeLandscapeLora , Replicate basic elements (Clouds, Leaves, Petals) in a fractal manner | Parameter | Setting | Effect | |--------------------|----------------------|--------| | Optimal Resolution | 1344x832 | Minimizes fractal compression artifacts | | CFG Scale | 8-10 | Enhances temporal dimensionality | | Sampler | DPM++ 3M SDE | Optimizes plasma fluid dynamics | | Steps | 25-45 | Required for fractal boundary definition | | LoRA Weight | 1.5-1.8 | 1.7 ideal for architectural recursion | Performance Profile - VRAM Usage: ~24GB at 1344x832 resolution - Fractal Controls: - Style Blending: - Anime (1.5) + Infinite (1.0) = Balanced nature/fractal synthesis

Genshin Impact Zhongli & Ningguang HunyuanVideo LoRA This repository contains the necessary setup and scripts to generate videos using the HunyuanVideo model with a LoRA (Low-Rank Adaptation) fine-tuned for Genshin Impact's characters Zhongli and Ningguang. Below are the instructions to install dependencies, download models, and run the demo. Step 1: Download HunyuanVideo Model Download the HunyuanVideo model and place it in the `ckpts` directory: Step 2: Download LLaVA Model Download the LLaVA model and preprocess it: Step 3: Download CLIP Model Download the CLIP model for the text encoder: Generate Video 1: Zhongli & Ningguang Cooking Rice Run the following command to generate a video of Zhongli and Ningguang cooking rice: Generate Video 2: Zhongli Drinking Tea Run the following command to generate a video of Zhongli drinking water: Notes - Ensure you have sufficient GPU resources for video generation. - Adjust the `--videosize`, `--videolength`, and `--infersteps` parameters as needed for different output qualities and lengths. - The `--prompt` parameter can be modified to generate videos with different scenes or actions.

This repository contains the necessary setup and scripts to generate videos using the HunyuanVideo model with a LoRA (Low-Rank Adaptation) fine-tuned for Ciel Phantomhive. Below are the instructions to install dependencies, download models, and run the demo. Step 1: Download HunyuanVideo Model Download the HunyuanVideo model and place it in the `ckpts` directory: Step 2: Download LLaVA Model Download the LLaVA model and preprocess it: Step 3: Download CLIP Model Download the CLIP model for the text encoder: Generate Video 1: Ciel Phantomhive Run the following command to generate a video of Ciel Phantomhive: Generate Video 2: Ciel Phantomhive Rain Run the following command to generate a video of Ciel Phantomhive in rain: - res: 848x480 - lora multi: 2 - prompt: Ciel Phantomhive, depicted in a semi-realistic art style. Ciel has short, silver hair with bangs, and an eyepatch over his right eye. He wears a black military-style uniform with white accents, including a high-collared shirt and a belt with a buckle. His expression is stern and focused. The background is a soft, pastel purple, contrasting with the darker tones of his outfit. The image has a clean, polished look with smooth shading and attention to detail in the uniform's textures and folds Notes - Ensure you have sufficient GPU resources for video generation. - Adjust the `--videosize`, `--videolength`, and `--infersteps` parameters as needed for different output qualities and lengths. - The `--prompt` parameter can be modified to generate videos with different scenes or actions.