
![ComfyUI Tips and Tricks Hero Image](https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1200&h=600&fit=crop)

ComfyUI has revolutionized the world of AI image generation with its powerful node-based interface. Whether you're just starting your journey or looking to optimize your existing workflows, these 25 essential tips and tricks will help you unlock ComfyUI's full potential and create stunning AI art more efficiently than ever before.

If you're completely new to ComfyUI, we recommend starting with the [official ComfyUI documentation](https://docs.comfy.org/) to understand the basic concepts before diving into these advanced techniques.

## Getting Started: Essential ComfyUI Basics

![ComfyUI Interface Basics](https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=800&h=400&fit=crop)

### 1. Master the Essential Keyboard Shortcuts

The most fundamental tip every ComfyUI user should know is the **Ctrl+Enter** shortcut. This instantly generates your image without needing to click the "Queue Prompt" button. This simple shortcut can save you hundreds of clicks during your creative sessions.

Other essential shortcuts include:
- **Ctrl+Shift+Enter**: Add to queue without clearing previous
- **Ctrl+Z**: Undo last action
- **Ctrl+Y**: Redo action
- **Delete**: Remove selected nodes
- **Ctrl+A**: Select all nodes

### 2. Organize Your Workflow with Node Groups

One of the most overlooked features is ComfyUI's ability to group nodes. Hold **Ctrl**, drag to select multiple nodes, then right-click and choose "Group Selected Nodes." This creates a collapsible group that makes complex workflows much more manageable.

Pro tip: Name your groups descriptively (e.g., "Text Encoding," "Image Processing," "Upscaling") to maintain clarity in complex workflows.

### 3. Save and Load Workflow Templates

ComfyUI automatically saves your workflow as a JSON file. Create a library of your best workflows by saving them with descriptive names. You can load these templates anytime by dragging the JSON file into the ComfyUI interface or using the "Load" button.

For more workflow examples and templates, check out the [ComfyUI Examples repository](https://comfyanonymous.github.io/ComfyUI_examples/) which contains a comprehensive collection of ready-to-use workflows for various tasks.

## Memory Management and Performance Optimization

![Memory Optimization](https://images.unsplash.com/photo-1558618666-fcd25c85cd64?w=800&h=400&fit=crop)

### 4. Optimize VRAM Usage for Better Performance

Memory management is crucial for smooth ComfyUI operation. Use these command-line arguments to optimize VRAM usage:

- `--lowvram`: For GPUs with 4GB or less VRAM
- `--normalvram`: For GPUs with 4-8GB VRAM  
- `--highvram`: For GPUs with 8GB+ VRAM
- `--cpu`: Force CPU processing (slower but uses system RAM)

### 5. Implement Smart Model Caching

ComfyUI's model caching system can significantly speed up your workflow. Keep frequently used models loaded by avoiding unnecessary model switches. When you must switch models, do it strategically to minimize loading times.

### 6. Use the Free Memory Nodes

Install the ComfyUI-FreeMemory custom node to manually clear memory between generations. This is especially useful for long workflow sessions or when working with large models like SDXL or Flux. You can find detailed installation instructions in the [ComfyUI installation guide](https://stable-diffusion-art.com/how-to-install-comfyui/) from Stable Diffusion Art.

### 7. Batch Processing for Efficiency

Instead of generating images one by one, use batch processing nodes to generate multiple variations simultaneously. This is more memory-efficient than running separate generations and saves significant time.

## Advanced Workflow Techniques

![Advanced ComfyUI Workflows](https://images.unsplash.com/photo-1633356122544-f134324a6cee?w=800&h=400&fit=crop)

### 8. Master the KSampler Settings

Understanding KSampler parameters is crucial for quality results:
- **Steps**: 20-30 for most models (higher isn't always better)
- **CFG Scale**: 7-12 for most prompts (lower for simple prompts)
- **Sampler**: DPM++ 2M Karras for quality, Euler for speed
- **Scheduler**: Karras for most cases, exponential for specific styles

### 9. Implement Conditional Workflows

Use Switch nodes to create conditional workflows that adapt based on input parameters. This allows you to build versatile workflows that can handle different scenarios without manual reconfiguration.

### 10. Optimize Image Preprocessing

Always preprocess your input images properly:
- Resize images to model-appropriate dimensions
- Use proper aspect ratios (1:1, 16:9, 4:3)
- Normalize image quality and lighting
- Consider using ControlNet preprocessors for better results

### 11. Leverage ControlNet Effectively

ControlNet is powerful but resource-intensive. Tips for optimal use:
- Use appropriate preprocessors for your control type
- Adjust control strength (0.8-1.2 typically works well)
- Combine multiple ControlNets carefully to avoid conflicts
- Consider using T2I-Adapter for lighter processing

For comprehensive ControlNet tutorials and advanced techniques, visit [ComfyUIWeb.com](https://comfyuiweb.com) where you'll find detailed guides on implementing various ControlNet workflows.

## Custom Nodes and Extensions

### 12. Essential Custom Nodes to Install

Expand ComfyUI's capabilities with these must-have custom nodes:
- **ComfyUI-Manager**: Essential for node management
- **ComfyUI-Advanced-ControlNet**: Enhanced ControlNet features
- **ComfyUI-AnimateDiff-Evolved**: For video generation
- **ComfyUI-IPAdapter-Plus**: Advanced image prompting
- **ComfyUI-Impact-Pack**: Quality of life improvements

You can install these nodes easily through the [ComfyUI-Manager](https://github.com/Comfy-Org/ComfyUI-Manager), which provides a user-friendly interface for managing all your custom nodes and extensions.

### 13. Keep Your Nodes Updated

Regularly update your custom nodes through ComfyUI-Manager. Outdated nodes can cause compatibility issues and prevent you from accessing new features and bug fixes.

### 14. Create Custom Node Shortcuts

Organize frequently used custom nodes in favorites or create custom menus. This reduces the time spent searching for specific nodes in complex workflows.

## Prompt Engineering and Text Processing

![AI Prompt Engineering](https://images.unsplash.com/photo-1677756119517-756a188d2d94?w=800&h=400&fit=crop)

### 15. Use Prompt Scheduling

Implement prompt scheduling to change prompts during the generation process. This technique allows for more dynamic and interesting results, especially in longer generation processes.

### 16. Implement Negative Prompt Strategies

Effective negative prompts are crucial:
- Use specific terms rather than generic ones
- Include common artifacts you want to avoid
- Consider model-specific negative prompts
- Don't over-complicate; 10-20 words usually suffice

### 17. Leverage CLIP Text Encoding Options

Experiment with different CLIP text encoding methods:
- Standard CLIP for most cases
- CLIP Vision for image-based prompting
- Multiple CLIP encoders for complex prompts
- Custom CLIP models for specific styles

For deeper insights into prompt engineering and CLIP optimization, the [Stable Diffusion Art ComfyUI guide](https://stable-diffusion-art.com/comfyui/) offers excellent tutorials on advanced text encoding techniques.

## Quality Enhancement and Post-Processing

### 18. Master Upscaling Techniques

Effective upscaling strategies:
- Use Real-ESRGAN for photorealistic images
- ESRGAN for general purpose upscaling
- Waifu2x for anime/cartoon styles
- Consider multi-stage upscaling for extreme enlargements

### 19. Implement Face Restoration

For portraits, use face restoration nodes:
- CodeFormer for balanced results
- GFPGAN for more natural faces
- RestoreFormer for detailed restoration
- Combine with inpainting for best results

### 20. Color Correction and Enhancement

Post-process your images for professional results:
- Adjust contrast and brightness
- Enhance color saturation selectively
- Apply film grain for artistic effect
- Use LUT nodes for cinematic looks

Advanced post-processing workflows and color grading techniques are covered extensively on [ComfyUIWeb.com](https://comfyuiweb.com), including professional-grade enhancement methods.

## Troubleshooting and Error Prevention

![ComfyUI Troubleshooting](https://images.unsplash.com/photo-1581291518857-4e27b48ff24e?w=800&h=400&fit=crop)

### 21. Common Error Solutions

**Out of Memory Errors:**
- Reduce batch size
- Lower image resolution
- Use --lowvram flag
- Clear GPU memory between runs

**Model Loading Issues:**
- Check file paths and permissions
- Verify model format compatibility
- Update ComfyUI to latest version
- Reinstall problematic models

### 22. Backup Your Workflows

Regularly backup your workflow files and custom node configurations. Store them in cloud storage or version control systems to prevent loss of your creative work.

### 23. Monitor System Resources

Keep an eye on:
- GPU memory usage
- System RAM consumption
- CPU temperature
- Storage space availability

Use system monitoring tools to identify bottlenecks and optimize accordingly. The [ComfyUI Community Manual](https://blenderneko.github.io/ComfyUI-docs/) provides detailed troubleshooting guides for common performance issues.

## Advanced Integration and Automation

### 24. API Integration for Automation

ComfyUI's API allows for powerful automation:
- Batch process multiple images
- Integrate with other applications
- Create custom interfaces
- Automate repetitive tasks

### 25. Cloud Deployment Strategies

For heavy workloads, consider cloud deployment:
- Use services like RunPod or Vast.ai
- Configure auto-scaling workflows
- Implement proper security measures
- Optimize costs with spot instances

The [official ComfyUI GitHub repository](https://github.com/comfyanonymous/ComfyUI) contains detailed documentation on API usage and deployment strategies for production environments.

<iframe width="560" height="315" src="https://www.youtube.com/embed/RFYvXm-c_Lc" title="ComfyUI Advanced Tips and Tricks" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

## Frequently Asked Questions

**Q: How much VRAM do I need for ComfyUI?**
A: Minimum 4GB for basic workflows, 8GB+ recommended for SDXL and advanced features, 12GB+ for professional use with large models.

**Q: Can I run ComfyUI on CPU only?**
A: Yes, but it's significantly slower. Use the --cpu flag for CPU-only processing. Consider cloud GPU services for better performance.

**Q: How do I share my ComfyUI workflows?**
A: Save your workflow as a JSON file and share it. Recipients can drag the file into their ComfyUI interface to load it.

**Q: What's the difference between ComfyUI and Automatic1111?**
A: ComfyUI uses a node-based interface offering more control and flexibility, while A1111 provides a more traditional web interface that's easier for beginners.

**Q: How often should I update ComfyUI?**
A: Check for updates monthly, but test new versions in a separate environment before updating your main installation.

## Conclusion

Mastering ComfyUI requires practice and experimentation, but these 25 tips and tricks will significantly accelerate your learning curve. Start with the basics like keyboard shortcuts and node organization, then gradually implement advanced techniques like memory optimization and custom node integration.

Remember that ComfyUI's strength lies in its flexibility and customization options. Don't be afraid to experiment with different approaches and create workflows that suit your specific creative needs. The community is incredibly supportive, so don't hesitate to share your discoveries and learn from others.

Whether you're creating art for personal projects, client work, or commercial applications, these tips will help you work more efficiently and achieve better results. Keep practicing, stay updated with the latest developments, and most importantly, have fun exploring the endless possibilities of AI-generated art with ComfyUI.

---

*Want to learn more about AI image generation? Check out our comprehensive guides on [ComfyUIWeb.com](https://comfyuiweb.com/tutorials) and explore the [Stable Diffusion Art tutorials](https://stable-diffusion-art.com/) for more advanced techniques and workflows.* 

Master ComfyUI with these expert tips and tricks that will transform your AI art workflow from beginner to professional level.

comfyui-keyboard-shortcuts-min.jpg

25 Essential ComfyUI Tips and Tricks to Master AI Image Generation in 2025

Today I'm introducing the newest, most powerful image-editing framework: **FLUX.1 Kontext**. Built by Black Forest Labs, FLUX.1 Kontext is a professional image-to-image model that intelligently understands context and delivers pinpoint editing—no convoluted prompts required. Whether you need object tweaks, style transfers, background swaps, consistent character edits or on-image text adjustments, Kontext has you covered. Its standout strength is deep context awareness coupled with character-consistency maintenance, so essential elements like subject traits and overall composition stay rock-steady across multiple revisions.

Right now, you can access Kontext via two paid API tiers—**Pro** and **Max**—and Black Forest Labs has confirmed that an open-source **Dev** edition is on the horizon.

Plus, ComfyUI already integrates with both paid tiers of the FLUX.1 Kontext API:

* **Kontext Pro**: tailored for seamless editing, compositing, and image mixing
* **Kontext Max**: optimized for top-tier typography, prompt fidelity, and blistering speed

Try it online: https://playground.bfl.ai/

![640 (2).webp](https://api.comfyuiweb.com/uploads/640_2_fad3183f61.webp)
![640 (1).webp](https://api.comfyuiweb.com/uploads/640_1_dfb9262b3e.webp)

Experience FLUX.1 Kontext Image Editing in ComfyUI

![flux.1 kontext image editing workflow](https://api.comfyuiweb.com/uploads/640_5_6e638ff204.webp)

**FLUX.1 Kontext Image Editing in ComfyUI: Prompt Guidelines & Best Practices**

---

### 1. Basic Prompts & Notes

* **Simple edits**

  * Direct:

    > “Change the car color to red.”
  * Style-aware:

    > “Change to daytime while maintaining the same style of the painting.”

* **Style transfer**

  * Name the style explicitly:

    > “Transform to Bauhaus art style.”
  * Describe key traits:

    > “Transform to oil painting with visible brushstrokes and thick paint texture.”
  * Preserve composition:

    > “Change to Bauhaus style while maintaining the original composition.”

* **Character consistency**

  * Be specific:

    > “The woman with short black hair,” rather than “her.”
  * Preserve defining features:

    > “…while maintaining the same facial features, hairstyle, and expression.”
  * Edit in steps:

  1. Change the background
  2. Adjust the pose or action

* **On-image text edits**

  * Use quotation marks around the text:

    > “Replace ‘joy’ with ‘BFL’.”
  * Maintain formatting:

    > “Replace the text while preserving the same font style.”

---

### 2. Core Prompting Principles

1. **Be Specific**
   Use precise descriptions; avoid vague terms.
2. **Step-by-Step**
   Break complex edits into a sequence of simple instructions.
3. **State What to Keep**
   Explicitly call out what must remain unchanged.
4. **Choose Clear Verbs**
   Prefer “change” or “replace” over generic “convert.”

---

### 3. Best-Practice Prompt Templates

* **Object Modification**

  > “Change **\[object]** to **\[new state]**, keep **\[elements to preserve]** unchanged.”
* **Style Transfer**

  > “Transform to **\[specific style]**, while maintaining **\[composition/character/other]** unchanged.”
* **Background Replacement**

  > “Change the background to **\[new background]**, keep the subject in the exact same position and pose.”
* **Text Editing**

  > “Replace ‘**\[original text]**’ with ‘**\[new text]**’, maintain the same font style.”

---

### 4. Common Pitfalls & Fixes

* **Unwanted Character Changes**

  * ❌ “Transform the person into a Viking.”
  * ✅ “Change the clothes to a Viking warrior outfit while preserving facial features.”

* **Composition Shifts**

  * ❌ “Put him on a beach.”
  * ✅ “Change the background to a beach while keeping the person in the exact same position, scale, and pose.”

* **Imprecise Style Application**

  * ❌ “Make it a sketch.”
  * ✅ “Convert to pencil sketch with natural graphite lines, cross-hatching, and visible paper texture.”

---

Follow these guidelines to get the most out of **FLUX.1 Kontext**’s powerful context-aware, consistency-preserving editing capabilities!




Consistent image editing reaches new heights as Black Forest rolls out a unified multi-task framework for subject and background modification, re-rendering, text editing, and more.

640 (2).webp

FLUX.1 Kontext: Game-changer

I'm gonna tell you about this cool tool called SkyReels-A2. It's changed how I make videos.


Looking at the image above, you can see what makes it special - combining multiple reference objects into one video!

## What Makes SkyReels-A2 Different From Other AI Video Tools?

SkyReels-A2 ain't just another video generator. It's way more clever than that.

The magic of this tool is "Compose Anything" - which means exactly what it says! I can take different pictures, objects, styles, and scenes, then blend them together into one video.

Look at them examples in the image. See how each video has multiple reference images on the side? That's the game-changer right here.

In the top left, there's a beach scene with a woman, but it's taking style cues from other images.

The middle one shows a lady with fantasy elements - combining a character with a magical door and nature setting.

This ain't just making videos from scratch. It's like being a video DJ - mixing and matching different visual elements!

When I first tried it, I was proper shocked. I could take:
- A picture of myself
- A beach image I liked
- A sunset color palette
- A walking motion reference

And boom! It made a video of me walking on that specific beach with that exact sunset feel!

The tech behind it is called "video diffusion transformers" which is fancy talk for "it understands how to blend different visual elements together naturally."

This is miles ahead of other tools that just make videos from text or a single image. SkyReels-A2 is actually understanding and combining multiple visual references at once!

## How To Get Started With Multi-Reference Video Creation

Setting up SkyReels-A2 on your computer ain't too hard. I'll walk you through it.

First, grab the code from GitHub:

```
git clone https://github.com/SkyworkAI/SkyReels-A2.git
cd SkyReels-A2
```

Set up your environment:

```
conda create -n skyreels-a2 python=3.10
conda activate skyreels-a2
pip install -r requirements.txt
```

Download the AI model:

```
huggingface-cli download Skywork/SkyReels-A2 --local-dir local_path --exclude "*.git*" "README.md" "docs"
```

Now for the exciting bit - preparing your reference images!

**This is where SkyReels-A2 shines compared to other tools.**

I usually create a folder with different references:
- Main subject (person, animal, object)
- Background scene or setting
- Style references
- Motion references

The system understands these different elements and combines them thoughtfully.

You can either use the command line:
```
python infer.py
```

Or the much friendlier interface:
```
python app.py
```

The interface lets you upload multiple reference images and control how much each influences the final video.

I had a bit of trouble first time round, mostly because my GPU wasn't powerful enough. You need a decent NVIDIA card to run this properly.

## Creating Multi-Reference AI Videos With SkyReels-A2

Making videos with SkyReels-A2 is where the real magic happens. Let me show you my process.

The most important thing to understand is that this ain't just text-to-video. It's "compose anything" - which means blending multiple visual elements together.

Here's my step-by-step:

1. Choose your main subject (person, object, character)
2. Select your background/setting image
3. Add style reference images (lighting, color, mood)
4. Include motion references if needed
5. Upload all references to the interface
6. Adjust how strongly each reference influences the result
7. Hit generate and wait for the magic!

I tried recreating one of the examples from the image above. I wanted a video of my mate in a spaceship corridor (like in the middle bottom example).

I uploaded:
- A picture of my mate
- An image of a sci-fi corridor
- A reference for cool lighting
- A phone-holding pose reference

The result was mind-blowing! It created a video that combined all these elements naturally.

What's clever about SkyReels-A2 is how it understands different types of references. It doesn't just copy-paste things together - it properly blends them.

If something doesn't look right, try adjusting the influence sliders. Sometimes lowering the influence of one reference can improve the overall result.

I've noticed it works best when your references are clear and not too complicated. High quality images give better results too.

## Advanced Tips For Combining Multiple References

After using SkyReels-A2 for a while, I've found some tricks for getting amazing multi-reference videos.

The biggest game-changer is understanding which types of references work well together.

**For best results combining multiple references:**

- Make sure lighting is consistent across references
- Choose style references that match the mood you want
- Use clear, high-quality images without busy backgrounds
- Try to match the angle of your subject and background references
- Add motion references that fit naturally with your subject

I've had amazing results mixing people with fantasy environments. Like putting my brother in a magical forest with glowing elements from another reference.

Another brilliant technique is using color palette references. I'll add a small image that just has the colors I want, and the AI picks up on that.

Sometimes less is more. I started by throwing in 5-6 references, but often 3 well-chosen ones give better results:
- Main subject
- Environment
- Style/mood

The system is really good at understanding clothing and accessories too. I can upload a reference of someone wearing a specific outfit, and it'll apply that style to my subject.

For those technical moments, you can actually adjust the "compositing weights" in the advanced settings. This controls how strongly each reference influences the final video.

I've notice that certain combinations are trickier - like trying to put realistic humans in cartoon worlds. The AI gets confused about which style to use.

My absolute favorite thing to create is travel scenarios - taking a picture of myself and blending it with amazing locations around the world. Instant holiday videos!

## The Technical Magic Behind SkyReels-A2's Multi-Reference System

Let me try to explain how this amazing tech actually works. I ain't no AI expert, but I'll keep it simple.

SkyReels-A2 uses something called "video diffusion transformers" which is proper clever. 

The real breakthrough is its ability to understand and compose multiple references together. Here's why that's special:

Traditional AI video tools work with:
- Text-to-video (from descriptions)
- Image-to-video (animating one image)

But SkyReels-A2 does "compose-to-video" - taking multiple visual elements and blending them naturally.

The system breaks down each reference into different aspects:
- Subject identity (what/who it is)
- Visual style (colors, lighting, texture)
- Motion patterns (how things move)
- Environment elements (setting, background)

Then it cleverly combines these elements while keeping everything consistent.

It's a bit like having a super-smart video editor who understands exactly which parts of each reference you want to use.

The A2-Wan2.1-14B model they've built has been trained to recognize these different elements and combine them in ways that make sense visually.

What's really impressive is how it handles conflicts between references. If one reference has a sunny day and another has rain, it makes smart decisions about which to follow.

I reckon this "compose anything" approach is going to be the future of AI video. Being able to mix and match visual elements gives so much more creative control!

## Practical Uses For Multi-Reference AI Videos

I've found loads of brilliant ways to use SkyReels-A2's multi-reference abilities. They're proper useful!

For my small business, I've been creating product videos by combining:
- Product photos
- Lifestyle setting references
- Motion references showing how the product works

This lets me make professional-looking videos without hiring a whole production team!

For social media content, I'm combining:
- Selfies or profile pics
- Trending locations or events
- Style references from popular creators

The videos get way more engagement than static images!

**Here are some clever uses I've discovered:**

- Travel content: Blend yourself into locations around the world
- Educational videos: Put subjects in historical settings using period references
- Fashion showcases: Combine clothing items with different models and settings
- Real estate: Create virtual tours by animating property photos with movement references
- Gaming content: Put yourself into game environments using screenshot references

The multi-reference approach solves so many problems with traditional AI video. Instead of trying to describe everything in text, you can just show visual references.

I helped my nephew with a school project using SkyReels-A2. We combined:
- Photos of him
- Images from his history textbook
- Motion references of people walking
- Style references for an old film look

He got top marks for his "time travel" presentation!

What's really exciting is how this changes storytelling. I can create little scenes with consistent characters across different settings by maintaining the same subject reference.

## The Future Of Multi-Reference AI Video Creation

The future of this tech is gonna be mental! I'm proper excited about where it's heading.

Right now, SkyReels-A2 can make short videos combining multiple references. But imagine what's coming next!

The Skywork team mentions they're working on an "infinity" version that'll make longer videos at higher resolution. That's gonna be game-changing!

I reckon we'll soon see:

- Full control over which elements from each reference to use
- Ability to specify exactly how references combine
- Longer narratives with multiple scenes
- Integration with other AI tools for full production workflows
- Mobile apps that make this tech accessible to everyone

The compose-anything approach is definitely the future of AI video. It just makes sense to build videos by combining visual elements rather than describing everything in text.

I'm already seeing how this changes content creation. Instead of needing expensive cameras, actors, and locations, I can just find reference images and combine them!

For small creators like me, this levels the playing field with big production companies. I can make professional-quality videos with just my laptop.

The most exciting thing is how it opens up creativity. I'm not limited by my resources anymore - if I can imagine a combination of elements, I can create it!

As the technology improves, I think we'll see entire films made this way. Imagine creating a movie by combining actor references, location shots, and style elements from your favorite directors!

SkyReels-A2's approach with video diffusion transformers is just the beginning of this revolution.

## Frequently Asked Questions About Multi-Reference Video Creation

### How many reference images can I combine in one video?

The current version works best with 3-5 references. You can try more, but it might get confused if you add too many conflicting elements.

### What types of references work best together?

A good combination is: main subject + environment/background + style reference. Make sure they have somewhat consistent lighting and perspective for best results.


### Are there any types of references that don't work well?

Very busy images with lots of elements can confuse the system. Also, extremely different art styles sometimes don't blend well. Keep references relatively clean and somewhat compatible.

### How long can videos be with the current version?

The preview version makes videos about 3 seconds long. They're working on an infinity version that'll make longer ones.

### Can I use these multi-reference videos commercially?

Check the latest terms on their GitHub. Generally, the license allows commercial use, but be careful about copyright if you're using references you don't own the rights to.

### Is SkyReels-A2 better than other AI video tools for specific tasks?

It's definitely better at combining multiple visual elements than most other tools. If you want to create videos that mix different references, SkyReels-A2 is currently one of the best options.

## Conclusion: Why SkyReels-A2's Multiple Reference System Changes Everything

SkyReels-A2 has completely changed how I think about making videos.

The ability to combine multiple references - taking subjects, environments, styles, and motions from different sources - gives me so much creative control.

Looking at the examples in the image above, you can see how powerful this approach is. Each video combines elements from multiple reference images in a way that feels natural and cohesive.

For anyone who creates content, this tool is a game-changer. You're no longer limited by what you can film yourself or describe in text.

Instead, you can build videos by combining visual references - just like a collage artist, but with moving images!

The "compose anything" approach of SkyReels-A2 is definitely the future of AI video creation. It's more intuitive, more flexible, and gives better results than traditional text-to-video methods.

I'm using it almost daily now and watching closely for updates.

This multi-reference video diffusion transformer technology is just getting started, and I can't wait to see where it goes next!


Discover how SkyReels-A2 is changing the game for video creators by allowing you to combine multiple reference images into one seamless video.

skyreels-a2.png

How I Use SkyReels-A2 To Create Amazing AI Videos With Multiple Reference Objects


Hey there, fellow AI enthusiasts! If you're anything like me, you're always on the lookout for the latest and greatest tools to take your text-to-image generations to the next level. Well, buckle up because I've got some exciting news that's going to blow your mind!
![new-vit-l-14-clip-l-text-encoder-finetune-for-flux-1-v0-kgtn2iad8mmd1.webp](https://api.comfyuiweb.com/uploads/new_vit_l_14_clip_l_text_encoder_finetune_for_flux_1_v0_kgtn2iad8mmd1_b0af0afb8a.webp)

Recently, a brilliant mind in the AI community, zer0int, has released a game-changing update to the CLIP-L text encoder. This new model, fine-tuned for Flux.1, boasts improved text adherence and detail accuracy. In other words, your generated images are about to look sharper and more true to your prompts than ever before!

But wait, there's more! zer0int has also provided a handy-dandy .safetensors download, making it a breeze to integrate this model into your workflow. Whether you're using Stable Diffusion, Flux, or any other text-to-image generator, this CLIP-L update is a must-have.

You can download the model: https://huggingface.co/zer0int/CLIP-GmP-ViT-L-14/tree/main

Now, I know what you're thinking: "But how does it compare to the Long-CLIP model?" Well, zer0int has got you covered there too. In a nutshell, the CLIP-L shines when it comes to shorter prompts and text-heavy tasks, while Long-CLIP excels at handling more elaborate scenes. It's like having a Swiss Army knife of text-to-image models!

So, what are you waiting for? Head over to the Hugging Face repository, grab the ViT-L-14-TEXT-detail-improved-hiT-GmP-TE-only-HF.safetensors file, and start generating some mind-blowing images! And if you're feeling generous, consider tossing a few bucks zer0int's way to help keep those AI critters fed and happy. After all, they're working hard to make our dreams of photorealistic images a reality!

## FAQs

1. **What is the difference between ViT-L-14-TEXT-detail-improved-hiT-GmP-HF.safetensors and ViT-L-14-TEXT-detail-improved-hiT-GmP-TE-only-HF.safetensors?**
The smaller file, ViT-L-14-TEXT-detail-improved-hiT-GmP-TE-only-HF.safetensors, contains only the text encoder, which is all you need for text-to-image generation. The larger file, ViT-L-14-TEXT-detail-improved-hiT-GmP-HF.safetensors, includes both the text encoder and the vision transformer, which is useful for other tasks but not necessary for generative AI.

2. **How do I use this CLIP-L update in my text-to-image workflow?**
Simply download the ViT-L-14-TEXT-detail-improved-hiT-GmP-TE-only-HF.safetensors file, place it in your models/clip folder (e.g., ComfyUI/models/clip), and select it in your CLIP loader. It's that easy!

3. **Will this CLIP-L update work with my existing Stable Diffusion or Flux models?**
Absolutely! This CLIP-L update is compatible with SD 1.5, SDXL, SD3, and Flux. These models all use the stock CLIP-L by default, so you can seamlessly integrate this improved version into your workflow.

## Conclusion

There you have it, folks! With zer0int's CLIP-L update, the sky's the limit when it comes to your text-to-image generations. Whether you're a seasoned pro or just starting out, this model is sure to take your creations to new heights. So, what are you waiting for? Get out there, experiment, and let your imagination run wild!

If you're looking to refine your images even further, you can also [sharpen image online](https://fixthephoto.com/sharpen-image-online.html) with easy-to-use tools that enhance clarity and detail.

Your generated images are about to look sharper and more true to your prompts than ever before!

Citations:
https://www.reddit.com/r/StableDiffusion/comments/1f83d0t/new_vitl14_clipl_text_encoder_finetune_for_flux1/
https://www.youtube.com/watch?v=gCzJI4sKr1I&t=1s

Discover the transformative potential of the newly released CLIP-L text encoder fine-tuned for Flux.1. 

new-vit-l-14-clip-l-text-encoder-finetune-for-flux-1-v0-z28p7r9e8mmd1.webp

New CLIP-L Model for Flux.1



I recently discovered **[Pollo.ai](https://pollo.ai?ref=zjjlzja)**, and it's been proper game-changing for my creative projects. This all-in-one AI video and image generator has made my life so much easier!

## What Makes Pollo.ai Special? My Personal Experience

I remember when creating videos required expensive software and loads of technical skills. Not anymore.

What makes [Pollo.ai](https://pollo.ai?ref=zjjlzja) brilliant is how it brings together loads of different AI platforms in one place. 

Instead of paying for 5 or 6 different subscriptions, I get everything in a single spot.
![pollo-video-ai.png](https://api.comfyuiweb.com/uploads/pollo_video_ai_6458089744.png)
![pollo-image-ai.png](https://api.comfyuiweb.com/uploads/pollo_image_ai_3ed4a33091.png)

It's dead simple to use.

The first time I tried it, I typed "sunset over mountain lake with birds flying" and within minutes had a beautiful video that looked proper professional.

You don't need to be a tech genius to use it. 

I'm certainly not one!

What I love most is how it lets me compare different AI models side-by-side. 

I can generate the same prompt with Runway, then Pika AI, then Luma AI, and see which one does the job best.

One day I was working on a project for my small business, needed a product demonstration video quickly. 

Traditional methods would've taken days or costed hundreds of pounds.

With [Pollo.ai](https://pollo.ai?ref=zjjlzja), I had it done in less than an hour.

The platform integrates APIs from top AI models like Kling AI, Runway, Luma AI and many others, so you don't need separate accounts everywhere.

I reckon I'm saving at least £80 a month by using Pollo instead of individual subscriptions.

Sometimes I use the Image to Video feature where I can take a still photo and transform it into a moving scene.

Other times I'm converting regular videos into anime-style creations that my kids absolutely love.

There's this brilliant feature called Consistent Character Video that keeps your main subject looking the same throughout the entire video.
![pollo-video-effects.png](https://api.comfyuiweb.com/uploads/pollo_video_effects_0b6b89c378.png)

This solves a massive problem with other AI tools where characters often change appearance between frames.

I've tried loads of similar tools, but none offer the combination of integrated platforms, ease-of-use and quality that [Pollo.ai](https://pollo.ai?ref=zjjlzja) does.

The variety of styles and effects available is proper impressive.

You can create anything from professional corporate videos to fun effects like the AI Squish or AI Inflate that always get laughs when shared with mates.

## Getting Started with Pollo.ai: My Top Tips for Beginners

When I first started using [Pollo.ai](https://pollo.ai?ref=zjjlzja), I made some silly mistakes that you can avoid.

The interface is straightforward but knowing a few tricks makes everything easier.

First thing you should do is explore the different integrated AI models available. 

Each one has its unique strengths.

Some are brilliant for realistic videos while others excel at stylized content.

The beauty of Pollo is that you don't need to create accounts on all these different platforms.

Everything's in one place, which saves loads of time and hassle.

Don't rush your prompts. 

The quality of what you get depends massively on what you put in.

Be specific about what you want to see in your video or image.

I've found that adding details about lighting, atmosphere, and movement gives much better results.

Try the same prompt across different AI models to see which one handles your specific request best.

This comparison feature has saved me from disappointment many times.

Start with the free plan to get a feel for everything. 

You'll have watermarks on your creations, but it's perfect for learning the ropes.

When you're ready to create professional content without watermarks, the paid plans are quite reasonable compared to hiring a video editor or subscribing to multiple AI platforms separately.

These are some prompt templates that have worked brilliantly for me:

* "Close-up of [subject] with [specific lighting] and [movement description]"
* "[Setting] with [weather condition] featuring [main subject] who is [action]"
* "Transform this portrait into [style] maintaining facial features and expression"

If you're creating marketing content, try using the Image to Video feature with your product images and  descriptions.

It's amazing for creating quick social media adverts that actually convert.

The AI Animation Generator is brilliant for creating engaging content for younger audiences or creative projects.

My nephew thinks I'm some kind of wizard now because of the cartoons I can make for him.

Don't be afraid to experiment with the video effects templates. 

Some of my most popular social posts have been simple videos using the AI Kissing or AI Hug templates.

Use the image generation features to create base images, then animate them with the Image to Video tool for a completely customised result.

This two-step approach gives you more control over the final output.

## How I Use [Pollo.ai](https://pollo.ai?ref=zjjlzja) for My Business and Personal Projects

[Pollo.ai](https://pollo.ai?ref=zjjlzja) hasn't just been a fun toy for me—it's become essential for my small business marketing.

I run an online shop selling handmade crafts, and professional product videos used to be a luxury I couldn't afford.

Before Pollo, I was spending over £200 a month on various AI platforms, each with different strengths.

Now I create new content weekly, showing my products from different angles and in various settings.

The engagement on my social media has gone through the roof since I started using AI-generated videos.

For my business, I mostly use the [Image to Video](https://pollo.ai/image-to-video?ref=zjjlzja) feature.

I take product photos then transform them into elegant videos that showcase the items being used in real-life scenarios.

Customers tell me these videos help them visualize owning the products themselves.

I can quickly test different AI models to see which one renders my products most accurately.

For personal projects, the [Video to Anime](https://pollo.ai/video-to-anime-ai?ref=zjjlzja) tool has been brilliant fun.

I converted old family videos into Pixar-style animations for my parent's anniversary, and they were proper chuffed with the results.

The AI effects templates are perfect for creating funny content to share with mates.

Last month I used the AI Muscle Generator to transform a video of my skinny brother and had the entire family in stitches.

What's really changed my workflow is the speed and convenience of having everything in one platform. 

I used to spend hours jumping between different AI websites, each with their own interfaces and quirks.

Now I can generate completely new content in minutes, all from the same dashboard.

For my blog, I use [Pollo.ai](https://pollo.ai?ref=zjjlzja) to create eye-catching header videos that keep readers engaged.
![pollo-ai-tools.png](https://api.comfyuiweb.com/uploads/pollo_ai_tools_5be9fdc350.png)

The analytics show that posts with AI-generated videos have almost double the average time on page.

I've even started offering video creation as an additional service to my clients, creating a new revenue stream I never anticipated.

The [Consistent Character Video](https://pollo.ai/consistent-character-video?ref=zjjlzja) feature has been particularly useful for creating a consistent brand mascot that appears across all my marketing materials.

When working with text prompts, I've learned to be very specific about the mood and atmosphere I want.

Adding words like "cinematic", "dramatic lighting", or "soft focus" makes a massive difference to the final output.

## Comparing Pollo.ai to Other AI Tools I've Tried

I've spent good money on various AI video tools over the past year, so I can give you a proper comparison.

[Pollo.ai](https://pollo.ai?ref=zjjlzja) stands out because it isn't just a single AI platform—it's an aggregator that brings together all the best ones.

Before Pollo, I was subscribing to Runway, Pika Labs, and several other platforms separately.

I was spending three times as much money and dealing with different user interfaces for each one.

With other tools, I needed separate subscriptions for image generation, video creation, and special effects.

![pollo-pricing.png](https://api.comfyuiweb.com/uploads/pollo_pricing_94aa39ec00.png)

The quality of Pollo.ai's output is consistently better because you can choose the best model for each specific task.

Some AI models are brilliant at landscapes but rubbish at human figures.

Others excel at cartoon styles but struggle with realism.

With Pollo, I just switch between models until I find the perfect match for my project.

I noticed that some other popular tools struggle with human figures, often creating weird hands or unnatural movements.

Pollo.ai lets me try different models until I find one that handles this better, especially when using the Consistent Character feature.

The speed is another area where [Pollo.ai](https://pollo.ai?ref=zjjlzja) outperforms others I've tried.

Some platforms had me waiting 20+ minutes for a simple video generation, while Pollo.ai typically delivers in just a few minutes.

The variety of AI models available in one place is what makes Pollo truly special.

Most competitors lock you into their proprietary model, but Pollo.ai lets you choose from different options depending on your specific needs.

This flexibility means you can select the perfect model for each project rather than forcing one approach for everything.

The user interface is much more intuitive than juggling multiple different AI platforms.

I'm not particularly tech-savvy, so I appreciate how straightforward everything is.

Price-wise, [Pollo.ai](https://pollo.ai?ref=zjjlzja) offers better value when you consider all the features included.

I was paying for three separate subscriptions before that together cost more than my current Pollo.ai plan.

The templates and effects library is constantly updated with new integrations, which isn't always the case with competitor platforms.

Last month they added several new transformation effects and models that weren't available anywhere else.

Customer support has been much more responsive than what I experienced with other AI tools.

When I had questions about optimizing my prompts for specific models, they provided helpful examples rather than generic answers.

If you're trying to decide between different AI video tools, the free plan at [Pollo.ai](https://pollo.ai?ref=zjjlzja) lets you test everything before committing, which not all competitors offer.

## FAQ: Things I Wished I'd Known Before Starting with Pollo.ai

**Is Pollo.ai difficult to learn if I'm not tech-savvy?**

Not at all! I'm properly rubbish with technology, but found it dead easy to use. The interface is designed for beginners, and you can create impressive videos within minutes of signing up at [Pollo.ai](https://pollo.ai?ref=zjjlzja).

**Why should I use Pollo.ai instead of going directly to platforms like Runway or Pika?**

The main advantage is having all these platforms in one place. You pay one subscription instead of many, can easily compare results from different AI models, and don't need to learn multiple interfaces. It's saved me loads of money and time.

**Do I need expensive equipment or software to use Pollo.ai?**

You only need a computer with internet access. Everything happens in your browser, so there's no need for downloads or special hardware. I use it on my old laptop without any problems.

**Can I use Pollo.ai videos for my business without copyright concerns?**

Yes, if you're on a paid plan. The commercial license lets you use generated content for business purposes. I use it for all my product videos and social media marketing.

**How do the different integrated AI models compare?**

Each model has different strengths. Runway is brilliant for realistic scenes, while Pika AI excels at stylized content. I recommend experimenting with different models for each project to find what works best. That's the beauty of having them all in one place.

**Can I edit the videos after they're generated?**

[Pollo.ai](https://pollo.ai?ref=zjjlzja) doesn't include video editing features, but you can download your creations and edit them in any video editing software. I use free tools like DaVinci Resolve to make small adjustments when needed.

**How many videos can I create on the free plan?**

The free plan gives you 20 credits. It's enough to test the features and different AI models, but if you're creating content regularly, you'll want to upgrade to a paid plan.

**Is my data safe when uploaded to Pollo.ai?**

According to their privacy policy, they don't claim ownership of your uploaded content. I've been using it for over six months with no concerns.

**Can I generate videos in different resolutions or aspect ratios?**

Yes, [Pollo.ai](https://pollo.ai?ref=zjjlzja) offers various output formats, including landscape, portrait, and square videos. This is brilliant for creating content optimized for different social platforms.

**Does Pollo.ai keep adding new AI models to their platform?**

Yes, they're constantly integrating new models as they become available. Just last month they added two new video generation models I hadn't seen anywhere else.

## My Final Thoughts on Pollo.ai

[Pollo.ai](https://pollo.ai?ref=zjjlzja) has fundamentally changed how I approach content creation for both personal and business projects.

The ability to access multiple leading AI platforms in one place has saved me both money and time.

I no longer need to juggle different subscriptions or learn multiple interfaces.

What impresses me most is how the platform continues to improve with regular updates and new model integrations.

Just last month they added several new AI models that I've already incorporated into my workflow.

If you're on the fence about trying [Pollo.ai](https://pollo.ai?ref=zjjlzja), I'd recommend starting with the free plan to experience the capabilities firsthand.

The platform offers the perfect balance of simplicity and powerful features that make AI video generation accessible to everyone.

Whether you're creating content for social media, business presentations, or just having fun with friends and family, Pollo.ai provides access to tools that previously required multiple expensive subscriptions.

I genuinely believe that all-in-one AI content creation platforms like [Pollo.ai](https://pollo.ai?ref=zjjlzja) represent the future of digital media, and I'm excited to see how it continues to evolve.

For anyone looking to enhance their content creation without learning complicated software, juggling multiple subscriptions, or hiring expensive professionals, **[Pollo.ai](https://pollo.ai?ref=zjjlzja)** is the solution I wholeheartedly recommend.


Discover how Pollo.ai combines multiple AI models in one platform, saving you time and money. Create stunning videos and images without technical skills or multiple subscriptions. Try it today!

polloai-index.png

Pollo.ai Review: The All-in-One AI Video & Image Generator That Saves Money

Hey there! I've been playing with the new Wan 2.1 model recently, and honestly, it's blown me away. Let me tell you everything about this incredible open-source video generation tool that's making waves in the AI community.

## What is Wan 2.1?

Wan 2.1 is a comprehensive open-source suite of video foundation models that's pushing the boundaries of what's possible with AI video generation. It's developed by the Wan team and available on [GitHub](https://github.com/Wan-Video/Wan2.1).

I first discovered it while looking for alternatives to expensive commercial video generation solutions. What caught my eye immediately was how it outperforms many closed-source models while being completely free and open.

The model comes in different sizes:
- A lightweight 1.3B parameter version that runs on consumer GPUs
- A more powerful 14B parameter version for professional results

What makes Wan 2.1 special is its versatility. It can handle:
* Text-to-Video generation
* Image-to-Video conversion
* Video editing capabilities
* Text-to-Image creation
* Video-to-Audio features

Perhaps the most impressive thing about Wan 2.1 is that it's the first video model that can actually generate both Chinese and English text within videos. This feature alone makes it incredibly useful for creating content with text overlays.

## Getting Started with Wan 2.1

Installing Wan 2.1 is pretty straightforward. You'll need to:

1. Clone the GitHub repository
```
git clone https://github.com/Wan-Video/Wan2.1.git
cd Wan2.1
```

2. Install the dependencies
```
pip install -r requirements.txt
```

3. Download the model weights (they offer several options depending on your needs)

The best part? The T2V-1.3B model only requires about 8.19 GB of VRAM, which means it can run on almost any consumer GPU. I've been using it on my RTX 4090, and it can generate a 5-second 480P video in about 4 minutes.

## Text-to-Video Generation with Wan 2.1

The text-to-video capabilities of Wan 2.1 are where it really shines. I've tried several prompts, and the results are impressively consistent.

To generate a video using the 14B model, you'd use:

```
python generate.py --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
```

If you're working with limited GPU memory like I was initially, you can use the 1.3B model with some memory optimization flags:

```
python generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --offload_model True --t5_cpu --sample_shift 8 --sample_guide_scale 6 --prompt "Two cats boxing"
```

One thing I've found that significantly improves results is using the prompt extension feature. This automatically enriches your prompt with more details, resulting in higher quality videos. You can use either the DashScope API or a local model for this.

## Image-to-Video: Bringing Photos to Life

I've been particularly impressed with the Image-to-Video capabilities. It's like magic seeing still photos suddenly come alive with movement.

To convert an image to video, use:

```
python generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --prompt "Summer beach vacation style"
```

The model preserves the aspect ratio of your original image while adding natural, coherent motion. I've tried it with landscape photos, portraits, and even abstract art - the results are consistently good.

## Why Wan 2.1 Stands Out from Other Models

After testing various video generation models, I can confidently say Wan 2.1 has several advantages:

1. **Performance**: It consistently outperforms other open-source models and even some commercial solutions
2. **Accessibility**: The 1.3B model runs on consumer hardware, making advanced video generation accessible to everyone
3. **Versatility**: From text-to-video to image animation, it handles multiple tasks well
4. **Text Generation**: Being able to generate readable text in videos is a huge advantage

The technical innovation behind Wan 2.1 is impressive too. It uses a novel 3D causal VAE architecture called Wan-VAE that can handle videos of any length while preserving temporal information.

## Common Questions About Wan 2.1

### What GPU do I need to run Wan 2.1?
For the 1.3B model, any GPU with 8GB+ VRAM should work. I've tested it on an RTX 4090, but it should run on cards like the RTX 3060 Ti or even older models.

### Can Wan 2.1 generate long videos?
Yes! The Wan-VAE architecture can encode and decode unlimited-length 1080P videos without losing historical temporal information.

### Is Wan 2.1 free to use?
Absolutely! It's released under the Apache 2.0 license, which means you can use it freely, even for commercial projects.

### How does Wan 2.1 compare to commercial models like Runway or Pika?
Based on the developer's evaluations, Wan 2.1 outperforms both open-source and many closed-source models in quality. I've found it produces results comparable to commercial solutions in many cases.

## Final Thoughts

Wan 2.1 represents a significant step forward for open-source video generation. It's democratizing access to high-quality video creation tools that were previously only available through expensive commercial services.

Whether you're a content creator, developer, or just someone interested in AI, Wan 2.1 is definitely worth checking out. The combination of performance, accessibility, and versatility makes it one of the most exciting developments in the generative AI space this year.

Have you tried Wan 2.1 yet? What kinds of videos have you created with it? I'd love to hear about your experiences!

[Check out Wan 2.1 on GitHub](https://github.com/Wan-Video/Wan2.1)


I discovered a free AI that creates better videos than paid tools! Wan 2.1 runs on your home PC & generates mind-blowing results in minutes. See what happened when I tried it...

wan21-open-source-video-generation-model.png

Wan 2.1: The Game-Changing Open Source Video Generation Model

Hey there! Let me tell you about something cool happening in the world of AI videos. It's called SkyReels V1, and it's changing how we create videos using AI.

## What Makes SkyReels V1 Special?

I've seen lots of AI video tools, but this one's different. Here's why:

- It's open for everyone to use (unlike some other tools that keep their secrets locked away)
- It's really good at showing faces and emotions - like, really good
- The videos it makes look like they could be from a movie

Let me break that down a bit more.

## The Secret Sauce Behind the Scenes

You know how some AI just doesn't get human expressions right? SkyReels fixed that. They trained their AI on millions of clips from movies and TV shows. That's why the videos look so natural.

Here's what makes it work:

1. It can spot 33 different facial expressions
2. It knows how people should move and stand in a scene
3. It can create over 400 different types of movements
4. It understands how lighting should look

## How Good Is It Really?

I'll be straight with you - it's pretty impressive. When they tested it against other AI video tools, SkyReels V1 came out on top. It scored 82.43 overall, beating other well-known tools like VideoCrafter and CogVideo.

But what does that mean for you? Well, the videos just look better. The people move more naturally, the lighting looks right, and everything feels more... real.

## Using SkyReels V1

Want to try it yourself? Here's what you need to know:

### What You'll Need
- A decent computer (if you've got an RTX 4090, you're golden)
- Some patience (good videos take time to make)
- Basic knowledge of using command lines
- CUDA version 12.2 (that's what works best)

### Getting Started

First, you'll need to get everything set up. Here's how:

1. Grab the code:
```bash
git clone https://github.com/SkyworkAI/SkyReels-V1
cd skyreelsinfer
```

2. Install what you need:
```bash
pip install -r requirements.txt
```

### Running Your First Video

Here's the simplest way to make a video:

```bash
python3 video_generate.py \
    --model_id Skywork/SkyReels-V1-Hunyuan-T2V \
    --task_type t2v \
    --guidance_scale 6.0 \
    --height 544 \
    --width 960 \
    --num_frames 97 \
    --prompt "FPS-24, A cat wearing sunglasses and working as a lifeguard at a pool" \
    --embedded_guidance_scale 1.0
```

Remember to start your prompt with "FPS-24" - it's important!

### Making It Work on Regular PCs

Got a RTX 4090? Here's how to make it work without using too much memory:

```bash
python3 video_generate.py \
    --model_id Skywork/SkyReels-V1-Hunyuan-T2V \
    --task_type t2v \
    --guidance_scale 6.0 \
    --height 544 \
    --width 960 \
    --num_frames 97 \
    --prompt "FPS-24, A cat wearing sunglasses and working as a lifeguard at a pool" \
    --embedded_guidance_scale 1.0 \
    --quant \
    --offload \
    --high_cpu_memory \
    --parameters_level
```

### Video Sizes That Work Well

Here are some sizes that work great:
- Vertical videos: 544x960 (97 frames)
- Horizontal videos: 960x544 (97 frames)
- Square videos: 720x720 (97 frames)

### Using Multiple GPUs

Got more than one GPU? Lucky you! Here's how to use them all:

```bash
python3 video_generate.py \
    --model_id Skywork/SkyReels-V1-Hunyuan-T2V \
    --guidance_scale 6.0 \
    --height 544 \
    --width 960 \
    --num_frames 97 \
    --prompt "FPS-24, A cat wearing sunglasses and working as a lifeguard at a pool" \
    --embedded_guidance_scale 1.0 \
    --quant \
    --offload \
    --high_cpu_memory \
    --gpu_num 2  # Change this number based on how many GPUs you have
```

### Speed Comparison

Just so you know what to expect:
- On a single RTX 4090: Takes about 15 minutes
- With 2 GPUs: About 7.5 minutes
- With 4 GPUs: About 5 minutes
- With 8 GPUs: Less than 3 minutes

That's way faster than other tools out there!

## FAQ

**Q: How long can the videos be?**
A: With a regular setup, you can make 4-second videos. If you've got more powerful hardware, you can go up to 12 seconds.

**Q: Do I need a super powerful computer?**
A: Not necessarily. It works better with fancy GPUs, but they've made it work on regular computers too.

**Q: Is it really free?**
A: Yep! The whole thing is open source. You can download and use it right now.

## Pro Tips

Here's what I've learned:

1. Start simple. Try making short videos first
2. Give clear, detailed prompts
3. Be patient - good videos take time to generate
4. Use multiple GPUs if you can - it makes things much faster

## What's Next?

And now comfyui supports loading and using SkyReels-V1-Hunyuan-I2V.

Need more help? Check out their GitHub page or join the community. There's always someone ready to help out!

github: https://github.com/SkyworkAI/SkyReels-V1

model: https://huggingface.co/Skywork/SkyReels-V1-Hunyuan-I2V

comfyui: https://github.com/comfyanonymous/ComfyUI/pull/6862

This free AI tool just dropped and it's making videos that look better than $100k productions! SkyReels V1 is the first AI that actually understands how humans move - we're talking 33 facial expressions and 400+ natural movements. Plus, it's completely FREE and open source!

comfyui-image-to-video_11zon.png

SkyReels V1 Deep Dive: Features, Setup, and Best Practices

Hey there! I've been playing around with this cool new tool called ComfyUI 3D Pack, and I want to tell you all about it. It's basically like having a 3D artist in your computer - but one that works really fast and doesn't need coffee breaks!

## What's This All About?

You know how making 3D stuff usually means spending hours learning complicated software? Well, this tool changes everything. It's like having a magic wand that turns regular pictures into 3D models. I'm not kidding - you can take a photo of your cat and turn it into a 3D model that you can spin around and look at from any angle!

The best part? It works right inside ComfyUI, which is already super popular for making AI art. If you're already using ComfyUI, you'll feel right at home.

## Cool Stuff You Can Do

Let me tell you about some of the awesome things this pack can do. It's like having a whole toolkit of 3D magic tricks:

## Single-Stage Models

### 1. TRELLIS (microsoft/TRELLIS)
- **Purpose**: Single image to 3D mesh with RGB texture
- **Model Location**: huggingface.co/JeffreyXiang/TRELLIS-image-large
- **Input**: Single image
- **Output**: Textured 3D mesh

**TRELLIS** introduces a structured 3D latent representation (SLAT) that combines sparse 3D grids with dense multi-view visual features from foundation models, enabling high-quality 3D generation across multiple formats (Radiance Fields, 3D Gaussians, meshes). The method employs Rectified Flow Transformers trained on 500K 3D assets, achieving state-of-the-art results through:

1. **Unified Representation** - SLAT's hybrid structure preserves both geometric detail (via sparse active voxels) and textural richness (through vision model features)
2. **Scalable Architecture** - 2B parameter model supporting text/image-conditioned generation
3. **Versatile Outputs** - Native support for different 3D representations without post-processing
4. **Editing Capabilities** - Enables asset variants and local manipulations through latent space operations


### 2. StableFast3D (Stability-AI/stable-fast-3d)
- **Purpose**: Quick single image to 3D mesh conversion
- **Special Note**: Requires Stability-AI terms agreement
- **Location**: Under Checkpoints/StableFast3D
- **Requirements**: Huggingface token in system.conf

**Stable Fast 3D** is Stability AI's real-time 3D generation model that converts single images into textured 3D meshes in 500ms. Key features:

1. **Ultra-Fast Conversion** - Generates UV-unwrapped meshes with material parameters in 0.5s (7GB VRAM) vs previous 10-minute generation times
2. **Enhanced Outputs** - Produces:
   - Illumination-reduced albedo textures
   - Optional quad/triangle remeshing (+100-200ms)
   - Normal maps and material parameters
3. **Applications** - Targets game development (background assets), AR/VR prototyping, e-commerce 3D modeling, and architectural visualization
4. **Access** - Available through:
   - Hugging Face (Community License for ≤$1M revenue)
   - Stability AI API
   - Stable Assistant chatbot with AR preview
5. **Technical Base** - Built on TripoSR architecture with retrained weights and mesh generation improvements

The model demonstrates a 1200x speed improvement over previous SV3D while maintaining output quality, positioning it as a rapid prototyping tool for 3D content creation.


## Two-Stage Models

### 3. Hunyuan3D_V2 (tencent/Hunyuan3D-2)
**Stage 1**: Single image → 3D mesh shape
**Stage 2**: Shape + reference image → Textured 3D mesh
- **Weights**: huggingface.co/tencent/Hunyuan3D-2/tree/main

**Hunyuan3D-2** is Tencent's scalable 3D generation system combining a 2.6B parameter shape model (Hunyuan3D-DiT) and 1.3B parameter texture model (Hunyuan3D-Paint). Key technical features:

1. **Architecture**  
   - Shape Generation: Flow-based diffusion transformer produces meshes from images/text
   - Texture Synthesis: Geometry-aware diffusion model (1024px+ resolution)
   - Unified pipeline supports both generated and custom meshes

2. **Performance**  
   - Outperforms SOTA in metrics: CMMD (3.193), FID_CLIP (49.165), CLIP-score (0.809)
   - Processes shape+texture in 24.5GB VRAM

3. **Deployment**  
   - Blender integration via addon
   - API server with GLB output

4. **Ecosystem**  
   - Community contributions: Windows tools, ComfyUI plugins
   - Enterprise platform (Hunyuan3D Studio) for asset management
   - Active development with TensorRT optimization roadmap

The system demonstrates state-of-the-art performance in conditional 3D asset generation while maintaining practical usability through multiple integration pathways.


### 4. Hunyuan3D_V1 (tencent/Hunyuan3D-1)
**Stage 1**: Single image → multi-views
**Stage 2**: Multi-views → Textured 3D mesh
- **Weights**: huggingface.co/tencent/Hunyuan3D-1/tree/main

Tencent Hunyuan3D-1.0 ([GitHub](https://github.com/Tencent/Hunyuan3D-1)) is a unified framework for 3D content generation that supports both text and image inputs. The system uses a novel two-stage pipeline:

1. **Multi-view Generation**: A diffusion model rapidly produces multi-view RGB images (4 seconds)
2. **3D Reconstruction**: A neural network converts these views into textured 3D meshes (7 seconds)

Key advantages include:
- Dual support for text/image prompts through integration with Hunyuan-DiT
- Significant speed improvements (10-25s generation time)
- Quality preservation through noise-handling reconstruction
- Open-source implementation with pre-trained models
- Commercial-friendly license (except baking module)

The framework offers lite and standard versions, with the standard model achieving superior quality while maintaining practical generation speeds. Installation requires CUDA-enabled GPUs and dependencies like PyTorch3D, with pre-trained models available via Hugging Face Hub.


## Advanced Multi-Stage Models

### 5. Unique3D (AiuniAI/Unique3D)
**Four-Stage Pipeline**:
1. Single image → 4 views (256x256)
2. Upscale to 512x512 → 2048x2048
3. Generate normal maps
4. Create textured 3D mesh

**Required Models**:
- sdv1.5-pruned-emaonly
- controlnet-tile
- ip-adapter_sd15
- OpenCLIP-ViT-H-14
- RealESRGAN_x4plus

**[Unique3D](https://github.com/AiuniAI/Unique3D)** ([Project Page](https://wukailu.github.io/Unique3D/)) is an open-source framework for rapid 3D mesh generation from single images, featuring:

**Core Capabilities**  
- 🚀 30-second generation of textured 3D meshes  
- 🖼️ Handles both object-centric and scene-level images  
- 🔄 Two-stage pipeline: Multi-view synthesis → Mesh reconstruction  

**Technical Highlights**  
- Hybrid architecture combining diffusion models with geometric processing  
- Adaptive view selection mechanism  
- Normal-map guided mesh refinement  

**Implementation Features**  
- Multiple deployment options:  
  - Local Gradio interface ([Demo](https://huggingface.co/spaces/AiuniAI/Unique3D))  
  - ComfyUI integration  
  - Docker support  
- Windows/Linux compatibility  
- MIT-licensed commercial use  

The system achieves 1280×1280 resolution outputs while maintaining 10× speed advantage over previous methods, though performance depends on input image quality and object orientation. Current limitations include sensitivity to occlusions and requirement for clear object boundaries in input images.


### 6. Era3D MVDiffusion
- **VRAM**: Minimum 16GB required
- **Output**: 6 multi-view images + normal maps (512x512)
- **Source**: pengHTYX/Era3D


**[Era3D](https://github.com/pengHTYX/Era3D)** ([Project Page](https://penghtyx.github.io/Era3D/)) introduces a high-resolution 3D generation framework with these key features:

**Core Innovation**  
- 🖼️ 512×512 multiview synthesis using row-wise attention  
- 🔄 Two-phase generation:  
  1. Multi-view RGB/Normal maps (4s)  
  2. Instant-NSR mesh reconstruction (7s)  

**Technical Highlights**  
- Orthogonal projection variant for input-view consistency  
- Hybrid training strategy combining 2D/3D supervision  
- Memory-efficient architecture (8GB VRAM usage)  

**Implementation**  
- Requirements:  
  - CUDA 11.8 + RTX 3090/4090 GPUs  
  - Specialized dependencies (xformers, nvdiffrast)  
- Deployment options:  
  - Local inference scripts  
  - Pre-trained models via Hugging Face  
  - Background removal integration  

The system achieves 3D reconstruction in ~11 seconds total while handling complex textures, though requires careful background preprocessing. Current limitations include AGPL-3.0 license constraints and sensitivity to input image perspective distortions.


### 7. InstantMesh Reconstruction
- **Input**: Multi-view images (white background)
- **Output**: RGB textured 3D mesh
- **Compatibility**: Works with Zero123++ and CRM MVDiffusion


**[InstantMesh](https://github.com/TencentARC/InstantMesh)** ([Paper](https://arxiv.org/abs/2404.07191)) is an efficient framework for single-image 3D generation with these key features:

**Core Architecture**  
- 🕒 30-second generation pipeline:  
  1. Multi-view synthesis (4s)  
  2. Sparse-view reconstruction (26s)  
- 🔄 Dual representation:  
  - Neural Radiance Fields (NeRF)  
  - Explicit textured meshes  

**Technical Highlights**  
- Hybrid mesh baking combining vertex colors & texture maps  
- Memory-optimized attention mechanisms (8GB VRAM usage)  
- Background-aware reconstruction via rembg integration  

**Implementation**  
- Multiple deployment options:  
  - Local Gradio/CLI  
  - Docker containers  
  - ComfyUI plugin  
- Training support for:  
  - Custom reconstruction models  
  - Zero123++ fine-tuning  
- Apache 2.0 licensed commercial use  

The system achieves 512×512 resolution outputs while maintaining 10× speed advantage over optimization-based methods, though requires high-quality input images for best results. Current limitations include dependency on CUDA 12.1+ and sensitivity to input image perspective.



## Specialized Models

### 8. CharacterGen (zjp-shadow/CharacterGen)
- **Purpose**: Character modeling from front view
- **Feature**: Works with Unique3D for enhanced results

**[CharacterGen](https://github.com/zjp-shadow/CharacterGen)** ([Paper](https://dl.acm.org/doi/10.1145/3658217)) introduces an anime-style 3D character generation system with these key features:

**Core Architecture**  
- 🎭 Two-phase generation:  
  1. Pose-canonicalized multi-view synthesis  
  2. Geometry-consistent 3D reconstruction  
- 🔄 VRM format support for animation-ready outputs  

**Technical Highlights**  
- Multi-view pose normalization for consistency  
- Hybrid rendering pipelines:  
  - Blender VRM add-on  
  - Three.js implementation  
- Anime3D dataset compatibility  

**Implementation**  
- Deployment options:  
  - Integrated Gradio web interface  
  - Modular 2D/3D stage separation  
- Requirements:  
  - CUDA-enabled GPUs  
  - PyTorch3D dependencies  
- Apache 2.0 licensed commercial use  

The system achieves 512×512 resolution character generation while maintaining anatomical consistency through pose calibration. Current limitations include dependency on VRM model preprocessing and requirement for high-quality input images. Pre-trained weights are available via [Hugging Face](https://huggingface.co/zjpshadow/CharacterGen).


### 9. Zero123++ (SUDO-AI-3D/zero123plus)
- **Output**: 6 view images (320x320)
- **Views**: Front, Back, Left, Right, Top, Down


**[Zero123++](https://github.com/SUDO-AI-3D/zero123plus)** ([Paper](https://arxiv.org/abs/2310.15110)) is a multi-view diffusion model for 3D generation with these key features:

**Core Architecture**  
- 🖼️ Single-image to 6-view synthesis (5GB VRAM)  
- 📐 Fixed camera parameters:  
  - Azimuth: 30° increments  
  - Elevation: 20°/-10° (v1.2)  
  - FOV: 30° fixed output  

**Technical Highlights**  
- Depth ControlNet for geometric consistency  
- Normal map generation for alpha matting  
- Adaptive step scheduling (28-100 steps)  

**Implementation**  
- Deployment options:  
  - Diffusers custom pipeline  
  - Streamlit/Gradio interfaces  
  - Hugging Face integration  
- Requirements:  
  - CUDA-enabled GPUs  
  - Torch 2.0+ recommended  

The model achieves 512×512 resolution outputs with CC-BY-NC licensed weights, requiring square input images (≥320px). Current limitations include non-commercial license restrictions and fixed viewpoint outputs. Pre-trained models available via [Hugging Face](https://huggingface.co/sudo-ai).


### 10. TripoSR
- **Function**: NeRF to 3D mesh conversion
- **Method**: Marching cubes algorithm
- **Source**: VAST-AI-Research/TripoSR

**[TripoSR](https://github.com/VAST-AI-Research/TripoSR)** ([Paper](https://arxiv.org/abs/2403.02151)) is a state-of-the-art open-source model for rapid 3D reconstruction from single images, featuring:

**Core Architecture**  
- ⚡️ 0.5-second generation on A100 GPUs  
- 🔄 Two-phase processing:  
  1. Multi-view synthesis  
  2. Feed-forward 3D reconstruction  

**Technical Highlights**  
- MIT-licensed commercial use  
- 6GB VRAM requirement per image  
- Texture baking support (up to 2048px)  

**Implementation**  
- Requirements:  
  - CUDA version matching PyTorch's CUDA version  
  - Python 3.8+  
- Deployment options:  
  - Command-line interface  
  - Local Gradio web demo  
  - Batch processing support  

The model achieves real-time performance while maintaining quality through its LRM-based architecture. Current limitations include dependency on CUDA version alignment and requirement for square input images. Pre-trained weights are included in the repository under MIT license.


### 11. Wonder3D
- **Output**: 6 consistent views + normal maps
- **Source**: xxlong0/Wonder3D

**[Wonder3D++](https://github.com/xxlong0/Wonder3D)** ([Project Page](https://www.xxlong.site/Wonder3D/)) introduces a cross-domain diffusion framework for 3D generation with these key features:

**Core Architecture**  
- 🕒 3-minute generation pipeline:  
  1. Multi-view RGB/Normal synthesis (2m)  
  2. Cascaded mesh reconstruction (1m)  
- 🔄 Cross-domain attention mechanism  

**Technical Highlights**  
- AGPL-3.0 licensed code (commercial restrictions)  
- Iterative refinement through `num_refine` parameter  
- Camera projection support:  
  - Orthographic (`--camera_type ortho`)  
  - Perspective (`--camera_type persp`)  

**Implementation**  
- Training requirements:  
  - 8x A100 GPUs (stage 1-3)  
  - Custom dataset preparation  
- Inference options:  
  - Local CLI/Gradio  
  - Hugging Face integration  
  - Colab demo via @camenduru  

The system achieves 512×512 resolution outputs while maintaining geometric consistency through multi-view attention. Current limitations include AGPL license constraints and dependency on accurate foreground masks. Pre-trained models require commercial licensing agreements.


### 12. Large Multiview Gaussian (3DTopia/LGM)
- **Speed**: Under 30 seconds on RTX3080
- **Feature**: Gaussian to mesh conversion
- **Source**: ashawkey/LGM

**[LGM (Large Multi-View Gaussian Model)](https://github.com/3DTopia/LGM)** ([Project Page](https://me.kiui.moe/lgm/)) introduces a 3D generation framework using Gaussian Splatting, featuring:

**Core Architecture**  
- 🌟 Dual-mode generation:  
  - Text-to-3D via MVDream/ImageDream  
  - Image-to-3D through multi-view synthesis  
- ⚡️ Real-time Gaussian Splatting reconstruction  

**Technical Highlights**  
- Modified depth-aware rasterization pipeline  
- Hybrid training strategy:  
  - 80K Objaverse subset pre-training  
  - Multi-GPU distributed training  
- MIT-licensed commercial use  

**Implementation**  
- Requirements:  
  - CUDA 11.8 + xformers  
  - RTX 3090/4090 GPUs recommended  
- Deployment options:  
  - Local Gradio interface  
  - Replicate API integration  
  - Mesh conversion to *.ply format  

The system achieves 512×512 resolution outputs while maintaining 10GB VRAM usage through optimized Gaussian parameterization. Current limitations include dependency on specific CUDA versions and requirement for square input images. Pre-trained weights available via [Hugging Face](https://huggingface.co/ashawkey/LGM).


### 13. Triplane Gaussian Transformers
- **Speed**: Under 10 seconds on RTX3080
- **Feature**: Quick Gaussian generation
- **Source**: VAST-AI-Research/TriplaneGaussian


**[TriplaneGaussian](https://github.com/VAST-AI-Research/TriplaneGaussian)** ([Paper](https://arxiv.org/abs/2312.09147)) introduces a hybrid 3D reconstruction framework with these key features:

**Core Architecture**  
- ⚡️ Sub-second reconstruction (A100 GPU)  
- 🔄 Dual representation:  
  * Triplane features (implicit)  
  * 3D Gaussian Splatting (explicit)  

**Technical Highlights**  
- Transformer-based geometry prediction  
- Adaptive camera distance parameter (1.9-2.1)  
- SAM integration for background removal  
- Apache 2.0 licensed commercial use  

**Implementation**  
- Requirements:  
  * CUDA-enabled GPUs  
  * PyTorch3D + diff-gaussian-rasterization  
- Deployment options:  
  * [Colab Notebook](https://colab.research.google.com/drive/1UyS_7RvQQaefC0HmGjsNHPUfQn2MlIcD)  
  * Local Gradio with [custom renderer](https://github.com/dylanebert/gradio-splatting)  
  * Hugging Face [model integration](https://huggingface.co/VAST-AI/TriplaneGaussian)  

The system achieves real-time performance through optimized Gaussian parameterization, though requires careful camera distance tuning for optimal results. Current limitations include dependency on foreground segmentation and training dataset scale constraints.


But wait - there's more! You can also:
- Make 3D models look smooth and professional
- Create spinning videos of your 3D models
- Save your work in different 3D file formats
- Fix up models that aren't quite right

## Getting Started Is Dead Easy

Want to try it yourself? Here's how:

1. The simplest way is through ComfyUI-Manager. It's like installing an app on your phone - just click and you're done!

2. If you're more of a DIY person, you can download it yourself from GitHub. Just copy a few commands, and you're ready to go.

3. Got a Windows PC? There's even a special version called Comfy3D-WinPortable that's super easy to set up.

## What's Coming Next?

The folks making this tool have big plans:
- They're working on making it even better at turning 3D point clouds into proper models
- New ways to estimate camera positions from photos
- More cool features for working with multiple views of objects

## Tips from Someone Who's Used It

Here are some things I learned while playing with it:

1. If you get any OpenGL errors (those annoying technical messages), just tick the 'force_cuda_rasterize' box. Works like a charm!

2. Having trouble with the coordinates? Remember it uses a system where:
   - Up is +y
   - Right is +x
   - Forward is +z
   
It's like a video game coordinate system - once you get it, it makes total sense!

## Wrapping Up

This tool is honestly amazing for anyone interested in 3D creation. Whether you're making game assets, doing 3D printing, or just playing around with 3D art, it makes everything so much easier.

The best part? It's always getting better. The team behind it is constantly adding new features and making improvements. And because it's open source, you can even help make it better if you're into coding!

Give it a try - you might be surprised at how easy it is to start creating cool 3D stuff!

github: https://github.com/MrForExample/ComfyUI-3D-Pack


Turn any image into professional 3D models instantly with ComfyUI 3D Pack. 13+ AI models, simple workflow, and no complex software needed. Start creating amazing 3D art today!

teaser.jpg

Best 3D Models With Comfyui

Hey there! I've been playing around with Ruyi-Models lately, and I'm excited to show you how this cool AI tool can transform your images into smooth, high-quality videos. 

## What is Ruyi-Models?

Ruyi is an open-source image-to-video model that does something pretty amazing - it takes your still images and turns them into cinematic videos. Here's what makes it special:

- Creates videos at 768 resolution (that's really crisp!)
- Runs at 24 frames per second (just like movies)
- Makes 5-second videos with 120 total frames
- Lets you control camera movement and motion effects
- Works great on RTX 3090 or 4090 GPUs

## Getting Started

Installing Ruyi is pretty straightforward. Just run these commands:

```bash
git clone https://github.com/IamCreateAI/Ruyi-Models
cd Ruyi-Models
pip install -r requirements.txt
```

### For ComfyUI Users

You've got two ways to set this up:

1. **The Easy Way**: Use ComfyUI Manager
   - Install the manager
   - Search for "Ruyi"
   - Click install
   - Also grab "ComfyUI-VideoHelperSuite"

2. **Manual Setup**:
```bash
cd ComfyUI/custom_nodes/
git clone https://github.com/IamCreateAI/Ruyi-Models.git
pip install -r Ruyi-Models/requirements.txt
```

## Making Your First Video

The simplest way to start is:

```python
python3 predict_i2v.py
```

This will:
- Download the model automatically
- Use sample images from the assets folder
- Generate a video based on those images

### Pro Tips

- Got a beefy GPU with 24GB+ memory? Use `predict_i2v_80g.py` for faster generation
- Working with less memory? No worries - there are optimization settings to help you out

## FAQ

**Q: What GPU do I need?**
A: An RTX 3090 or 4090 works best, but you can run it on less powerful cards with memory optimization.

**Q: How long does video generation take?**
A: It varies by settings and hardware, but expect anywhere from 1-20 minutes for a 5-second clip.

**Q: Can I control the camera movement?**
A: Yep! You can set different camera motions like pan, tilt, and zoom.

github:[https://github.com/IamCreateAI/Ruyi-Models](https://github.com/IamCreateAI/Ruyi-Models)


Learn how to transform still images into cinematic 24fps videos with Ruyi-Models, an open-source AI tool. This guide covers installation, usage tips, and best practices for generating high-quality 768p videos from images. 

Latest News for ComfyUI