https://normalcrafter.github.io

https://github.com/Binyr/NormalCrafter

https://huggingface.co/spaces/Yanrui95/NormalCrafter

https://huggingface.co/Yanrui95/NormalCrafter

https://github.com/Comfy-Org/comfy-cli

comfy-cli is a command line tool that helps users easily install and manage ComfyUI, a powerful open-source machine learning framework. With comfy-cli, you can quickly set up ComfyUI, install packages, and manage custom nodes, all from the convenience of your terminal.

C:\<PATH_TO>\python.exe -m venv C:\comfyUI_cli_install
cd C:\comfyUI_env
C:\comfyUI_env\Scripts\activate.bat
C:\<PATH_TO>\python.exe -m pip install comfy-cli
comfy --workspace=C:\comfyUI_env\ComfyUI install

# then
comfy launch
# or
comfy launch -- --cpu --listen 0.0.0.0

If you are trying to clone a different install, pip freeze it first. Then run those requirements.

# from the original env
python.exe -m pip freeze > M:\requirements.txt

# under the new venv env
pip install -r M:\requirements.txt

https://ali-vilab.github.io/VACE-Page

https://github.com/ali-vilab/VACE

https://huggingface.co/collections/ali-vilab/vace-67eca186ff3e3564726aff38

https://github.com/kijai/ComfyUI-WanVideoWrapper/tree/main/example_workflows

https://github.com/ltdrdata/ComfyUI-Inspire-Pack

https://github.com/crystian/ComfyUI-Crystools

https://aejion.github.io/accvideo

https://github.com/aejion/AccVideo

https://huggingface.co/aejion/AccVideo

AccVideo is a novel efficient distillation method to accelerate video diffusion models with synthetic datset. This method is 8.5x faster than HunyuanVideo.

https://higgsfield.ai

https://runwayml.com/research/introducing-runway-gen-4

With Gen-4, you are now able to precisely generate consistent characters, locations and objects across scenes. Simply set your look and feel and the model will maintain coherent world environments while preserving the distinctive style, mood and cinematographic elements of each frame. Then, regenerate those elements from multiple perspectives and positions within your scenes.

https://github.com/AIDC-AI/ComfyUI-Copilot

https://openai.com/index/introducing-4o-image-generation

https://preview.reve.art

https://decrypt.co/311375/new-reve-image-generator-beats-ai-art-heavyweights-midjourney-and-flux-at-a-penny-per-image

A little-known AI image generator called Reve Image 1.0 is trying to make a name in the text-to-image space, potentially outperforming established tools like Midjourney, Flux, and Ideogram. Users receive 100 free credits to test the service after signing up, with additional credits available at $5 for 500 generations—pretty cheap when compared to options like MidJourney or Ideogram, which start at $8 per month and can reach $120 per month, depending on the usage. It also offers 20 free generations per day.

https://arxiv.org/pdf/2503.17347

https://abuuu122.github.io/DAI.github.io

https://github.com/Abuuu122/Dereflection-Any-Image

https://huggingface.co/spaces/sjtu-deepvision/Dereflection-Any-Image

https://fenghora.github.io/Personalize-Anything-Page

Customize any subject with advanced DiT without additional fine-tuning.

https://stability.ai/news/introducing-stable-virtual-camera-multi-view-video-generation-with-3d-camera-control

https://static1.squarespace.com/static/6213c340453c3f502425776e/t/67d9986bf7c111252695fa9b/1742313585359/stable-virtual-camera.pdf

Capabilities

Stable Virtual Camera offers advanced capabilities for generating 3D videos, including:

• Dynamic Camera Control: Supports user-defined camera trajectories as well as multiple dynamic camera paths, including: 360°, Lemniscate (∞ shaped path), Spiral, Dolly Zoom In, Dolly Zoom Out, Zoom In, Zoom Out, Move Forward, Move Backward, Pan Up, Pan Down, Pan Left, Pan Right, and Roll. 
• Flexible Inputs: Generates 3D videos from just one input image or up to 32.

• Multiple Aspect Ratios: Capable of producing videos in square (1:1), portrait (9:16), landscape (16:9), and other custom aspect ratios without additional training.
• Long Video Generation: Ensures 3D consistency in videos up to 1,000 frames, enabling seamless

Model limitations

In its initial version, Stable Virtual Camera may produce lower-quality results in certain scenarios. Input images featuring humans, animals, or dynamic textures like water often lead to degraded outputs. Additionally, highly ambiguous scenes, complex camera paths that intersect objects or surfaces, and irregularly shaped objects can cause flickering artifacts, especially when target viewpoints differ significantly from the input images.