Game of Thrones: The Animated Series

• This Midjourney-created animated series is our pop culture pick of the week.

HalimAlrasihi: Kling AI Beautiful Characters

• Halim Alrasihi used Kling AI to bring beautiful and cinematic models to life. The characters interact beautifully with lights and shadows.

• He noted that he used creativity at 0.5 and used a negative prompt with added words like slow, low quality, morphing, blurry.

Dave Clark: BloodSpace

• Filmmaker Dave Clark unveils his latest passion project (and best AI film trailer yet): "BloodSpace," an AI-generated trailer blending vampires and lunar landscapes.

• Clark shares his creative process, highlighting his use of cutting-edge AI tools like Runway ML and Midjourney to bring his vision to life

• Clark’s enthusiasm is palpable as seen in his innovative approach to filmmaking, including the use of tools like runway’s Gen 3, Midjourney 6.1, Kling AI, and Luma’s Dream Machine.

Jeff Synthesized: Alice in Wonderland

• A reimagined young woman's journey into a bizarre, modern fantasy world, where she navigates absurdities and challenges mirroring today's society.

• Uploaded to the new platform ‘Dreamflaire.’ It’s Jeff’s most in-depth and developed AI film yet

  Watch here!

Everyone is talking about FLUX.1: new open-source image generator

@MokadyRon compared the difference between Flux v. SD3:

Rotary Position Embedding (RoPE):

• Flux: Injected before each attention layer.

• SD3: Not specified.

Model Size and Architecture:

• Flux: 12B parameters, 57 layers (19 MMDIT + 38 Single DIT similar to AuraFlow), hidden dimension 3072.

• SD3: 8B parameters.

Text Embedding:

• Flux: Uses T5XXL text embedding with additional pooled embedding from a single CLIP.

• SD3: No mention of T5XXL, pooled embedding in SDXL not significant.

Noise Scheduler:

• Flux: Dynamic noise shifting based on input size.

• SD3: Static noise shifting.

Distillation:

• Flux: Model distilled to avoid computing CFG, reduces inference time, but does not support negative prompts.

• SD3: Not specified.

Variational Autoencoder (VAE):

• Similar performance, visually hard to spot differences.

Dataset and Compute:

• Possible significant differences in datasets and compute resources used for training.
  

  

AI Dubbing Tools Market Size to Grow USD 1883.2 Million by 2030

• The global AI Dubbing Tools market is expected to grow from $783 million in 2023 to $1883.2 million by 2030, with a CAGR of 14.2%.

• Major factors driving growth include demand for localized content, advancements in voice synthesis and NLP technology, and the rise of streaming platforms.

• AI dubbing tools offer quick, cost-effective solutions for dubbing content in multiple languages, improving accessibility and viewership.

• The market is segmented into cloud-based and on-premises solutions, with applications in content creation, films, animation, and other areas.

• North America, particularly the US, is a key player in the market due to its advanced tech infrastructure and entertainment industry needs.

  Read more