Blender camera motion + MiniMax H3 ref2vid in ComfyUI (workflow + prompts)

I’ve been experimenting with building the camera move in Blender before generating the AI video. This example combines a five-second mannequin render with a character reference sheet and a waterfront background image in MiniMax H3’s reference-to-video workflow. How I made it: Used Codex CLI with Blender MCP to build a simple mannequin scene and animate …

Update to the KREA 2 Turbo Style Gallery: 397 styles re-rendered with a prompt fix, 5 rewritten, 1 new

Update to the [KREA 2 Turbo Style Gallery](https://www.reddit.com/r/StableDiffusion/comments/1v4u1bu/krea_2_turbo_style_gallery/) from July. There’s an actual finding in this one, so here’s what changed: The fix: the style’s name was missing from the prompt. u/Dear-Spend-2865‘s wildcards are written as Style Name: description. When I converted them into the node I only kept the description, so for 290 of …

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Trading a Cloud Identity for Your Own: Workload Attestation on Managed Compute

By Dhruv Pratap Introduction Organizations that have been around for a while usually run two identity systems side by side. One belongs to the cloud provider: IAM roles, instance profiles, execution roles. The other is your own, and it is the one your internal services actually check when they decide whether to answer a request. On infrastructure …

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Scaling MoE reinforcement learning on Amazon EKS with EFA and DeepEP with 40% more throughput

When you post-train a Mixture-of-Experts (MoE) model with Reinforcement Learning from Human Feedback (RLHF) or Group Relative Policy Optimization (GRPO) at scale, three simultaneous challenges emerge. The first requires coordinating heterogeneous compute for rollout generation and policy training. Second, sustaining high-throughput communication across hundreds of accelerators. And third, dynamically orchestrating every subsystem to keep them …

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Best practices guide for customizing Gemini models via Reinforcement Learning (RL)

Reinforcement learning (RL) has been a keystone of modern LLM post-training, but it demands large training clusters and access to model internals that external customers can’t have with proprietary models like Gemini. So here at Google Cloud, we packaged it into a managed RL fine-tuning service (RLFT service) — you bring prompts and a reward …