What I load in ComfyUI, and which models do true instruction edits vs. needing img2img. Includes the encoders, VAEs and ControlNets you need.
🔄 In a Training Loop
Alex Jones
axjns
AI & ML interests
I build AI/ML infrastructure
Recent Activity
reacted to ProCreations's post with 👍 14 minutes ago
I have hit 300 followers, and I think this calls for a bit of a giveaway 👀 a unique one, too.
I have had countless AI projects I have wanted to make but have been (brutally) blocked by compute. Now that I finally have just enough compute to sort of get around (i still don't have enough 😭) and for hitting 300 followers (tysm!) I will be funding three of the communities projects via HuggingFace jobs, giving them 150 dollars max worth of compute each. I will personally be picking the winners, I am looking for projects that genuinely hit the compute wall: great ideas, blocked by compute, just like the countless ideas I've had.
To join, head over to https://giveaway.ssh.codes
RULES:
- Final result must be open weight or open source
- Only one submission per person
- Have fun! liked a Space 22 minutes ago
DontPlanToEnd/UGI-Leaderboard upvoted a changelog about 14 hours ago
MCP Server EnhancementsOrganizations
Inference cost decides your architecture. These papers, read top to bottom, build the mental model: where the FLOPs and the bytes actually go.
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Efficiently Scaling Transformer Inference
Paper • 2211.05102 • Published • 4 -
Scaling Laws for Neural Language Models
Paper • 2001.08361 • Published • 11 -
Training Compute-Optimal Large Language Models
Paper • 2203.15556 • Published • 13 -
Fast Transformer Decoding: One Write-Head is All You Need
Paper • 1911.02150 • Published • 9
What I load in ComfyUI, and which models do true instruction edits vs. needing img2img. Includes the encoders, VAEs and ControlNets you need.
Everything I run on a 64GiB unified-memory Strix Halo box, with my own measured numbers. The finding: MoE decodes ~6x faster than dense here.
Inference cost decides your architecture. These papers, read top to bottom, build the mental model: where the FLOPs and the bytes actually go.
-
Efficiently Scaling Transformer Inference
Paper • 2211.05102 • Published • 4 -
Scaling Laws for Neural Language Models
Paper • 2001.08361 • Published • 11 -
Training Compute-Optimal Large Language Models
Paper • 2203.15556 • Published • 13 -
Fast Transformer Decoding: One Write-Head is All You Need
Paper • 1911.02150 • Published • 9