huggingface / huggingface/candle
Latent Consistency Models Support
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- Dominant language
- Rust
- Stars
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- Forks
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- Avg merge
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- Merged PRs (30d)
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Description
Brief introduction to Latent Consistency Models
Latent Consistency Models (LCMs) are a novel approach in image synthesis that enhance pre-trained Latent Diffusion Models, such as Stable Diffusion 1.5 / SDX, by enabling high-resolution image generation with significantly fewer diffusion steps.
They achieve this by predicting the solution of the underlying ODE instead of predicting the score function and then propagate it.
Current state
The original version of LCMs could be used with two method:
- By applying a LoRA to a pretrain Latent Diffusion Model (LDM), checkpoints available here: Latent Consistency Models LoRAs
- By directly using the Standalone LCMs version, checkpoints available here: Latent Consistency Models Weights
Currently Standalone LCMs are already available in 🤗 Diffusers under LCM Pipeline and LCM Scheduler.
A pretrain version of LCM on SSD1B is available and achieve very good result, I would like to implement it here in Candle because this might enable a fast image generation directly in browser with WASM.
Are you guys interested in implementing LCM here ? The most direct way would be to implement Stable Diffusion LoRA, so it will also enable the usage of any custom LoRA aside of LCM
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the linked Diffusers LCM Pipeline and LCM Scheduler, along with the referenced Stable Diffusion LoRA approach. Define the Candle scope before implementation; done would mean LCM support, including the proposed LoRA path and browser WASM use.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- rust, wasm
- Domain
- ai, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100