How is the Flux Klein 4b fp8 model supposed to be run with diffusers with rtx4000ada? Can I run native fp8 mat_mult?

[vgabbo]

Thank you very much,

[aniruddhaadak80]

Hi @vgabbo!

Thanks for starting this discussion! When dealing with AI/LLM integrations, Vector DBs, or agent frameworks, quirks like this can usually be traced back to a few specific moving parts:

1. Environment & Dependency Versions: The AI tooling ecosystem evolves incredibly rapidly. Double-check your local environment (Node/Python versions) to ensure packages like langchain, transformers, or openai are on the latest stable releases. Breaking API changes upstream happen often.
2. Context Window & Embeddings: If you are seeing strange truncation, blank responses, or timeouts, make sure the payload you are sending fits well within the specific model???s maximum context window. Also verify your vector chunk sizes match the dimensionality of your chosen embedding model.
3. Rate Limits: If you are hitting external APIs, occasionally verify that your API key is correctly scoped and that you are not hitting concurrency or token rate limits which might silently fail or hang the process.

If you are still blocked, providing a minimal reproducible snippet or logging the raw request/response payload (scrubbed of secrets) usually helps pinpoint the exact failure layer much faster.

Hope this helps point you in the right direction. Let me know if you make any progress!

[adi776borate]

Photoroom has converted the weights from the ComfyUI format to Diffusers.
You can find the weights and usage here: https://huggingface.co/Photoroom/FLUX.2-klein-4b-fp8-diffusers

Regarding fp8 support, in this article, NVIDIA claims that their Fourth-generation Tensor Cores support acceleration of the FP8 precision data type. So, if you go with transformer_fp8_static/ variant, you can leverage native fp8 matmul.