Flux.1-Dev BNB NF4 (v1 & v2):
Source: https://huggingface.co/lllyasviel/flux1-dev-bnb-nf4/tree/main from lllyasviel
Flux.1-Schnell BNB NF4:
Source: https://huggingface.co/silveroxides/flux1-nf4-weights/tree/main from silveroxides
ComfyUI: https://github.com/comfyanonymous/ComfyUI_bitsandbytes_NF4
Forge: https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/981
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Flux.1-Dev (v1+v2) Flux.1-Schnell BNB NF4 is a highly specialized Image generation AI Model of type Safetensors / Checkpoint AI Model created by AI community user RalFinger. Derived from the powerful Stable Diffusion (Flux.1 S) model, Flux.1-Dev (v1+v2) Flux.1-Schnell BNB NF4 has undergone an extensive fine-tuning process, leveraging the power of a dataset consisting of images generated by other AI models or user-contributed data. This fine-tuning process ensures that Flux.1-Dev (v1+v2) Flux.1-Schnell BNB NF4 is capable of generating images that are highly relevant to the specific use-cases it was designed for, such as base model, flux1.d, flux1.s.
With a rating of 0 and over 0 ratings, Flux.1-Dev (v1+v2) Flux.1-Schnell BNB NF4 is a popular choice among users for generating high-quality images from text prompts.
Yes! You can download the latest version of Flux.1-Dev (v1+v2) Flux.1-Schnell BNB NF4 from here.
To use Flux.1-Dev (v1+v2) Flux.1-Schnell BNB NF4, download the model checkpoint file and set up an UI for running Stable Diffusion models (for example, AUTOMATIC1111). Then, provide the model with a detailed text prompt to generate an image. Experiment with different prompts and settings to achieve the desired results. If this sounds a bit complicated, check out our initial guide to Stable Diffusion – it might be of help. And if you really want to dive deep into AI image generation and understand how set up AUTOMATIC1111 to use Safetensors / Checkpoint AI Models like Flux.1-Dev (v1+v2) Flux.1-Schnell BNB NF4, check out our crash course in AI image generation.
V2 is 0.5 GB larger than the previous version, since the chunk 64 norm is now stored in full precision float32, making it much more precise than the previous version. Also, since V2 does not have second compression stage, it now has less computation overhead for on-the-fly decompression, making the inference a bit faster!