# F-Lite Guide to Open Weights, ComfyUI and Detailed Prompts > Published 2026-04-05, updated 2026-09-06 ยท https://www.promptzone.com/wei_saito/nouveau-f-lite-efficient-ai-image-tool-4hmg --- title: F-Lite Guide to Open Weights, ComfyUI and Detailed Prompts published: true description: Run F-Lite through its official CLI or ComfyUI nodes, understand the published model variants and write prompts suited to its documented limits. tags: ai,imagegeneration,comfyui,opensource --- F-Lite is a text-to-image diffusion model developed by Freepik and fal. Its original 10B weights are downloadable from Hugging Face, and the official repository supplies command-line generation and ComfyUI integration. [Model card][card] [Repository][repo] ## What are the key facts about F-Lite? | Field | Verified information | |---|---| | Developer | Freepik and fal. [Model card][card] | | Released | Original model: April 29, 2025; 7B version: May 9, 2025. [Repository][repo] | | Type | Text-to-image diffusion model. [Model card][card] | | Size or parameters | Original F-Lite: 10B; separately released F-Lite 7B: 7B. [Model cards][card] [7B card][small] | | License and access | Downloadable weights under CreativeML Open RAIL-M. [Model card][card] | | Where it runs | Official code supports CLI and ComfyUI workflows; the model card links hosted demos. [Repository][repo] [Model card][card] | | Hardware baseline | The repository specifies at least 24 GB of VRAM for its original model workflow. [Repository][repo] | ## What distinguishes F-Lite, Texture and the 7B variant? The developers describe training on approximately 80 million images from Freepik's collection, characterized as copyright-safe and safe for work. This is the developers' account of their data. [Texture card][texture] That provenance emphasis is useful when deciding which model documentation to investigate. Keep the distinction between the stated training collection and your own requirements for a generated asset; review each output for its intended use. The Texture variant emphasizes surface detail, and the later 7B model offers a distilled alternative. [Texture card][texture] [7B card][small] F-Lite 7B is described as a distilled version of the larger model. The developer reports improved speed and memory efficiency, but those statements do not provide a universal runtime for your workstation. [7B card][small] For a first evaluation, choose scenes where you can judge materials and composition directly. A ceramic bowl on a wooden shelf, for example, gives you clear surfaces, edges and spatial relationships to inspect. Build your first prompt from visible requirements. Describe the bowl's shape and color, the shelf material, the camera position and the direction of light. Decide which details matter before viewing candidate outputs. ## What hardware and image-generation limits apply to F-Lite? The model card warns about malformed outputs, limited text capabilities and possible bias. It also recommends longer prompts and generation above one megapixel, noting poorer quality with small images or short requests. [Model card][card] That suggests a useful baseline: try a detailed scene without demanding accurate small lettering. If exact typography is essential, include a separate acceptance test for every required word instead of judging only the overall illustration. The repository describes the Texture variant as more prone to malformations and less effective for vector-style imagery. Select the variant according to the visual task and verify the behavior yourself. [Repository][repo] The weight license is CreativeML Open RAIL-M. Its name and conditions are distinct from the licenses of auxiliary components listed in the card; review the model's own terms when choosing a deployment. [Model card][card] The repository specifies a GPU with at least 24 GB of VRAM for the original F-Lite workflow. Use that published requirement when planning the installation, then measure the complete configuration before scaling up the workload. [Repository][repo] ## How do you run F-Lite with its CLI or ComfyUI? ### Start with the official command-line workflow 1. Clone the fal-ai F-Lite repository into a working directory. Use a dedicated Python environment so the project's dependencies are easy to identify. 2. Install its requirements and choose the published model identifier. Begin with the original standard model to establish a reference result. 3. Run the documented generation module with a detailed prompt and the example's dimensions. Save the output and generation settings together. 4. Review the image before adding a different checkpoint, prompt-expansion tool or fine-tuning step. The repository documents this CLI structure. The prompt below is an original example; the module and options follow its generation implementation. [CLI source][cli] ```bash git clone https://github.com/fal-ai/f-lite.git cd f-lite pip install -r requirements.txt python -m f_lite.generate \ --model "Freepik/F-Lite" \ --prompt "A blue ceramic bowl on an oak shelf beside a folded linen cloth, side view, soft daylight from a window on the left, matte surfaces, quiet interior photograph" \ --output_file "bowl.png" \ --width 1344 --height 896 \ --steps 30 --guidance_scale 6 --seed 42 ``` Inspect whether the bowl, shelf and cloth remain separate objects. Then assess surface appearance and lighting. Write down failures in those terms so that your next prompt revision addresses an observable problem. Change one part of the brief at a time. If the bowl shape is wrong, describe its rim and depth before adding more style language. If the lighting is wrong, clarify the light source without replacing the entire scene. ### Use the repository's ComfyUI integration The official instructions place the repository in ComfyUI's `custom_nodes` directory and install its requirements in ComfyUI's Python environment. The included simple workflow needs no additional extensions. [Repository][repo] Start with `F-lite-simple.json` from the repository's workflows directory. After loading it, check the selected checkpoint and prompt before queueing an image, then retain the workflow with any result you approve. [Simple workflow][workflow] Use the [ComfyUI complete guide](/tomas_novak/comfyui-2026-the-complete-guide-to-power-user-ai-image-generation-1g17) for general graph setup. Follow F-Lite's own workflow for its model-specific loading and generation controls. The repository also offers a SuperPrompt workflow that adds prompt expansion and requires extra extensions. Establish the simple workflow first so you can distinguish model behavior from changes introduced by expanded prompts. [Repository][repo] If you use expansion, read the resulting prompt. Remove invented product attributes or unwanted scene objects before accepting a generation, and keep the final expanded text with the image for later comparison. ### Consider training only after evaluating the base model The project's fine-tuning instructions support full tuning and LoRA training with images and captions. They document a CSV format pairing each image path with its caption. [Training guide][training] Prepare a held-out set of briefs before training. Evaluate whether the base model already meets them, then compare any adapted checkpoint with the same requirements instead of evaluating only the images used to train it. ## How does F-Lite compare with its 7B variant and SDXL? | Option | Published distinction | Useful comparison | |---|---|---| | F-Lite | Original 10B model with documented training-data provenance. [Model card][card] | Establish the standard checkpoint's scene quality. | | F-Lite 7B | Smaller distilled family member. [7B card][small] | Measure its actual resource use and accepted outputs. | | Stable Diffusion XL | Separate downloadable image model with its own pipeline and license. [SDXL card][sdxl] | Compare complete workflows for the same brief. | The [SDXL model guide](/tara_suzuki/best-sdxl-models-in-2026-realistic-anime-and-all-purpose-checkpoints-116) supplies context for that alternative. Avoid transferring settings or extensions between unrelated architectures without checking compatibility. ## What else should you know before running F-Lite? ### How much VRAM does the original F-Lite workflow require? The official F-Lite repository specifies a GPU with at least 24 GB of VRAM for its original workflow. F-Lite 7B is a separate distilled checkpoint; its model card describes improved memory efficiency without giving a universal hardware minimum. [Repository][repo] [7B card][small] ### Does F-Lite work in ComfyUI? Yes, F-Lite's official repository provides ComfyUI custom nodes and example workflows. Begin with the included simple workflow, then add optional components only after the baseline works. [Repository][repo] ### How should I prompt F-Lite? F-Lite's model card recommends longer prompts. Start with a clear subject, environment, composition and lighting description, then assess whether each added detail improves your intended image. [Model card][card] ### Can I fine-tune the weights? F-Lite's repository includes full fine-tuning and LoRA instructions. Review the weight license and prepare images, captions and evaluation cases before starting a training run. [Training guide][training] ## Sources - [Original F-Lite model card][card] - [Official F-Lite repository][repo] - [F-Lite 7B model card][small] - [F-Lite Texture model card][texture] - [F-Lite command-line implementation][cli] - [F-Lite simple ComfyUI workflow][workflow] - [Official fine-tuning guide][training] - [Stable Diffusion XL model card][sdxl] [card]: https://huggingface.co/Freepik/F-Lite [repo]: https://github.com/fal-ai/f-lite [small]: https://huggingface.co/Freepik/F-Lite-7B [texture]: https://huggingface.co/Freepik/F-Lite-Texture [cli]: https://raw.githubusercontent.com/fal-ai/f-lite/main/f_lite/generate.py [workflow]: https://raw.githubusercontent.com/fal-ai/f-lite/main/workflows/F-lite-simple.json [training]: https://raw.githubusercontent.com/fal-ai/f-lite/main/FINE-TUNING.md [sdxl]: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 ## Related guides on PromptZone - [Best SDXL Models in 2026](/tara_suzuki/best-sdxl-models-in-2026-realistic-anime-and-all-purpose-checkpoints-116) - [ComfyUI 2026: The Complete Guide](/tomas_novak/comfyui-2026-the-complete-guide-to-power-user-ai-image-generation-1g17) - [AI Model Releases Timeline](/ai-model-releases)