FinesseV2PF16LORA
by ModelsLabHow to generate images:
Base Model + Finesse LORA: For more customization, combine the base model (Flux1-Dev-FP8) with the FinesseV2 LORA. Think of the LORA as a special ingredient that gives the images a unique style.
Why use the LORA?
Save space: The LORA is much smaller than the full model. So you don´t have to repeat and reapeat downloading checkpoints or unets. The specialiced information added to a base model only needs to stay in a smaller LORA
More flexibility: Experiment with different styles by combining the LORA with other base models (Flux1-dev-fp8 checkpoints). If you use ComfyUi you can make a unet, just use two nodes, LoadCheckpoint to load flux1-de-fp8 and ModelSave to save the unet, just link only the model points from both
Something you can´t do with checkpoints and unets, you can play with the strength of the lora. For some features you can enhance the image, e.g, make a woman more curvy (1.2 is enough)
Using the Flux1.Dev base model you prefer wheather checkpoint or unet and the Finesse LORA together saves you space in disk and makes it easier to experiment with different styles. It's like having a modular system where you can customize your cake with different toppings
This is an attempt to distribute a modification of a basic model in the LORA format instead of a full trained or merged model. Every time we download a trained model, for each model we download we download again: the basic model, the VAE, Clip-L and T5, in total about 17Gb and if you you use unet the penalty in each download is 11 GB. If you believe that GGUF is a solution, the penalty is only reduced by half (5Gb). That is to say that if we download "n" models based on FluxDevfp8 checkpoint , we have a redundancy of n x 17Gb. SSD vendors are very happy and grateful. Using a distribution based on LORAs, you just download your prefered base model Flux1.Dev, with the included or not VAE, Clip-L and T5 only once and then the specific LORA.
The base model to make the sample images was:
https://huggingface.co/lllyasviel/flux1_dev/resolve/main/flux1-dev-fp8.safetensors
If you want an image in 6-8 steps download Bytedance also include in the prompt (strength 0.125),
https://huggingface.co/ByteDance/Hyper-SD/resolve/main/Hyper-FLUX.1-dev-8steps-lora.safetensors
For those who like GGUF, there are several cuantizations versions with 6-8 steps accelerator included
https://huggingface.co/mhnakif/flux-hyp8/tree/main
finessev2pf16loraInput
Per image generation will cost 0.0047$
For premium plan image generation will cost 0.00$ i.e Free.
Output
Unknown content type
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About FinesseV2PF16LORA
How to generate images:Base Model + Finesse LORA: For more customization, combine the base model (Flux1-Dev-FP8) with the FinesseV2 LORA. Think of the LORA as a special ingredient that gives the images a unique style.Why use the LORA?Save space: The LORA is much smaller than the full model. So you don´t have to repeat and reapeat downloading checkpoints or unets. The specialiced information added to a base model only needs to stay in a smaller LORAMore flexibility: Experiment with different styl
Technical Specifications
- Model ID
- finessev2pf16lora
- Provider
- Modelslab
- Category
- Image Models
- Task
- Text to Image
- Price
- $0.0047 per API call
- Added
- June 14, 2025
Key Features
- High-resolution AI image generation from text prompts
- Negative prompt support for precise control
- Multiple output formats and aspect ratios
- Adjustable inference steps and guidance scale
- Batch generation support via API
Quick Start
Integrate FinesseV2PF16LORA into your application with a single API call. Get your API key from the pricing page to get started.
import requestsimport jsonurl = "https://modelslab.com/api/v6/images/text2img"headers = {"Content-Type": "application/json"}data = {"model_id": "finessev2pf16lora","prompt": "your prompt here","key": "YOUR_API_KEY"}try:response = requests.post(url, headers=headers, json=data)response.raise_for_status() # Raises an HTTPError for bad responses (4XX or 5XX)result = response.json()print("API Response:")print(json.dumps(result, indent=2))except requests.exceptions.HTTPError as http_err:print(f"HTTP error occurred: {http_err} - {response.text}")except Exception as err:print(f"Other error occurred: {err}")
View the full API documentation for SDKs, code examples in Python, JavaScript, and more.
Pricing
FinesseV2PF16LORA API costs $0.0047 per API call. Pay only for what you use with no minimum commitments. View pricing plans
Use Cases
- Product photography and e-commerce visuals
- Marketing and social media content creation
- Concept art and design prototyping
- Custom illustrations and artwork
FinesseV2PF16LORA FAQ
How to generate images:Base Model + Finesse LORA: For more customization, combine the base model (Flux1-Dev-FP8) with the FinesseV2 LORA. Think of the LORA as a special ingredient that gives the images a unique style.Why use the LORA?Save space: The LORA is much smaller than the full model. So you d
You can integrate FinesseV2PF16LORA into your application with a single API call. Sign up on ModelsLab to get your API key, then use the model ID "finessev2pf16lora" in your API requests. We provide SDKs for Python, JavaScript, and cURL examples in the API documentation.
FinesseV2PF16LORA costs $0.0047 per API call. ModelsLab uses pay-per-use pricing with no minimum commitments. A free tier is available to get started.
The model ID for FinesseV2PF16LORA is "finessev2pf16lora". Use this ID in your API requests to specify this model.
Yes, ModelsLab offers a free tier that lets you try FinesseV2PF16LORA and other AI models. Sign up to get free API credits and start building immediately.