instruct-pix2pix
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve timbrooks/instruct-pix2pixNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
instruct-pix2pix is an open-weight image editing model from timbrooks, published on Hugging Face in January 2023. It has 860M parameters and is released under the mit license.
HardwareLink to this section
At full FP16 precision the weights need about 2 GB of memory; 8-bit quantization brings that to roughly 1 GB. That is small enough for most consumer GPUs, Apple Silicon Macs, and many CPUs.
PopularityLink to this section
instruct-pix2pix had 82,445 downloads on Hugging Face in the last month and has 1,191 likes.
Run it locallyLink to this section
The weights are on Hugging Face and load with Hugging Face Diffusers (or ComfyUI). Follow the model card for the exact setup and license terms.
Frequently asked
Quick answers to common questions
How much VRAM does instruct-pix2pix need?
instruct-pix2pix with 0.86B parameters needs approximately 1 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is instruct-pix2pix better than other timbrooks models?
instruct-pix2pix has 0.86B parameters with 8,192 context - a strong choice for image-editing.
What license is instruct-pix2pix under?
instruct-pix2pix is released under the mit license, making it suitable for most commercial and personal projects.
What hardware runs instruct-pix2pix well?
With 0.86B parameters, instruct-pix2pix requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for instruct-pix2pix?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~1 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
What models compete with instruct-pix2pix?
instruct-pix2pix competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec