Text-to-Image
Diffusers
Safetensors
StableDiffusionXLInstructPix2PixPipeline
stable-diffusion-xl
stable-diffusion-xl-diffusers
instruct-pix2pix
Instructions to use diffusers/sdxl-instructpix2pix-768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use diffusers/sdxl-instructpix2pix-768 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("diffusers/sdxl-instructpix2pix-768", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Clarification on Total Batch Size with 8xA100 GPUs
#3
by WensongSong - opened
Compute
one 8xA100 machine
Batch size
Data parallel with a single gpu batch size of 8 for a total batch size of 32.
It’s stated that with data parallelism, each GPU handles a single GPU batch size of 8, leading to a total batch size of 32 across 8 GPUs. However, my understanding is that if each of the 8 GPUs processes a batch of 8 samples, the total batch size should logically be 8 * 8 = 64. Could someone please clarify why the total batch size is noted as 32 instead of 64?