Air_waves_homo_1

🌊 Air_waves_homo_1 — Trillion-Parameter Image Generator (Scaffold)

Air_waves_homo_1 is an open, extensible repository scaffold for training a trillion-parameter image generation model based on latent diffusion (VAE + UNet + conditional text encoder). It is built to be trainable and finetunable by the community using DeepSpeed / FSDP and adapter/LoRA methods.

⚠️ This repository contains architecture, training code, and recipes only — no pretrained weights.

Features

• Latent diffusion pipeline (VAE encoder/decoder + conditional UNet denoiser)
• Text conditioning (supports any text encoder — CLIP, T5, or custom)
• Trainer with DeepSpeed Zero-3 / FSDP readiness + activation checkpointing
• LoRA / Adapter support for safe, low-cost fine-tuning
• Inference sampling utilities and a Gradio demo
• Clear config-driven scaling to trillions of parameters

Quickstart

1. Install

git clone https://huggingface.co/your-username/Air_waves_homo_1
cd Air_waves_homo_1
pip install -r requirements.txt


## Way of Using


2. Inference (Text  to image)

from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained("your-username/Air_waves_homo_1", torch_dtype=torch.float16)
pipe.to("cuda")

prompt = "A futuristic desert city with glowing crystal towers"
image = pipe(prompt).images[0]
image.save("sample_output.png")


## Training and finetuning


3.Finetune model

accelerate launch train.py \
  --config config.json \
  --dataset /content/my_custom_dataset \
  --epochs 5 \
  --batch_size 2 \
  --lr 2e-5 \
  --resume_from_checkpoint ./base_checkpoint \
  --use_lora True


## Pushing model


4.Push model to hub

huggingface-cli login
git add .
git commit -m "Added fine-tuned Air_waves_homo_1"
git push


## App


5.deployment

import gradio as gr
from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained("your-username/Air_waves_homo_1", torch_dtype=torch.float16)
pipe.to("cuda")

def generate(prompt):
    return pipe(prompt).images[0]

gr.Interface(
    fn=generate,
    inputs="text",
    outputs="image",
    title="🌊 Air_waves_homo_1 - Trillion-Parameter Image Generator",
    description="Enter any text prompt and generate stunning images!"
).launch()