prunaai/hidream-l1-dev - AI Image Models Tool

Overview

prunaai/hidream-l1-dev is an optimized HiDream family image model endpoint published on Replicate. The model is distributed by prunaai and uses the Pruna optimization toolkit to reduce inference cost and improve throughput compared with unoptimized weights, while remaining available as an open endpoint on Replicate (and runnable locally via Docker). ([github.com](https://github.com/PrunaAI/pruna?utm_source=openai)) On Replicate the model is advertised as an optimized HIDream L1 developer endpoint that runs on Nvidia H100 GPU hardware with very low latency (predictions typically complete in about 3 seconds) and a low cost per run (approximately $0.0031 per inference). The Replicate page also shows tens of thousands of public runs, indicating active usage and experimentation via the hosted API. ([replicate.com](https://replicate.com/prunaai/hidream-l1-dev)) Practically, hidream-l1-dev is used for prompt-driven image generation and image editing workflows (HiDream is an image generation/editing project), and the pruna optimizations are intended to make these workflows cheaper and faster in production. Community commentary around HiDream models is mixed — users report strong prompt understanding and fast runtimes, but the outputs and quality trade-offs (versus other top-tier image models) are discussed openly in forums. ([github.com](https://github.com/HiDream-ai/HiDream-E1?utm_source=openai))

Key Features

• Pruna-optimized weights for reduced latency and lower inference cost.
• Hosted on Replicate with an API for programmatic image generation.
• Typical hosted latency around 3 seconds per prediction.
• Low approximate cost per run (~$0.0031 on Replicate).
• Runs on NVIDIA H100 hardware when hosted by Replicate.
• Open-source friendly — Replicate page links to Docker/self-hosting.
• Built from the HiDream family (supports text-to-image and image editing).

Example Usage

Example (python):

import os
import replicate

# Set REPLICATE_API_TOKEN in your environment before running
# pip install replicate
# Example: export REPLICATE_API_TOKEN="r8_xxx"

client = replicate.Client()
# Run the default (latest) version of the model hosted on Replicate
# Input keys vary by model; 'prompt' is commonly used for HiDream endpoints.
output = client.run("prunaai/hidream-l1-dev", input={
    "prompt": "A cinematic futuristic city at sunset, high detail, volumetric lighting",
    "num_inference_steps": 28,
    "seed": 42
})

print(output)

# Note: exact input parameter names and options are documented on the model's
# Replicate API page. If you plan to self-host, the Replicate page links to
# Docker/self-hosting instructions when available. ([replicate.com](https://replicate.com/prunaai/hidream-l1-dev))

Pricing

Replicate-hosted runs are listed at approximately $0.0031 per inference (≈322 runs per $1), but actual cost varies by input and usage. The model is also open-source and can be run locally via Docker to avoid hosted costs. Source: Replicate model page. ([replicate.com](https://replicate.com/prunaai/hidream-l1-dev))

Benchmarks

Replicate run count (public): 47.7K runs (public) (Source: https://replicate.com/prunaai/hidream-l1-dev)

Typical prediction latency (hosted): ~3 seconds per prediction (Replicate hosted H100) (Source: https://replicate.com/prunaai/hidream-l1-dev)

Approximate hosted cost per run: $0.0031 per run (≈322 runs per $1) — varies by input (Source: https://replicate.com/prunaai/hidream-l1-dev)

Hosted GPU hardware: NVIDIA H100 (Replicate hosted) (Source: https://replicate.com/prunaai/hidream-l1-dev)

Optimization toolkit: Pruna optimization framework (speed/size/cost focused) (Source: https://github.com/PrunaAI/pruna)

Key Information

• Category: Image Models
• Type: AI Image Models Tool