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HiDream-I1 is an open-source image generative foundation model with 17B parameters, designed for high-quality image generation within seconds. It uses a sparse Diffusion Transformer (DiT) structure with a dynamic Mixture-of-Experts (MoE) architecture.
HiDream-I1 employs a dual-stream decoupled design for its sparse DiT, where separate encoders independently process image and text tokens. This is followed by a single-stream sparse DiT with dynamic MoE architecture to enable multi-modal interaction for efficient image generation. Hidream nsfw is built to balance quality and computational efficiency, addressing the trade-off between inference latency and output fidelity. The architecture supports flexible accessibility through three variants: HiDream-I1-Full, HiDream-I1-Dev, and HiDream-I1-Fast, each optimized for different inference steps and use cases. The full model requires 50 inference steps, while the distilled versions reduce this to 28 and 16 steps respectively, making it adaptable for various hardware and speed requirements.
Hidream nsfw and its code are released under the MIT License, allowing broad use and modification. The repository includes inference scripts, a Gradio demo, and support for the Diffusers library. Users can download model weights from Hugging Face for the Full, Dev, and Fast variants. The open-source nature enables developers to integrate the model into their own applications, though the documentation does not specify built-in content moderation or filtering mechanisms. Users are responsible for complying with applicable laws and platform terms when generating content. The model's technical report is available on arXiv, providing further details on its design and evaluation.
HiDream-I1 offers state-of-the-art image generation quality, as demonstrated by benchmarks like DPG-Bench, GenEval, and HPSv2.1. It also supports instruction-based image editing through the HiDream-E1 model.
HiDream-I1 achieves high scores on multiple evaluation benchmarks. On DPG-Bench, it scores 85.89 overall, with strong performance in attributes like 'Other' (91.83) and 'Relation' (93.74). In GenEval, it achieves an overall score of 0.83, with perfect single-object generation (1.00) and high two-object (0.98) and counting (0.79) scores. The HPSv2.1 benchmark shows an averaged score of 33.82, with top performance in Animation (35.05) and Painting (33.88). These metrics indicate hidream nsfw's ability to generate diverse and accurate images across various categories, though the documentation does not specify performance on adult content specifically.
Users can access HiDream-I1 through multiple channels. Hidream nsfw is available on Hugging Face Spaces for direct experimentation, and the code can be run locally using the provided inference scripts. Integration with the Diffusers library is supported, allowing seamless use within existing pipelines. The model requires Flash Attention and CUDA 12.4 for optimal performance. Additionally, the HiDream-E1 model enables instruction-based image editing, and the HiDream-A1 agent combines generation and editing for interactive creation. These tools are accessible via the project website and GitHub repository, though the documentation does not detail content moderation policies for these interfaces.
HiDream-I1 is available as open-source software, with model weights hosted on Hugging Face and code on GitHub. Users can run it locally or use hosted demos.
To run HiDream-I1 locally, users need to install Flash Attention and CUDA 12.4. The repository provides a requirements.txt file and inference scripts for the Full, Dev, and Fast models. The inference script automatically downloads the Llama-3.1-8B-Instruct model, requiring users to agree to its license and log in via huggingface-cli. A Gradio demo is also available for interactive generation. The model supports various parameters like guidance scale and inference steps, allowing customization for different quality and speed needs. The documentation does not specify any content restrictions for local use, but users should adhere to legal and ethical guidelines.
HiDream-I1 is accessible via Hugging Face Spaces, where users can try the Dev model directly without local setup. The project also provides a technical report and citation for academic use. The model is supported in the Diffusers library, enabling integration with other tools. The HiDream-E1 editing model has its own Hugging Face Space for experimentation. These resources facilitate community engagement and development, though the documentation does not outline specific content policies for these hosted services. Users should review the terms of service for each platform when generating content.
HiDream-I1 comes in three variants: Full, Dev, and Fast, each optimized for different inference steps and performance characteristics. The configurations allow users to balance quality and speed.
The HiDream-I1-Full variant requires 50 inference steps and is designed for maximum quality. It uses the full model weights and is suitable for applications where generation time is less critical. The configuration includes a guidance scale parameter that can be set to 5.0 for the Full model, while Dev and Fast variants use 0.0. The Full model achieves the highest benchmark scores but requires more computational resources. Users can adjust the number of inference steps and other parameters to fine-tune output. The documentation does not specify any content-specific configurations for adult content generation.
The HiDream-I1-Dev and HiDream-I1-Fast variants are distilled versions that require 28 and 16 inference steps respectively. These models offer faster generation with slightly reduced quality, making them suitable for real-time applications. The Dev variant is available on Hugging Face Spaces for quick experimentation. The Fast variant is optimized for speed, using fewer steps while maintaining competitive performance. All variants use the same underlying architecture but with different weight configurations. The documentation does not provide specific guidance on content filtering for these variants, and users should refer to current service terms for any restrictions.
When using HiDream-I1, users should consider hardware requirements, integration options, and legal responsibilities. The model's open-source nature provides flexibility but requires user awareness of content guidelines.
HiDream-I1 requires CUDA 12.4 and Flash Attention for optimal performance. The model uses a Llama-3.1-8B-Instruct text encoder, which must be downloaded separately with user consent. The inference scripts are designed for GPU usage, and the model's 17B parameter size necessitates significant VRAM. Users with limited hardware may opt for the Dev or Fast variants, which require fewer inference steps. The documentation does not specify minimum VRAM requirements, but users should expect high memory usage. For local deployment, ensure compatibility with the Diffusers library and PyTorch.
The HiDream-I1 model is released under the MIT License, which permits broad use but does not address content moderation. Users are responsible for ensuring their generated content complies with applicable laws and platform policies. The technical documentation does not establish an adult-content policy, and users should consult current service terms for specific guidelines. The open-source nature allows for customization, including potential integration of content filters, but this is not provided out-of-the-box. Researchers and developers should consider ethical implications when deploying the model in public-facing applications.
Frequently asked questions about HiDream-I1 and its capabilities.
The term 'hidream nsfw' refers to inquiries about the HiDream-I1 model's capabilities regarding adult content. The cited technical documentation does not establish an adult-content policy or built-in NSFW filtering. Users should refer to current service terms for specific content guidelines. The model is open-source and can be run locally, but users are responsible for complying with applicable laws and platform policies.
HiDream-I1 is available on GitHub and Hugging Face. Users can download model weights and run inference locally using the provided scripts, or try the Dev model on Hugging Face Spaces. The model is also integrated with the Diffusers library for seamless use in existing pipelines.
HiDream-I1 requires CUDA 12.4, Flash Attention, and a GPU with sufficient VRAM to handle the 17B parameter model. The distilled variants (Dev and Fast) require fewer inference steps and may be more suitable for limited hardware. The documentation does not specify exact VRAM requirements.
