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Chroma1-HD: Open-Source 8.9B Text-to-Image Model for AI Creators & Developers

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Chroma1-HD: A Powerful Open-Source Text-to-Image Model for Creators and Developers

In the rapidly evolving world of artificial intelligence, text-to-image models have become indispensable tools for artists, developers, and researchers alike. Among the latest innovations in this space is Chroma1-HD, an 8.9B parameter text-to-image foundational model that’s making waves for its performance, flexibility, and open accessibility. Built on the robust FLUX.1-schnell architecture, Chroma1-HD stands out as a versatile base model designed to empower users to create, modify, and build upon it—all under the permissive Apache 2.0 license. Whether you’re a seasoned developer looking to fine-tune a specialized model or an artist exploring new creative horizons, Chroma1-HD offers a strong foundation to bring your ideas to life.

What Makes Chroma1-HD Unique?

Chroma1-HD isn’t just another text-to-image model—it’s a carefully engineered tool designed with flexibility and adaptability in mind. Let’s break down its key features to understand why it’s gaining attention in the AI community.

High-Performance Base Model

At its core, Chroma1-HD boasts 8.9 billion parameters, placing it in the upper echelon of text-to-image models in terms of capacity. This substantial size allows it to capture complex patterns, details, and styles from text prompts, resulting in high-quality, coherent images. What’s more, it’s built on the FLUX.1 architecture, a proven framework known for its ability to generate realistic and diverse visuals. This combination of scale and a strong architectural foundation ensures that Chroma1-HD delivers consistent performance across a wide range of creative tasks.

Designed for Easy Finetuning

One of Chroma1-HD’s most notable strengths is its focus on being an excellent starting point for finetuning. Finetuning is the process of adapting a pre-trained model to specialize in a specific style, subject, or task—think creating a model that excels at generating vintage photography, cartoon characters, or even technical diagrams. Chroma1-HD’s neutral, well-balanced training makes it ideal for this purpose. Unlike models that are heavily biased toward certain styles or themes, it provides a clean slate, allowing developers and artists to mold it to their exact needs without fighting against pre-existing tendencies.

Community-Driven and Open-Source

In an era where many advanced AI models are locked behind proprietary walls, Chroma1-HD takes a different approach. It’s fully open-source with an Apache 2.0 license, which means anyone can use it, modify it, and redistribute it—even for commercial purposes. This openness fosters collaboration and innovation: researchers can study its inner workings, developers can build new tools on top of it, and artists can experiment without restrictions. Additionally, the model’s training history is transparent, giving users insight into how it was developed and ensuring accountability.

Flexibility for Diverse Tasks

Chroma1-HD isn’t limited to a single use case. Its design prioritizes flexibility, making it suitable for a wide range of generative tasks. Whether you’re generating realistic photographs, abstract art, product designs, or even 3D-rendered concepts, the model adapts to different prompts and styles with ease. This versatility makes it a valuable tool for professionals across industries, from marketing and design to education and research.

A Note on Chroma1-Flash

If you’re looking for a faster version of Chroma1-HD optimized for speed, be sure to check out Chroma1-Flash. This “baked” variant is designed with a fast CFG (Classifier-Free Guidance) setup, making it ideal for scenarios where quick image generation is a priority—such as real-time applications or workflows that require rapid iterations. Both models share the same core strengths, but Chroma1-Flash trades some of the HD variant’s granular control for speed, giving users options based on their specific needs.

Special Thanks: The Minds Behind Chroma1-HD

No project of this scale is possible without support, and Chroma1-HD owes its existence to several key contributors.

First, a massive thank you goes to an anonymous donor whose generous funding made the pretraining run and data collection possible. Their support has been transformative for open-source AI, enabling the development of a tool that will benefit the entire community.

Additionally, Fictional.ai played a crucial role in supporting the project and pushing the boundaries of open-source AI. If you’re eager to try Chroma1-HD in action, you can experiment with it directly on their platform by visiting Fictional.ai.

How to Use Chroma1-HD: Step-by-Step Guides

Whether you’re a developer comfortable with coding or an advanced user preferring visual workflows, Chroma1-HD offers multiple ways to integrate it into your projects. Below, we’ll walk through two popular methods: using the diffusers library for code-based generation and ComfyUI for a more visual, customizable approach.

Using the diffusers Library

The diffusers library, developed by Hugging Face, is a popular choice for working with state-of-the-art diffusion models like Chroma1-HD. It simplifies the process of loading models, setting up pipelines, and generating images. Here’s how to get started:

Step 1: Install Required Libraries

First, you’ll need to install the necessary dependencies. Open your terminal or command prompt and run the following command:

pip install transformers diffusers sentencepiece accelerate

This installs:

  • transformers: For loading and working with pre-trained models.
  • diffusers: The core library for diffusion-based image generation.
  • sentencepiece: For processing text prompts.
  • accelerate: To help with optimizing model performance across different hardware setups.

Step 2: Basic Image Generation with Python

Once the libraries are installed, you can use the following Python code to generate your first image with Chroma1-HD:

import torch
from diffusers import ChromaPipeline

# Load the Chroma1-HD model
pipe = ChromaPipeline.from_pretrained("lodestones/Chroma1-HD", torch_dtype=torch.bfloat16)
# Enable CPU offloading to save GPU memory (useful for systems with limited GPU resources)
pipe.enable_model_cpu_offload()

# Define your prompt: be as detailed as possible for best results
prompt = [
    "A high-fashion close-up portrait of a blonde woman in clear sunglasses. The image uses a bold teal and red color split for dramatic lighting. The background is a simple teal-green. The photo is sharp and well-composed, and is designed for viewing with anaglyph 3D glasses for optimal effect. It looks professionally done."
]

# Define what you want to avoid in the image (negative prompt)
negative_prompt =  ["low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"]

# Generate the image
image = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    # Set a seed for reproducibility (same seed = same image)
    generator=torch.Generator("cpu").manual_seed(433),
    # Number of steps the model takes to generate the image (more steps = finer details)
    num_inference_steps=40,
    # How strongly the model follows the prompt (lower = more creative, higher = more literal)
    guidance_scale=3.0,
    # Number of images to generate per prompt
    num_images_per_prompt=1,
).images[0]

# Save the generated image
image.save("chroma.png")

Let’s break down what each part does:

  • torch_dtype=torch.bfloat16: This sets the data type to 16-bit floating point, which reduces memory usage without significantly impacting image quality—great for GPUs with limited VRAM.
  • pipe.enable_model_cpu_offload(): This feature moves parts of the model to the CPU when they’re not in use, freeing up GPU memory. It’s especially helpful if you’re working on a laptop or a system with a mid-range GPU.
  • prompt and negative_prompt: These guide the model. The prompt describes what you want, while the negative prompt lists qualities to avoid (like “low quality” or “blurry”).
  • generator=torch.Generator("cpu").manual_seed(433): Using a fixed seed ensures that you can reproduce the same image later, which is useful for testing or refining prompts.
  • num_inference_steps=40: The model builds the image step by step, reducing noise each time. 40 steps is a good balance between speed and quality—you can increase this for more details (but it will take longer) or decrease it for faster generation.
  • guidance_scale=3.0: This controls how closely the model sticks to your prompt. A lower value (like 1-3) gives the model more creative freedom, while a higher value (like 7-10) makes it follow the prompt more strictly.

Step 3: Quantized Inference with Gemlite (For Faster Performance)

If you want to speed up image generation without sacrificing too much quality, you can use quantized inference with Gemlite. Quantization reduces the model’s size and speeds up computation by using lower-precision numbers (like 8-bit instead of 16-bit) for certain calculations. Here’s how to set it up:

import torch
from diffusers import ChromaPipeline

# Load the model with 16-bit floating point precision
pipe = ChromaPipeline.from_pretrained("lodestones/Chroma1-HD", torch_dtype=torch.float16)
# Note: We comment out CPU offload here because we'll use a GPU with Gemlite
# pipe.enable_model_cpu_offload()

#######################################################
# Set up Gemlite for quantized inference
import gemlite
device = 'cuda:0'  # Use the first GPU (change if you have multiple GPUs)

# Choose a quantization method:
# A8W8_int8_dynamic: Balances speed and quality
# A8W8_fp8_dynamic: Slightly faster but may reduce quality
# A16W4_MXFP: Most aggressive quantization (fastest, but quality may drop more)
processor = gemlite.helper.A8W8_int8_dynamic

# Prepare the model for Gemlite by naming modules
for name, module in pipe.transformer.named_modules():
    module.name = name

# Function to replace linear layers with quantized versions using Gemlite
def patch_linearlayers(model, fct):
    for name, layer in model.named_children():
        if isinstance(layer, torch.nn.Linear):
            setattr(model, name, fct(layer, name))
        else:
            patch_linearlayers(layer, fct)

def patch_linear_to_gemlite(layer, name):
    # Move the layer to the GPU
    layer = layer.to(device, non_blocking=True)
    try:
        # Convert the layer to a quantized version using Gemlite
        return processor(device=device).from_linear(layer)
    except Exception as exception:
        print('Skipping gemlite conversion for: ' + str(layer.name), exception)
        return layer

# Apply the quantization to the model's transformer layers
patch_linearlayers(pipe.transformer, patch_linear_to_gemlite)
# Clean up GPU memory
torch.cuda.synchronize()
torch.cuda.empty_cache()

# Move the entire pipeline to the GPU
pipe.to(device)
# Compile the model's forward pass for faster execution
pipe.transformer.forward = torch.compile(pipe.transformer.forward, fullgraph=True)
pipe.vae.forward = torch.compile(pipe.vae.forward, fullgraph=True)
# Uncomment below to hide the progress bar
# pipe.set_progress_bar_config(disable=True)
#######################################################

# Use the same prompt and negative prompt as before
prompt = [
    "A high-fashion close-up portrait of a blonde woman in clear sunglasses. The image uses a bold teal and red color split for dramatic lighting. The background is a simple teal-green. The photo is sharp and well-composed, and is designed for viewing with anaglyph 3D glasses for optimal effect. It looks professionally done."
]
negative_prompt =  ["low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"]

# Test the speed of the quantized model
import time
for _ in range(3):
    t_start = time.time()
    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        generator=torch.Generator("cpu").manual_seed(433),
        num_inference_steps=40,
        guidance_scale=3.0,
        num_images_per_prompt=1,
    ).images[0]
    t_end = time.time()
    print(f"Took: {t_end - t_start} secs.")  # Example output: ~27 seconds (down from ~66 seconds without Gemlite)

# Save the generated image
image.save("chroma.png")

As shown in the code comments, using Gemlite can significantly speed up generation—reducing the time from around 66 seconds to 27 seconds in testing. This makes it a great option for workflows where speed is important, such as batch processing or interactive applications.

Using ComfyUI for Advanced Workflows

For users who prefer a visual, node-based interface (or need more control over the image generation process), ComfyUI is an excellent choice. ComfyUI allows you to build custom workflows by connecting different components (nodes) together, giving you fine-grained control over every step of the generation process. Here’s how to set up Chroma1-HD with ComfyUI:

Requirements

Before you start, make sure you have the following:

  • A working installation of ComfyUI. If you don’t have it installed, follow the official ComfyUI setup guide for your operating system.
  • The latest Chroma checkpoint, available from Hugging Face.
  • The T5 XXL Text Encoder, which you can download from here.
  • The FLUX VAE (Variational Autoencoder), available here.
  • The Chroma Workflow JSON file, which you can get from here. This file contains a pre-built workflow to get you started.

Setup Steps

  1. Install the T5 XXL Text Encoder: This component processes your text prompts into a format the model can understand. Place the downloaded t5xxl_fp16.safetensors file in your ComfyUI’s models/clip folder.

  2. Install the FLUX VAE: The VAE is responsible for converting the model’s output into a final image. Place the ae.safetensors file in your ComfyUI’s models/vae folder.

  3. Install the Chroma Checkpoint: This is the main model file. Place the downloaded Chroma checkpoint in your ComfyUI’s models/diffusion_models folder.

  4. Load the Workflow: Open ComfyUI, then go to File > Load Workflow and select the ChromaSimpleWorkflow20250507.json file you downloaded. This will load a pre-configured workflow with all the necessary nodes connected.

  5. Run the Workflow: Once the workflow is loaded, you can modify the prompt (look for the “CLIP Text Encode (T5 XXL)” node), adjust settings like inference steps or guidance scale, and click “Queue Prompt” to generate an image. The output will appear in the “Save Image” node’s preview window.

ComfyUI is particularly useful for advanced users who want to experiment with custom pipelines—for example, combining Chroma1-HD with other models (like upscalers or style transfer models) or creating complex workflows with multiple steps (e.g., generating a base image, refining it, and then upscaling it).

Model Details: What Powers Chroma1-HD?

To truly understand Chroma1-HD’s capabilities, it helps to look under the hood at its architecture, training data, and development process.

Architecture

Chroma1-HD is based on the FLUX.1-schnell model, a state-of-the-art diffusion model known for its efficiency and high-quality outputs. While FLUX.1-schnell originally has 12 billion parameters, Chroma1-HD has been optimized to 8.9 billion parameters through careful architectural modifications (more on that later). This reduction in size makes it more accessible—requiring less computational power to run—without sacrificing significant performance.

Training Data

Chroma1-HD was trained on a curated dataset of 5 million samples, selected from a larger pool of 20 million. This dataset includes a diverse range of content, from artistic works and photographs to niche styles, ensuring the model can handle a wide variety of prompts. The curation process focused on quality and diversity, helping the model learn to generate coherent, detailed images across different themes and aesthetics.

Technical Report

A comprehensive technical paper detailing Chroma1-HD’s architectural modifications, training process, and performance benchmarks is currently in the works. This report will provide researchers and developers with deeper insights into how the model works, making it easier to understand its strengths, limitations, and potential for further improvement.

Intended Use: Who Should Use Chroma1-HD?

Chroma1-HD is designed to be a foundational tool, meaning its primary purpose is to serve as a starting point for further development and creativity. Here are some of the key use cases it’s best suited for:

Finetuning for Specialized Tasks

As a base model, Chroma1-HD excels when finetuned on specific styles, concepts, or characters. For example:

  • A graphic designer could finetune it to generate images in a specific brand’s style.
  • A game developer might train it to create consistent character designs or environments.
  • An educator could adapt it to generate educational illustrations tailored to a particular subject.

Its neutral foundation ensures that these finetuned models stay true to the target style without inheriting unwanted biases from the base model.

Research into Generative Models

Researchers studying generative AI will find Chroma1-HD to be a valuable tool. Its open-source nature and transparent training history make it ideal for experiments into:

  • How diffusion models learn and represent visual concepts.
  • Methods for improving model alignment (ensuring outputs match user intent).
  • Safety measures to prevent harmful or biased outputs.
  • New techniques for finetuning, quantization, or model compression.

Foundational Component in Larger Systems

Chroma1-HD can also serve as a building block in more complex AI systems. For example:

  • It could be integrated into a content creation platform, allowing users to generate images from text within a larger workflow.
  • It might power a chatbot that can generate visual aids to accompany its responses.
  • It could be part of a tool for rapid prototyping, where designers quickly generate visual concepts from text descriptions.

Limitations and Bias: Important Considerations

While Chroma1-HD is a powerful tool, it’s important to be aware of its limitations and potential biases. Like many AI models trained on internet data, it may reflect the biases, stereotypes, and inconsistencies present in its training data. This could include biases related to gender, race, culture, or other social factors.

Additionally, the model is released “as is” without specific safety filters. This means it has the potential to generate content that some may find harmful, explicit, or offensive, depending on the input prompts. It’s the responsibility of users to implement appropriate safeguards when deploying Chroma1-HD in applications—especially those accessible to the public.

By being mindful of these limitations, users can make informed decisions about how to use the model responsibly and ethically.

Architectural Modifications: How Chroma1-HD Was Optimized

The development of Chroma1-HD involved several key architectural changes to improve efficiency and performance. While a full technical report is forthcoming, here’s a simplified overview of the most important modifications:

Reducing Parameters: From 12B to 8.9B

The original FLUX.1-schnell model includes a 3.3B parameter timestep-encoding layer—a component that helps the model understand the “time” step in the diffusion process (i.e., how much noise to remove at each stage). Through analysis, the developers found that this layer was significantly larger than needed for its task. They replaced it with a more efficient 250M parameter feed-forward network (FFN), reducing the total parameter count by over 3 billion while maintaining (and in some cases improving) performance. This makes the model more lightweight and easier to run on consumer hardware.

MMDiT Masking for Better Fidelity

Another key change was the implementation of MMDiT (Masked Multi-Decoder Transformer) masking. In text processing, padding tokens (<pad>) are often used to make all text inputs the same length, but they don’t carry meaningful information. By masking these padding tokens during training, the model was prevented from focusing on irrelevant data, which improved the overall fidelity of generated images and made the training process more stable.

Custom Timestep Distributions

Diffusion models learn to generate images by reversing a noise process—starting with random noise and gradually refining it into a coherent image. The way timesteps (the stages of this process) are sampled during training can significantly impact model performance. Chroma1-HD uses a custom timestep sampling distribution based on -x² (a quadratic function). This distribution helps prevent sudden spikes in training loss and ensures the model learns effectively across both high-noise (early stages) and low-noise (late stages) regions, resulting in more consistent image quality.

A Quick Note: Chroma1-HD vs. Older Versions

It’s important to note that Chroma1-HD is not the same as the older Chroma-v.50 model. It has been retrained from version v.48, incorporating lessons learned and improvements from earlier iterations. This means it offers better performance, more stability, and a more neutral foundation than its predecessors.

Citation

If you use Chroma1-HD in your research or projects, please cite it using the following format:

@misc{rock2025chroma,
  author = {Lodestone Rock},
  title = {Chroma1-HD},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  howpublished = {\url{https://huggingface.co/lodestones/Chroma1-HD}},
}

Citing the model helps support its continued development and ensures that the community recognizes the work that went into creating it.

Conclusion

Chroma1-HD represents a significant step forward in open-source text-to-image modeling. With its 8.9B parameters, flexible design, and Apache 2.0 license, it empowers developers, researchers, and artists to explore new creative and technical possibilities. Whether you’re finetuning it for a specific task, integrating it into a larger system, or simply experimenting with text-to-image generation, Chroma1-HD provides a strong, accessible foundation.

As the AI community continues to push the boundaries of what’s possible with generative models, tools like Chroma1-HD will play a crucial role in ensuring that innovation remains open, collaborative, and accessible to all. So why not give it a try? Download the model, follow the guides above, and start creating—your next great idea might be just a prompt away.

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