--- license: apache-2.0 pipeline_tag: text-to-image --- # Chroma1-HD Chroma1-HD is an **8.9B** parameter text-to-image foundational model based on **FLUX.1-schnell**. It is fully **Apache 2.0 licensed**, ensuring that anyone can use, modify, and build upon it. As a **base model**, Chroma1 is intentionally designed to be an excellent starting point for **finetuning**. It provides a strong, neutral foundation for developers, researchers, and artists to create specialized models. for the fast CFG "baked" version please go to [Chroma1-Flash](https://huggingface.co/lodestones/Chroma1-Flash). ### Key Features * **High-Performance Base:** 8.9B parameters, built on the powerful FLUX.1 architecture. * **Easily Finetunable:** Designed as an ideal checkpoint for creating custom, specialized models. * **Community-Driven & Open-Source:** Fully transparent with an Apache 2.0 license, and training history. * **Flexible by Design:** Provides a flexible foundation for a wide range of generative tasks. ## Special Thanks A massive thank you to our supporters who make this project possible. * **Anonymous donor** whose incredible generosity funded the pretraining run and data collections. Your support has been transformative for open-source AI. * **Fictional.ai** for their fantastic support and for helping push the boundaries of open-source AI. You can try Chroma on their platform: [](https://fictional.ai/?ref=chroma_hf) ## How to Use ### `diffusers` Library install the requirements `pip install transformers diffusers sentencepiece accelerate` ```python import torch from diffusers import ChromaPipeline pipe = ChromaPipeline.from_pretrained("lodestones/Chroma1-HD", torch_dtype=torch.bfloat16) pipe.enable_model_cpu_offload() 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"] 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] image.save("chroma.png") ``` Quantized inference using gemlite ```py import torch from diffusers import ChromaPipeline pipe = ChromaPipeline.from_pretrained("lodestones/Chroma1-HD", torch_dtype=torch.float16) #pipe.enable_model_cpu_offload() ####################################################### import gemlite device = 'cuda:0' processor = gemlite.helper.A8W8_int8_dynamic #processor = gemlite.helper.A8W8_fp8_dynamic #processor = gemlite.helper.A16W4_MXFP for name, module in pipe.transformer.named_modules(): module.name = name 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): layer = layer.to(device, non_blocking=True) try: return processor(device=device).from_linear(layer) except Exception as exception: print('Skipping gemlite conversion for: ' + str(layer.name), exception) return layer patch_linearlayers(pipe.transformer, patch_linear_to_gemlite) torch.cuda.synchronize() torch.cuda.empty_cache() pipe.to(device) pipe.transformer.forward = torch.compile(pipe.transformer.forward, fullgraph=True) pipe.vae.forward = torch.compile(pipe.vae.forward, fullgraph=True) #pipe.set_progress_bar_config(disable=True) ####################################################### 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"] 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.") #66.1242527961731 -> 27.72 sec image.save("chroma.png") ``` ComfyUI For advanced users and customized workflows, you can use Chroma with ComfyUI. **Requirements:** * A working ComfyUI installation. * [Chroma checkpoint](https://huggingface.co/lodestones/Chroma) (latest version). * [T5 XXL Text Encoder](https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp16.safetensors). * [FLUX VAE](https://huggingface.co/lodestones/Chroma/resolve/main/ae.safetensors). * [Chroma Workflow JSON](https://huggingface.co/lodestones/Chroma1-HD/resolve/main/ComfyUI_Chroma1-HD_T2I-workflow.json).   **Setup:** 1. Place the `T5_xxl` model in your `ComfyUI/models/clip` folder. 2. Place the `FLUX VAE` in your `ComfyUI/models/vae` folder. 3. Place the `Chroma checkpoint` in your `ComfyUI/models/diffusion_models` folder. 4. Load the Chroma workflow file into ComfyUI and run. ## Model Details * **Architecture:** Based on the 8.9B parameter FLUX.1-schnell model. * **Training Data:** Trained on a 5M sample dataset curated from a 20M pool, including artistic, photographic, and niche styles. * **Technical Report:** A comprehensive technical paper detailing the architectural modifications and training process is forthcoming. ## Intended Use Chroma is intended to be used as a **base model** for researchers and developers to build upon. It is ideal for: * Finetuning on specific styles, concepts, or characters. * Research into generative model behavior, alignment, and safety. * As a foundational component in larger AI systems. ## Limitations and Bias Statement Chroma is trained on a broad, filtered dataset from the internet. As such, it may reflect the biases and stereotypes present in its training data. The model is released in a state as is and has not been aligned with a specific safety filter. Users are responsible for their own use of this model. It has the potential to generate content that may be considered harmful, explicit, or offensive. I encourage developers to implement appropriate safeguards and ethical considerations in their downstream applications. ## Summary of Architectural Modifications *(For a full breakdown, tech report soon-ish.)* * **12B → 8.9B Parameters:** * **TL;DR:** I replaced a 3.3B parameter timestep-encoding layer with a more efficient 250M parameter FFN, as the original was vastly oversized for its task. * **MMDiT Masking:** * **TL;DR:** Masking T5 padding tokens enhanced fidelity and increased training stability by preventing the model from focusing on irrelevant ` ` tokens. * **Custom Timestep Distributions:** * **TL;DR:** I implemented a custom timestep sampling distribution (`-x^2`) to prevent loss spikes and ensure the model trains effectively on both high-noise and low-noise regions. ## P.S Chroma1-HD is not the old Chroma-v.50 it has been retrained from v.48 ## Citation ``` @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}}, } ```
