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Chroma

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Chroma

LoRA MPS

Chroma is a text to image generation model based on Flux.

Original model checkpoints for Chroma can be found here.

Chroma can use all the same optimizations as Flux.

Inference (Single File)

The ChromaTransformer2DModel supports loading checkpoints in the original format. This is also useful when trying to load finetunes or quantized versions of the models that have been published by the community.

The following example demonstrates how to run Chroma from a single file.

Then run the following example

import torch
from diffusers import ChromaTransformer2DModel, ChromaPipeline
from transformers import T5EncoderModel

bfl_repo = "black-forest-labs/FLUX.1-dev"
dtype = torch.bfloat16

transformer = ChromaTransformer2DModel.from_single_file("https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v35.safetensors", torch_dtype=dtype)

text_encoder = T5EncoderModel.from_pretrained(bfl_repo, subfolder="text_encoder_2", torch_dtype=dtype)
tokenizer = T5Tokenizer.from_pretrained(bfl_repo, subfolder="tokenizer_2", torch_dtype=dtype)

pipe = ChromaPipeline.from_pretrained(bfl_repo, transformer=transformer, text_encoder=text_encoder, tokenizer=tokenizer, torch_dtype=dtype)

pipe.enable_model_cpu_offload()

prompt = "A cat holding a sign that says hello world"
image = pipe(
    prompt,
    guidance_scale=4.0,
    output_type="pil",
    num_inference_steps=26,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]

image.save("image.png")

ChromaPipeline

class diffusers.ChromaPipeline

< >

( scheduler: FlowMatchEulerDiscreteScheduler vae: AutoencoderKL text_encoder: T5EncoderModel tokenizer: T5TokenizerFast transformer: ChromaTransformer2DModel image_encoder: CLIPVisionModelWithProjection = None feature_extractor: CLIPImageProcessor = None )

Parameters

  • transformer (ChromaTransformer2DModel) — Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
  • scheduler (FlowMatchEulerDiscreteScheduler) — A scheduler to be used in combination with transformer to denoise the encoded image latents.
  • vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representation
  • text_encoder (T5EncoderModel) — T5, specifically the google/t5-v1_1-xxl variant.
  • tokenizer (T5TokenizerFast) — Second Tokenizer of class T5TokenizerFast.

The Chroma pipeline for text-to-image generation.

Reference: https://huggingface.co/lodestones/Chroma/

__call__

< >

( prompt: typing.Union[str, typing.List[str]] = None negative_prompt: typing.Union[str, typing.List[str]] = None height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 28 sigmas: typing.Optional[typing.List[float]] = None guidance_scale: float = 3.5 num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None ip_adapter_image_embeds: typing.Optional[typing.List[torch.Tensor]] = None negative_ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None negative_ip_adapter_image_embeds: typing.Optional[typing.List[torch.Tensor]] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True joint_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None callback_on_step_end: typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] max_sequence_length: int = 512 ) → ~pipelines.chroma.ChromaPipelineOutput or tuple

Parameters

  • prompt (str or List[str], optional) — The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds. instead.
  • negative_prompt (str or List[str], optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass negative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is not greater than 1).
  • height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image. This is set to 1024 by default for the best results.
  • width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated image. This is set to 1024 by default for the best results.
  • num_inference_steps (int, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
  • sigmas (List[float], optional) — Custom sigmas to use for the denoising process with schedulers which support a sigmas argument in their set_timesteps method. If not defined, the default behavior when num_inference_steps is passed will be used.
  • guidance_scale (float, optional, defaults to 3.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance. guidance_scale is defined as w of equation 2. of Imagen Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality.
  • num_images_per_prompt (int, optional, defaults to 1) — The number of images to generate per prompt.
  • generator (torch.Generator or List[torch.Generator], optional) — One or a list of torch generator(s) to make generation deterministic.
  • latents (torch.FloatTensor, optional) — Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random generator.
  • prompt_embeds (torch.FloatTensor, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument.
  • ip_adapter_image — (PipelineImageInput, optional): Optional image input to work with IP Adapters.
  • ip_adapter_image_embeds (List[torch.Tensor], optional) — Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape (batch_size, num_images, emb_dim). If not provided, embeddings are computed from the ip_adapter_image input argument.
  • negative_ip_adapter_image — (PipelineImageInput, optional): Optional image input to work with IP Adapters.
  • negative_ip_adapter_image_embeds (List[torch.Tensor], optional) — Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape (batch_size, num_images, emb_dim). If not provided, embeddings are computed from the ip_adapter_image input argument.
  • negative_prompt_embeds (torch.FloatTensor, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input argument.
  • output_type (str, optional, defaults to "pil") — The output format of the generate image. Choose between PIL: PIL.Image.Image or np.array.
  • return_dict (bool, optional, defaults to True) — Whether or not to return a ~pipelines.flux.ChromaPipelineOutput instead of a plain tuple.
  • joint_attention_kwargs (dict, optional) — A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor.
  • callback_on_step_end (Callable, optional) — A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified by callback_on_step_end_tensor_inputs.
  • callback_on_step_end_tensor_inputs (List, optional) — The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list will be passed as callback_kwargs argument. You will only be able to include variables listed in the ._callback_tensor_inputs attribute of your pipeline class.
  • max_sequence_length (int defaults to 512) — Maximum sequence length to use with the prompt.

Returns

~pipelines.chroma.ChromaPipelineOutput or tuple

~pipelines.chroma.ChromaPipelineOutput if return_dict is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images.

Function invoked when calling the pipeline for generation.

Examples:

>>> import torch
>>> from diffusers import ChromaPipeline

>>> pipe = ChromaPipeline.from_single_file(
...     "chroma-unlocked-v35-detail-calibrated.safetensors", torch_dtype=torch.bfloat16
... )
>>> pipe.to("cuda")
>>> prompt = "A cat holding a sign that says hello world"
>>> image = pipe(prompt, num_inference_steps=28, guidance_scale=4.0).images[0]
>>> image.save("chroma.png")

disable_vae_slicing

< >

( )

Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to computing decoding in one step.

disable_vae_tiling

< >

( )

Disable tiled VAE decoding. If enable_vae_tiling was previously enabled, this method will go back to computing decoding in one step.

enable_vae_slicing

< >

( )

Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.

enable_vae_tiling

< >

( )

Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images.

encode_prompt

< >

( prompt: typing.Union[str, typing.List[str]] negative_prompt: typing.Union[str, typing.List[str]] = None device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None do_classifier_free_guidance: bool = True max_sequence_length: int = 512 lora_scale: typing.Optional[float] = None )

Parameters

  • prompt (str or List[str], optional) — prompt to be encoded
  • negative_prompt (str or List[str], optional) — The prompt not to guide the image generation. If not defined, one has to pass negative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
  • device — (torch.device): torch device
  • num_images_per_prompt (int) — number of images that should be generated per prompt
  • prompt_embeds (torch.FloatTensor, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument.
  • lora_scale (float, optional) — A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
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