diffusers/pr_11739/en/api/pipelines/chroma.md

Chroma

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

Original model checkpoints for Chroma can be found here:

• High-resolution finetune: lodestones/Chroma1-HD
• Base model: lodestones/Chroma1-Base
• Original repo with progress checkpoints: lodestones/Chroma (loading this repo with from_pretrained will load a Diffusers-compatible version of the unlocked-v37 checkpoint)

Chroma can use all the same optimizations as Flux.

Inference

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")

Loading from a single file

To use updated model checkpoints that are not in the Diffusers format, you can use the ChromaTransformer2DModel class to load the model from a single file 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

model_id = "lodestones/Chroma1-HD"
dtype = torch.bfloat16

transformer = ChromaTransformer2DModel.from_single_file("https://huggingface.co/lodestones/Chroma1-HD/blob/main/Chroma1-HD.safetensors", torch_dtype=dtype)

pipe = ChromaPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=dtype)
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,
).images[0]

image.save("chroma-single-file.png")

ChromaPipeline[[diffusers.ChromaPipeline]]

diffusers.ChromaPipeline[[diffusers.ChromaPipeline]]

Source

The Chroma pipeline for text-to-image generation.

Reference: https://huggingface.co/lodestones/Chroma1-HD/

__call__diffusers.ChromaPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/pipelines/chroma/pipeline_chroma.py#L641[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "height", "val": ": typing.Optional[int] = None"}, {"name": "width", "val": ": typing.Optional[int] = None"}, {"name": "num_inference_steps", "val": ": int = 35"}, {"name": "sigmas", "val": ": typing.Optional[typing.List[float]] = None"}, {"name": "guidance_scale", "val": ": float = 5.0"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "negative_ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "negative_ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "joint_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 512"}]- 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.Tensor, 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 be generated by sampling using the supplied random generator.
• prompt_embeds (torch.Tensor, 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.Tensor, 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.
• prompt_attention_mask (torch.Tensor, optional) -- Attention mask for the prompt embeddings. Used to mask out padding tokens in the prompt sequence. Chroma requires a single padding token remain unmasked. Please refer to https://huggingface.co/lodestones/Chroma#tldr-masking-t5-padding-tokens-enhanced-fidelity-and-increased-stability-during-training
• negative_prompt_attention_mask (torch.Tensor, optional) -- Attention mask for the negative prompt embeddings. Used to mask out padding tokens in the negative prompt sequence. Chroma requires a single padding token remain unmasked. PLease refer to https://huggingface.co/lodestones/Chroma#tldr-masking-t5-padding-tokens-enhanced-fidelity-and-increased-stability-during-training
• 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.0~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

>>> model_id = "lodestones/Chroma1-HD"
>>> ckpt_path = "https://huggingface.co/lodestones/Chroma1-HD/blob/main/Chroma1-HD.safetensors"
>>> transformer = ChromaTransformer2DModel.from_single_file(ckpt_path, torch_dtype=torch.bfloat16)
>>> pipe = ChromaPipeline.from_pretrained(
...     model_id,
...     transformer=transformer,
...     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, negative_prompt=negative_prompt).images[0]
>>> image.save("chroma.png")

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.

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.

disable_vae_slicing[[diffusers.ChromaPipeline.disable_vae_slicing]]

Source

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[[diffusers.ChromaPipeline.disable_vae_tiling]]

Source

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[[diffusers.ChromaPipeline.enable_vae_slicing]]

Source

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[[diffusers.ChromaPipeline.enable_vae_tiling]]

Source

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[[diffusers.ChromaPipeline.encode_prompt]]

Source

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.Tensor, 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.

ChromaImg2ImgPipeline[[diffusers.ChromaImg2ImgPipeline]]

diffusers.ChromaImg2ImgPipeline[[diffusers.ChromaImg2ImgPipeline]]

Source

The Chroma pipeline for image-to-image generation.

Reference: https://huggingface.co/lodestones/Chroma1-HD/

__call__diffusers.ChromaImg2ImgPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/pipelines/chroma/pipeline_chroma_img2img.py#L700[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "height", "val": ": typing.Optional[int] = None"}, {"name": "width", "val": ": typing.Optional[int] = None"}, {"name": "num_inference_steps", "val": ": int = 35"}, {"name": "sigmas", "val": ": typing.Optional[typing.List[float]] = None"}, {"name": "guidance_scale", "val": ": float = 5.0"}, {"name": "strength", "val": ": float = 0.9"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "negative_ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "negative_ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_attention_mask", "val": ": typing.Optional[] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "joint_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 512"}]- 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 35) -- 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.
• strength (`float, optional, defaults to 0.9) -- Conceptually, indicates how much to transform the reference image. Must be between 0 and 1. image will be used as a starting point, adding more noise to it the larger the strength. The number of denoising steps depends on the amount of noise initially added. When strength is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in num_inference_steps. A value of 1, therefore, essentially ignores image.
• 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.Tensor, 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 be generated by sampling using the supplied random generator.
• prompt_embeds (torch.Tensor, 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.Tensor, 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.
• prompt_attention_mask (torch.Tensor, optional) -- Attention mask for the prompt embeddings. Used to mask out padding tokens in the prompt sequence. Chroma requires a single padding token remain unmasked. Please refer to https://huggingface.co/lodestones/Chroma#tldr-masking-t5-padding-tokens-enhanced-fidelity-and-increased-stability-during-training
• negative_prompt_attention_mask (torch.Tensor, optional) -- Attention mask for the negative prompt embeddings. Used to mask out padding tokens in the negative prompt sequence. Chroma requires a single padding token remain unmasked. PLease refer to https://huggingface.co/lodestones/Chroma#tldr-masking-t5-padding-tokens-enhanced-fidelity-and-increased-stability-during-training
• 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.0~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 ChromaTransformer2DModel, ChromaImg2ImgPipeline

>>> model_id = "lodestones/Chroma1-HD"
>>> ckpt_path = "https://huggingface.co/lodestones/Chroma1-HD/blob/main/Chroma1-HD.safetensors"
>>> pipe = ChromaImg2ImgPipeline.from_pretrained(
...     model_id,
...     transformer=transformer,
...     torch_dtype=torch.bfloat16,
... )
>>> pipe.enable_model_cpu_offload()
>>> init_image = load_image(
...     "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
... )
>>> prompt = "a scenic fastasy landscape with a river and mountains in the background, vibrant colors, detailed, high resolution"
>>> negative_prompt = "low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"
>>> image = pipe(prompt, image=init_image, negative_prompt=negative_prompt).images[0]
>>> image.save("chroma-img2img.png")

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.

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.

disable_vae_slicing[[diffusers.ChromaImg2ImgPipeline.disable_vae_slicing]]

Source

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[[diffusers.ChromaImg2ImgPipeline.disable_vae_tiling]]

Source

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[[diffusers.ChromaImg2ImgPipeline.enable_vae_slicing]]

Source

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[[diffusers.ChromaImg2ImgPipeline.enable_vae_tiling]]

Source

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[[diffusers.ChromaImg2ImgPipeline.encode_prompt]]

Source

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.Tensor, 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.