DreamLite

DreamLite is a text-to-image and image-editing model from ByteDance. It pairs a custom 2D U-Net (DreamLiteUNetModel) with the Qwen3-VL multimodal encoder as its prompt / image-instruction encoder, and uses an AutoencoderTiny (TAESD-style) VAE for fast latent encode/decode.

Two pipelines are exposed:

Pipeline	Modes	CFG	Use case
DreamLitePipeline	text-to-image and image-editing (auto-selected by whether `image` is `None`)	3-branch dual CFG (`guidance_scale` on text branch, `image_guidance_scale` on image branch, à la InstructPix2Pix)	Highest quality
DreamLiteMobilePipeline	text-to-image and image-editing (auto-selected by whether `image` is `None`)	None — distilled, single UNet forward per step	On-device / low-latency

Official checkpoints:

Base model: carlofkl/DreamLite-base
Distilled mobile model: carlofkl/DreamLite-mobile

Both pipelines auto-detect text-to-image vs. image-editing mode from whether the image argument is provided. There is no separate Img2Img class.

When loading an input image for editing, prefer diffusers.utils.load_image(...) over raw PIL.Image.open(...). load_image enforces an RGB conversion and applies EXIF orientation, both of which the pipeline assumes. A plain Image.open of an RGBA / palette / EXIF-rotated source will silently produce a different latent conditioning and degrade output quality.

Text-to-image (Base)

import torch
from diffusers import DreamLitePipeline

pipe = DreamLitePipeline.from_pretrained("carlofkl/DreamLite-base", revision="diffusers", torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")

image = pipe(
    prompt="a dog running on the grass",
    negative_prompt="",
    height=1024,
    width=1024,
    num_inference_steps=28,
    generator=torch.Generator("cpu").manual_seed(42),
).images[0]
image.save("dreamlite_t2i.png")

Image editing (Base)

Pass an image to enter edit mode. Both guidance_scale (text branch) and image_guidance_scale (image branch) are active here.

import torch
from diffusers import DreamLitePipeline
from diffusers.utils import load_image

pipe = DreamLitePipeline.from_pretrained("carlofkl/DreamLite-base", revision="diffusers", torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")

source = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")

image = pipe(
    prompt="turn the cat into a corgi",
    image=source,
    height=1024,
    width=1024,
    num_inference_steps=28,
    generator=torch.Generator("cpu").manual_seed(42),
).images[0]
image.save("dreamlite_edit.png")

Text-to-image (Mobile)

The mobile pipeline is distilled and skips CFG entirely — a single UNet forward per step. It accepts the same prompt / height / width / num_inference_steps arguments, but ignores guidance_scale and image_guidance_scale if passed (a warning is logged).

import torch
from diffusers import DreamLiteMobilePipeline

pipe = DreamLiteMobilePipeline.from_pretrained("carlofkl/DreamLite-mobile", revision="diffusers", torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")

image = pipe(
    prompt="a dog running on the grass",
    height=1024,
    width=1024,
    num_inference_steps=4,
    generator=torch.Generator("cpu").manual_seed(42),
).images[0]
image.save("dreamlite_mobile_t2i.png")

Image editing (Mobile)

import torch
from diffusers import DreamLiteMobilePipeline
from diffusers.utils import load_image

pipe = DreamLiteMobilePipeline.from_pretrained("carlofkl/DreamLite-mobile", revision="diffusers", torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")

source = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")

image = pipe(
    prompt="turn the cat into a corgi",
    image=source,
    height=1024,
    width=1024,
    num_inference_steps=4,
    generator=torch.Generator("cpu").manual_seed(42),
).images[0]
image.save("dreamlite_mobile_edit.png")

Notes and limitations

Both pipelines force batch_size = 1 internally; num_images_per_prompt controls how many samples are drawn from the same prompt rather than parallel batching.
The prompt encoder is Qwen3-VL, which is a multimodal model. Loading the full pipeline therefore requires sufficient GPU memory for both the U-Net and the Qwen3-VL text encoder (~4 GB + ~0.7 GB in bf16 for the base release).
The VAE is AutoencoderTiny and exposes encoder_block_out_channels; vae_scale_factor is derived from it at pipeline init time.

DreamLitePipeline

class diffusers.DreamLitePipeline

< source >

( text_encoder: Qwen3VLForConditionalGeneration tokenizer: AutoTokenizer processor: Qwen3VLProcessor vae: AutoencoderTiny unet: DreamLiteUNetModel scheduler: FlowMatchEulerDiscreteScheduler )

DreamLite pipeline for text-to-image and instruction-based image editing.

The same pipeline supports both modes; the operating mode is auto-detected from the inputs:

image is None -> text-to-image (single CFG on text).
image is not None -> image-to-image / instruction edit (dual CFG: text + image).

Components: text_encoder ([~transformers.Qwen3VLForConditionalGeneration]): Multimodal text/vision encoder used to produce conditioning embeddings. tokenizer ([~transformers.AutoTokenizer]): Tokenizer for text-only (generate) mode. processor ([~transformers.Qwen3VLProcessor]): Multimodal processor for edit mode (text + image template). vae ([~diffusers.AutoencoderTiny]): Mobile-friendly tiny VAE for latent encode/decode. unet ([~diffusers.DreamLiteUNetModel]): DreamLite UNet (GQA + qk_norm + depthwise-separable convs). scheduler ([~diffusers.FlowMatchEulerDiscreteScheduler]): Flow-matching Euler scheduler with dynamic shift.

Note: batch_size is currently forced to 1; num_images_per_prompt is supported.

call

< source >

( prompt: typing.Optional[str] = None negative_prompt: typing.Optional[str] = None image: typing.Optional[PIL.Image.Image] = None height: typing.Optional[int] = None width: typing.Optional[int] = None guidance_scale: float = 3.5 image_guidance_scale: float = 1.5 num_inference_steps: int = 30 sigmas: typing.Optional[typing.List[float]] = None num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True max_sequence_length: int = 200 text_pad_embedding: typing.Optional[torch.Tensor] = None )

Parameters

prompt — Text prompt.
negative_prompt — Negative text prompt (defaults to empty string).
image — Optional input image. If provided, the pipeline runs in edit / image-to-image mode with dual classifier-free guidance; otherwise it runs in text-to-image mode.
height — Output resolution (height). Defaults to default_sample_size * vae_scale_factor (1024). The same default applies in both T2I and I2I; pass an explicit value to override.
width — Output resolution (width). Defaults to default_sample_size * vae_scale_factor (1024). The same default applies in both T2I and I2I; pass an explicit value to override.
guidance_scale — CFG scale on the text branch (both modes).
image_guidance_scale — Additional CFG scale on the image branch (edit mode only).
num_inference_steps — Number of denoising steps.
sigmas — Optional explicit FlowMatch sigmas; defaults to a uniform linspace.
num_images_per_prompt — Output images per prompt (note: batch_size is forced to 1).
generator — Random generator(s).
output_type — "pil", "np", "pt" or "latent".
return_dict — If True, returns a DreamLitePipelineOutput; else a tuple (images,).
max_sequence_length — Maximum number of user-prompt tokens kept after dropping the chat-template prefix. Only applies to generate mode (the edit mode uses the multimodal processor’s native padding).
text_pad_embedding — Optional learned pad embedding for masked positions.

Run the DreamLite pipeline.

DreamLiteMobilePipeline

class diffusers.DreamLiteMobilePipeline

< source >

( text_encoder: Qwen3VLForConditionalGeneration tokenizer: AutoTokenizer processor: Qwen3VLProcessor vae: AutoencoderTiny unet: DreamLiteUNetModel scheduler: FlowMatchEulerDiscreteScheduler )

DreamLite Mobile pipeline: a distilled, classifier-free-guidance-free variant of DreamLitePipeline for fast few-step inference (default 4 steps).

The operating mode is auto-detected from inputs (same as the base pipeline):

image is None -> text-to-image.
image is not None -> image-to-image / instruction edit.

Because classifier-free guidance is distilled away, guidance_scale and image_guidance_scale are accepted for API parity with DreamLitePipeline but are ignored in the denoising loop. negative_prompt is intentionally absent.

Components (identical to the base pipeline): text_encoder ([~transformers.Qwen3VLForConditionalGeneration]): Multimodal text/vision encoder. tokenizer ([~transformers.AutoTokenizer]): Tokenizer for text-only (generate) mode. processor ([~transformers.Qwen3VLProcessor]): Multimodal processor for edit mode. vae ([~diffusers.AutoencoderTiny]): Mobile-friendly tiny VAE. unet ([~diffusers.DreamLiteUNetModel]): DreamLite UNet. scheduler ([~diffusers.FlowMatchEulerDiscreteScheduler]): Flow-matching Euler scheduler with dynamic shift.

Note: batch_size is currently forced to 1; num_images_per_prompt is supported.

call

< source >

( prompt: typing.Union[str, typing.List[str]] = None image: typing.Optional[PIL.Image.Image] = None height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 4 guidance_scale: typing.Optional[float] = None image_guidance_scale: typing.Optional[float] = None sigmas: typing.Optional[typing.List[float]] = None num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True max_sequence_length: int = 200 text_pad_embedding: typing.Optional[torch.Tensor] = None )

Parameters

prompt — Text prompt.
image — Optional input image. If provided, runs in edit / image-to-image mode; otherwise runs in text-to-image mode.
height — Output resolution (height). Defaults to default_sample_size * vae_scale_factor (1024).
width — Output resolution (width). Defaults to default_sample_size * vae_scale_factor (1024).
num_inference_steps — Number of denoising steps. Defaults to 4 (distilled).
guidance_scale — Accepted for API parity with DreamLitePipeline; ignored because CFG was distilled away.
image_guidance_scale — Accepted for API parity with DreamLitePipeline; ignored because CFG was distilled away.
sigmas — Optional explicit FlowMatch sigmas; defaults to a uniform linspace.
num_images_per_prompt — Output images per prompt (note: batch_size is forced to 1).
generator — Random generator(s).
output_type — "pil", "np", "pt" or "latent".
return_dict — If True, returns a DreamLitePipelineOutput; else (images,).
max_sequence_length — Maximum number of user-prompt tokens kept after dropping the chat-template prefix. Only applies to generate mode (the edit mode uses the multimodal processor’s native padding).
text_pad_embedding — Optional learned pad embedding for masked positions.

Run the distilled DreamLite Mobile pipeline.

DreamLitePipelineOutput

class diffusers.DreamLitePipelineOutput

< source >

( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] )

Parameters

images (List[PIL.Image.Image] or np.ndarray) — List of denoised PIL images of length batch_size or NumPy array of shape (batch_size, height, width, num_channels). PIL images or NumPy array present the denoised images of the diffusion pipeline.

Output class for DreamLite pipelines.

Update on GitHub

Diffusers

DreamLite

Text-to-image (Base)

Image editing (Base)

Text-to-image (Mobile)

Image editing (Mobile)

Notes and limitations

DreamLitePipeline

class diffusers.DreamLitePipeline

__call__

DreamLiteMobilePipeline

class diffusers.DreamLiteMobilePipeline

__call__

DreamLitePipelineOutput

class diffusers.DreamLitePipelineOutput

call

call