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library_name: diffusers
tags:
- modular-diffusers
- diffusers
- krea2
- text-to-image
---
This is a modular diffusion pipeline built with 🧨 Diffusers' modular pipeline framework.
**Pipeline Type**: Krea2AutoBlocks
**Description**: Auto Modular pipeline for text-to-image generation using Krea 2: encode text -> core denoise (symmetric CFG) -> decode.
This pipeline uses a 3-block architecture that can be customized and extended.
## Example Usage
[TODO]
## Pipeline Architecture
This modular pipeline is composed of the following blocks:
1. **text_encoder** (`Krea2TextEncoderStep`)
- Text encoder step that tokenizes the prompt(s) with the Krea 2 chat template, runs the Qwen3-VL text encoder, and stacks a fixed set of decoder-layer hidden states per token as the transformer's text conditioning. When `guidance_scale > 0` the negative prompt is encoded the same way for CFG.
2. **denoise** (`Krea2CoreDenoiseStep`)
- Core denoising workflow for Krea 2 text-to-image: prepares the batch/latents/timesteps and the shared position ids, then runs the symmetric-CFG denoising loop, producing the denoised packed latents for the decoder.
3. **decode** (`Krea2DecodeStep`)
- Step that unpacks the denoised packed latents back to the spatial grid, de-normalizes them with the VAE's per-channel statistics, and decodes them through the Qwen-Image VAE into images.
## Model Components
1. text_encoder (`Qwen3VLModel`): The Qwen3-VL text encoder.
2. tokenizer (`AutoTokenizer`): The tokenizer paired with the text encoder.
3. transformer (`Krea2Transformer2DModel`)
4. scheduler (`FlowMatchEulerDiscreteScheduler`)
5. vae (`AutoencoderKLQwenImage`)
6. image_processor (`VaeImageProcessor`)
## Configuration Parameters
is_distilled (default: False)
## Workflow Input Specification
<details>
<summary><strong>text2image</strong></summary>
- `prompt` (`str`): The prompt or prompts to guide image generation.
</details>
## Input/Output Specification
**Inputs:**
- `prompt` (`str`): The prompt or prompts to guide image generation.
- `negative_prompt` (`str`, *optional*): The negative prompt(s) for CFG.
- `guidance_scale` (`float`, *optional*, defaults to `4.5`): CFG scale; the negative prompt is only encoded when this is > 0.
- `max_sequence_length` (`int`, *optional*, defaults to `512`): Maximum sequence length for prompt encoding.
- `num_images_per_prompt` (`int`, *optional*, defaults to `1`): The number of images to generate per prompt.
- `latents` (`Tensor`, *optional*): Pre-generated noisy latents for image generation.
- `height` (`int`): The height in pixels of the generated image.
- `width` (`int`): The width in pixels of the generated image.
- `generator` (`Generator`, *optional*): Torch generator for deterministic generation.
- `num_inference_steps` (`int`, *optional*, defaults to `28`): The number of denoising steps.
- `sigmas` (`list`, *optional*): Custom sigma schedule (defaults to a linear ramp).
- `attention_kwargs` (`dict`, *optional*): Additional kwargs for attention processors.
- `output_type` (`str`, *optional*, defaults to `pil`): Output format: 'pil', 'np', 'pt'.
**Outputs:**
- `images` (`list`): Generated images.
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