FLUX.2-klein-4B-modular

This is a modular diffusion pipeline built with 🧨 Diffusers' modular pipeline framework.

Pipeline Type: Flux2KleinAutoBlocks

Description: Auto blocks that perform the text-to-image and image-conditioned generation using Flux2-Klein.

• for image-conditioned generation, you need to provide image (list of PIL images).
• for text-to-image generation, all you need to provide is prompt.

This pipeline uses a 4-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 (Flux2KleinTextEncoderStep)
   • Text Encoder step that generates text embeddings using Qwen3 to guide the image generation
2. vae_encoder (Flux2KleinAutoVaeEncoderStep)
   • VAE encoder step that encodes the image inputs into their latent representations.
   • img_conditioning: Flux2KleinVaeEncoderSequentialStep
     • VAE encoder step that preprocesses and encodes the image inputs into their latent representations.
3. denoise (Flux2KleinCoreDenoiseStep)
   • Core denoise step that performs the denoising process for Flux2-Klein (distilled model).
   • input: Flux2TextInputStep
     • This step:
   • prepare_image_latents: Flux2PrepareImageLatentsStep
     • Step that prepares image latents and their position IDs for Flux2 image conditioning.
   • prepare_latents: Flux2PrepareLatentsStep
     • Prepare latents step that prepares the initial noise latents for Flux2 text-to-image generation
   • set_timesteps: Flux2SetTimestepsStep
     • Step that sets the scheduler's timesteps for Flux2 inference using empirical mu calculation
   • prepare_rope_inputs: Flux2RoPEInputsStep
     • Step that prepares the 4D RoPE position IDs for Flux2 denoising. Should be placed after text encoder and latent preparation steps.
   • denoise: Flux2KleinDenoiseStep
     • Denoise step that iteratively denoises the latents for Flux2.
   • after_denoise: Flux2UnpackLatentsStep
     • Step that unpacks the latents from the denoising step
4. decode (Flux2DecodeStep)
   • Step that decodes the denoised latents into images using Flux2 VAE with batch norm denormalization

Conditional Execution

This pipeline contains blocks that are selected at runtime based on inputs:

• Trigger Inputs: image

Model Components

1. text_encoder (Qwen3ForCausalLM)
2. tokenizer (Qwen2Tokenizer)
3. image_processor (Flux2ImageProcessor)
4. vae (AutoencoderKLFlux2)
5. scheduler (FlowMatchEulerDiscreteScheduler)
6. transformer (Flux2Transformer2DModel)

Configuration Parameters

is_distilled (default: True)

Input/Output Specification

Inputs Optional:

• prompt (Any): No description provided
• max_sequence_length (int), default: 512: No description provided
• text_encoder_out_layers (Tuple), default: (9, 18, 27): No description provided
• image (Any): No description provided
• height (Any): No description provided
• width (Any): No description provided
• generator (Any): No description provided
• num_images_per_prompt (Any), default: 1: No description provided
• image_latents (List): No description provided
• latents (Optional): No description provided
• num_inference_steps (Any), default: 50: No description provided
• timesteps (Any): No description provided
• sigmas (Any): No description provided
• joint_attention_kwargs (Any): No description provided
• output_type (Any), default: pil: No description provided

Outputs - images (List): The images from the decoding step.