app.py

import random

import gradio as gr
import numpy as np
import spaces
import torch

from diffusers import ModularPipeline
from diffusers.modular_pipelines import SequentialPipelineBlocks
from diffusers.modular_pipelines.flux2.decoders import Flux2UnpackLatentsStep


repo_id = "black-forest-labs/FLUX.2-klein-4B"

# Take the pipeline apart into stages: each stage only loads the components it needs.
blocks = ModularPipeline.from_pretrained(repo_id).blocks
text_encoder_block = blocks.sub_blocks.pop("text_encoder")
decode_block = blocks.sub_blocks.pop("decode")
blocks.sub_blocks.pop("vae_encoder")  # image-conditioning branch, unused in this text-to-image demo

text_encoder_pipe = text_encoder_block.init_pipeline(repo_id)  # text encoder + tokenizer
pipe = blocks.init_pipeline(repo_id)  # transformer + scheduler

# The preview decoder unpacks the in-loop (packed) latents, then runs the pipeline's own decode
# block — the same block popped from the pipeline above.
preview = SequentialPipelineBlocks.from_blocks_dict(
    {"unpack": Flux2UnpackLatentsStep(), "decode": decode_block}
).init_pipeline(repo_id)  # vae + image processor

for stage in (text_encoder_pipe, pipe, preview):
    stage.load_components(dtype=torch.bfloat16)
    stage.to("cuda")

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048


@spaces.GPU(duration=120)
def infer(
    prompt,
    seed=42,
    randomize_seed=False,
    width=1024,
    height=1024,
    num_inference_steps=4,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)

    text_embeddings = text_encoder_pipe(prompt=prompt).get_by_kwargs("denoiser_input_fields")

    # `pipe.stream()` yields an event with the live pipeline state after every denoising step
    stream = pipe.stream(
        **text_embeddings,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
    )
    for event in stream:
        # flow matching: after step i the latents sit at sigmas[i + 1]; project to the predicted
        # clean image x0 = x_t - sigma * v so the preview shows the image forming, not noise.
        # At the last step sigma is 0, so the last preview is exactly the final image.
        latents = event.state.get("latents")
        sigma = pipe.scheduler.sigmas[event.loop_kwargs["i"] + 1].to(latents.device, latents.dtype)
        x0 = latents - sigma * event.state.get("noise_pred")
        image = preview(
            latents=x0,
            latent_ids=event.state.get("latent_ids"),
            output="images",
        )[0]
        yield image, seed


examples = [
    "a tiny astronaut hatching from an egg on the moon",
    "a cat holding a sign that says hello world",
    "an anime illustration of a wiener schnitzel",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

with gr.Blocks() as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(
            """# FLUX.2 [klein] — Live Preview with Modular Diffusers
Live latent preview powered by `pipe.stream()`: the pipeline yields its live state after every
denoising step, and a preview pipeline built from flux2's own unpack + decode blocks renders it.
No custom blocks, queues, or threads — see [huggingface/diffusers#14159](https://github.com/huggingface/diffusers/pull/14159).
"""
        )

        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )

            run_button = gr.Button("Run", scale=0)

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )

            num_inference_steps = gr.Slider(
                label="Number of inference steps",
                minimum=1,
                maximum=16,
                step=1,
                value=4,
            )

        gr.Examples(examples=examples, fn=infer, inputs=[prompt], outputs=[result, seed], cache_examples=False)

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
        outputs=[result, seed],
    )

demo.launch(css=css, show_error=True)