Instructions to use OzzyGT/krea2_reference_blocks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use OzzyGT/krea2_reference_blocks with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OzzyGT/krea2_reference_blocks", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("OzzyGT/krea2_reference_blocks", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Krea 2 reference-image modular blocks
Custom Modular Diffusers blocks that let Krea 2 take reference images. Its text encoder is the full Qwen3-VL, vision tower included, so a reference can condition the generation through the encoder's vision path without any extra model. Two community edit LoRAs go further and feed the reference into the transformer as clean VAE latents.
reference_mode picks the path:
reference_mode="off" Qwen3-VL vision path only. No LoRA, works on the stock checkpoint
reference_mode="append" vision + clean reference tokens after the target. Ostris style-reference LoRA
reference_mode="prepend" vision + clean source before the target. Identity-Edit LoRA
With reference_mode="off" and no images, the blocks are stock text-to-image. No mode patches the
transformer.
Loading & running
import sdnq # needed to load the quantized text encoder
import torch
from diffusers import ModularPipeline
from diffusers.utils import load_image
pipe = ModularPipeline.from_pretrained(
"OzzyGT/krea2_reference_blocks", trust_remote_code=True
)
pipe.load_components(dtype=torch.bfloat16)
pipe.to("cuda")
image = pipe(
prompt="the same cat, sitting on a stone wall at sunset",
reference_images=load_image("cat.png"),
height=1024,
width=1024,
output="images",
)[0]
image.save("reference.png")
The LoRA modes need their adapter loaded first:
pipe.load_lora_weights("ostris/krea2_turbo_style_reference", weight_name="krea2_style_reference.safetensors")
image = pipe(
prompt="the same subject in a snowy forest",
reference_images=load_image("subject.png"),
reference_mode="append",
output="images",
)[0]
For loading a different checkpoint or swapping components, see Modular pipeline in the diffusers docs.
Reference inputs
reference_images one image or a list
reference_subject_strength scales content and subject. 1.0 default, 0 drops it
reference_style_strength scales texture and style. 1.0 default, 0 drops it
grounding_px longest side of the view the VLM sees, 768 default
Either strength takes one value for every reference, or a list matching them one to one, so a reference can
give its look without its content or the other way round. grounding_px is the identity-versus-adherence
dial: lower follows the prompt, higher holds the reference, 0 is native resolution.
Masks
reference_masks restricts a reference to part of the picture, painted being the part to use. One mask per
image, or None for a slot without one.
reference_mask_mode decides what happens to the rest:
"exclude_blank" default. Mask the vision attention and blank the masked-out tokens
"exclude" mask the attention only, leaving the masked-out tokens in the sequence
"deemphasize" blank the tokens only, which attenuates the region instead of removing it
Masks always apply to the vision path. In the LoRA modes the clean reference latents are unmasked by default,
so the edit still sees the whole image and a masked edit can show hints of the unpainted parts. Set
mask_reference_latents=True to drop those patches from the sequence too.
These blocks only work with Krea 2, Turbo or raw.
Credits
ostris/krea2_turbo_style_reference-- the style-reference LoRA behindappendconradlocke/krea2-identity-edit-- the Identity-Edit LoRA behindprepend- Both recipes come from ai-toolkit;
appendfollows its reference/edit setup,prependitspredict_velocity_edit.
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