Instructions to use kimi000/crystal-field-29 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kimi000/crystal-field-29 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kimi000/crystal-field-29", 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
FLUX.2 Klein 4B Step-400 EMA Diffusers Export
This repository contains a complete native Hugging Face Diffusers export of a trained FLUX.2 Klein Base 4B checkpoint. The public repository name is intentionally neutral; full experiment provenance is recorded below for reproducibility.
Provenance
- Source experiment:
flux2_klein_base_4b_diffusionnft_dvreward_prompt_rubric_v4_3_1_geneval_16prompts_group14_7train_1dvreward_tp1_2node_512px_20step_cfg4_nrt - Experiment variant: prompt-rubric v4.3.1, GenEval
- Formal run:
flux2-v431-geneval-formal-505-142eef2e-a12 - Source run code revision:
142eef2e8ea30d30eaff17e3ddb2547f908fc3cc - Source checkpoint:
models/step_400.pt - Global step:
400 - Exported weights: EMA
- Source checkpoint SHA-256:
7aca3ce4549f66635e82e6ee7e6bedad9ba9d1193defc16ca59aeb0457015ad1 - Base model:
black-forest-labs/FLUX.2-klein-base-4B - Base revision:
a3b4f4849157f664bdbc776fd7453c2783562f4d - Training profile: 16 prompts, group size 14, 512 px, 20 rollout steps, CFG 4, 7 policy ranks and 1 DVReward replica per node across 2 nodes
- LoRA rank / alpha:
32 / 64
The EMA LoRA weights were merged into the native BF16 FLUX.2 transformer. The export contains 169 transformer tensors and 3,875,544,576 transformer parameters. DiffusionNFT old-policy training-state tensors were excluded from the inference artifact.
Format
- Pipeline:
diffusers.Flux2KleinPipeline - Transformer:
diffusers.Flux2Transformer2DModel - Precision: BF16
- Scheduler: 1,000 training timesteps, dynamic shifting enabled, deterministic sampling
- Transformer weights: 9 shards
- Text encoder weights: 9 shards
- Maximum shard size: below 1 GB
Verification
The export was strictly reloaded offline with stock Diffusers and generated a 512 x 512 smoke image using 4 inference steps, CFG 4, and seed 0. The merged transformer differs from Base in 60 tensors and 2,403,252,754 elements, with L2 delta 26.236759527279407 and maximum absolute delta 0.007568359375.
A same-seed Base render was also generated:
- Changed channel values:
763,458 - Mean absolute channel delta:
39.71453730265299 - Maximum absolute channel delta:
255
These checks verify that the exported transformer contains the trained Step-400 EMA weights rather than an unchanged Base transformer. verification.json, smoke images, the export manifest, and SHA-256 checksums are included.
Loading
import torch
from diffusers import Flux2KleinPipeline
pipe = Flux2KleinPipeline.from_pretrained(
"kimi000/crystal-field-29",
torch_dtype=torch.bfloat16,
)
pipe.enable_model_cpu_offload()
image = pipe(
prompt="A red cube beside a blue glass sphere.",
height=1024,
width=1024,
guidance_scale=4.0,
num_inference_steps=20,
).images[0]
See export_manifest.json and verification.json for machine-readable export details.
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Model tree for kimi000/crystal-field-29
Base model
black-forest-labs/FLUX.2-klein-base-4B