Diffusers Model Export
This public repository contains a native Diffusers export of an EMA training checkpoint. The neutral repository name is intentional; complete provenance is recorded below.
Provenance
- Source experiment:
flux2_klein_base_4b_diffusionnft_dvreward_prompt_rubric_v4_3_visual_task_program_16prompts_group14_7train_1dvreward_tp1_2node_512px_20step_cfg4_nrt - Formal run:
flux2-v43-formal-70bfccfe - Source checkpoint:
models/step_500.pt - Global step:
500 - Checkpoint weight:
EMA - Source checkpoint SHA-256:
1e8e9d503c7f6bdfedbc41ae8a2d2fd49b8b03beb796b27c8de3c965cb1a3f84 - Base model:
black-forest-labs/FLUX.2-klein-base-4B - Base revision:
a3b4f4849157f664bdbc776fd7453c2783562f4d - Training profile: DiffusionNFT with prompt-rubric v4.3 visual-task-program DVReward, 512 px, 20 rollout steps, CFG 4
- LoRA merge: rank 32, alpha 64, 60 adapter pairs, merged into BF16 EMA transformer weights
Verification
The export was strictly loaded with native Diffusers in offline mode and passed a deterministic 512 px smoke generation.
- Strict transformer load: passed
- Offline pipeline reload: passed
- Transformer tensors changed from Base: 60
- Changed transformer elements: 2,434,099,540
- Transformer L2 delta from Base: 30.570273992298265
- Maximum absolute parameter delta: 0.009368896484375
- Smoke mean absolute channel delta from Base: 51.595113118489586
- Transformer shards: 9
- Text encoder shards: 9
- Maximum shard size: 1 GB
See export_manifest.json, verification.json, and artifact_checksums.sha256 for machine-readable provenance and integrity data.
Loading
import torch
from diffusers import Flux2KleinPipeline
pipe = Flux2KleinPipeline.from_pretrained(
"kimi000/quiet-cascade-57",
torch_dtype=torch.bfloat16,
)
pipe.to("cuda")
image = pipe(
"A cinematic photograph of a red fox walking through a snowy forest",
guidance_scale=4.0,
num_inference_steps=20,
).images[0]