import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("SceneWorks/Lens", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Lens (base)
Self-contained diffusers-layout snapshot of Microsoft's Lens text-to-image model, re-assembled for
in-house (SceneWorks) use after Microsoft removed the original microsoft/Lens repository from the Hub.
This is a repackage, not a retrain — every weight is byte-identical to a public upstream source (verified by tensor-level comparison against Comfy-Org's authentic redistribution):
| Component | Source | License |
|---|---|---|
transformer/ |
Lens DiT, bf16 — from Comfy-Org/Lens (diffusion_models/lens_bf16.safetensors) |
MIT |
text_encoder/ |
gpt-oss-20b (MXFP4), used encoder-only — from openai/gpt-oss-20b |
Apache-2.0 |
tokenizer/ |
gpt-oss tokenizer — from openai/gpt-oss-20b |
Apache-2.0 |
vae/ |
FLUX.2 VAE (AutoencoderKLFlux2) — from black-forest-labs/FLUX.2-dev |
FLUX.2-dev license |
The Lens text encoder is stock, frozen gpt-oss-20b (tensor-verified identical to Comfy's
gpt_oss_20b_nvfp4 up to quantization), and the VAE is stock FLUX.2-dev (full-file identical to
Comfy's flux2-vae). Only the transformer/ DiT is Lens-specific.
Layout
tokenizer/ tokenizer.json, tokenizer_config.json, special_tokens_map.json
text_encoder/ model-0000*-of-00002.safetensors (MXFP4), model.safetensors.index.json, config.json
transformer/ lens_bf16.safetensors
vae/ diffusion_pytorch_model.safetensors, config.json
Sampling defaults for the base model: 20 steps, guidance 5.0. For the distilled variant see
SceneWorks/Lens-Turbo (4 steps, guidance 1.0).
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