mlboydaisuke / GLM-Image-CoreAI

huggingface.co
Total runs: 27
24-hour runs: 0
7-day runs: 27
30-day runs: 27
Model's Last Updated: August 15 2026
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Introduction of GLM-Image-CoreAI

Model Details of GLM-Image-CoreAI

GLM-Image — Core AI

ZhipuAI's GLM-Image (16B, MIT) converted to Core AI for on-device image generation on Apple Silicon (macOS 27+) — the zoo's first autoregressive + diffusion hybrid .

GLM-Image generates in two stages: a 9B GLM-4 autoregressive model writes the image as a grid of discrete visual prior tokens (sampled left-to-right like an LLM, ~36 tok/s on M4 Max), and a 7B flow-matching DiT denoises the actual pixels conditioned on those tokens, followed by a 16-channel VAE decode. Both native 1024×1024 and a faster 512×512 variant are included.

macOS only. The AR (9.6 GB) + DiT (9.2 GB) weights are resolution-independent, so the 512 variant is no smaller — an iPhone port needs int4 + sequential component loading.

Components
Component Description Size
glm_image_ar.aimodelc GLM-4-9B visual-token AR decoder (int8, S=1 decode, AOT h16c GPU) 9.6 GB
glm_image_dit_1024.aimodelc 30-block flow-matching DiT, 1024² (int8, AOT h16c GPU) 9.2 GB
glm_image_dit_512.aimodelc Same DiT exported at 512² 9.2 GB
glm_image_vae_1024.aimodel 16ch AutoencoderKL decoder, 1024² (fp32, CPU) 0.9 GB
glm_image_vae_512.aimodel Same VAE exported at 512² 0.9 GB
tokenizer/ GLM tokenizer (vocab 168 064; visual tokens < 16 512) —
ehs.f32 Empty-glyph text embedding [1×1×1472] — the DiT text input for glyph-free prompts (no T5 needed at runtime) 6 KB

Weights are int8 (per-block-32) for AR/DiT; the VAE stays fp32 (fp16 overflows its activations). Compute precision float16 on GPU. One resolution needs ~19.7 GB on disk (AR + one DiT + one VAE).

Usage
Sample app (easiest)

CoreAIImageGen (macOS) — run the CoreAIImageGenMac scheme, pick GLM-Image 1024 (AR+diffusion) from the model menu, tap Download & Load , type a prompt, Generate .

  • Steps 20 (default) ≈ 4.5 min/image on M4 Max — reference quality
  • Steps 12 ≈ 2.5 min — nearly indistinguishable
  • The 512 variant generates in ~1 min

The AR stage is sampled (temperature 0.9 / top-p 0.75, the upstream generation_config ), so re-rolling the seed meaningfully changes composition.

Pipeline contract (build your own)

The hybrid does not fit Apple's high-level CoreAIDiffusionPipeline ; the host loop is ~300 lines of Swift (see the app's GlmImagePipeline ):

  1. AR : tokenize(prompt) + fixed grid suffix → S=1 prefill/decode with host-computed 3D-mRoPE cos/sin → sample visual tokens → 2× upsample the large grid → prior[1, N] (N = 1024 @512, 4096 @1024).
  2. DiT : 20-step CFG (guidance 1.5, prior_scale 1/0 for cond/uncond). Condition the DiT on the raw integer schedule trunc(linspace(1000,1,steps+1)) − 1 ; step the latent with the mu-shifted sigmas σ' = μ/(μ + (1/σ − 1)) , μ = 0.75·side/256 + 0.25.
  3. VAE (fp32, CPU): latent → RGB.

⚠️ Feeding the DiT the shifted timesteps (sigma×1000) instead of the raw schedule skews the adaLN time conditioning every step and drifts colors — resolution-dependently (mild at 512, strong at 1024). This is the one non-obvious contract in the port.

Limitations
  • Glyph rendering (text in images) is not wired : prompts containing quoted text ( '…' , "…" , 「…」 ) route through a T5 glyph encoder that is not part of this bundle. Keep prompts glyph-free.
  • Negative prompts are a no-op (GLM's CFG drops the prior tokens instead).
  • Image-to-image / editing ( GlmImageKVCache ) not yet exported.
Quality

Verified against the bf16 diffusers reference: DiT single-forward cosine 0.9999, VAE byte-exact, AR prior-injection reproduces the reference composition; end-to-end output is visually on par with GlmImagePipeline at the same step count.

License

MIT (inherited from zai-org/GLM-Image ).

Runs of mlboydaisuke GLM-Image-CoreAI on huggingface.co

27
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3-day runs
27
7-day runs
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