Local-only parity and smoke tests for the glm_image port: FastVideo vs the official HF transformers + diffusers GLM-Image implementation. These are not run in CI — they need the full weights, CUDA, and (for the reference side) the diffusers GLM-Image classes; each test skips with an actionable message when a prerequisite is missing.

What you need


Weights	`zai-org/GLM-Image` → `official_weights/glm_image` (~30 GB: AR encoder 4 shards, transformer 3 shards, vae + text_encoder)
Runtime deps	`transformers>=5.0.0` (first release with the AR encoder `GlmImageForConditionalGeneration`) and `diffusers>=0.38.0` — both committed in `pyproject.toml`
Test-only deps	the diffusers `GlmImageTransformer2DModel` / `GlmImagePipeline` reference classes need `diffusers>=0.37.0.dev0` (used only by the parity tests)

Download weights (from the repo root; cache via HF_HOME if / is small):

python ".agents/skills/add-model-01-prep/scripts/download_hf_weights.py" \
    "zai-org/GLM-Image" "official_weights/glm_image"

Optionally install the diffusers reference classes to run the parity side:

uv pip install -U "git+https://github.com/huggingface/diffusers.git@ff3b86b4"

Parity was checked against transformers a65bf6c9 + diffusers ff3b86b4.

Parity results (GB200)

Component	Test	Result
`transformer` (DiT)	`test_transformer_parity.py`	fp32 cosine `1.000000` (100% within 5e-3); bf16 cosine `0.999969` (element-wise rounding over 30 layers vs a different SDPA kernel — not a bug; RoPE proven exactly equivalent). fp32 + bf16 + strict-load
`vae` (`AutoencoderKL`)	`test_vae_parity.py`	decode + encode + config, 3/3
`text_encoder` (T5) + ByT5 tokenizer	`test_t5_parity.py`	tokenizer + glyph extract + embeds, 3/3
`vision_language_encoder` (GLM-4-9B AR)	`test_ar_parity.py`	surface + to/eval + greedy determinism, 3/3
`pipeline` (T2I)	`test_pipeline_parity.py`	injected-input determinism: latent cosine `0.999997`, decoded-image MAE `0.159/255`. validity + deterministic parity, 2/2
`pipeline` (I2I / edit)	`test_edit_pipeline_parity.py`	validity / wiring gate (condition image enters via a KV-cache write pass, not noise-the-latent). diffusers distribution parity deferred (stochastic AR)

processor (GlmImageProcessor) and scheduler (FlowMatchEulerDiscreteScheduler, shared flow-matching) are exercised via the AR and pipeline tests.

Run:

pytest tests/local_tests/glm_image/test_transformer_parity.py -v -s
pytest tests/local_tests/glm_image/test_vae_parity.py -v -s
pytest tests/local_tests/glm_image/test_t5_parity.py -v -s
pytest tests/local_tests/glm_image/test_ar_parity.py -v -s
pytest tests/local_tests/glm_image/test_pipeline_parity.py -v -s
pytest tests/local_tests/glm_image/test_edit_pipeline_parity.py -v -s

Design notes (load-bearing — don't regress)

Pipeline parity is by injection. Full-pipeline pixel parity is ill-posed (stochastic AR + independent latent RNG), so the pipeline tests inject matched (prompt_embeds, prior_token_ids, latents) into both the diffusers pipeline and the real GlmImageDenoisingStage, then compare denoised latents + decoded image — not raw end-to-end pixels.
HF imports are confined to the lazy-wrapper loader fastvideo/models/encoders/glm_image_ar_loader.py, never the production loaders (production-boundary rule).
Tokenizer is ByT5, matching the diffusers reference (not plain T5); compute_glyph_embeds mirrors diffusers _get_glyph_embeds.
Model-specific stages live under fastvideo/pipelines/basic/glm_image/stages/ per the add-model Files Map.
Param mapping is handled in-place at load via param_names_mapping (fastvideo/configs/models/dits/glm_image.py) + the native VAE/encoder configs — no conversion script; the loader raises on any unmatched param and test_transformer_strict_load.py asserts completeness with strict=True.