Davids048andClaude Opus 5.5
4117696a4f
- Docs, AGENTS.md files, and skills describe ResolvedGeneratorConfig, resolve_inference_config, typed YAML, and --config plus dotted overrides for training and preprocessing instead of FastVideoArgs and flat flags. - The local tests build resolved configs (or small fakes that expose typed paths) instead of FastVideoArgs; the LoRA extraction scripts build pipelines with from_pretrained(model_path, resolved_config=...). - The mixkit README's NVFP4 snippet uses from_config with engine.quantization.transformer_quant (the from_pretrained keyword it used is rejected). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
123 lines
4.6 KiB
Python
123 lines
4.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Loader smoke tests for FLUX.1-dev component loading from a local checkpoint.
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Verifies that tokenizers, CLIP/T5 text encoders, VAE, and FlowMatch scheduler
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all load correctly from a Diffusers-layout ``FLUX.1-dev`` directory.
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Requires ``official_weights/FLUX.1-dev`` (or set ``FLUX_DEV_ROOT``) and CUDA.
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Run from repo root::
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pytest tests/local_tests/flux/test_flux_dev_component_loaders.py -vs
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"""
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from __future__ import annotations
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import os
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from dataclasses import dataclass, field
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from pathlib import Path
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import pytest
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import torch
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from fastvideo.configs.models.encoders import (
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BaseEncoderOutput,
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CLIPTextConfig,
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T5LargeConfig,
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)
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from fastvideo.configs.models.vaes.autoencoder_kl import AutoencoderKLVAEConfig
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from fastvideo.configs.pipelines.base import PipelineConfig, preprocess_text
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os.environ.setdefault("MASTER_ADDR", "localhost")
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os.environ.setdefault("MASTER_PORT", "29517")
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_REPO_ROOT = Path(__file__).resolve().parents[3]
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_FLUX_DEV_ROOT = os.environ.get(
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"FLUX_DEV_ROOT",
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str(_REPO_ROOT / "official_weights" / "FLUX.1-dev"),
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)
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def _flux_clip_post(outputs: BaseEncoderOutput) -> torch.Tensor:
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if outputs.pooler_output is None:
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raise RuntimeError("CLIP pooled output required")
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return outputs.pooler_output
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def _flux_t5_post(outputs: BaseEncoderOutput) -> torch.Tensor:
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if outputs.last_hidden_state is None:
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raise RuntimeError("T5 last_hidden_state required")
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return outputs.last_hidden_state
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@dataclass
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class _FluxDevLoaderPipelineConfig(PipelineConfig):
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"""Two text encoders (CLIP + T5) matching FLUX.1-dev model_index.json."""
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vae_config: AutoencoderKLVAEConfig = field(default_factory=AutoencoderKLVAEConfig)
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text_encoder_configs: tuple[CLIPTextConfig,
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T5LargeConfig] = field(default_factory=lambda: (CLIPTextConfig(), T5LargeConfig()))
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text_encoder_precisions: tuple[str, ...] = ("fp32", "bf16")
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preprocess_text_funcs: tuple = field(default_factory=lambda: (preprocess_text, preprocess_text))
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postprocess_text_funcs: tuple = field(default_factory=lambda: (_flux_clip_post, _flux_t5_post))
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def test_flux_dev_model_index_components_load() -> None:
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"""CLIP/T5 encoders, tokenizers, VAE, and FlowMatch scheduler load."""
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if not torch.cuda.is_available():
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pytest.skip("FLUX component loader test requires CUDA")
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if not Path(_FLUX_DEV_ROOT, "model_index.json").is_file():
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pytest.skip("official_weights/FLUX.1-dev missing "
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"(set FLUX_DEV_ROOT or download black-forest-labs/FLUX.1-dev)")
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from fastvideo.api.inference_resolution import resolve_inference_config
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from fastvideo.distributed import (
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cleanup_dist_env_and_memory,
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maybe_init_distributed_environment_and_model_parallel,
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)
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from fastvideo.models.loader.component_loader import (
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SchedulerLoader,
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TextEncoderLoader,
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TokenizerLoader,
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VAELoader,
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)
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maybe_init_distributed_environment_and_model_parallel(1, 1)
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try:
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args = resolve_inference_config({
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"model_path": _FLUX_DEV_ROOT,
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"engine": {
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"parallelism": {
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"hsdp_shard_dim": 1
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},
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"offload": {
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"pin_cpu_memory": False
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},
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},
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"pipeline": {
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"experimental": {
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"pipeline_config": _FluxDevLoaderPipelineConfig()
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}
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},
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})
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tok_clip = TokenizerLoader().load(os.path.join(_FLUX_DEV_ROOT, "tokenizer"), args)
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tok_t5 = TokenizerLoader().load(os.path.join(_FLUX_DEV_ROOT, "tokenizer_2"), args)
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assert "CLIP" in tok_clip.__class__.__name__
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assert "T5" in tok_t5.__class__.__name__
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te_clip = TextEncoderLoader().load(os.path.join(_FLUX_DEV_ROOT, "text_encoder"), args)
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te_t5 = TextEncoderLoader().load(os.path.join(_FLUX_DEV_ROOT, "text_encoder_2"), args)
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# Distributed init may wrap encoders in an FSDP shell.
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assert te_clip.__class__.__name__.endswith("CLIPTextModel")
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assert te_t5.__class__.__name__.endswith("T5EncoderModel")
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vae = VAELoader().load(os.path.join(_FLUX_DEV_ROOT, "vae"), args)
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assert vae.__class__.__name__ == "AutoencoderKL"
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scheduler = SchedulerLoader().load(os.path.join(_FLUX_DEV_ROOT, "scheduler"), args)
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assert scheduler.__class__.__name__ == "FlowMatchEulerDiscreteScheduler"
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scheduler.set_timesteps(4, mu=0.7)
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assert len(scheduler.timesteps) == 4
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finally:
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cleanup_dist_env_and_memory()
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