417 lines
15 KiB
Python
417 lines
15 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import argparse
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import os
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import time
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from pathlib import Path
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from typing import Any
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import torch
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from vllm_omni.diffusion.data import DiffusionParallelConfig, logger
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from vllm_omni.entrypoints.omni import Omni
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from vllm_omni.inputs.data import OmniDiffusionSamplingParams
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from vllm_omni.lora.request import LoRARequest
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from vllm_omni.lora.utils import stable_lora_int_id
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from vllm_omni.outputs import OmniRequestOutput
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from vllm_omni.platforms import current_omni_platform
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def is_nextstep_model(model_name: str) -> bool:
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"""Check if the model is a NextStep model by reading its config."""
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from vllm.transformers_utils.config import get_hf_file_to_dict
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try:
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cfg = get_hf_file_to_dict("config.json", model_name)
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if cfg and cfg.get("model_type") == "nextstep":
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return True
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except Exception:
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pass
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return False
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Generate an image with supported diffusion models.")
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parser.add_argument(
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"--model",
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default="Qwen/Qwen-Image",
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help="Diffusion model name or local path. Supported models: "
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"Qwen/Qwen-Image, Tongyi-MAI/Z-Image-Turbo, Qwen/Qwen-Image-2512, stepfun-ai/NextStep-1.1",
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)
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parser.add_argument("--prompt", default="a cup of coffee on the table", help="Text prompt for image generation.")
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parser.add_argument(
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"--negative-prompt",
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default=None,
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help="negative prompt for classifier-free conditional guidance.",
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)
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parser.add_argument("--seed", type=int, default=142, help="Random seed for deterministic results.")
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parser.add_argument(
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"--cfg-scale",
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type=float,
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default=4.0,
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help="True classifier-free guidance scale specific to Qwen-Image.",
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)
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parser.add_argument(
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"--guidance-scale",
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type=float,
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default=1.0,
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help="Classifier-free guidance scale.",
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)
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parser.add_argument("--height", type=int, default=1024, help="Height of generated image.")
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parser.add_argument("--width", type=int, default=1024, help="Width of generated image.")
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parser.add_argument(
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"--output",
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type=str,
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default="qwen_image_output.png",
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help="Path to save the generated image (PNG).",
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)
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parser.add_argument(
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"--num-images-per-prompt",
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type=int,
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default=1,
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help="Number of images to generate for the given prompt.",
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)
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parser.add_argument(
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"--num-inference-steps",
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type=int,
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default=50,
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help="Number of denoising steps for the diffusion sampler.",
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)
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parser.add_argument(
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"--cache-backend",
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type=str,
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default=None,
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choices=["cache_dit", "tea_cache"],
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help=(
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"Cache backend to use for acceleration. "
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"Options: 'cache_dit' (DBCache + SCM + TaylorSeer), 'tea_cache' (Timestep Embedding Aware Cache). "
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"Default: None (no cache acceleration)."
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),
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)
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parser.add_argument(
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"--enable-cache-dit-summary",
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action="store_true",
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help="Enable cache-dit summary logging after diffusion forward passes.",
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)
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parser.add_argument(
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"--ulysses-degree",
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type=int,
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default=1,
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help="Number of GPUs used for ulysses sequence parallelism.",
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)
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parser.add_argument(
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"--ring-degree",
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type=int,
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default=1,
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help="Number of GPUs used for ring sequence parallelism.",
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)
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parser.add_argument(
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"--cfg-parallel-size",
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type=int,
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default=1,
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choices=[1, 2],
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help="Number of GPUs used for classifier free guidance parallel size.",
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)
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parser.add_argument(
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"--enforce-eager",
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action="store_true",
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help="Disable torch.compile and force eager execution.",
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)
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parser.add_argument(
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"--enable-cpu-offload",
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action="store_true",
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help="Enable CPU offloading for diffusion models.",
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)
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parser.add_argument(
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"--enable-layerwise-offload",
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action="store_true",
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help="Enable layerwise (blockwise) offloading on DiT modules.",
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)
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parser.add_argument(
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"--quantization",
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type=str,
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default=None,
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choices=["fp8"],
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help="Quantization method for the transformer. "
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"Options: 'fp8' (FP8 W8A8 on Ada/Hopper, weight-only on older GPUs). "
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"Default: None (no quantization, uses BF16).",
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)
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parser.add_argument(
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"--ignored-layers",
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type=str,
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default=None,
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help="Comma-separated list of layer name patterns to skip quantization. "
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"Only used when --quantization is set. "
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"Available layers: to_qkv, to_out, add_kv_proj, to_add_out, img_mlp, txt_mlp, proj_out. "
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"Example: --ignored-layers 'add_kv_proj,to_add_out'",
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)
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parser.add_argument(
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"--vae-use-slicing",
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action="store_true",
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help="Enable VAE slicing for memory optimization.",
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)
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parser.add_argument(
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"--vae-use-tiling",
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action="store_true",
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help="Enable VAE tiling for memory optimization.",
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)
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parser.add_argument(
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"--tensor-parallel-size",
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type=int,
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default=1,
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help="Number of GPUs used for tensor parallelism (TP) inside the DiT.",
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)
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parser.add_argument(
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"--lora-path",
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type=str,
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default=None,
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help="Path to LoRA adapter folder (PEFT format). Loaded at initialization and used for generation.",
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)
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parser.add_argument(
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"--lora-scale",
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type=float,
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default=1.0,
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help="Scale factor for LoRA weights (default: 1.0).",
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)
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parser.add_argument(
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"--vae-patch-parallel-size",
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type=int,
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default=1,
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help="Number of ranks used for VAE patch/tile parallelism (decode/encode).",
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)
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# NextStep-1.1 specific arguments
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parser.add_argument(
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"--guidance-scale-2",
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type=float,
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default=1.0,
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help="Secondary guidance scale (e.g. image-level CFG for NextStep-1.1).",
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)
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parser.add_argument(
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"--timesteps-shift",
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type=float,
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default=1.0,
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help="[NextStep-1.1 only] Timesteps shift parameter for sampling.",
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)
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parser.add_argument(
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"--cfg-schedule",
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type=str,
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default="constant",
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choices=["constant", "linear"],
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help="[NextStep-1.1 only] CFG schedule type.",
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)
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parser.add_argument(
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"--use-norm",
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action="store_true",
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help="[NextStep-1.1 only] Apply layer normalization to sampled tokens.",
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)
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return parser.parse_args()
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def main():
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args = parse_args()
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generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed)
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use_nextstep = is_nextstep_model(args.model)
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cache_config = None
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cache_backend = args.cache_backend
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if cache_backend == "cache_dit":
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# cache-dit configuration: Hybrid DBCache + SCM + TaylorSeer
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# All parameters marked with [cache-dit only] in DiffusionCacheConfig
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cache_config = {
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# DBCache parameters [cache-dit only]
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"Fn_compute_blocks": 1, # Optimized for single-transformer models
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"Bn_compute_blocks": 0, # Number of backward compute blocks
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"max_warmup_steps": 4, # Maximum warmup steps (works for few-step models)
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"residual_diff_threshold": 0.24, # Higher threshold for more aggressive caching
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"max_continuous_cached_steps": 3, # Limit to prevent precision degradation
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# TaylorSeer parameters [cache-dit only]
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"enable_taylorseer": False, # Disabled by default (not suitable for few-step models)
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"taylorseer_order": 1, # TaylorSeer polynomial order
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# SCM (Step Computation Masking) parameters [cache-dit only]
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"scm_steps_mask_policy": None, # SCM mask policy: None (disabled), "slow", "medium", "fast", "ultra"
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"scm_steps_policy": "dynamic", # SCM steps policy: "dynamic" or "static"
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}
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elif cache_backend == "tea_cache":
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# TeaCache configuration
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# All parameters marked with [tea_cache only] in DiffusionCacheConfig
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cache_config = {
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# TeaCache parameters [tea_cache only]
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"rel_l1_thresh": 0.2, # Threshold for accumulated relative L1 distance
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# Note: coefficients will use model-specific defaults based on model_type
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# (e.g., QwenImagePipeline or FluxPipeline)
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}
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# assert args.ring_degree == 1, "Ring attention is not supported yet"
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parallel_config = DiffusionParallelConfig(
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ulysses_degree=args.ulysses_degree,
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ring_degree=args.ring_degree,
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cfg_parallel_size=args.cfg_parallel_size,
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tensor_parallel_size=args.tensor_parallel_size,
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vae_patch_parallel_size=args.vae_patch_parallel_size,
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)
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# Check if profiling is requested via environment variable
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profiler_enabled = bool(os.getenv("VLLM_TORCH_PROFILER_DIR"))
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# Prepare LoRA kwargs for Omni initialization
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lora_args: dict[str, Any] = {}
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if args.lora_path:
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lora_args["lora_path"] = args.lora_path
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print(f"Using LoRA from: {args.lora_path}")
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# Build quantization kwargs: use quantization_config dict when
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# ignored_layers is specified so the list flows through OmniDiffusionConfig
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quant_kwargs: dict[str, Any] = {}
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ignored_layers = [s.strip() for s in args.ignored_layers.split(",") if s.strip()] if args.ignored_layers else None
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if args.quantization and ignored_layers:
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quant_kwargs["quantization_config"] = {
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"method": args.quantization,
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"ignored_layers": ignored_layers,
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}
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elif args.quantization:
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quant_kwargs["quantization"] = args.quantization
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omni_kwargs = {
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"model": args.model,
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"enable_layerwise_offload": args.enable_layerwise_offload,
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"vae_use_slicing": args.vae_use_slicing,
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"vae_use_tiling": args.vae_use_tiling,
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"cache_backend": args.cache_backend,
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"cache_config": cache_config,
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"enable_cache_dit_summary": args.enable_cache_dit_summary,
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"parallel_config": parallel_config,
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"enforce_eager": args.enforce_eager,
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"enable_cpu_offload": args.enable_cpu_offload,
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**lora_args,
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**quant_kwargs,
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}
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if use_nextstep:
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# NextStep-1.1 requires explicit pipeline class
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omni_kwargs["model_class_name"] = "NextStep11Pipeline"
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omni = Omni(**omni_kwargs)
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if profiler_enabled:
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print("[Profiler] Starting profiling...")
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omni.start_profile()
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# Time profiling for generation
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print(f"\n{'=' * 60}")
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print("Generation Configuration:")
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print(f" Model: {args.model}")
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print(f" Inference steps: {args.num_inference_steps}")
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print(f" Cache backend: {cache_backend if cache_backend else 'None (no acceleration)'}")
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print(f" Quantization: {args.quantization if args.quantization else 'None (BF16)'}")
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if ignored_layers:
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print(f" Ignored layers: {ignored_layers}")
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print(
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f" Parallel configuration: tensor_parallel_size={args.tensor_parallel_size}, "
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f"ulysses_degree={args.ulysses_degree}, ring_degree={args.ring_degree}, cfg_parallel_size={args.cfg_parallel_size}, "
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f"vae_patch_parallel_size={args.vae_patch_parallel_size}"
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)
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print(f" CPU offload: {args.enable_cpu_offload}")
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print(f" Image size: {args.width}x{args.height}")
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if args.lora_path:
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print(f" LoRA: scale={args.lora_scale}")
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print(f"{'=' * 60}\n")
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# Build LoRA request when --lora-path is set
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lora_request = None
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if args.lora_path:
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lora_request_id = stable_lora_int_id(args.lora_path)
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lora_request = LoRARequest(
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lora_name=Path(args.lora_path).stem,
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lora_int_id=lora_request_id,
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lora_path=args.lora_path,
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)
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generation_start = time.perf_counter()
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extra_args = {
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"timesteps_shift": args.timesteps_shift,
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"cfg_schedule": args.cfg_schedule,
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"use_norm": args.use_norm,
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}
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if lora_request:
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extra_args["lora_request"] = lora_request
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extra_args["lora_scale"] = args.lora_scale
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outputs = omni.generate(
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{
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"prompt": args.prompt,
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"negative_prompt": args.negative_prompt,
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},
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OmniDiffusionSamplingParams(
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height=args.height,
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width=args.width,
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generator=generator,
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true_cfg_scale=args.cfg_scale,
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guidance_scale=args.guidance_scale,
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guidance_scale_2=args.guidance_scale_2,
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num_inference_steps=args.num_inference_steps,
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num_outputs_per_prompt=args.num_images_per_prompt,
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extra_args=extra_args,
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),
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)
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generation_end = time.perf_counter()
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generation_time = generation_end - generation_start
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# Print profiling results
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print(f"Total generation time: {generation_time:.4f} seconds ({generation_time * 1000:.2f} ms)")
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if profiler_enabled:
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print("\n[Profiler] Stopping profiler and collecting results...")
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profile_results = omni.stop_profile()
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if profile_results and isinstance(profile_results, dict):
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traces = profile_results.get("traces", [])
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print("\n" + "=" * 60)
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print("PROFILING RESULTS:")
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for rank, trace in enumerate(traces):
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print(f"\nRank {rank}:")
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if trace:
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print(f" • Trace: {trace}")
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if not traces:
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print(" No traces collected.")
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print("=" * 60)
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else:
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print("[Profiler] No valid profiling data returned.")
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# Extract images from OmniRequestOutput
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# omni.generate() returns list[OmniRequestOutput], extract images from the first output
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if not outputs or len(outputs) == 0:
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raise ValueError("No output generated from omni.generate()")
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logger.info(f"Outputs: {outputs}")
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# Extract images from request_output[0]['images']
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first_output = outputs[0]
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if not hasattr(first_output, "request_output") or not first_output.request_output:
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raise ValueError("No request_output found in OmniRequestOutput")
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req_out = first_output.request_output[0]
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if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
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raise ValueError("Invalid request_output structure or missing 'images' key")
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images = req_out.images
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if not images:
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raise ValueError("No images found in request_output")
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output_path = Path(args.output)
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output_path.parent.mkdir(parents=True, exist_ok=True)
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suffix = output_path.suffix or ".png"
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stem = output_path.stem or "qwen_image_output"
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if len(images) <= 1:
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images[0].save(output_path)
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print(f"Saved generated image to {output_path}")
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else:
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for idx, img in enumerate(images):
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save_path = output_path.parent / f"{stem}_{idx}{suffix}"
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img.save(save_path)
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print(f"Saved generated image to {save_path}")
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if __name__ == "__main__":
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main()
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