vllm-omni/docs/user_guide/examples/offline_inference/bagel.md

BAGEL-7B-MoT

Source https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/bagel.

Set up

Please refer to the stage configuration documentation to configure memory allocation appropriately for your hardware setup.

Run examples

Note: These examples work with the default configuration on an NVIDIA A100 (80GB). We also tested on dual NVIDIA RTX 5000 Ada (32GB each). For dual-GPU setups, please modify the stage configuration to distribute the model across devices.

Get into the bagel folder

cd examples/offline_inference/bagel

Modality Control

BAGEL-7B-MoT supports multiple modality modes. You can control the mode using the --modality argument:

Text to Image (text2img)

• Pipeline: Text → Thinker → DiT → VAE Decode → Image
• Stages Used: Stage 0 (Thinker) + Stage 1 (DiT)
• KV Transfer: Thinker sends KV cache to DiT for conditioned generation

Generate images from text prompts:

python end2end.py --model ByteDance-Seed/BAGEL-7B-MoT \
                  --modality text2img \
                  --prompts "A cute cat"

Image to Image (img2img)

• Pipeline: Image → VAE Encode → DiT → VAE Decode → New Image
• Stages Used: Stage 1 (DiT) only
• Special: Bypasses the Thinker stage, direct image-to-image transformation

Transform images based on text prompts:

python end2end.py --model ByteDance-Seed/BAGEL-7B-MoT \
                  --modality img2img \
                  --image-path /path/to/image.jpg \
                  --prompts "Let the woman wear a blue dress"

Image to Text (img2text)

• Pipeline: Image → ViT + VAE Encode → Thinker → Text Output
• Stages Used: Stage 0 (Thinker) only
• Special: Uses both VAE latent encoding AND ViT semantic encoding for comprehensive image understanding

Generate text descriptions from images:

python end2end.py --model ByteDance-Seed/BAGEL-7B-MoT \
                  --modality img2text \
                  --image-path /path/to/image.jpg \
                  --prompts "Describe this image in detail"

Text to Text (text2text)

• Pipeline: Text → Thinker → Text Output
• Stages Used: Stage 0 (Thinker) only
• Special: No visual components involved, operates as pure language model

Pure text generation:

python end2end.py --model ByteDance-Seed/BAGEL-7B-MoT \
                  --modality text2text \
                  --prompts "What is the capital of France?"

# You can load prompts from a text file (one prompt per line):  
python end2end.py --model ByteDance-Seed/BAGEL-7B-MoT \
                  --modality text2text \
                  --txt-prompts /path/to/prompts.txt

Inference Steps

Control the number of inference steps for image generation:

# You can adjust steps to 100 to improve image quality
python end2end.py --model ByteDance-Seed/BAGEL-7B-MoT \
                  --modality text2img \
                  --steps 50 \
                  --prompts "A cute cat"

Key arguments

BAGEL-7B-MoT supports multiple modality modes for different use cases.

The default yaml configuration deploys Thinker and DiT on the same GPU. You can use the default configuration file: bagel.yaml

📌 Command Line Arguments (end2end.py)

Argument	Type	Default	Description
--model	string	ByteDance-Seed/BAGEL-7B-MoT	Model path or name
--modality	choice	text2img	Modality mode: text2img, img2img, img2text, text2text
--prompts	list	None	Input text prompts directly
--txt-prompts	string	None	Path to txt file with one prompt per line
--image-path	string	None	Input image path (for img2img/img2text)
--steps	int	50	Number of inference steps
--stage-configs-path	string	None	Custom stage config file path
--worker-backend	choice	process	Worker backend: process or ray
--ray-address	string	None	Ray cluster address
--enable-stats	flag	False	Enable statistics logging
--init-sleep-seconds	int	20	Initialization sleep time
--batch-timeout	int	5	Batch timeout
--init-timeout	int	300	Initialization timeout

---

⚙️ Stage Configuration Parameters (bagel.yaml)

Stage 0 - Thinker (LLM Stage)

Parameter	Value	Description
stage_type	llm	Stage type
devices	"0"	GPU device ID
max_batch_size	1	Maximum batch size
model_stage	thinker	Model stage identifier
model_arch	BagelForConditionalGeneration	Model architecture
gpu_memory_utilization	0.4	GPU memory utilization
tensor_parallel_size	1	Tensor parallel size
max_num_batched_tokens	32768	Maximum batched tokens
omni_kv_config.need_send_cache	true	Whether to send KV cache

---

Stage 1 - DiT (Diffusion Stage)

Parameter	Value	Description
stage_type	diffusion	Stage type
devices	"0"	GPU device ID
max_batch_size	1	Maximum batch size
model_stage	dit	Model stage identifier
gpu_memory_utilization	0.4	GPU memory utilization
omni_kv_config.need_recv_cache	true	Whether to receive KV cache
engine_input_source	[0]	Input source from Stage 0

---

🔗 Runtime Configuration

Parameter	Value	Description
window_size	-1	Window size (-1 means unlimited)
max_inflight	1	Maximum inflight requests
shm_threshold_bytes	65536	Shared memory threshold (64KB)

FAQ

• If you encounter an error about the backend of librosa, try to install ffmpeg with the command below.

sudo apt update
sudo apt install ffmpeg

• If you don’t know how much VRAM is needed for the model or encounter the OOM error, you can try to decrease the max_model_len.

Stage	VRAM
Stage-0 (Thinker)	15.04 GiB + KV Cache
Stage-1 (DiT)	26.50 GiB
Total	~42 GiB + KV Cache

Example materials

??? abstract "end2end.py" py --8<-- "examples/offline_inference/bagel/end2end.py"