vllm-omni/recipes/Boogu/Boogu-Image.md

Boogu-Image

Text-to-image and image-editing online serving (Boogu-Image-0.1-Base / -Edit / -Edit-Turbo)

Summary

• Vendor: Boogu
• Model: Boogu/Boogu-Image-0.1-Base (text-to-image), Boogu/Boogu-Image-0.1-Edit (image editing), and Boogu/Boogu-Image-0.1-Edit-Turbo (four-step image editing)
• Task: Text-to-image generation and text-guided image editing (TI2I)
• Mode: Online serving with the OpenAI-compatible API
• Maintainer: Community

When to use this recipe

Use this recipe when you want a known-good starting point for serving Boogu/Boogu-Image-0.1-Base, Boogu/Boogu-Image-0.1-Edit, or Boogu/Boogu-Image-0.1-Edit-Turbo with vLLM-Omni's native pipeline (no --diffusion-load-format diffusers, and the upstream boogu package is not required).

Boogu-Image-0.1 is an Apache-2.0 unified image generation and editing model family. The Base text-to-image checkpoint pairs a Qwen3-VL multimodal encoder with a Diffusion Transformer (DiT) and a flow-match Euler scheduler with time-shift. It handles photorealistic generation and Chinese/English text rendering. The Edit and Edit-Turbo checkpoints use the same native BooguImagePipeline for image editing.

References

• Upstream model card: https://huggingface.co/Boogu/Boogu-Image-0.1-Base
• Edit model card: https://huggingface.co/Boogu/Boogu-Image-0.1-Edit
• Edit-Turbo 1K hotfix: https://huggingface.co/Boogu/Boogu-Image-0.1-Edit-Turbo/tree/hotfix-1k-20260708
• Project page: https://boogu.org
• GitHub: https://github.com/boogu-project/Boogu-Image
• Related example: examples/online_serving/text_to_image/

Hardware Support

This recipe documents supported configurations for CUDA GPU serving. The native pipeline supports single-GPU inference and classifier-free-guidance (CFG) parallelism. CPU offload, cache acceleration, and other multi-GPU dimensions remain unsupported (see Notes).

GPU

1 x A100/H100 (Single GPU, 40GB+ VRAM)

The model footprint is roughly 34.6 GiB on GPU, so a 40GB+ card is recommended.

Command

vllm serve Boogu/Boogu-Image-0.1-Base --omni --port 8091

!!! note If you hit Out-of-Memory (OOM) on a smaller card, enable VAE slicing and tiling to reduce peak memory: --vae-use-slicing --vae-use-tiling.

Verification

After the server is ready, test with a simple request:

curl -X POST http://localhost:8091/v1/images/generations \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Boogu/Boogu-Image-0.1-Base",
    "prompt": "A mountain lake at sunset, photorealistic, cinematic lighting",
    "size": "1024x1024",
    "num_inference_steps": 28,
    "guidance_scale": 4.0,
    "seed": 42
  }' | jq -r '.data[0].b64_json' | base64 -d > output.png

Or via the chat-completions endpoint (parameters go in extra_body):

curl -s http://localhost:8091/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "A mountain lake at sunset, photorealistic, cinematic lighting"}
    ],
    "extra_body": {
      "height": 1024,
      "width": 1024,
      "num_inference_steps": 28,
      "guidance_scale": 4.0,
      "seed": 42
    }
  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > output.png

Notes

• Memory usage: ~34.6 GiB on GPU. Use --vae-use-slicing --vae-use-tiling to trim peak VRAM if needed.
• Key flags:
  • --omni — enables vLLM-Omni diffusion serving.
• Guidance: Boogu-Image uses guidance_scale (mapped to the upstream text_guidance_scale); the default is 4.0. Classifier-free guidance is active whenever guidance_scale > 1.0.
• Recommended settings: num_inference_steps=28-50, guidance_scale=4.0. The model's maximum native resolution is 2K.
• Known limitations (not yet supported): CPU offload (--enable-cpu-offload / --enable-layerwise-offload), Cache-DiT (--cache-backend cache_dit), and TP / SP / HSDP multi-GPU parallelism.

2 x H100 (CFG parallel, Base T2I)

Base T2I has two predictions per guided denoising step, so two CFG ranks map one-to-one to the positive and negative-text branches:

vllm serve Boogu/Boogu-Image-0.1-Base \
  --omni \
  --cfg-parallel-size 2 \
  --port 8091

Use guidance_scale > 1.0 to activate the parallel two-branch path. Requests with guidance_scale=1.0 remain valid on the same server: every rank evaluates only the positive branch, no negative embeddings are built, and no guidance is applied.

Image editing (Boogu-Image-0.1-Edit)

The Edit checkpoint is served by the same native pipeline (BooguImagePipeline); the image-editing (TI2I) path activates automatically when a request carries a reference image. The Base text-to-image path is unaffected (no reference image is sent).

Command

vllm serve Boogu/Boogu-Image-0.1-Edit --omni --port 8091

For text-only or image-only guidance, use two CFG ranks:

vllm serve Boogu/Boogu-Image-0.1-Edit \
  --omni \
  --cfg-parallel-size 2 \
  --port 8091

Double guidance has three predictions per denoising step. It supports either two ranks (round-robin assignment: rank 0 runs branches 0 and 2) or three ranks (one branch per rank):

# Practical two-GPU configuration
vllm serve Boogu/Boogu-Image-0.1-Edit \
  --omni \
  --cfg-parallel-size 2 \
  --port 8091

# Maximum branch parallelism
vllm serve Boogu/Boogu-Image-0.1-Edit \
  --omni \
  --cfg-parallel-size 3 \
  --port 8091

Verification

Edit an image with /v1/images/edits (the model-card example — change a photo to a colored-pencil drawing). Diffusion parameters are plain multipart form fields; add guidance_scale_2 to enable image guidance (double-guidance path):

curl -s http://localhost:8091/v1/images/edits \
  -F model="Boogu/Boogu-Image-0.1-Edit" \
  -F image="@input.png" \
  -F prompt="Change the style to a colored pencil drawing." \
  -F num_inference_steps=28 \
  -F guidance_scale=5.0 \
  -F guidance_scale_2=2.0 \
  -F seed=42 \
  | jq -r '.data[0].b64_json' | base64 -d > edited.png

Or via chat completions (attach the image as a data URL; parameters go in extra_body):

curl -s http://localhost:8091/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": [
        {"type": "image_url", "image_url": {"url": "data:image/png;base64,<BASE64>"}},
        {"type": "text", "text": "Change the style to a colored pencil drawing."}
      ]}
    ],
    "extra_body": {
      "num_inference_steps": 28,
      "guidance_scale": 5.0,
      "guidance_scale_2": 1.0,
      "seed": 42
    }
  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > edited.png

Notes

• Single reference image: only one input image is supported for now (the upstream "Only support 1 reference image for now" limit).

• Guidance semantics:

  • guidance_scale = text guidance (upstream text_guidance_scale, default 4.0); > 1.0 enables text CFG. Editing typically uses 5.0.
  • guidance_scale_2 = image guidance (upstream image_guidance_scale, default 1.0 = off). Setting it > 1.0 enables the double-guidance path (3 model predictions per step), steering more strongly toward the reference image.

• CFG-size recommendations:

  Request mode	Scales	Predictions per step	Recommended CFG size
  Base T2I text guidance	guidance_scale > 1, no reference	2	2
  Edit text-only guidance	guidance_scale > 1, guidance_scale_2 = 1	2	2
  Edit image-only guidance	guidance_scale = 1, guidance_scale_2 > 1	2	2
  Edit double guidance	both scales > 1	3	2 (practical) or 3 (maximum parallelism)
  CFG off	both scales = 1	1	Reuse a size-2 server safely; no speedup expected

  Double guidance preserves Boogu's original combination:

  pred = positive_with_reference
       + (text_scale - 1) * (positive_with_reference - negative_with_reference)
       + (image_scale - 1) * (negative_with_reference - uncond)
  

• Output resolution: the output size follows the reference image (upstream align_res, on by default for a single reference), so height/width are derived from the input and requested sizes are not applied for edits.

• Same limitations as the Base checkpoint apply except that CFG parallelism is supported as described above; CPU offload, Cache-DiT, TP, SP, and HSDP are still unsupported.

• Offline editing: the shared offline example examples/offline_inference/image_to_image/image_edit.py supports Boogu-Image-Edit directly (--model Boogu/Boogu-Image-0.1-Edit --guidance-scale 5.0, optional --guidance-scale-2).

Fast image editing (Boogu-Image-0.1-Edit-Turbo)

Edit-Turbo is the distilled image-editing checkpoint. Its model_index.json declares BooguImagePipeline, so it uses the same native TI2I path as Edit. The upstream 1K hotfix is recommended for stable results and is pinned below to avoid silently loading the older checkpoint from the repository's default revision.

Command

vllm serve Boogu/Boogu-Image-0.1-Edit-Turbo \
  --omni \
  --revision hotfix-1k-20260708 \
  --port 8091

Verification

Edit an image with the distilled four-step settings:

curl -s http://localhost:8091/v1/images/edits \
  -F model="Boogu/Boogu-Image-0.1-Edit-Turbo" \
  -F image="@input.png" \
  -F prompt="Change the style to a colored pencil drawing." \
  -F num_inference_steps=4 \
  -F guidance_scale=1.0 \
  -F seed=42 \
  | jq -r '.data[0].b64_json' | base64 -d > edited-turbo.png

Or via chat completions:

curl -s http://localhost:8091/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": [
        {"type": "image_url", "image_url": {"url": "data:image/png;base64,<BASE64>"}},
        {"type": "text", "text": "Change the style to a colored pencil drawing."}
      ]}
    ],
    "extra_body": {
      "num_inference_steps": 4,
      "guidance_scale": 1.0,
      "seed": 42
    }
  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > edited-turbo.png

Notes

• Pinned revision: use hotfix-1k-20260708; upstream recommends the 1K hotfix over the 1.5K variant for more stable results.
• Recommended settings: num_inference_steps=4 and guidance_scale=1.0. Edit-Turbo is guidance-distilled, meaning the guidance behavior is already baked into the distilled checkpoint. A scale of 1.0 avoids applying additional classifier-free guidance; unlike the regular Edit checkpoint, it should not be increased to 5.0.
• Reference images: the same single-reference and align_res behavior as the regular Edit checkpoint applies.
• Known limitations: the same single-GPU limitations as Base and Edit apply; CPU offload, Cache-DiT, and multi-GPU parallelism are not yet validated.

Performance validation

The checked-in single-device A40 measurements remain the current Boogu baseline: Base T2I is about 16.37 s request latency at concurrency 1 and Edit text-only guidance is about 29.64 s for 512x512, 28-step requests, with roughly 36.8 GB peak memory. CFG-parallel A/B numbers must be collected on the same hardware and software revision before reporting a speedup; do not compare a multi-GPU result against these A40 numbers as if it were a controlled A/B.

Run the dedicated perf rows in tests/dfx/perf/tests/test_boogu_image_vllm_omni.json and tests/dfx/perf/tests/test_boogu_image_edit_vllm_omni.json on 2x/3x H100, then record latency, throughput, and per-GPU peak memory beside the single-device run. The expected shape is close to the general CFG-parallel range (roughly 1.5x-1.8x for balanced two-branch workloads); the three-branch Edit result will depend on whether two-rank round-robin or full three-rank execution is used and must be reported from measurement rather than assumed.