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fix multimodal gen issues (#12765)

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Co-authored-by: Mick <mickjagger19@icloud.com>
This commit is contained in:
Yuhao Yang
2025-11-07 13:19:49 +08:00
committed by GitHub
parent 3b1cc466c0
commit d8736c756a
6 changed files with 56 additions and 42 deletions
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2
python/sglang/multimodal_gen/README.md
View File

@@ -24,7 +24,7 @@ sgl-diffusion has the following features:
## Getting Started
```bash
uv pip install sglang[.diffusion] --prerelease=allow
uv pip install 'sglang[diffusion]' --prerelease=allow
```
For more information, check the [docs](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen/docs/install.md).

2
python/sglang/multimodal_gen/docs/cli.md
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@@ -143,7 +143,7 @@ SERVER_ARGS=(
--ring-degree=2
)
sglang serve"${SERVER_ARGS[@]}"
sglang serve "${SERVER_ARGS[@]}"
```
- **--model-path**: Which model to load. The example uses `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`.

10
python/sglang/multimodal_gen/docs/install.md
View File

@@ -11,7 +11,7 @@ It is recommended to use uv for a faster installation:
```bash
pip install --upgrade pip
pip install uv
uv pip install sglang[.diffusion] --prerelease=allow
uv pip install 'sglang[diffusion]' --prerelease=allow
```
## Method 2: From source
@@ -29,13 +29,9 @@ pip install -e "python/.[diffusion]"
uv pip install --prerelease=allow -e "python/.[diffusion]"
```
**Quick fixes for common problems:**
- If you want to develop sgl-diffusion, it is recommended to use Docker. The Docker image is `lmsysorg/sgl-diffusion:latest`.
## Method 3: Using Docker
The Docker images are available on Docker Hub at [lmsysorg/sgl-diffusion](), built from the [Dockerfile](https://github.com/sgl-project/sgl-diffusion/tree/main/docker).
The Docker images are available on Docker Hub at [lmsysorg/sglang](), built from the [Dockerfile](https://github.com/sgl-project/sglang/tree/main/docker).
Replace `<secret>` below with your HuggingFace Hub [token](https://huggingface.co/docs/hub/en/security-tokens).
```bash
@@ -45,7 +41,7 @@ docker run --gpus all \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:diffusion \
lmsysorg/sglang:dev \
sglang generate --model-path black-forest-labs/FLUX.1-dev \
--prompt "A logo With Bold Large text: SGL Diffusion" \
--save-output

2
python/sglang/multimodal_gen/runtime/entrypoints/openai/video_api.py
View File

@@ -69,8 +69,8 @@ def _build_sampling_params_from_request(
save_output=True,
)
sampling_params = sampling_params.from_user_sampling_params(user_params)
sampling_params.set_output_file_name()
sampling_params.log(server_args)
sampling_params.set_output_file_ext()
return sampling_params

18
python/sglang/multimodal_gen/runtime/server_args.py
View File

@@ -777,13 +777,6 @@ class ServerArgs:
)
self.sp_degree = self.ulysses_degree = self.ring_degree = 1
if (
self.ring_degree is not None
and self.ring_degree > 1
and self.attention_backend != "fa3"
):
raise ValueError("Ring Attention is only supported for fa3 backend for now")
if self.sp_degree == -1:
# assume we leave all remaining gpus to sp
num_gpus_per_group = self.dp_size * self.tp_size
@@ -815,6 +808,17 @@ class ServerArgs:
f"Ring degree not set, " f"using default value {self.ring_degree}"
)
if self.ring_degree > 1:
if self.attention_backend != None and self.attention_backend != "fa3":
raise ValueError(
"Ring Attention is only supported for fa3 backend for now"
)
else:
self.attention_backend = "fa3"
logger.info(
"Ring Attention is currently only supported for fa3, attention_backend has been automatically set to fa3"
)
if self.sp_degree == -1:
self.sp_degree = self.ring_degree * self.ulysses_degree
logger.info(

64
python/sglang/multimodal_gen/test/cli/test_serve.py
View File

@@ -3,9 +3,13 @@
import asyncio
import base64
import subprocess
import tempfile
import time
import unittest
import uuid
from contextlib import contextmanager
from pathlib import Path
from urllib.request import urlopen
from openai import OpenAI
@@ -13,6 +17,20 @@ from sglang.multimodal_gen.runtime.utils.common import kill_process_tree
from sglang.multimodal_gen.test.test_utils import is_mp4, is_png, wait_for_port
@contextmanager
def downloaded_temp_file(url: str, prefix: str = "i2v_input_", suffix: str = ".jpg"):
tmp_path = Path(tempfile.gettempdir()) / f"{prefix}{uuid.uuid4().hex}{suffix}"
with urlopen(url) as resp:
tmp_path.write_bytes(resp.read())
try:
yield tmp_path
finally:
try:
tmp_path.unlink(missing_ok=True)
except Exception:
pass
def wait_for_video_completion(client, video_id, timeout=300, check_interval=3):
start = time.time()
video = client.videos.retrieve(video_id)
@@ -27,7 +45,7 @@ def wait_for_video_completion(client, video_id, timeout=300, check_interval=3):
class TestVideoHttpServer(unittest.TestCase):
model_name = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
timeout = 120
timeout = 500
extra_args = []
def _create_wait_and_download(
@@ -77,7 +95,7 @@ class TestVideoHttpServer(unittest.TestCase):
api_key="sk-proj-1234567890", base_url="http://localhost:30010/v1"
)
content = self._create_wait_and_download(
client, "A calico cat playing a piano on stage", "832x480"
client, "A plane is taking off.", "832x480"
)
self.assertTrue(is_mp4(content))
@@ -97,7 +115,7 @@ class TestVideoHttpServer(unittest.TestCase):
async def send_concurrent_requests():
tasks = [
generate_and_check_video(
"A dog playing a piano on stage",
"A ship is beside the port.",
"832x480",
)
for _ in range(num_requests)
@@ -107,14 +125,6 @@ class TestVideoHttpServer(unittest.TestCase):
asyncio.run(send_concurrent_requests())
class TestFastWan2_1HttpServer(TestVideoHttpServer):
model_name = "FastVideo/FastWan2.1-T2V-1.3B-Diffusers"
class TestFastWan2_2HttpServer(TestVideoHttpServer):
model_name = "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers"
class TestImage2VideoHttpServer(unittest.TestCase):
model_name = "Wan-AI/Wan2.2-I2V-A14B-Diffusers"
timeout = 1200
@@ -124,15 +134,17 @@ class TestImage2VideoHttpServer(unittest.TestCase):
self, client: OpenAI, prompt: str, size: str
) -> bytes:
image_path = "https://github.com/Wan-Video/Wan2.2/blob/990af50de458c19590c245151197326e208d7191/examples/i2v_input.JPG?raw=true"
image_path = Path(image_path)
video = client.videos.create(
prompt=prompt,
input_reference=image_path,
size=size,
seconds=10,
extra_body={"fps": 16, "num_frames": 125},
)
image_url = "https://github.com/Wan-Video/Wan2.2/blob/990af50de458c19590c245151197326e208d7191/examples/i2v_input.JPG?raw=true"
with downloaded_temp_file(
image_url, prefix="i2v_input_", suffix=".jpg"
) as tmp_path:
video = client.videos.create(
prompt=prompt,
input_reference=tmp_path,
size=size,
seconds=10,
extra_body={"fps": 16, "num_frames": 125},
)
# TODO: Some combinations of num_frames and fps may cause errors and need further investigation.
video_id = video.id
self.assertEqual(video.status, "queued")
@@ -149,18 +161,20 @@ class TestImage2VideoHttpServer(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.base_command = [
"sgl-diffusion",
"sglang",
"serve",
"--model-path",
f"{cls.model_name}",
"--num-gpus",
"4",
"--ulysses-degree",
"4",
"--port",
"30010",
]
process = subprocess.Popen(
cls.base_command + cls.extra_args,
# stdout=subprocess.PIPE,
# stderr=subprocess.PIPE,
text=True,
bufsize=1,
)
@@ -176,7 +190,7 @@ class TestImage2VideoHttpServer(unittest.TestCase):
api_key="sk-proj-1234567890", base_url="http://localhost:30010/v1"
)
content = self._create_wait_and_download(
client, "A girl is fighting a monster.", "832x480"
client, "A cat surfing on the sea.", "832x480"
)
self.assertTrue(is_mp4(content))
@@ -196,7 +210,7 @@ class TestImage2VideoHttpServer(unittest.TestCase):
async def send_concurrent_requests():
tasks = [
generate_and_check_video(
"A dog playing a piano on stage",
"A cat surfing on the sea.",
"832x480",
)
for _ in range(num_requests)