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README.md 0 → 100644
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# Emu3.5_pytorch
## 论文
[Emu3.5](https://arxiv.org/pdf/2510.26583)
## 模型结构
Emu3.5在“Next-Token Prediction”范式的基础上，模拟人类自然学习方式，以自回归架构实现了对多模态序列的“Next-State Prediction (NSP)”，获得了可泛化的世界建模能力。
<div align=center>
<img src="./doc/arch.png"/>
</div>
## 算法原理
Emu3.5 是由北京智源人工智能研究院发布的多模态世界大模型。它通过在超过10万亿的多模态Token（主要源自互联网视频，总时长约790年）上进行端到端预训练，具备了原生的世界建模能力，能够理解和生成文本、图像和视频等多种模态的数据。
## 环境配置
### 硬件需求
DCU型号：BW1000,节点数量：1台,卡数：2张。
`-v 路径`、`docker_name`和`imageID`根据实际情况修改
### Docker（方法一）
```bash
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.7.1-ubuntu22.04-dtk25.04.2-py3.10-alpha
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
pip install http://10.16.4.1:8000/debug/torchaudio/dtk25.04.2-beta-bug-fix/torch251-audio/torch251-audio-fastpt/torchaudio-2.5.1a0%2Bd178b24-cp310-cp310-manylinux_2_28_x86_64.whl
pip install http://10.16.4.1:8000/debug/flash_attn/dtk25.04.2-rc1/dtk25.04-llvm0106/flash_attn-2.6.1%2Bdas.opt1.dtk2504-cp310-cp310-manylinux_2_28_x86_64.whl
pip install transformers -U
cd /your_code_path/emu3.5_pytorch
pip install -r requirements.txt
```
### Dockerfile（方法二）
```bash
cd docker
docker build --no-cache -t emu3.5_pytorch:latest .
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
pip install http://10.16.4.1:8000/debug/torchaudio/dtk25.04.2-beta-bug-fix/torch251-audio/torch251-audio-fastpt/torchaudio-2.5.1a0%2Bd178b24-cp310-cp310-manylinux_2_28_x86_64.whl
pip install http://10.16.4.1:8000/debug/flash_attn/dtk25.04.2-rc1/dtk25.04-llvm0106/flash_attn-2.6.1%2Bdas.opt1.dtk2504-cp310-cp310-manylinux_2_28_x86_64.whl
pip install transformers -U
cd /your_code_path/emu3.5_pytorch
pip install -r requirements.txt
```
### Anaconda（方法三）
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装。
```bash
DTK: 25.04.2
python: 3.10
torch: 2.7.1a0+das.opt1.dtk25042
accelerate：1.11.0
transformers: 4.48.2
flash_attn：2.6.1+das.opt1.dtk2504
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`
```bash
pip install http://10.16.4.1:8000/debug/torchaudio/dtk25.04.2-beta-bug-fix/torch251-audio/torch251-audio-fastpt/torchaudio-2.5.1a0%2Bd178b24-cp310-cp310-manylinux_2_28_x86_64.whl
pip install http://10.16.4.1:8000/debug/flash_attn/dtk25.04.2-rc1/dtk25.04-llvm0106/flash_attn-2.6.1%2Bdas.opt1.dtk2504-cp310-cp310-manylinux_2_28_x86_64.whl
pip install transformers -U
cd /your_code_path/emu3.5_pytorch
pip install -r requirements.txt
```
## 数据集
无
## 训练
暂无
## 推理
样例模型：[Emu3.5](https://huggingface.co/BAAI/Emu3.5-Image)
不同任务推理命令如下：
```bash
# 🖼️ Text-to-Image (T2I) task
python inference.py --cfg configs/example_config_t2i.py
# 🔄 Any-to-Image (X2I) task
python inference.py --cfg configs/example_config_x2i.py
# 🎯 Visual Guidance task
python inference.py --cfg configs/example_config_visual_guidance.py
# 📖 Visual Narrative task
python inference.py --cfg configs/example_config_visual_narrative.py
# After running inference, the model will generate results in protobuf format (.pb files) for each input prompt.
```
可视化Protobuf文件输出
```bash
python src/utils/vis_proto.py --input <input_proto_file> --output <output_dir> [--video]
```
## result
```bash
Handling prompt: <|extra_203|>You are a helpful assistant for t2i task. USER: A lively comic-style illustration depicting two humorous cartoon dogs interacting near a freshly dug backyard hole surrounded by scattered dirt, garden tools, blooming flowers, and a wooden fence background. At the upper-left side, Dog One stands nervously near the messy hole, ears down and eyes wide open with an expression of concern. Its speech bubble is an oval shape, outlined neatly with smooth, slightly rounded corners, positioned clearly above Dog One's head. Inside, clearly readable playful handwritten-style text emphasizes the dog's worried tone, saying, "You sure the humans won't notice this giant hole here?". Toward the lower-right side, Dog Two sits calmly and confidently with a cheerful, carefree expression, wagging its tail gently. Its speech bubble is rectangular with softly rounded edges, placed slightly overlapping with Dog One's speech bubble to guide the reader naturally downward diagonally across the frame. Dog Two's friendly, humorous response appears in a whimsical italicized comic font, clearly stating, "Relax! We'll just blame it on the neighbor's cat again!". Each speech bubble creats the playful and engaging backyard scene. ASSISTANT: <|extra_100|>
```
<div align=center>
<img src="./doc/result.png"/>
</div>
### 精度
DCU与GPU精度一致，推理框架：pytorch。
## 应用场景
### 算法类别
多模态
### 热点应用行业
制造,广媒,家居,教育
## 预训练权重
- [Emu3.5](https://huggingface.co/BAAI/Emu3.5/tree/main)
- [Emu3.5-Image](https://huggingface.co/BAAI/Emu3.5-Image/tree/main)
- [Emu3.5-VisionTokenizer](https://huggingface.co/BAAI/Emu3.5-VisionTokenizer/tree/main)
## 源码仓库及问题反馈
- https://developer.sourcefind.cn/codes/modelzoo/emu3.5_pytorch
## 参考资料
- https://huggingface.co/BAAI/Emu3.5-Image
configs/config.py 0 → 100644
View file @ f17c9671
# Copyright 2025 BAAI. and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
from pathlib import Path
from src.utils.logging_utils import setup_logger
cfg_name = Path(__file__).stem
model_path = "/home/zzg_zone/LLM-Models/BAAI/Emu3.5-Image" # download from hf
vq_path = "./BAAI/Emu3.5-VisionTokenizer" # download from hf
tokenizer_path = "./src/tokenizer_emu3_ibq"
vq_type = "ibq"
# task_type in {"t2i", "x2i", "howto", "story", "explore", "vla"}
task_type = "story"
# whether prompts include an input image token and provide reference_image paths
use_image = True
# saving config
exp_name = "emu3p5"
save_path = f"./outputs/{exp_name}"
save_to_proto = True
setup_logger(save_path)
hf_device = "auto"
vq_device = "cuda:0"
streaming = False
unconditional_type = "no_text"
classifier_free_guidance = 3.0 # (recommended）Emu3.5 interleaved: 3, T2I/X2I: 2; Emu3.5-Image T2I/X2I: 5
max_new_tokens = 32768
image_area = 518400
def build_unc_and_template(task: str, with_image: bool):
# System prompt header and role formatting remain consistent
task_str = task.lower()
if with_image:
unc_p = "<|extra_203|>You are a helpful assistant. USER: <|IMAGE|> ASSISTANT: <|extra_100|>"
tmpl = "<|extra_203|>You are a helpful assistant for %s task. USER: {question}<|IMAGE|> ASSISTANT: <|extra_100|>" % task_str
else:
unc_p = "<|extra_203|>You are a helpful assistant. USER: ASSISTANT: <|extra_100|>"
tmpl = "<|extra_203|>You are a helpful assistant for %s task. USER: {question} ASSISTANT: <|extra_100|>" % task_str
return unc_p, tmpl
unc_prompt, template = build_unc_and_template(task_type, use_image)
sampling_params = dict(
use_cache=True,
# text token sampling config
text_top_k=1024,
text_top_p=0.9,
text_temperature=1.0,
# image token sampling config
image_top_k=10240,
image_top_p=1.0,
image_temperature=1.0,
# general config
top_k=131072, # default topk (backward compatible)
top_p=1.0, # default top_p (backward compatible)
temperature=1.0, # default temperature (backward compatible)
num_beams_per_group=1,
num_beam_groups=1,
diversity_penalty=0.0,
max_new_tokens=max_new_tokens,
guidance_scale=1.0,
# enable differential sampling
use_differential_sampling=True,
)
sampling_params["do_sample"] = sampling_params["num_beam_groups"] <= 1
sampling_params["num_beams"] = sampling_params["num_beams_per_group"] * sampling_params["num_beam_groups"]
special_tokens = dict(
BOS="<|extra_203|>",
EOS="<|extra_204|>",
PAD="<|endoftext|>",
EOL="<|extra_200|>",
EOF="<|extra_201|>",
TMS="<|extra_202|>",
IMG="<|image token|>",
BOI="<|image start|>",
EOI="<|image end|>",
BSS="<|extra_100|>",
ESS="<|extra_101|>",
BOG="<|extra_60|>",
EOG="<|extra_61|>",
BOC="<|extra_50|>",
EOC="<|extra_51|>",
)
seed = 6666
# prompts config
# If use_image=True, each item should be a dict with {"prompt", "reference_image"}.
# If use_image=False, each item is a plain text string.
_prompts_base = [
{
"prompt": "Tell a story about a clay astronaut exploring Mars and discovering a new continent hidden beneath the red dust.",
"reference_image": "assets/ref_img.png",
},
# {
# "prompt": "Generate a clay astronaut in the given image exploring Mars.",
# "reference_image": "assets/ref_img.png",
# },
]
if use_image:
prompts = _prompts_base
else:
prompts = [p["prompt"] for p in _prompts_base]
configs/example_config_t2i.py 0 → 100644
View file @ f17c9671
# Copyright 2025 BAAI. and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
from pathlib import Path
from src.utils.logging_utils import setup_logger
cfg_name = Path(__file__).stem
model_path = "/home/zzg_zone/LLM-Models/BAAI/Emu3.5-Image" # download from hf
vq_path = "./BAAI/Emu3.5-VisionTokenizer" # download from hf
tokenizer_path = "./src/tokenizer_emu3_ibq"
vq_type = "ibq"
# task_type in {"t2i", "x2i", "howto", "story", "explore", "vla"}
task_type = "t2i"
# whether prompts include an input image token and provide reference_image paths
use_image = False
# saving config
exp_name = "emu3p5-image"
save_path = f"./outputs/{exp_name}/{task_type}"
save_to_proto = True
setup_logger(save_path)
hf_device = "auto"
vq_device = "cuda:0"
streaming = False
unconditional_type = "no_text"
classifier_free_guidance = 5.0
max_new_tokens = 5120
image_area = 1048576
aspect_ratios = {
"4:3": "55*73",
"21:9": "41*97",
"16:9": "47*85",
"3:2": "52*78",
"1:1": "64*64",
"3:4": "73*55",
"9:16": "85*47",
"2:3": "78*52",
"default": "55*73",
"auto": None,
}
def get_target_size(aspect_ratio: str):
value = aspect_ratios.get(aspect_ratio, None)
if value is None:
return None, None
h, w = map(int, value.split("*"))
return h, w
# --- example usage ---
aspect_ratio = "default" # User input, which can be replaced by "4:3", "1:1", "auto" etc.
target_height, target_width = get_target_size(aspect_ratio)
print(f"Aspect Ratio = {aspect_ratio}")
print(f"target_height = {target_height}, target_width = {target_width}")
def build_unc_and_template(task: str, with_image: bool):
# System prompt header and role formatting remain consistent
task_str = task.lower()
if with_image:
unc_p = "<|extra_203|>You are a helpful assistant. USER: <|IMAGE|> ASSISTANT: <|extra_100|>"
tmpl = "<|extra_203|>You are a helpful assistant for %s task. USER: {question}<|IMAGE|> ASSISTANT: <|extra_100|>" % task_str
else:
unc_p = "<|extra_203|>You are a helpful assistant. USER: ASSISTANT: <|extra_100|>"
tmpl = "<|extra_203|>You are a helpful assistant for %s task. USER: {question} ASSISTANT: <|extra_100|>" % task_str
return unc_p, tmpl
unc_prompt, template = build_unc_and_template(task_type, use_image)
sampling_params = dict(
use_cache=True,
# text token sampling config
text_top_k=1024,
text_top_p=0.9,
text_temperature=1.0,
# image token sampling config
image_top_k=5120,
image_top_p=1.0,
image_temperature=1.0,
# general config
top_k=131072, # default topk (backward compatible)
top_p=1.0, # default top_p (backward compatible)
temperature=1.0, # default temperature (backward compatible)
num_beams_per_group=1,
num_beam_groups=1,
diversity_penalty=0.0,
max_new_tokens=max_new_tokens,
guidance_scale=1.0,
# enable differential sampling
use_differential_sampling=True,
)
sampling_params["do_sample"] = sampling_params["num_beam_groups"] <= 1
sampling_params["num_beams"] = sampling_params["num_beams_per_group"] * sampling_params["num_beam_groups"]
special_tokens = dict(
BOS="<|extra_203|>",
EOS="<|extra_204|>",
PAD="<|endoftext|>",
EOL="<|extra_200|>",
EOF="<|extra_201|>",
TMS="<|extra_202|>",
IMG="<|image token|>",
BOI="<|image start|>",
EOI="<|image end|>",
BSS="<|extra_100|>",
ESS="<|extra_101|>",
BOG="<|extra_60|>",
EOG="<|extra_61|>",
BOC="<|extra_50|>",
EOC="<|extra_51|>",
)
seed = 6666
# prompts config
# If use_image=True, each item should be a dict with {"prompt", "reference_image"}.
# If use_image=False, each item is a plain text string.
_prompts_base = [
{
"prompt":"""A lively comic-style illustration depicting two humorous cartoon dogs interacting near a freshly dug backyard hole surrounded by scattered dirt, garden tools, blooming flowers, and a wooden fence background. At the upper-left side, Dog One stands nervously near the messy hole, ears down and eyes wide open with an expression of concern. Its speech bubble is an oval shape, outlined neatly with smooth, slightly rounded corners, positioned clearly above Dog One's head. Inside, clearly readable playful handwritten-style text emphasizes the dog's worried tone, saying, "You sure the humans won't notice this giant hole here?". Toward the lower-right side, Dog Two sits calmly and confidently with a cheerful, carefree expression, wagging its tail gently. Its speech bubble is rectangular with softly rounded edges, placed slightly overlapping with Dog One's speech bubble to guide the reader naturally downward diagonally across the frame. Dog Two's friendly, humorous response appears in a whimsical italicized comic font, clearly stating, "Relax! We'll just blame it on the neighbor's cat again!". Each speech bubble creats the playful and engaging backyard scene.""",
},
]
if use_image:
prompts = _prompts_base
else:
prompts = [p["prompt"] for p in _prompts_base]
configs/example_config_visual_guidance.py 0 → 100644
View file @ f17c9671
# Copyright 2025 BAAI. and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
from pathlib import Path
from src.utils.logging_utils import setup_logger
cfg_name = Path(__file__).stem
model_path = "./weights/Emu3.5" # download from hf
vq_path = "./weights/Emu3.5-VisionTokenizer" # download from hf
tokenizer_path = "./src/tokenizer_emu3_ibq"
vq_type = "ibq"
# task_type in {"t2i", "x2i", "howto", "story", "explore", "vla"}
task_type = "howto"
# whether prompts include an input image token and provide reference_image paths
use_image = False
# saving config
exp_name = "emu3p5"
save_path = f"./outputs/{exp_name}/{task_type}"
save_to_proto = True
setup_logger(save_path)
hf_device = "auto"
vq_device = "cuda:0"
streaming = False
unconditional_type = "no_text"
classifier_free_guidance = 3.0
max_new_tokens = 32768
image_area = 518400
def build_unc_and_template(task: str, with_image: bool):
# System prompt header and role formatting remain consistent
task_str = task.lower()
if task_str == 'howto':
extra_system_prompt = ' Please generate a response with interleaved text and images.'
else:
extra_system_prompt = ''
if with_image:
unc_p = "<|extra_203|>You are a helpful assistant. USER: <|IMAGE|> ASSISTANT: <|extra_100|>"
tmpl = "<|extra_203|>You are a helpful assistant for %s task.%s USER: {question}<|IMAGE|> ASSISTANT: <|extra_100|>"% (task_str, extra_system_prompt)
else:
unc_p = "<|extra_203|>You are a helpful assistant. USER: ASSISTANT: <|extra_100|>"
tmpl = "<|extra_203|>You are a helpful assistant for %s task.%s USER: {question} ASSISTANT: <|extra_100|>" % (task_str, extra_system_prompt)
return unc_p, tmpl
unc_prompt, template = build_unc_and_template(task_type, use_image)
sampling_params = dict(
use_cache=True,
# text token sampling config
text_top_k=200,
text_top_p=0.8,
text_temperature=0.7,
# image token sampling config
image_top_k=10240,
image_top_p=1.0,
image_temperature=1.0,
# general config
top_k=131072, # default topk (backward compatible)
top_p=1.0, # default top_p (backward compatible)
temperature=1.0, # default temperature (backward compatible)
num_beams_per_group=1,
num_beam_groups=1,
diversity_penalty=0.0,
max_new_tokens=max_new_tokens,
guidance_scale=1.0,
# enable differential sampling
use_differential_sampling=True,
)
sampling_params["do_sample"] = sampling_params["num_beam_groups"] <= 1
sampling_params["num_beams"] = sampling_params["num_beams_per_group"] * sampling_params["num_beam_groups"]
special_tokens = dict(
BOS="<|extra_203|>",
EOS="<|extra_204|>",
PAD="<|endoftext|>",
EOL="<|extra_200|>",
EOF="<|extra_201|>",
TMS="<|extra_202|>",
IMG="<|image token|>",
BOI="<|image start|>",
EOI="<|image end|>",
BSS="<|extra_100|>",
ESS="<|extra_101|>",
BOG="<|extra_60|>",
EOG="<|extra_61|>",
BOC="<|extra_50|>",
EOC="<|extra_51|>",
)
seed = 6666
# prompts config
# If use_image=True, each item should be a dict with {"prompt", "reference_image"}.
# If use_image=False, each item is a plain text string.
_prompts_base = [
{
"prompt": "How to cook Shrimp, Celery, and Pork Dumplings.",
"reference_image": "",
},
# You can specify the length of the generated steps of Visual Guidance task by adding 'Please provide xxx steps for the task.'.
# {
# "prompt": "How to cook Shrimp, Celery, and Pork Dumplings. Please provide 7 steps for the task.",
# "reference_image": "",
# },
]
if use_image:
prompts = _prompts_base
else:
prompts = [p["prompt"] for p in _prompts_base]
\ No newline at end of file
configs/example_config_visual_narrative.py 0 → 100644
View file @ f17c9671
# Copyright 2025 BAAI. and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
from pathlib import Path
from src.utils.logging_utils import setup_logger
cfg_name = Path(__file__).stem
model_path = "./weights/Emu3.5" # download from hf
vq_path = "./weights/Emu3.5-VisionTokenizer" # download from hf
tokenizer_path = "./src/tokenizer_emu3_ibq"
vq_type = "ibq"
# task_type in {"t2i", "x2i", "howto", "story", "explore", "vla"}
task_type = "story"
# whether prompts include an input image token and provide reference_image paths
use_image = False
# saving config
exp_name = "emu3p5"
save_path = f"./outputs/{exp_name}/{task_type}"
save_to_proto = True
setup_logger(save_path)
hf_device = "auto"
vq_device = "cuda:0"
streaming = False
unconditional_type = "no_text"
classifier_free_guidance = 3.0
max_new_tokens = 32768
image_area = 518400
def build_unc_and_template(task: str, with_image: bool):
# System prompt header and role formatting remain consistent
task_str = task.lower()
if with_image:
unc_p = "<|extra_203|>You are a helpful assistant. USER: <|IMAGE|> ASSISTANT: <|extra_100|>"
tmpl = "<|extra_203|>You are a helpful assistant for %s task. USER: {question}<|IMAGE|> ASSISTANT: <|extra_100|>" % task_str
else:
unc_p = "<|extra_203|>You are a helpful assistant. USER: ASSISTANT: <|extra_100|>"
tmpl = "<|extra_203|>You are a helpful assistant for %s task. USER: {question} ASSISTANT: <|extra_100|>" % task_str
return unc_p, tmpl
unc_prompt, template = build_unc_and_template(task_type, use_image)
sampling_params = dict(
use_cache=True,
# text token sampling config
text_top_k=1024,
text_top_p=0.9,
text_temperature=1.0,
# image token sampling config
image_top_k=10240,
image_top_p=1.0,
image_temperature=1.0,
# general config
top_k=131072, # default topk (backward compatible)
top_p=1.0, # default top_p (backward compatible)
temperature=1.0, # default temperature (backward compatible)
num_beams_per_group=1,
num_beam_groups=1,
diversity_penalty=0.0,
max_new_tokens=max_new_tokens,
guidance_scale=1.0,
# enable differential sampling
use_differential_sampling=True,
)
sampling_params["do_sample"] = sampling_params["num_beam_groups"] <= 1
sampling_params["num_beams"] = sampling_params["num_beams_per_group"] * sampling_params["num_beam_groups"]
special_tokens = dict(
BOS="<|extra_203|>",
EOS="<|extra_204|>",
PAD="<|endoftext|>",
EOL="<|extra_200|>",
EOF="<|extra_201|>",
TMS="<|extra_202|>",
IMG="<|image token|>",
BOI="<|image start|>",
EOI="<|image end|>",
BSS="<|extra_100|>",
ESS="<|extra_101|>",
BOG="<|extra_60|>",
EOG="<|extra_61|>",
BOC="<|extra_50|>",
EOC="<|extra_51|>",
)
seed = 6666
# prompts config
# If use_image=True, each item should be a dict with {"prompt", "reference_image"}.
# If use_image=False, each item is a plain text string.
_prompts_base = [
{
"prompt": "Imagine a heartwarming tale about a little hedgehog who overcomes his fear of the dark with the help of glowing fireflies.",
"reference_image": "",
},
# {
# "prompt": "Tell a story about a clay astronaut exploring Mars and discovering a new continent hidden beneath the red dust.",
# "reference_image": "assets/ref_img.png",
# },
# {
# "prompt": "Generate a clay astronaut in the given image exploring Mars.",
# "reference_image": "assets/ref_img.png",
# },
]
if use_image:
prompts = _prompts_base
else:
prompts = [p["prompt"] for p in _prompts_base]
configs/example_config_x2i.py 0 → 100644
View file @ f17c9671
# Copyright 2025 BAAI. and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
from pathlib import Path
from src.utils.logging_utils import setup_logger
cfg_name = Path(__file__).stem
model_path = "path_to_emu3.5_model" # download from hf
vq_path = "path_to_vq_model" # download from hf
tokenizer_path = "path_to_tokenizer"
vq_type = "ibq"
task_type = "x2i"
# whether prompts include an input image token and provide reference_image paths
use_image = True
# saving config
exp_name = "emu3p5-image"
save_path = f"./outputs/{exp_name}/{task_type}"
save_to_proto = True
setup_logger(save_path)
hf_device = "auto"
vq_device = "cuda:0"
streaming = False
unconditional_type = "no_text"
classifier_free_guidance = 3.0 # For Emu3.5 model: we recommend set to 2
max_new_tokens = 5120
image_area = 1048576
# prompts config
# If use_image=True, each item should be a dict with {"prompt", "reference_image"}. reference_image should be a list of image (maximum 3 images).
# If use_image=False, each item is a plain text string.
_prompts_base = [
{
"prompt": "As shown in the second figure: The ripe strawberry rests on a green leaf in the garden. Replace the chocolate truffle in first image with ripe strawberry from 2nd image",
"reference_image": ["./assets/ref_0.png", "./assets/ref_1.png"],
},
]
if use_image:
prompts = _prompts_base
else:
prompts = [p["prompt"] for p in _prompts_base]
def build_unc_and_template(task: str, with_image: bool):
# System prompt header and role formatting remain consistent
task_str = task.lower()
if with_image:
unc_p = "<|extra_203|>You are a helpful assistant. USER: <|IMAGE|> ASSISTANT: <|extra_100|>"
tmpl = "<|extra_203|>You are a helpful assistant for %s task. USER: <|IMAGE|>{question} ASSISTANT: <|extra_100|>" % task_str
else:
unc_p = "<|extra_203|>You are a helpful assistant. USER: ASSISTANT: <|extra_100|>"
tmpl = "<|extra_203|>You are a helpful assistant for %s task. USER: {question} ASSISTANT: <|extra_100|>" % task_str
return unc_p, tmpl
unc_prompt, template = build_unc_and_template(task_type, use_image)
# sampling paras config
sampling_params = dict(
use_cache=True,
# text token sampling config
text_top_k=1024,
text_top_p=0.9,
text_temperature=1.0,
# image token sampling config
image_top_k=5120,
image_top_p=1.0,
image_temperature=1.0,
# general config
top_k=131072, # default topk (backward compatible)
top_p=1.0, # default top_p (backward compatible)
temperature=1.0, # default temperature (backward compatible)
num_beams_per_group=1,
num_beam_groups=1,
diversity_penalty=0.0,
max_new_tokens=max_new_tokens,
guidance_scale=1.0,
# enable differential sampling
use_differential_sampling=True,
)
sampling_params["do_sample"] = sampling_params["num_beam_groups"] <= 1
sampling_params["num_beams"] = sampling_params["num_beams_per_group"] * sampling_params["num_beam_groups"]
special_tokens = dict(
BOS="<|extra_203|>",
EOS="<|extra_204|>",
PAD="<|endoftext|>",
EOL="<|extra_200|>",
EOF="<|extra_201|>",
TMS="<|extra_202|>",
IMG="<|image token|>",
BOI="<|image start|>",
EOI="<|image end|>",
BSS="<|extra_100|>",
ESS="<|extra_101|>",
BOG="<|extra_60|>",
EOG="<|extra_61|>",
BOC="<|extra_50|>",
EOC="<|extra_51|>",
)
seed = 6666
docker/Dockerfile 0 → 100644
View file @ f17c9671
FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.7.1-ubuntu22.04-dtk25.04.2-py3.10-alpha
\ No newline at end of file
icon.png 0 → 100644
View file @ f17c9671
icon.png

50.3 KB

inference.py 0 → 100644
View file @ f17c9671
# Copyright 2025 BAAI. and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
import argparse
import importlib as imp
import os
import os.path as osp
from pathlib import Path
import random
from time import sleep
from PIL import Image
import torch
from tqdm import tqdm
from src.utils.model_utils import build_emu3p5
from src.utils.generation_utils import generate, multimodal_decode
from src.utils.painting_utils import ProtoWriter
from src.utils.input_utils import build_image, smart_resize
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--cfg", default="", type=str)
parser.add_argument("--num_workers", default=1, type=int)
parser.add_argument("--worker_id", default=0, type=int)
args = parser.parse_args()
return args
def inference(
cfg,
model,
tokenizer,
vq_model,
):
save_path = cfg.save_path
os.makedirs(save_path, exist_ok=True)
os.makedirs(f"{save_path}/proto", exist_ok=True)
proto_writer = ProtoWriter()
for name, question in tqdm(cfg.prompts, total=len(cfg.prompts)):
if osp.exists(f"{save_path}/proto/{name}.pb"):
print(f"[WARNING] Result already exists, skipping {name}", flush=True)
continue
torch.cuda.empty_cache()
reference_image = None
if not isinstance(question, str):
if isinstance(question["reference_image"], list):
print(f"[INFO] {len(question['reference_image'])} reference images are provided")
reference_image = []
for img in question["reference_image"]:
reference_image.append(Image.open(img).convert("RGB"))
else:
print (f"[INFO] 1 reference image is provided")
reference_image = Image.open(question["reference_image"]).convert("RGB")
question = question["prompt"]
else:
print(f"[INFO] No reference image is provided")
proto_writer.clear()
proto_writer.extend([["question", question]])
if reference_image is not None:
if isinstance(reference_image, list):
for idx, img in enumerate(reference_image):
proto_writer.extend([[f"reference_image", img]])
else:
proto_writer.extend([["reference_image", reference_image]])
success = True
prompt = cfg.template.format(question=question)
print(f"[INFO] Handling prompt: {prompt}")
if reference_image is not None:
if isinstance(reference_image, list):
image_str = ""
for img in reference_image:
image_str += build_image(img, cfg, tokenizer, vq_model)
else:
image_str = build_image(reference_image, cfg, tokenizer, vq_model)
prompt = prompt.replace("<|IMAGE|>", image_str)
unc_prompt = cfg.unc_prompt.replace("<|IMAGE|>", image_str)
else:
unc_prompt = cfg.unc_prompt
input_ids = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)
if input_ids[0, 0] != cfg.special_token_ids["BOS"]:
BOS = torch.Tensor([[cfg.special_token_ids["BOS"]]], device=input_ids.device, dtype=input_ids.dtype)
input_ids = torch.cat([BOS, input_ids], dim=1)
unconditional_ids = tokenizer.encode(unc_prompt, return_tensors="pt", add_special_tokens=False).to(model.device)
if hasattr(cfg, "img_unc_prompt"):
full_unc_ids = tokenizer.encode(cfg.img_unc_prompt, return_tensors="pt", add_special_tokens=False).to(model.device)
else:
full_unc_ids = None
force_same_image_size = True
# for x2i task, if multiple reference images are provided as a list, force_same_image_size should be False
if isinstance(reference_image, list) and len(reference_image) > 1:
force_same_image_size = False
for result_tokens in generate(cfg, model, tokenizer, input_ids, unconditional_ids, full_unc_ids, force_same_image_size):
try:
result = tokenizer.decode(result_tokens, skip_special_tokens=False)
mm_out = multimodal_decode(result, tokenizer, vq_model)
proto_writer.extend(mm_out)
except Exception as e:
success = False
print(f"[ERROR] Failed to generate token sequence: {e}")
break
if not success:
continue
proto_writer.save(f"{save_path}/proto/{name}.pb")
def main():
args = parse_args()
cfg_name = Path(args.cfg).stem
cfg_package = Path(args.cfg).parent.__str__().replace("/", ".")
cfg = imp.import_module(f".{cfg_name}", package=cfg_package)
rank, world_size = args.worker_id, args.num_workers
cfg.rank = rank
cfg.world_size = world_size
if isinstance(cfg.prompts, dict):
cfg.prompts = [(n, p) for n, p in cfg.prompts.items()]
else:
cfg.prompts = [(f"{idx:03d}", p) for idx, p in enumerate(cfg.prompts)]
cfg.prompts = [(n, p) for n, p in cfg.prompts if not osp.exists(f"{cfg.save_path}/proto/{n}.pb")]
cfg.prompts = cfg.prompts[rank::world_size]
cfg.num_prompts = len(cfg.prompts)
hf_device, vq_device = cfg.hf_device, cfg.vq_device
model, tokenizer, vq_model = build_emu3p5(
cfg.model_path,
cfg.tokenizer_path,
cfg.vq_path,
vq_type=cfg.vq_type,
model_device=hf_device,
vq_device=vq_device,
**getattr(cfg, "diffusion_decoder_kwargs", {}),
)
print(f"[INFO] Model loaded successfully")
cfg.special_token_ids = {}
for k, v in cfg.special_tokens.items():
cfg.special_token_ids[k] = tokenizer.encode(v)[0]
random.seed(cfg.seed + rank)
inference(
cfg=cfg,
model=model,
tokenizer=tokenizer,
vq_model=vq_model,
)
print(f"[INFO] Inference finished")
if __name__ == "__main__":
main()
model.properties 0 → 100644
View file @ f17c9671
# 模型唯一标识
modelCode=1806
# 模型名称
modelName=Emu3.5_pytorch
# 模型描述
modelDescription=通过在超过10万亿的多模态Token（主要源自互联网视频，总时长约790年）上进行端到端预训练，具备了原生的世界建模能力，能够理解和生成文本、图像和视频等多种模态的数据。
# 应用场景
appScenario=推理,制造,广媒,家居,教育
# 框架类型
frameType=pytorch
#加速卡类型
accelerateType=BW1000
requirements.txt 0 → 100644
View file @ f17c9671
#torch>=2.6.0
#torchvision>=0.15.0
#torchaudio>=2.0.0
transformers==4.48.2
accelerate>=0.20.0
pillow>=9.0.0
numpy>=1.21.0
tqdm>=4.64.0
protobuf>=3.20.0
tiktoken>=0.12.0
imageio==2.37.0
imageio-ffmpeg==0.6.0
omegaconf==2.3.0
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