add readme and test pre commit

.github/ISSUE_TEMPLATE/bug_report.md

@@ -11,7 +11,7 @@ assignees: strint
 #### A clear and concise description of what the bug is.
 
 ### Your environment
-#### OS 
+#### OS
 
 #### OneDiff git commit id
 

.github/workflows/examples.yml

@@ -242,7 +242,7 @@ jobs:
           SDXL_BASE: ${{ env.SDXL_BASE }}
           UNET_INT8: ${{ env.UNET_INT8 }}
           SILICON_ONEDIFF_LICENSE_KEY: ${{ secrets.SILICON_ONEDIFF_LICENSE_KEY }}
-          
+
       - name: Setup docker for WebUI Test
         if: matrix.test-suite == 'webui'
         run: |
@@ -351,7 +351,7 @@ jobs:
         run: |
           docker exec -w /src/onediff ${{ env.CONTAINER_NAME }} python3 onediff_diffusers_extensions/examples/text_to_image_sd_enterprise.py  --model /share_nfs/hf_models/stable-diffusion-v1-5-int8 --width 512 --height 512 --saved_image /src/onediff/output_enterprise_sd.png
           docker exec -w /src/onediff ${{ env.CONTAINER_NAME }} python3 tests/test_quantitative_quality.py
-      
+
       - name: Install Requirements for WebUI
         if: matrix.test-suite == 'webui'
         run: |

README_ENTERPRISE.md

@@ -122,7 +122,7 @@ Ensure that you have installed [OneDiff ComfyUI Nodes](onediff_comfy_nodes/READM
 
   For more information and to **access the model files and Workflow below**, please visit [Hugging Face - stable-diffusion-v1-5-onediff-enterprise-v1](https://huggingface.co/siliconflow/stable-diffusion-v1-5-onediff-comfy-enterprise-v1/tree/main).
 
-<details> 
+<details>
 <summary> Download the required model files </summary>
 
 1. Download the [`v1-5-pruned.safetensors`](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.safetensors) file and place it in the `ComfyUI/models/checkpoints/` directory:
@@ -149,7 +149,7 @@ Click the links below to view the workflow images, or load them directly into Co
 
   For more information and to **access the model files and Workflow below**, please visit [Hugging Face - stable-diffusion-v2-1-onediff-enterprise](https://huggingface.co/siliconflow/stable-diffusion-v2-1-onediff-comfy-enterprise/tree/main).
 
-<details> 
+<details>
 <summary> Download the required model files </summary>
 
 1. Download the [`v2-1_768-ema-pruned.zip`](https://huggingface.co/siliconflow/stable-diffusion-v2-1-onediff-comfy-enterprise/blob/main/v2-1_768-ema-pruned.zip) file and unzip ,then place the .safetensors in the `ComfyUI/models/checkpoints/` directory:
@@ -166,15 +166,15 @@ wget https://huggingface.co/siliconflow/stable-diffusion-v2-1-onediff-comfy-ente
 
 </details>
 
-Click the links below to view the workflow images, or load them directly into ComfyUI. 
+Click the links below to view the workflow images, or load them directly into ComfyUI.
 
   - Workflow: [SD 2.1](https://huggingface.co/siliconflow/stable-diffusion-v2-1-onediff-comfy-enterprise/blob/main/onediff_stable_diffusion_2_1.png)
 
 ### ComfyUI SDXL
 
   For model details and to **access the model files and Workflow below**, please  visit [Hugging Face - sdxl-base-1.0-onediff-comfy-enterprise-v1](https://huggingface.co/siliconflow/sdxl-base-1.0-onediff-comfy-enterprise-v1/tree/main).
 
-<details> 
+<details>
 <summary> Download the required model files </summary>
 
 1. Download the [`sd_xl_base_1.0.safetensors`](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors) file and place it in the `ComfyUI/models/checkpoints/` directory:
@@ -373,20 +373,20 @@ To download the necessary models, please visit the [siliconflow/stable-video-dif
 Run [image_to_video.py](benchmarks/image_to_video.py):
 
 ```bash
-python3 benchmarks/image_to_video.py \     
-  --model $model_path \    
-  --input-image path/to/input_image.jpg \     
-  --output-video path/to/output_image.mp4   
+python3 benchmarks/image_to_video.py \
+  --model $model_path \
+  --input-image path/to/input_image.jpg \
+  --output-video path/to/output_image.mp4
 ```
 
 #### SVD + DeepCache
 
 ```bash
-python3 benchmarks/image_to_video.py \     
-  --model $model_path \     
-  --deepcache \     
-  --input-image path/to/input_image.jpg \     
-  --output-video path/to/output_image.mp4 
+python3 benchmarks/image_to_video.py \
+  --model $model_path \
+  --deepcache \
+  --input-image path/to/input_image.jpg \
+  --output-video path/to/output_image.mp4
 ```
 
 ## Quantitative model

benchmarks/docker/_utils.py

@@ -1,12 +1,13 @@
 import hashlib
 import os
 import subprocess
-import yaml
 
-from git import Repo
+import yaml
 
 from _logger import logger
 
+from git import Repo
+
 
 def load_yaml(*, file):
     if not os.path.exists(file):

benchmarks/docker/main.py

@@ -1,35 +1,44 @@
 import argparse
-from datetime import datetime
 import os
 import sys
+from datetime import datetime
 from pathlib import Path
 
 ONEDIFFBOX_ROOT = Path(os.path.abspath(__file__)).parents[0]
 sys.path.insert(0, str(ONEDIFFBOX_ROOT))
 
+from _logger import logger
 from _utils import (
-    calculate_sha256,
-    setup_repo,
-    load_yaml,
-    generate_docker_file,
     build_image,
+    calculate_sha256,
     gen_docker_compose_yaml,
+    generate_docker_file,
+    load_yaml,
+    setup_repo,
 )
-from _logger import logger
 
 
 def parse_args():
     parser = argparse.ArgumentParser(description="Build OneDiff Box")
     formatted_datetime = datetime.now().strftime("%Y%m%d-%H%M")
 
     parser.add_argument(
-        "-y", "--yaml", type=str, default="config/community-default.yaml",
+        "-y",
+        "--yaml",
+        type=str,
+        default="config/community-default.yaml",
     )
     parser.add_argument(
-        "-i", "--image", type=str, default="onediff",
+        "-i",
+        "--image",
+        type=str,
+        default="onediff",
     )
     parser.add_argument(
-        "-t", "--tag", type=str, default=f"benchmark",
+        "-t",
+        "--tag",
+        type=str,
+        default=f"benchmark",
     )
     parser.add_argument(
         "-o",
@@ -39,10 +48,17 @@ def parse_args():
         help="the output directory of Dockerfile and Docker-compose file",
     )
     parser.add_argument(
-        "-c", "--context", type=str, default=".", help="the path to build context",
+        "-c",
+        "--context",
+        type=str,
+        default=".",
+        help="the path to build context",
     )
     parser.add_argument(
-        "-q", "--quiet", action="store_true", help="quiet mode",
+        "-q",
+        "--quiet",
+        action="store_true",
+        help="quiet mode",
     )
     args = parser.parse_args()
     return args
@@ -77,7 +93,10 @@ def parse_args():
 
     envs = image_config.pop("envs", [])
     volumes = image_config.pop(
-        "volumes", ["$BENCHMARK_MODEL_PATH:/benchmark_model:ro",],
+        "volumes",
+        [
+            "$BENCHMARK_MODEL_PATH:/benchmark_model:ro",
+        ],
     )
     compose_file, run_command = gen_docker_compose_yaml(
         f"onediff-benchmark-{version}", image_name, envs, volumes, args.output

benchmarks/image_to_video.py

@@ -30,19 +30,19 @@
 CACHE_INTERVAL = 3
 CACHE_BRANCH = 0
 
-import os
+import argparse
 import importlib
 import inspect
-import argparse
-import time
 import json
+import os
 import random
-from PIL import Image, ImageDraw
+import time
 
 import oneflow as flow
 import torch
-from onediffx import compile_pipe, OneflowCompileOptions 
-from diffusers.utils import load_image, export_to_video
+from diffusers.utils import export_to_video, load_image
+from onediffx import compile_pipe, OneflowCompileOptions
+from PIL import Image, ImageDraw
 
 
 def parse_args():
@@ -84,10 +84,14 @@ def parse_args():
         default=ATTENTION_FP16_SCORE_ACCUM_MAX_M,
     )
     parser.add_argument(
-        "--alter-height", type=int, default=ALTER_HEIGHT,
+        "--alter-height",
+        type=int,
+        default=ALTER_HEIGHT,
     )
     parser.add_argument(
-        "--alter-width", type=int, default=ALTER_WIDTH,
+        "--alter-width",
+        type=int,
+        default=ALTER_WIDTH,
     )
     return parser.parse_args()
 
@@ -110,7 +114,8 @@ def load_pipe(
         from diffusers import ControlNetModel
 
         controlnet = ControlNetModel.from_pretrained(
-            controlnet, torch_dtype=torch.float16,
+            controlnet,
+            torch_dtype=torch.float16,
         )
         extra_kwargs["controlnet"] = controlnet
     if os.path.exists(os.path.join(model_name, "calibrate_info.txt")):
@@ -218,7 +223,12 @@ def main():
                 control_image = Image.new("RGB", (width, height))
                 draw = ImageDraw.Draw(control_image)
                 draw.ellipse(
-                    (width // 4, height // 4, width // 4 * 3, height // 4 * 3,),
+                    (
+                        width // 4,
+                        height // 4,
+                        width // 4 * 3,
+                        height // 4 * 3,
+                    ),
                     fill=(255, 255, 255),
                 )
                 del draw

benchmarks/instant_id.py

@@ -26,23 +26,24 @@
 CACHE_LAYER_ID = 0
 CACHE_BLOCK_ID = 0
 
-import sys
-import os
+import argparse
 import importlib
 import inspect
-import argparse
-import time
 import json
-import torch
-from PIL import Image, ImageDraw
-import numpy as np
+import os
+import sys
+import time
+
 import cv2
-from huggingface_hub import snapshot_download
-from diffusers.utils import load_image
-from insightface.app import FaceAnalysis
+import numpy as np
 
 import oneflow as flow
+import torch
+from diffusers.utils import load_image
+from huggingface_hub import snapshot_download
+from insightface.app import FaceAnalysis
 from onediffx import compile_pipe
+from PIL import Image, ImageDraw
 
 
 def parse_args():
@@ -97,7 +98,8 @@ def load_pipe(
         from diffusers import ControlNetModel
 
         controlnet = ControlNetModel.from_pretrained(
-            controlnet, torch_dtype=torch.float16,
+            controlnet,
+            torch_dtype=torch.float16,
         )
         extra_kwargs["controlnet"] = controlnet
     if os.path.exists(os.path.join(model_name, "calibrate_info.txt")):
@@ -183,13 +185,14 @@ def main():
     if args.repo is None:
         custom_pipeline = args.custom_pipeline
         from diffusers import DiffusionPipeline
+
         pipeline_cls = DiffusionPipeline
     else:
         sys.path.insert(0, args.repo)
 
         from pipeline_stable_diffusion_xl_instantid import (
-            StableDiffusionXLInstantIDPipeline as pipeline_cls,
             draw_kps,
+            StableDiffusionXLInstantIDPipeline as pipeline_cls,
         )
 
     if os.path.exists(args.controlnet):

benchmarks/patch_stable_cascade.py

@@ -78,11 +78,11 @@ def pixel_shuffle(input, upscale_factor):
     *batch_dims, channels, height, width = input.shape
 
     assert (
-        channels % (upscale_factor ** 2) == 0
+        channels % (upscale_factor**2) == 0
     ), "Number of channels must be divisible by the square of the upscale factor"
 
     # Calculate new channels after applying upscale_factor
-    new_channels = channels // (upscale_factor ** 2)
+    new_channels = channels // (upscale_factor**2)
 
     # Reshape input to (*batch_dims, new_channels, upscale_factor, upscale_factor, height, width)
     reshaped = input.reshape(
@@ -143,7 +143,7 @@ def pixel_unshuffle(input, downscale_factor):
     # Final reshape
     output = permuted.reshape(
         *batch_dims,
-        channels * downscale_factor ** 2,
+        channels * downscale_factor**2,
         height // downscale_factor,
         width // downscale_factor,
     )

benchmarks/patch_stable_cascade_of.py

@@ -1,11 +1,12 @@
+import importlib.metadata
 from typing import Optional
+
 import oneflow as torch
 import oneflow.nn as nn
 import oneflow.nn.functional as F
-from packaging import version
-import importlib.metadata
 
 from onediff.infer_compiler.backends.oneflow.transform import transform_mgr
+from packaging import version
 
 diffusers_of = transform_mgr.transform_package("diffusers")
 StableCascadeUnet_OF_CLS = (
@@ -114,14 +115,15 @@ def forward(
         return self.clf(x).to(torch.float16)
 
 
+from contextlib import contextmanager
+
 # diffusers.pipelines.stable_cascade.modeling_stable_cascade_common.StableCascadeUnet
 from diffusers.pipelines.stable_cascade.modeling_stable_cascade_common import (
     StableCascadeUnet,
 )
 
 # torch2oflow_class_map.update({StableCascadeUnet: StableCascadeUnetOflow})
 from onediff.infer_compiler.backends.oneflow.transform import register
-from contextlib import contextmanager
 
 
 @contextmanager

benchmarks/run_image_to_video_benchmark.sh

@@ -24,7 +24,7 @@ while getopts 'm:w:c:o:h' opt; do
     o)
       OUTPUT_FILE=$OPTARG
       ;;
-   
+
     ?|h)
       echo "Usage: $(basename $0) [-m model_dir] [-w warmups] [-c compiler] [-o output_file]"
       echo "  -m model_dir: the directory of the models, if not set, use HF models"

benchmarks/run_sdxl_light_benchmarks.sh

@@ -43,5 +43,3 @@ TXT2IMG_ONEFLOW_OUTPUT_FILE=${OUTPUT_DIR}/sdxl_light_oneflow.md
 BENCHMARK_RESULT_TEXT="${BENCHMARK_RESULT_TEXT}\n\n### Text to Image (OneFlow)\n\n$(cat ${TXT2IMG_ONEFLOW_OUTPUT_FILE})\n\n"
 
 echo -e "${BENCHMARK_RESULT_TEXT}" > ${OUTPUT_FILE}
-
-