Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,28 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Comic_Generation.ipynb filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/gradio/_frontend_code/lite/dist/assets/Canvas3D-JkRyAMo4.js.map filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/gradio/frpc_windows_amd64_v0.2 filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/gradio/frpc_windows_amd64_v0.3 filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/gradio/templates/frontend/assets/Canvas3D-Y-FPpH_E.js.map filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/gradio/templates/node/build/client/_app/immutable/chunks/Canvas3D.rJxgLkEw.js.br filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/gradio/templates/node/build/server/chunks/Canvas3D-qDqWI6YT.js.map filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/gradio/templates/node/build/server/chunks/PlotlyPlot-P3y0I-J2.js.map filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/numpy.libs/libopenblas64__v0.3.23-293-gc2f4bdbb-gcc_10_3_0-2bde3a66a51006b2b53eb373ff767a3f.dll filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/torch/bin/fbgemm.dll filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/torch/bin/protoc.exe filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/torch/lib/dnnl.lib filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/torch/lib/fbgemm.dll filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/torch/lib/fbgemm.lib filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/torch/lib/kineto.lib filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/torch/lib/libiomp5md.dll filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/torch/lib/libprotobuf-lite.lib filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/torch/lib/libprotobuf.lib filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/torch/lib/libprotoc.lib filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/torch/lib/torch_cpu.dll filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/torch/lib/torch_cpu.lib filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/torch/lib/torch_python.dll filter=lfs diff=lfs merge=lfs -text
+myenv/Lib/site-packages/torch/lib/XNNPACK.lib filter=lfs diff=lfs merge=lfs -text
+myenv/Scripts/ruff.exe filter=lfs diff=lfs merge=lfs -text
+results_examples/image1.png filter=lfs diff=lfs merge=lfs -text

.github/workflows/update_space.yml

@@ -0,0 +1,28 @@
+name: Run Python script
+
+on:
+  push:
+    branches:
+      - Mytest
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install Gradio
+      run: python -m pip install gradio
+
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+
+    - name: Deploy to Spaces
+      run: gradio deploy

.gitignore

@@ -0,0 +1,101 @@
+# Byte-compile / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.aider*
+
+# ignore ALL .log files
+*.log
+# ignore ALL files in ANY directory named temp
+.ipynb_checkpoints/
+images/.ipynb_checkpoints/
+results/
+.vscode/settings.json
+fonts/agency.ttf
+fonts/calibri.ttf
+data/
+
+# myenv/
+models/

.gradio/certificate.pem

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+-----END CERTIFICATE-----

Comic_Generation.ipynb

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+oid sha256:c6376c7f98b4d0419d9310a3debfaa76b403c6f5a994f8f0de687a65adc6564c
+size 19371692

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,12 +1,158 @@
----
-title: StoryDiffusion
-emoji: 🐠
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 5.9.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: StoryDiffusion
+app_file: app.py
+sdk: gradio
+sdk_version: 3.41.2
+---
+<p align="center">
+  <img src="https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/f79da6b7-0b3b-4dd7-8dd0-ba0b15306fe6" height=100>
+</p>
+
+<div align="center">
+  
+## StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation  [![Paper page](https://huggingface.co/datasets/huggingface/badges/resolve/main/paper-page-md-dark.svg)]()
+
+[[Paper](https://arxiv.org/abs/2405.01434)] &emsp; [[Project Page](https://storydiffusion.github.io/)] &emsp;  [[Jittor Version](https://github.com/JittorCV/jittordiffusion/tree/master)]&emsp; [[🤗 Comic Generation Demo ](https://huggingface.co/spaces/YupengZhou/StoryDiffusion)] [![Replicate](https://replicate.com/cjwbw/StoryDiffusion/badge)](https://replicate.com/cjwbw/StoryDiffusion) [![Run Comics Demo in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/HVision-NKU/StoryDiffusion/blob/main/Comic_Generation.ipynb) <br>
+</div>
+
+
+---
+
+Official implementation of **[StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation]()**.
+
+### **Demo Video**
+
+https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/d5b80f8f-09b0-48cd-8b10-daff46d422af
+
+
+### Update History
+
+***You can visit [here](update.md) to visit update history.***
+
+### 🌠  **Key Features:**
+StoryDiffusion can create a magic story by generating consistent images and videos. Our work mainly has two parts: 
+1. Consistent self-attention for character-consistent image generation over long-range sequences. It is hot-pluggable and compatible with all SD1.5 and SDXL-based image diffusion models. For the current implementation, the user needs to provide at least 3 text prompts for the consistent self-attention module. We recommend at least 5 - 6 text prompts for better layout arrangement.
+2. Motion predictor for long-range video generation, which predicts motion between Condition Images in a compressed image semantic space, achieving larger motion prediction. 
+
+
+
+## 🔥 **Examples**
+
+
+### Comics generation 
+
+
+![1](https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/b3771cbc-b6ca-4e26-bdc5-d944daf9f266)
+
+
+
+### Image-to-Video generation （Results are HIGHLY compressed for speed）
+Leveraging the images produced through our Consistent Self-Attention mechanism, we can extend the process to create videos by seamlessly transitioning between these images. This can be considered as a two-stage long video generation approach.
+
+Note: results are **highly compressed** for speed, you can visit [our website](https://storydiffusion.github.io/) for the high-quality version.
+#### Two-stage Long Videos Generation (New Update)
+Combining the two parts, we can generate very long and high-quality AIGC videos.
+| Video1 | Video2  | Video3  |
+| --- | --- | --- |
+| <img src="https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/4e7e0f24-5f90-419b-9a1e-cdf36d361b26" width=224>  | <img src="https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/f509343d-d691-4e2a-b615-7d96381ef7c1" width=224> | <img src="https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/4f0f7abb-4ae4-47a6-b692-5bdd8d9c8006" width=224>  |
+
+
+#### Long Video Results using Condition Images
+Our Image-to-Video model can generate a video by providing a sequence of user-input condition images.
+| Video1 | Video2  | Video3  |
+| --- | --- | --- |
+| <img src="https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/af6f5c50-c773-4ef2-a757-6d7a46393f39" width=224>  | <img src="https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/d58e4037-d8df-4f90-8c81-ce4b6d2d868e" width=224> |  <img src="https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/40da15ba-f5c1-48d8-84d6-8d327207d696" width=224>  |
+
+| Video4 | Video5  | Video6  |
+| --- | --- | --- |
+| <img src="https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/8f04c9fc-3031-49e3-9de8-83d582b80a1f" width=224>  | <img src="https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/604107fb-8afe-4052-bda4-362c646a756e" width=224> |  <img src="https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/b05fa6a0-12e6-4111-abf8-18b8cd84f3ff" width=224>  |
+
+
+
+
+#### Short Videos 
+
+| Video1 | Video2  | Video3  |
+| --- | --- | --- |
+| <img src="https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/5e7f717f-daad-46f6-b3ba-c087bd843158" width=224>  | <img src="https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/79aa52b2-bf37-4c9c-8555-c7050aec0cdf" width=224> | <img src="https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/9fdfd091-10e6-434e-9ce7-6d6e6d8f4b22" width=224>  |
+
+
+
+| Video4 | Video5  | Video6  |
+| --- | --- | --- |
+| <img src="https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/0b219b60-a998-4820-9657-6abe1747cb6b" width=224>  | <img src="https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/d387aef0-ffc8-41b0-914f-4b0392d9f8c5" width=224> | <img src="https://github.com/HVision-NKU/StoryDiffusion/assets/49511209/3c64958a-1079-4ca0-a9cf-e0486adbc57f" width=224>  |
+
+
+
+
+## 🚩 **TODO/Updates**
+- [x] Comic Results of StoryDiffusion.
+- [x] Video Results of StoryDiffusion.
+- [x] Source code of Comic Generation
+- [x] Source code of gradio demo
+- [ ] Source code of Video Generation Model
+- [ ] Pretrained weight of Video Generation Model
+---
+
+# 🔧 Dependencies and Installation
+
+- Python >= 3.8 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
+- [PyTorch >= 2.0.0](https://pytorch.org/)
+```bash
+conda create --name storydiffusion python=3.10
+conda activate storydiffusion
+pip install -U pip
+
+# Install requirements
+pip install -r requirements.txt
+```
+# How to use
+
+Currently, we provide two ways for you to generate comics.
+
+## Use the jupyter notebook
+
+You can open the `Comic_Generation.ipynb` and run the code.
+
+## Start a local gradio demo
+Run the following command:
+
+
+**(Recommend)** We provide a low GPU Memory cost version, it was tested on a machine with 24GB GPU-memory(Tesla A10) and 30GB RAM, and expected to work well with >20 G GPU-memory.
+
+```python
+python gradio_app_sdxl_specific_id_low_vram.py
+```
+
+
+## Contact
+If you have any questions, you are very welcome to email ypzhousdu@gmail.com and zhoudaquan21@gmail.com
+
+   
+
+
+# Disclaimer
+This project strives to impact the domain of AI-driven image and video generation positively. Users are granted the freedom to create images and videos using this tool, but they are expected to comply with local laws and utilize it responsibly. The developers do not assume any responsibility for potential misuse by users.
+
+# Related Resources
+Following are some third-party implementations of StoryDiffusion.
+
+
+## API
+
+- [runpod.io serverless worker](https://github.com/bes-dev/story-diffusion-runpod-serverless-worker) provided by [BeS](https://github.com/bes-dev).
+- [Replicate worker](https://github.com/camenduru/StoryDiffusion-replicate) provided by [camenduru](https://github.com/camenduru).
+
+
+
+
+# BibTeX
+If you find StoryDiffusion useful for your research and applications, please cite using this BibTeX:
+
+```BibTeX
+@article{zhou2024storydiffusion,
+  title={StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation},
+  author={Zhou, Yupeng and Zhou, Daquan and Cheng, Ming-Ming and Feng, Jiashi and Hou, Qibin},
+  journal={NeurIPS 2024},
+  year={2024}
+}

app.py

@@ -0,0 +1,750 @@
+from email.policy import default
+import gradio as gr
+import numpy as np
+import spaces
+import torch
+import requests
+import random
+import os
+import sys
+import pickle
+from PIL import Image
+from tqdm.auto import tqdm
+from datetime import datetime
+from utils.gradio_utils import is_torch2_available
+if is_torch2_available():
+    from utils.gradio_utils import \
+        AttnProcessor2_0 as AttnProcessor
+    # from utils.gradio_utils import SpatialAttnProcessor2_0
+else:
+    from utils.gradio_utils  import AttnProcessor
+
+import diffusers
+from diffusers import StableDiffusionXLPipeline
+from utils import PhotoMakerStableDiffusionXLPipeline
+from diffusers import DDIMScheduler
+import torch.nn.functional as F
+from utils.gradio_utils import cal_attn_mask_xl
+import copy
+import os
+from huggingface_hub import hf_hub_download
+from diffusers.utils import load_image
+from utils.utils import get_comic
+from utils.style_template import styles
+image_encoder_path = "./data/models/ip_adapter/sdxl_models/image_encoder"
+ip_ckpt = "./data/models/ip_adapter/sdxl_models/ip-adapter_sdxl_vit-h.bin"
+os.environ["no_proxy"] = "localhost,127.0.0.1,::1"
+STYLE_NAMES = list(styles.keys())
+DEFAULT_STYLE_NAME = "Japanese Anime"
+global models_dict
+use_va = True
+models_dict = {
+#    "Juggernaut": "RunDiffusion/Juggernaut-XL-v8",
+#    "RealVision": "SG161222/RealVisXL_V4.0" ,
+#    "SDXL":"stabilityai/stable-diffusion-xl-base-1.0" ,
+   "Unstable": "stablediffusionapi/sdxl-unstable-diffusers-y"
+}
+photomaker_path =  hf_hub_download(repo_id="TencentARC/PhotoMaker", filename="photomaker-v1.bin", repo_type="model")
+MAX_SEED = np.iinfo(np.int32).max
+def setup_seed(seed):
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    torch.backends.cudnn.deterministic = True
+def set_text_unfinished():
+    return gr.update(visible=True, value="<h3>(Not Finished) Generating ···  The intermediate results will be shown.</h3>")
+def set_text_finished():
+    return gr.update(visible=True, value="<h3>Generation Finished</h3>")
+#################################################
+def get_image_path_list(folder_name):
+    image_basename_list = os.listdir(folder_name)
+    image_path_list = sorted([os.path.join(folder_name, basename) for basename in image_basename_list])
+    return image_path_list
+
+#################################################
+class SpatialAttnProcessor2_0(torch.nn.Module):
+    r"""
+    Attention processor for IP-Adapater for PyTorch 2.0.
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+        text_context_len (`int`, defaults to 77):
+            The context length of the text features.
+        scale (`float`, defaults to 1.0):
+            the weight scale of image prompt.
+    """
+
+    def __init__(self, hidden_size = None, cross_attention_dim=None,id_length = 4,device = "cuda",dtype = torch.float16):
+        super().__init__()
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+        self.device = device
+        self.dtype = dtype
+        self.hidden_size = hidden_size
+        self.cross_attention_dim = cross_attention_dim
+        self.total_length = id_length + 1
+        self.id_length = id_length
+        self.id_bank = {}
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None):
+        # un_cond_hidden_states, cond_hidden_states = hidden_states.chunk(2)
+        # un_cond_hidden_states = self.__call2__(attn, un_cond_hidden_states,encoder_hidden_states,attention_mask,temb)
+        # 生成一个0到1之间的随机数
+        global total_count,attn_count,cur_step,mask1024,mask4096
+        global sa32, sa64
+        global write
+        global height,width
+        if write:
+            # print(f"white:{cur_step}")
+            self.id_bank[cur_step] = [hidden_states[:self.id_length], hidden_states[self.id_length:]]
+        else:
+            encoder_hidden_states = torch.cat((self.id_bank[cur_step][0].to(self.device),hidden_states[:1],self.id_bank[cur_step][1].to(self.device),hidden_states[1:]))
+        # 判断随机数是否大于0.5
+        if cur_step <5:
+            hidden_states = self.__call2__(attn, hidden_states,encoder_hidden_states,attention_mask,temb)
+        else:   # 256 1024 4096
+            random_number = random.random()
+            if cur_step <20:
+                rand_num = 0.3
+            else:
+                rand_num = 0.1
+            # print(f"hidden state shape {hidden_states.shape[1]}")
+            if random_number > rand_num:
+                # print("mask shape",mask1024.shape,mask4096.shape)
+                if not write:
+                    if hidden_states.shape[1] == (height//32) * (width//32):
+                        attention_mask = mask1024[mask1024.shape[0] // self.total_length * self.id_length:]
+                    else:
+                        attention_mask = mask4096[mask4096.shape[0] // self.total_length * self.id_length:]
+                else:
+                    # print(self.total_length,self.id_length,hidden_states.shape,(height//32) * (width//32))
+                    if hidden_states.shape[1] == (height//32) * (width//32):
+                        attention_mask = mask1024[:mask1024.shape[0] // self.total_length * self.id_length,:mask1024.shape[0] // self.total_length * self.id_length]
+                    else:
+                        attention_mask = mask4096[:mask4096.shape[0] // self.total_length * self.id_length,:mask4096.shape[0] // self.total_length * self.id_length]
+                   # print(attention_mask.shape)
+                # print("before attention",hidden_states.shape,attention_mask.shape,encoder_hidden_states.shape if encoder_hidden_states is not None else "None")
+                hidden_states = self.__call1__(attn, hidden_states,encoder_hidden_states,attention_mask,temb)
+            else:
+                hidden_states = self.__call2__(attn, hidden_states,None,attention_mask,temb)
+        attn_count +=1
+        if attn_count == total_count:
+            attn_count = 0
+            cur_step += 1
+            mask1024,mask4096 = cal_attn_mask_xl(self.total_length,self.id_length,sa32,sa64,height,width, device=self.device, dtype= self.dtype)
+
+        return hidden_states
+    def __call1__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        # print("hidden state shape",hidden_states.shape,self.id_length)
+        residual = hidden_states
+        # if encoder_hidden_states is not None:
+        #     raise Exception("not implement")
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            total_batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(total_batch_size, channel, height * width).transpose(1, 2)
+        total_batch_size,nums_token,channel = hidden_states.shape
+        img_nums = total_batch_size//2
+        hidden_states = hidden_states.view(-1,img_nums,nums_token,channel).reshape(-1,img_nums * nums_token,channel)
+
+        batch_size, sequence_length, _ = hidden_states.shape
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states  # B, N, C
+        else:
+            encoder_hidden_states = encoder_hidden_states.view(-1,self.id_length+1,nums_token,channel).reshape(-1,(self.id_length+1) * nums_token,channel)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        # print(key.shape,value.shape,query.shape,attention_mask.shape)
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        #print(query.shape,key.shape,value.shape,attention_mask.shape)
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(total_batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        # if input_ndim == 4:
+        #     tile_hidden_states = tile_hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        # if attn.residual_connection:
+        #     tile_hidden_states = tile_hidden_states + residual
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(total_batch_size, channel, height, width)
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+        hidden_states = hidden_states / attn.rescale_output_factor
+        # print(hidden_states.shape)
+        return hidden_states
+    def __call2__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, channel = (
+            hidden_states.shape
+        )
+        # print(hidden_states.shape)
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states  # B, N, C
+        else:
+            encoder_hidden_states = encoder_hidden_states.view(-1,self.id_length+1,sequence_length,channel).reshape(-1,(self.id_length+1) * sequence_length,channel)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+def set_attention_processor(unet,id_length,is_ipadapter = False):
+    global total_count
+    total_count = 0
+    attn_procs = {}
+    for name in unet.attn_processors.keys():
+        cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
+        if name.startswith("mid_block"):
+            hidden_size = unet.config.block_out_channels[-1]
+        elif name.startswith("up_blocks"):
+            block_id = int(name[len("up_blocks.")])
+            hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+        elif name.startswith("down_blocks"):
+            block_id = int(name[len("down_blocks.")])
+            hidden_size = unet.config.block_out_channels[block_id]
+        if cross_attention_dim is None:
+            if name.startswith("up_blocks") :
+                attn_procs[name] = SpatialAttnProcessor2_0(id_length = id_length)
+                total_count +=1
+            else:    
+                attn_procs[name] = AttnProcessor()
+        else:
+            if is_ipadapter:
+                attn_procs[name] = IPAttnProcessor2_0(
+                    hidden_size=hidden_size,
+                    cross_attention_dim=cross_attention_dim,
+                    scale=1,
+                    num_tokens=4,
+                ).to(unet.device, dtype=torch.float16)
+            else:
+                attn_procs[name] = AttnProcessor()
+
+    unet.set_attn_processor(copy.deepcopy(attn_procs))
+    print("successsfully load paired self-attention")
+    print(f"number of the processor : {total_count}")
+#################################################
+#################################################
+canvas_html = "<div id='canvas-root' style='max-width:400px; margin: 0 auto'></div>"
+load_js = """
+async () => {
+const url = "https://huggingface.co/datasets/radames/gradio-components/raw/main/sketch-canvas.js"
+fetch(url)
+  .then(res => res.text())
+  .then(text => {
+    const script = document.createElement('script');
+    script.type = "module"
+    script.src = URL.createObjectURL(new Blob([text], { type: 'application/javascript' }));
+    document.head.appendChild(script);
+  });
+}
+"""
+
+get_js_colors = """
+async (canvasData) => {
+  const canvasEl = document.getElementById("canvas-root");
+  return [canvasEl._data]
+}
+"""
+
+css = '''
+#color-bg{display:flex;justify-content: center;align-items: center;}
+.color-bg-item{width: 100%; height: 32px}
+#main_button{width:100%}
+<style>
+'''
+
+
+#################################################
+title = r"""
+<h1 align="center">StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation</h1>
+"""
+
+description = r"""
+<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/HVision-NKU/StoryDiffusion' target='_blank'><b>StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation</b></a>.<br>
+❗️❗️❗️[<b>Important</b>] Personalization steps:<br>
+1️⃣ Enter a Textual Description for Character, if you add the Ref-Image, making sure to <b>follow the class word</b> you want to customize with the <b>trigger word</b>: `img`, such as: `man img` or `woman img` or `girl img`.<br>
+2️⃣ Enter the prompt array, each line corrsponds to one generated image.<br>
+3️⃣ Choose your preferred style template.<br>
+4️⃣ Click the <b>Submit</b> button to start customizing.
+"""
+
+article = r"""
+
+If StoryDiffusion is helpful, please help to ⭐ the <a href='https://github.com/HVision-NKU/StoryDiffusion' target='_blank'>Github Repo</a>. Thanks! 
+[![GitHub Stars](https://img.shields.io/github/stars/HVision-NKU/StoryDiffusion?style=social)](https://github.com/HVision-NKU/StoryDiffusion)
+---
+📝 **Citation**
+<br>
+If our work is useful for your research, please consider citing:
+
+```bibtex
+@article{Zhou2024storydiffusion,
+  title={StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation},
+  author={Zhou, Yupeng and Zhou, Daquan and Cheng, Ming-Ming and Feng, Jiashi and Hou, Qibin},
+  year={2024}
+}
+```
+📋 **License**
+<br>
+The Contents you create are under Apache-2.0 LICENSE. The Code are under Attribution-NonCommercial 4.0 International. 
+
+📧 **Contact**
+<br>
+If you have any questions, please feel free to reach me out at <b>ypzhousdu@gmail.com</b>.
+"""
+version = r"""
+<h3 align="center">StoryDiffusion Version 0.01 (test version)</h3>
+
+<h5 >1. Support image ref image. (Cartoon Ref image is not support now)</h5>
+<h5 >2. Support Typesetting Style and Captioning.(By default, the prompt is used as the caption for each image. If you need to change the caption, add a # at the end of each line. Only the part after the # will be added as a caption to the image.)</h5>
+<h5 >3. [NC]symbol (The [NC] symbol is used as a flag to indicate that no characters should be present in the generated scene images. If you want do that, prepend the "[NC]" at the beginning of the line. For example, to generate a scene of falling leaves without any character, write: "[NC] The leaves are falling."),Currently, support is only using Textual Description</h5>
+<h5 align="center">Tips: Not Ready Now! Just Test</h5>
+"""
+#################################################
+global attn_count, total_count, id_length, total_length,cur_step, cur_model_type
+global write
+global  sa32, sa64
+global height,width
+attn_count = 0
+total_count = 0
+cur_step = 0
+id_length = 4
+total_length = 5
+cur_model_type = ""
+device="cuda"
+global attn_procs,unet
+attn_procs = {}
+###
+write = False
+###
+sa32 = 0.5
+sa64 = 0.5
+height = 768
+width = 768
+###
+global sd_model_path
+sd_model_path = models_dict["Unstable"]#"SG161222/RealVisXL_V4.0"
+use_safetensors= False
+### LOAD Stable Diffusion Pipeline
+pipe1 = StableDiffusionXLPipeline.from_pretrained(sd_model_path, torch_dtype=torch.float16, use_safetensors= use_safetensors)
+pipe1 = pipe1.to("cuda")
+pipe1.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
+# pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+pipe1.scheduler.set_timesteps(50)
+### 
+pipe2 = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
+    sd_model_path, torch_dtype=torch.float16, use_safetensors=use_safetensors)
+pipe2 = pipe2.to("cuda")
+pipe2.load_photomaker_adapter(
+    os.path.dirname(photomaker_path),
+    subfolder="",
+    weight_name=os.path.basename(photomaker_path),
+    trigger_word="img"  # define the trigger word
+)
+pipe2 = pipe2.to("cuda")
+pipe2.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
+pipe2.fuse_lora()
+
+######### Gradio Fuction #############
+
+def swap_to_gallery(images):
+    return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False)
+
+def upload_example_to_gallery(images, prompt, style, negative_prompt):
+    return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False)
+
+def remove_back_to_files():
+    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
+
+def remove_tips():
+    return gr.update(visible=False)
+
+def apply_style_positive(style_name: str, positive: str):
+    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
+    return p.replace("{prompt}", positive) 
+
+def apply_style(style_name: str, positives: list, negative: str = ""):
+    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
+    return [p.replace("{prompt}", positive) for positive in positives], n + ' ' + negative
+
+def change_visiale_by_model_type(_model_type):
+    if _model_type == "Only Using Textual Description":
+        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+    elif _model_type == "Using Ref Images":
+        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)
+    else:
+        raise ValueError("Invalid model type",_model_type)
+
+
+######### Image Generation ##############
+@spaces.GPU
+def process_generation(_sd_type,_model_type,_upload_images, _num_steps,style_name, _Ip_Adapter_Strength ,_style_strength_ratio, guidance_scale, seed_,  sa32_, sa64_, id_length_,  general_prompt, negative_prompt,prompt_array,G_height,G_width,_comic_type):
+    _model_type = "Photomaker" if _model_type == "Using Ref Images" else "original"
+    if _model_type == "Photomaker" and "img" not in general_prompt:
+        raise gr.Error("Please add the triger word \" img \"  behind the class word you want to customize, such as: man img or woman img")
+    if _upload_images is None and _model_type != "original":
+        raise gr.Error(f"Cannot find any input face image!")
+    global sa32, sa64,id_length,total_length,attn_procs,unet,cur_model_type,device
+    global write
+    global cur_step,attn_count
+    global height,width
+    height = G_height
+    width = G_width
+    global pipe1,pipe2
+    global sd_model_path,models_dict
+    sd_model_path = models_dict[_sd_type]
+    use_safe_tensor = True
+    if _model_type == "original":
+        pipe = pipe1
+        set_attention_processor(pipe.unet,id_length_,is_ipadapter = False)
+    elif _model_type == "Photomaker":
+        pipe = pipe2
+        set_attention_processor(pipe.unet,id_length_,is_ipadapter = False)
+    else:
+        raise NotImplementedError("You should choice between original and Photomaker!",f"But you choice {_model_type}")
+        ##### ########################
+    pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+    pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
+    cur_model_type = _sd_type+"-"+_model_type+""+str(id_length_)
+    if _model_type != "original":
+        input_id_images = []
+        for img in _upload_images:
+            print(img)
+            input_id_images.append(load_image(img))
+    prompts = prompt_array.splitlines()
+    start_merge_step = int(float(_style_strength_ratio) / 100 * _num_steps)
+    if start_merge_step > 30:
+        start_merge_step = 30
+    print(f"start_merge_step:{start_merge_step}")
+    generator = torch.Generator(device="cuda").manual_seed(seed_)
+    sa32, sa64 =  sa32_, sa64_
+    id_length = id_length_
+    clipped_prompts = prompts[:]
+    prompts = [general_prompt + "," + prompt if "[NC]" not in prompt else prompt.replace("[NC]","")  for prompt in clipped_prompts]
+    prompts = [prompt.rpartition('#')[0] if "#" in prompt else prompt for prompt in prompts]
+    print(prompts)
+    id_prompts = prompts[:id_length]
+    real_prompts = prompts[id_length:]
+    torch.cuda.empty_cache()
+    write = True
+    cur_step = 0
+
+    attn_count = 0
+    id_prompts, negative_prompt = apply_style(style_name, id_prompts, negative_prompt)
+    setup_seed(seed_)
+    total_results = []
+    if _model_type == "original":
+        id_images = pipe(id_prompts, num_inference_steps=_num_steps, guidance_scale=guidance_scale,  height = height, width = width,negative_prompt = negative_prompt,generator = generator).images
+    elif _model_type == "Photomaker":
+        id_images = pipe(id_prompts,input_id_images=input_id_images, num_inference_steps=_num_steps, guidance_scale=guidance_scale, start_merge_step = start_merge_step, height = height, width = width,negative_prompt = negative_prompt,generator = generator).images
+    else: 
+        raise NotImplementedError("You should choice between original and Photomaker!",f"But you choice {_model_type}")
+    total_results = id_images + total_results
+    yield total_results
+    real_images = []
+    write = False
+    for real_prompt in real_prompts:
+        setup_seed(seed_)
+        cur_step = 0
+        real_prompt = apply_style_positive(style_name, real_prompt)
+        if _model_type == "original":   
+            real_images.append(pipe(real_prompt,  num_inference_steps=_num_steps, guidance_scale=guidance_scale,  height = height, width = width,negative_prompt = negative_prompt,generator = generator).images[0])
+        elif _model_type == "Photomaker":      
+            real_images.append(pipe(real_prompt, input_id_images=input_id_images, num_inference_steps=_num_steps, guidance_scale=guidance_scale,  start_merge_step = start_merge_step, height = height, width = width,negative_prompt = negative_prompt,generator = generator).images[0])
+        else:
+            raise NotImplementedError("You should choice between original and Photomaker!",f"But you choice {_model_type}")
+        total_results = [real_images[-1]] + total_results
+        yield total_results
+    if _comic_type != "No typesetting (default)":
+        captions= prompt_array.splitlines()
+        captions = [caption.replace("[NC]","") for caption in captions]
+        captions = [caption.split('#')[-1] if "#" in caption else caption for caption in captions]
+        from PIL import ImageFont
+        total_results = get_comic(id_images + real_images, _comic_type,captions= captions,font=ImageFont.truetype("./fonts/Inkfree.ttf", int(45))) + total_results
+    set_attention_processor(pipe.unet,id_length_,is_ipadapter = False)
+    yield total_results
+
+
+
+def array2string(arr):
+    stringtmp = ""
+    for i,part in enumerate(arr):
+        if i != len(arr)-1:
+            stringtmp += part +"\n"
+        else:
+            stringtmp += part
+
+    return stringtmp
+
+
+#################################################
+#################################################
+### define the interface
+with gr.Blocks(css=css) as demo:
+    binary_matrixes = gr.State([])
+    color_layout = gr.State([])
+
+    # gr.Markdown(logo)
+    gr.Markdown(title)
+    gr.Markdown(description)
+
+    with gr.Row():
+        with gr.Group(elem_id="main-image"):
+            # button_run = gr.Button("generate id images ! 😺", elem_id="main_button", interactive=True)
+
+            prompts = []
+            colors = []
+            # with gr.Column(visible=False) as post_sketch:
+            #     for n in range(MAX_COLORS):
+            #         if n == 0 :
+            #             with gr.Row(visible=False) as color_row[n]:
+            #                 colors.append(gr.Image(shape=(100, 100), label="background", type="pil", image_mode="RGB", width=100, height=100))
+            #                 prompts.append(gr.Textbox(label="Prompt for the background (white region)", value=""))
+            #         else:
+            #             with gr.Row(visible=False) as color_row[n]:
+            #                 colors.append(gr.Image(shape=(100, 100), label="segment "+str(n), type="pil", image_mode="RGB", width=100, height=100))
+            #                 prompts.append(gr.Textbox(label="Prompt for the segment "+str(n)))
+
+            #     get_genprompt_run = gr.Button("(2) I've finished segment labeling ! 😺", elem_id="prompt_button", interactive=True)
+
+            with gr.Column(visible=True) as gen_prompt_vis:
+                sd_type = gr.Dropdown(choices=list(models_dict.keys()), value = "Unstable",label="sd_type", info="Select pretrained model")
+                model_type = gr.Radio(["Only Using Textual Description", "Using Ref Images"], label="model_type", value = "Only Using Textual Description",  info="Control type of the Character")
+                with gr.Group(visible=False) as control_image_input:
+                    files = gr.Files(
+                                label="Drag (Select) 1 or more photos of your face",
+                                file_types=["image"],
+                            )
+                    uploaded_files = gr.Gallery(label="Your images", visible=False, columns=5, rows=1, height=200)
+                    with gr.Column(visible=False) as clear_button:
+                        remove_and_reupload = gr.ClearButton(value="Remove and upload new ones", components=files, size="sm")
+                general_prompt = gr.Textbox(value='', label="(1) Textual Description for Character", interactive=True)
+                negative_prompt = gr.Textbox(value='', label="(2) Negative_prompt", interactive=True)
+                style = gr.Dropdown(label="Style template", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
+                prompt_array = gr.Textbox(lines = 3,value='', label="(3) Comic Description (each line corresponds to a frame).", interactive=True)
+                with gr.Accordion("(4) Tune the hyperparameters", open=True):
+                    #sa16_ = gr.Slider(label=" (The degree of Paired Attention at 16 x 16 self-attention layers) ", minimum=0, maximum=1., value=0.3, step=0.1)
+                    sa32_ = gr.Slider(label=" (The degree of Paired Attention at 32 x 32 self-attention layers) ", minimum=0, maximum=1., value=0.7, step=0.1)
+                    sa64_ = gr.Slider(label=" (The degree of Paired Attention at 64 x 64 self-attention layers) ", minimum=0, maximum=1., value=0.7, step=0.1)
+                    id_length_ = gr.Slider(label= "Number of id images in total images" , minimum=2, maximum=4, value=2, step=1)
+                    # total_length_ = gr.Slider(label= "Number of total images", minimum=1, maximum=20, value=1, step=1)
+                    seed_ = gr.Slider(label="Seed", minimum=-1, maximum=MAX_SEED, value=0, step=1)
+                    num_steps = gr.Slider( 
+                        label="Number of sample steps",
+                        minimum=20,
+                        maximum=100,
+                        step=1,
+                        value=50,
+                    )
+                    G_height = gr.Slider( 
+                        label="height",
+                        minimum=256,
+                        maximum=1024,
+                        step=32,
+                        value=768,
+                    )
+                    G_width = gr.Slider( 
+                        label="width",
+                        minimum=256,
+                        maximum=1024,
+                        step=32,
+                        value=768,
+                    )
+                    comic_type = gr.Radio(["No typesetting (default)", "Four Pannel", "Classic Comic Style"], value = "Classic Comic Style", label="Typesetting Style", info="Select the typesetting style ")
+                    guidance_scale = gr.Slider(
+                        label="Guidance scale",
+                        minimum=0.1,
+                        maximum=10.0,
+                        step=0.1,
+                        value=5,
+                    )
+                    style_strength_ratio = gr.Slider(
+                        label="Style strength of Ref Image (%)",
+                        minimum=15,
+                        maximum=50,
+                        step=1,
+                        value=20,
+                        visible=False
+                    )
+                    Ip_Adapter_Strength = gr.Slider(
+                        label="Ip_Adapter_Strength",
+                        minimum=0,
+                        maximum=1,
+                        step=0.1,
+                        value=0.5,
+                        visible=False
+                    )
+                final_run_btn = gr.Button("Generate ! 😺")
+
+
+        with gr.Column():
+            out_image = gr.Gallery(label="Result", columns=2, height='auto')
+            generated_information = gr.Markdown(label="Generation Details", value="",visible=False)
+            gr.Markdown(version)
+    model_type.change(fn = change_visiale_by_model_type , inputs = model_type, outputs=[control_image_input,style_strength_ratio,Ip_Adapter_Strength])
+    files.upload(fn=swap_to_gallery, inputs=files, outputs=[uploaded_files, clear_button, files])
+    remove_and_reupload.click(fn=remove_back_to_files, outputs=[uploaded_files, clear_button, files])
+
+    final_run_btn.click(fn=set_text_unfinished, outputs = generated_information
+    ).then(process_generation, inputs=[sd_type,model_type,files, num_steps,style, Ip_Adapter_Strength,style_strength_ratio, guidance_scale, seed_, sa32_, sa64_, id_length_, general_prompt, negative_prompt, prompt_array,G_height,G_width,comic_type], outputs=out_image
+    ).then(fn=set_text_finished,outputs = generated_information)
+
+
+    gr.Examples(
+        examples=[
+            [1,0.5,0.5,3,"a woman img, wearing a white T-shirt, blue loose hair",
+                   "bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs",
+                   array2string(["wake up in the bed",
+                                "have breakfast",
+                                "is on the road, go to company",
+                                "work in the company",
+                                "Take a walk next to the company at noon",
+                                "lying in bed at night"]),
+                                "Japanese Anime",  "Using Ref Images",get_image_path_list('./examples/taylor'),768,768
+                ],
+                [0,0.5,0.5,2,"a man, wearing black jacket",
+                   "bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs",
+                   array2string(["wake up in the bed",
+                                "have breakfast",
+                                "is on the road, go to the company,  close look",
+                                "work in the company",
+                                "laughing happily",
+                                "lying in bed at night"
+                                ]),
+                                "Japanese Anime","Only Using Textual Description",get_image_path_list('./examples/taylor'),768,768
+                ],
+                [0,0.3,0.5,2,"a girl, wearing white shirt, black skirt, black tie, yellow hair",
+                   "bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs",
+                    array2string([
+                            "at home #at home, began to go to drawing",
+                            "sitting alone on a park bench.",
+                            "reading a book on a park bench.",
+                            "[NC]A squirrel approaches, peeking over the bench. ",
+                            "look around in the park. # She looks around and enjoys the beauty of nature.",
+                            "[NC]leaf falls from the tree, landing on the sketchbook.",
+                            "picks up the leaf, examining its details closely.",
+                            "starts sketching the leaf with intricate lines.",
+                            "holds up the sketch drawing of the leaf.",
+                            "[NC]The brown squirrel appear.",
+                            "is very happy # She is very happy to see the squirrel again",
+                            "[NC]The brown squirrel takes the cracker and scampers up a tree. # She gives the squirrel cracker",
+                            "laughs and tucks the leaf into her book as a keepsake.",
+                            "ready to leave.",]),
+                    "Japanese Anime","Only Using Textual Description",get_image_path_list('./examples/taylor'),768,768
+                ]
+                ],
+        inputs=[seed_, sa32_, sa64_, id_length_,  general_prompt, negative_prompt, prompt_array,style,model_type,files,G_height,G_width],
+        # outputs=[post_sketch, binary_matrixes, *color_row, *colors, *prompts, gen_prompt_vis, general_prompt, seed_],
+        # run_on_click=True,
+        label='😺 Examples 😺',
+    )
+    gr.Markdown(article)
+
+    # demo.load(None, None, None, _js=load_js)
+
+demo.launch(server_name="0.0.0.0", share = True if use_va else False)

cog.yaml

@@ -0,0 +1,23 @@
+# Configuration for Cog ⚙️
+# Reference: https://cog.run/yaml
+
+build:
+  gpu: true
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+  python_version: "3.11"
+  python_packages:
+    - xformers==0.0.20
+    - torch==2.0.1
+    - torchvision==0.15.2
+    - diffusers==0.25.0
+    - transformers==4.36.2
+    - gradio==3.48.0
+    - accelerate
+    - safetensors
+    - peft
+    - Pillow==9.5.0
+  run:
+    - curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/download/v0.6.0/pget_linux_x86_64" && chmod +x /usr/local/bin/pget
+predict: "predict.py:Predictor"

config/models.yaml

@@ -0,0 +1,19 @@
+Juggernaut: 
+  path: "https://huggingface.co/RunDiffusion/Juggernaut-XL-v9/blob/main/Juggernaut-XL_v9_RunDiffusionPhoto_v2.safetensors"
+  single_files: true   ### if true, is a civitai model
+  use_safetensors: true
+
+RealVision:
+  path: "SG161222/RealVisXL_V4.0"
+  single_files: false
+  use_safetensors: true
+
+SDXL:
+  path: "stabilityai/stable-diffusion-xl-base-1.0"
+  single_files: false
+  use_safetensors: true
+
+Unstable:
+  path: "stablediffusionapi/sdxl-unstable-diffusers-y"
+  single_files: false
+  use_safetensors: false

gradio_app_sdxl_specific_id_low_vram.py

@@ -0,0 +1,1346 @@
+from this import d
+import gradio as gr
+import numpy as np
+import torch
+import gc
+import copy
+import os
+import random
+import datetime
+from PIL import ImageFont
+from utils.gradio_utils import (
+    character_to_dict,
+    process_original_prompt,
+    get_ref_character,
+    cal_attn_mask_xl,
+    cal_attn_indice_xl_effcient_memory,
+    is_torch2_available,
+)
+
+if is_torch2_available():
+    from utils.gradio_utils import AttnProcessor2_0 as AttnProcessor
+else:
+    from utils.gradio_utils import AttnProcessor
+from huggingface_hub import hf_hub_download
+from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import (
+    StableDiffusionXLPipeline,
+)
+from diffusers.schedulers.scheduling_ddim import DDIMScheduler
+import torch.nn.functional as F
+from diffusers.utils.loading_utils import load_image
+from utils.utils import get_comic
+from utils.style_template import styles
+from utils.load_models_utils import get_models_dict, load_models
+
+STYLE_NAMES = list(styles.keys())
+DEFAULT_STYLE_NAME = "Japanese Anime"
+global models_dict
+
+models_dict = get_models_dict()
+
+# Automatically select the device
+device = (
+    "cuda"
+    if torch.cuda.is_available()
+    else "mps" if torch.backends.mps.is_available() else "cpu"
+)
+print(f"@@device:{device}")
+
+
+# check if the file exists locally at a specified path before downloading it.
+# if the file doesn't exist, it uses `hf_hub_download` to download the file
+# and optionally move it to a specific directory. If the file already
+# exists, it simply uses the local path.
+local_dir = "data/"
+photomaker_local_path = f"{local_dir}photomaker-v1.bin"
+if not os.path.exists(photomaker_local_path):
+    photomaker_path = hf_hub_download(
+        repo_id="TencentARC/PhotoMaker",
+        filename="photomaker-v1.bin",
+        repo_type="model",
+        local_dir=local_dir,
+    )
+else:
+    photomaker_path = photomaker_local_path
+
+MAX_SEED = np.iinfo(np.int32).max
+
+
+def setup_seed(seed):
+    torch.manual_seed(seed)
+    if device == "cuda":
+        torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    torch.backends.cudnn.deterministic = True
+
+
+def set_text_unfinished():
+    return gr.update(
+        visible=True,
+        value="<h3>(Not Finished) Generating ···  The intermediate results will be shown.</h3>",
+    )
+
+
+def set_text_finished():
+    return gr.update(visible=True, value="<h3>Generation Finished</h3>")
+
+
+#################################################
+def get_image_path_list(folder_name):
+    image_basename_list = os.listdir(folder_name)
+    image_path_list = sorted(
+        [os.path.join(folder_name, basename) for basename in image_basename_list]
+    )
+    return image_path_list
+
+
+#################################################
+class SpatialAttnProcessor2_0(torch.nn.Module):
+    r"""
+    Attention processor for IP-Adapater for PyTorch 2.0.
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+        text_context_len (`int`, defaults to 77):
+            The context length of the text features.
+        scale (`float`, defaults to 1.0):
+            the weight scale of image prompt.
+    """
+
+    def __init__(
+        self,
+        hidden_size=None,
+        cross_attention_dim=None,
+        id_length=4,
+        device=device,
+        dtype=torch.float16,
+    ):
+        super().__init__()
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+            )
+        self.device = device
+        self.dtype = dtype
+        self.hidden_size = hidden_size
+        self.cross_attention_dim = cross_attention_dim
+        self.total_length = id_length + 1
+        self.id_length = id_length
+        self.id_bank = {}
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        # un_cond_hidden_states, cond_hidden_states = hidden_states.chunk(2)
+        # un_cond_hidden_states = self.__call2__(attn, un_cond_hidden_states,encoder_hidden_states,attention_mask,temb)
+        # 生成一个0到1之间的随机数
+        global total_count, attn_count, cur_step, indices1024, indices4096
+        global sa32, sa64
+        global write
+        global height, width
+        global character_dict, character_index_dict, invert_character_index_dict, cur_character, ref_indexs_dict, ref_totals, cur_character
+        if attn_count == 0 and cur_step == 0:
+            indices1024, indices4096 = cal_attn_indice_xl_effcient_memory(
+                self.total_length,
+                self.id_length,
+                sa32,
+                sa64,
+                height,
+                width,
+                device=self.device,
+                dtype=self.dtype,
+            )
+        if write:
+            assert len(cur_character) == 1
+            if hidden_states.shape[1] == (height // 32) * (width // 32):
+                indices = indices1024
+            else:
+                indices = indices4096
+            # print(f"white:{cur_step}")
+            total_batch_size, nums_token, channel = hidden_states.shape
+            img_nums = total_batch_size // 2
+            hidden_states = hidden_states.reshape(-1, img_nums, nums_token, channel)
+            # print(img_nums,len(indices),hidden_states.shape,self.total_length)
+            if cur_character[0] not in self.id_bank:
+                self.id_bank[cur_character[0]] = {}
+            self.id_bank[cur_character[0]][cur_step] = [
+                hidden_states[:, img_ind, indices[img_ind], :]
+                .reshape(2, -1, channel)
+                .clone()
+                for img_ind in range(img_nums)
+            ]
+            hidden_states = hidden_states.reshape(-1, nums_token, channel)
+            # self.id_bank[cur_step] = [hidden_states[:self.id_length].clone(), hidden_states[self.id_length:].clone()]
+        else:
+            # encoder_hidden_states = torch.cat((self.id_bank[cur_step][0].to(self.device),self.id_bank[cur_step][1].to(self.device)))
+            # TODO: ADD Multipersion Control
+            encoder_arr = []
+            for character in cur_character:
+                encoder_arr = encoder_arr + [
+                    tensor.to(self.device)
+                    for tensor in self.id_bank[character][cur_step]
+                ]
+        # 判断随机数是否大于0.5
+        if cur_step < 1:
+            hidden_states = self.__call2__(
+                attn, hidden_states, None, attention_mask, temb
+            )
+        else:  # 256 1024 4096
+            random_number = random.random()
+            if cur_step < 20:
+                rand_num = 0.3
+            else:
+                rand_num = 0.1
+            # print(f"hidden state shape {hidden_states.shape[1]}")
+            if random_number > rand_num:
+                if hidden_states.shape[1] == (height // 32) * (width // 32):
+                    indices = indices1024
+                else:
+                    indices = indices4096
+                # print("before attention",hidden_states.shape,attention_mask.shape,encoder_hidden_states.shape if encoder_hidden_states is not None else "None")
+                if write:
+                    total_batch_size, nums_token, channel = hidden_states.shape
+                    img_nums = total_batch_size // 2
+                    hidden_states = hidden_states.reshape(
+                        -1, img_nums, nums_token, channel
+                    )
+                    encoder_arr = [
+                        hidden_states[:, img_ind, indices[img_ind], :].reshape(
+                            2, -1, channel
+                        )
+                        for img_ind in range(img_nums)
+                    ]
+                    for img_ind in range(img_nums):
+                        # print(img_nums)
+                        # assert img_nums != 1
+                        img_ind_list = [i for i in range(img_nums)]
+                        # print(img_ind_list,img_ind)
+                        img_ind_list.remove(img_ind)
+                        # print(img_ind,invert_character_index_dict[img_ind])
+                        # print(character_index_dict[invert_character_index_dict[img_ind]])
+                        # print(img_ind_list)
+                        # print(img_ind,img_ind_list)
+                        encoder_hidden_states_tmp = torch.cat(
+                            [encoder_arr[img_ind] for img_ind in img_ind_list]
+                            + [hidden_states[:, img_ind, :, :]],
+                            dim=1,
+                        )
+
+                        hidden_states[:, img_ind, :, :] = self.__call2__(
+                            attn,
+                            hidden_states[:, img_ind, :, :],
+                            encoder_hidden_states_tmp,
+                            None,
+                            temb,
+                        )
+                else:
+                    _, nums_token, channel = hidden_states.shape
+                    # img_nums = total_batch_size // 2
+                    # encoder_hidden_states = encoder_hidden_states.reshape(-1,img_nums,nums_token,channel)
+                    hidden_states = hidden_states.reshape(2, -1, nums_token, channel)
+                    # print(len(indices))
+                    # encoder_arr = [encoder_hidden_states[:,img_ind,indices[img_ind],:].reshape(2,-1,channel) for img_ind in range(img_nums)]
+                    encoder_hidden_states_tmp = torch.cat(
+                        encoder_arr + [hidden_states[:, 0, :, :]], dim=1
+                    )
+                    # print(len(encoder_arr),encoder_hidden_states_tmp.shape)
+                    hidden_states[:, 0, :, :] = self.__call2__(
+                        attn,
+                        hidden_states[:, 0, :, :],
+                        encoder_hidden_states_tmp,
+                        None,
+                        temb,
+                    )
+                hidden_states = hidden_states.reshape(-1, nums_token, channel)
+            else:
+                hidden_states = self.__call2__(
+                    attn, hidden_states, None, attention_mask, temb
+                )
+        attn_count += 1
+        if attn_count == total_count:
+            attn_count = 0
+            cur_step += 1
+            indices1024, indices4096 = cal_attn_indice_xl_effcient_memory(
+                self.total_length,
+                self.id_length,
+                sa32,
+                sa64,
+                height,
+                width,
+                device=self.device,
+                dtype=self.dtype,
+            )
+
+        return hidden_states
+
+    def __call2__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+
+        batch_size, sequence_length, channel = hidden_states.shape
+        # print(hidden_states.shape)
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(
+                attention_mask, sequence_length, batch_size
+            )
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(
+                batch_size, attn.heads, -1, attention_mask.shape[-1]
+            )
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
+                1, 2
+            )
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states  # B, N, C
+        # else:
+        #     encoder_hidden_states = encoder_hidden_states.view(-1,self.id_length+1,sequence_length,channel).reshape(-1,(self.id_length+1) * sequence_length,channel)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size, -1, attn.heads * head_dim
+        )
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+def set_attention_processor(unet, id_length, is_ipadapter=False):
+    global attn_procs
+    attn_procs = {}
+    for name in unet.attn_processors.keys():
+        cross_attention_dim = (
+            None
+            if name.endswith("attn1.processor")
+            else unet.config.cross_attention_dim
+        )
+        if name.startswith("mid_block"):
+            hidden_size = unet.config.block_out_channels[-1]
+        elif name.startswith("up_blocks"):
+            block_id = int(name[len("up_blocks.")])
+            hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+        elif name.startswith("down_blocks"):
+            block_id = int(name[len("down_blocks.")])
+            hidden_size = unet.config.block_out_channels[block_id]
+        if cross_attention_dim is None:
+            if name.startswith("up_blocks"):
+                attn_procs[name] = SpatialAttnProcessor2_0(id_length=id_length)
+            else:
+                attn_procs[name] = AttnProcessor()
+        else:
+            if is_ipadapter:
+                attn_procs[name] = IPAttnProcessor2_0(
+                    hidden_size=hidden_size,
+                    cross_attention_dim=cross_attention_dim,
+                    scale=1,
+                    num_tokens=4,
+                ).to(unet.device, dtype=torch.float16)
+            else:
+                attn_procs[name] = AttnProcessor()
+
+    unet.set_attn_processor(copy.deepcopy(attn_procs))
+
+
+#################################################
+#################################################
+canvas_html = "<div id='canvas-root' style='max-width:400px; margin: 0 auto'></div>"
+load_js = """
+async () => {
+const url = "https://huggingface.co/datasets/radames/gradio-components/raw/main/sketch-canvas.js"
+fetch(url)
+  .then(res => res.text())
+  .then(text => {
+    const script = document.createElement('script');
+    script.type = "module"
+    script.src = URL.createObjectURL(new Blob([text], { type: 'application/javascript' }));
+    document.head.appendChild(script);
+  });
+}
+"""
+
+get_js_colors = """
+async (canvasData) => {
+  const canvasEl = document.getElementById("canvas-root");
+  return [canvasEl._data]
+}
+"""
+
+css = """
+#color-bg{display:flex;justify-content: center;align-items: center;}
+.color-bg-item{width: 100%; height: 32px}
+#main_button{width:100%}
+<style>
+"""
+
+
+def save_single_character_weights(unet, character, description, filepath):
+    """
+    保存 attention_processor 类中的 id_bank GPU Tensor 列表到指定文件中。
+    参数:
+    - model: 包含 attention_processor 类实例的模型。
+    - filepath: 权重要保存到的文件路径。
+    """
+    weights_to_save = {}
+    weights_to_save["description"] = description
+    weights_to_save["character"] = character
+    for attn_name, attn_processor in unet.attn_processors.items():
+        if isinstance(attn_processor, SpatialAttnProcessor2_0):
+            # 将每个 Tensor 转到 CPU 并转为列表，以确保它可以被序列化
+            weights_to_save[attn_name] = {}
+            for step_key in attn_processor.id_bank[character].keys():
+                weights_to_save[attn_name][step_key] = [
+                    tensor.cpu()
+                    for tensor in attn_processor.id_bank[character][step_key]
+                ]
+    # 使用torch.save保存权重
+    torch.save(weights_to_save, filepath)
+
+
+def load_single_character_weights(unet, filepath):
+    """
+    从指定文件中加载权重到 attention_processor 类的 id_bank 中。
+    参数:
+    - model: 包含 attention_processor 类实例的模型。
+    - filepath: 权重文件的路径。
+    """
+    # 使用torch.load来读取权重
+    weights_to_load = torch.load(filepath, map_location=torch.device("cpu"))
+    character = weights_to_load["character"]
+    description = weights_to_load["description"]
+    for attn_name, attn_processor in unet.attn_processors.items():
+        if isinstance(attn_processor, SpatialAttnProcessor2_0):
+            # 转移权重到GPU（如果GPU可用的话）并赋值给id_bank
+            attn_processor.id_bank[character] = {}
+            for step_key in weights_to_load[attn_name].keys():
+                attn_processor.id_bank[character][step_key] = [
+                    tensor.to(unet.device)
+                    for tensor in weights_to_load[attn_name][step_key]
+                ]
+
+
+def save_results(unet, img_list):
+
+    timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+    folder_name = f"results/{timestamp}"
+    weight_folder_name = f"{folder_name}/weights"
+    # 创建文件夹
+    if not os.path.exists(folder_name):
+        os.makedirs(folder_name)
+        os.makedirs(weight_folder_name)
+
+    for idx, img in enumerate(img_list):
+        file_path = os.path.join(folder_name, f"image_{idx}.png")  # 图片文件名
+        img.save(file_path)
+    global character_dict
+    # for char in character_dict:
+    #     description = character_dict[char]
+    #     save_single_character_weights(unet,char,description,os.path.join(weight_folder_name, f'{char}.pt'))
+
+
+#################################################
+title = r"""
+<h1 align="center">StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation</h1>
+"""
+
+description = r"""
+<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/HVision-NKU/StoryDiffusion' target='_blank'><b>StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation</b></a>.<br>
+❗️❗️❗️[<b>Important</b>] Personalization steps:<br>
+1️⃣ Enter a Textual Description for Character, if you add the Ref-Image, making sure to <b>follow the class word</b> you want to customize with the <b>trigger word</b>: `img`, such as: `man img` or `woman img` or `girl img`.<br>
+2️⃣ Enter the prompt array, each line corrsponds to one generated image.<br>
+3️⃣ Choose your preferred style template.<br>
+4️⃣ Click the <b>Submit</b> button to start customizing.
+"""
+
+article = r"""
+
+If StoryDiffusion is helpful, please help to ⭐ the <a href='https://github.com/HVision-NKU/StoryDiffusion' target='_blank'>Github Repo</a>. Thanks! 
+[![GitHub Stars](https://img.shields.io/github/stars/HVision-NKU/StoryDiffusion?style=social)](https://github.com/HVision-NKU/StoryDiffusion)
+---
+📝 **Citation**
+<br>
+If our work is useful for your research, please consider citing:
+
+```bibtex
+@article{Zhou2024storydiffusion,
+  title={StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation},
+  author={Zhou, Yupeng and Zhou, Daquan and Cheng, Ming-Ming and Feng, Jiashi and Hou, Qibin},
+  year={2024}
+}
+```
+📋 **License**
+<br>
+Apache-2.0 LICENSE. 
+
+📧 **Contact**
+<br>
+If you have any questions, please feel free to reach me out at <b>ypzhousdu@gmail.com</b>.
+"""
+version = r"""
+<h3 align="center">StoryDiffusion Version 0.02 (test version)</h3>
+
+<h5 >1. Support image ref image. (Cartoon Ref image is not support now)</h5>
+<h5 >2. Support Typesetting Style and Captioning.(By default, the prompt is used as the caption for each image. If you need to change the caption, add a # at the end of each line. Only the part after the # will be added as a caption to the image.)</h5>
+<h5 >3. [NC]symbol (The [NC] symbol is used as a flag to indicate that no characters should be present in the generated scene images. If you want do that, prepend the "[NC]" at the beginning of the line. For example, to generate a scene of falling leaves without any character, write: "[NC] The leaves are falling.")</h5>
+<h5 align="center">Tips: </h4>
+"""
+#################################################
+global attn_count, total_count, id_length, total_length, cur_step, cur_model_type
+global write
+global sa32, sa64
+global height, width
+attn_count = 0
+total_count = 0
+cur_step = 0
+id_length = 4
+total_length = 5
+cur_model_type = ""
+global attn_procs, unet
+attn_procs = {}
+###
+write = False
+###
+sa32 = 0.5
+sa64 = 0.5
+height = 768
+width = 768
+###
+global pipe
+global sd_model_path
+pipe = None
+sd_model_path = models_dict["Unstable"]["path"]  # "SG161222/RealVisXL_V4.0"
+single_files = models_dict["Unstable"]["single_files"]
+### LOAD Stable Diffusion Pipeline
+if single_files:
+    pipe = StableDiffusionXLPipeline.from_single_file(
+        sd_model_path, torch_dtype=torch.float16
+    )
+else:
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        sd_model_path, torch_dtype=torch.float16, use_safetensors=False
+    )
+pipe = pipe.to(device)
+pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
+# pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+pipe.scheduler.set_timesteps(50)
+pipe.enable_vae_slicing()
+if device != "mps":
+    pipe.enable_model_cpu_offload()
+unet = pipe.unet
+cur_model_type = "Unstable" + "-" + "original"
+### Insert PairedAttention
+for name in unet.attn_processors.keys():
+    cross_attention_dim = (
+        None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
+    )
+    if name.startswith("mid_block"):
+        hidden_size = unet.config.block_out_channels[-1]
+    elif name.startswith("up_blocks"):
+        block_id = int(name[len("up_blocks.")])
+        hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+    elif name.startswith("down_blocks"):
+        block_id = int(name[len("down_blocks.")])
+        hidden_size = unet.config.block_out_channels[block_id]
+    if cross_attention_dim is None and (name.startswith("up_blocks")):
+        attn_procs[name] = SpatialAttnProcessor2_0(id_length=id_length)
+        total_count += 1
+    else:
+        attn_procs[name] = AttnProcessor()
+print("successsfully load paired self-attention")
+print(f"number of the processor : {total_count}")
+unet.set_attn_processor(copy.deepcopy(attn_procs))
+global mask1024, mask4096
+mask1024, mask4096 = cal_attn_mask_xl(
+    total_length,
+    id_length,
+    sa32,
+    sa64,
+    height,
+    width,
+    device=device,
+    dtype=torch.float16,
+)
+
+######### Gradio Fuction #############
+
+
+def swap_to_gallery(images):
+    return (
+        gr.update(value=images, visible=True),
+        gr.update(visible=True),
+        gr.update(visible=False),
+    )
+
+
+def upload_example_to_gallery(images, prompt, style, negative_prompt):
+    return (
+        gr.update(value=images, visible=True),
+        gr.update(visible=True),
+        gr.update(visible=False),
+    )
+
+
+def remove_back_to_files():
+    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
+
+
+def remove_tips():
+    return gr.update(visible=False)
+
+
+def apply_style_positive(style_name: str, positive: str):
+    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
+    return p.replace("{prompt}", positive)
+
+
+def apply_style(style_name: str, positives: list, negative: str = ""):
+    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
+    return [
+        p.replace("{prompt}", positive) for positive in positives
+    ], n + " " + negative
+
+
+def change_visiale_by_model_type(_model_type):
+    if _model_type == "Only Using Textual Description":
+        return (
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=False),
+        )
+    elif _model_type == "Using Ref Images":
+        return (
+            gr.update(visible=True),
+            gr.update(visible=True),
+            gr.update(visible=False),
+        )
+    else:
+        raise ValueError("Invalid model type", _model_type)
+
+
+def load_character_files(character_files: str):
+    if character_files == "":
+        raise gr.Error("Please set a character file!")
+    character_files_arr = character_files.splitlines()
+    primarytext = []
+    for character_file_name in character_files_arr:
+        character_file = torch.load(
+            character_file_name, map_location=torch.device("cpu")
+        )
+        primarytext.append(character_file["character"] + character_file["description"])
+    return array2string(primarytext)
+
+
+def load_character_files_on_running(unet, character_files: str):
+    if character_files == "":
+        return False
+    character_files_arr = character_files.splitlines()
+    for character_file in character_files_arr:
+        load_single_character_weights(unet, character_file)
+    return True
+
+
+######### Image Generation ##############
+def process_generation(
+    _sd_type,
+    _model_type,
+    _upload_images,
+    _num_steps,
+    style_name,
+    _Ip_Adapter_Strength,
+    _style_strength_ratio,
+    guidance_scale,
+    seed_,
+    sa32_,
+    sa64_,
+    id_length_,
+    general_prompt,
+    negative_prompt,
+    prompt_array,
+    G_height,
+    G_width,
+    _comic_type,
+    font_choice,
+    _char_files,
+):  # Corrected font_choice usage
+    if len(general_prompt.splitlines()) >= 3:
+        raise gr.Error(
+            "Support for more than three characters is temporarily unavailable due to VRAM limitations, but this issue will be resolved soon."
+        )
+    _model_type = "Photomaker" if _model_type == "Using Ref Images" else "original"
+    if _model_type == "Photomaker" and "img" not in general_prompt:
+        raise gr.Error(
+            'Please add the triger word " img "  behind the class word you want to customize, such as: man img or woman img'
+        )
+    if _upload_images is None and _model_type != "original":
+        raise gr.Error(f"Cannot find any input face image!")
+    global sa32, sa64, id_length, total_length, attn_procs, unet, cur_model_type
+    global write
+    global cur_step, attn_count
+    global height, width
+    height = G_height
+    width = G_width
+    global pipe
+    global sd_model_path, models_dict
+    sd_model_path = models_dict[_sd_type]
+    use_safe_tensor = True
+    for attn_processor in pipe.unet.attn_processors.values():
+        if isinstance(attn_processor, SpatialAttnProcessor2_0):
+            for values in attn_processor.id_bank.values():
+                del values
+            attn_processor.id_bank = {}
+            attn_processor.id_length = id_length
+            attn_processor.total_length = id_length + 1
+    gc.collect()
+    torch.cuda.empty_cache()
+    if cur_model_type != _sd_type + "-" + _model_type:
+        # apply the style template
+        ##### load pipe
+        del pipe
+        gc.collect()
+        if device == "cuda":
+            torch.cuda.empty_cache()
+        model_info = models_dict[_sd_type]
+        model_info["model_type"] = _model_type
+        pipe = load_models(model_info, device=device, photomaker_path=photomaker_path)
+        set_attention_processor(pipe.unet, id_length_, is_ipadapter=False)
+        ##### ########################
+        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+        pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
+        cur_model_type = _sd_type + "-" + _model_type
+        pipe.enable_vae_slicing()
+        if device != "mps":
+            pipe.enable_model_cpu_offload()
+    else:
+        unet = pipe.unet
+        # unet.set_attn_processor(copy.deepcopy(attn_procs))
+
+    load_chars = load_character_files_on_running(unet, character_files=_char_files)
+
+    prompts = prompt_array.splitlines()
+    global character_dict, character_index_dict, invert_character_index_dict, ref_indexs_dict, ref_totals
+    character_dict, character_list = character_to_dict(general_prompt)
+
+    start_merge_step = int(float(_style_strength_ratio) / 100 * _num_steps)
+    if start_merge_step > 30:
+        start_merge_step = 30
+    print(f"start_merge_step:{start_merge_step}")
+    generator = torch.Generator(device=device).manual_seed(seed_)
+    sa32, sa64 = sa32_, sa64_
+    id_length = id_length_
+    clipped_prompts = prompts[:]
+    nc_indexs = []
+    for ind, prompt in enumerate(clipped_prompts):
+        if "[NC]" in prompt:
+            nc_indexs.append(ind)
+            if ind < id_length:
+                raise gr.Error(
+                    f"The first {id_length} row is id prompts, cannot use [NC]!"
+                )
+    prompts = [
+        prompt if "[NC]" not in prompt else prompt.replace("[NC]", "")
+        for prompt in clipped_prompts
+    ]
+
+    prompts = [
+        prompt.rpartition("#")[0] if "#" in prompt else prompt for prompt in prompts
+    ]
+    print(prompts)
+    # id_prompts = prompts[:id_length]
+    (
+        character_index_dict,
+        invert_character_index_dict,
+        replace_prompts,
+        ref_indexs_dict,
+        ref_totals,
+    ) = process_original_prompt(character_dict, prompts.copy(), id_length)
+    if _model_type != "original":
+        input_id_images_dict = {}
+        if len(_upload_images) != len(character_dict.keys()):
+            raise gr.Error(
+                f"You upload images({len(_upload_images)}) is not equal to the number of characters({len(character_dict.keys())})!"
+            )
+        for ind, img in enumerate(_upload_images):
+            input_id_images_dict[character_list[ind]] = [load_image(img)]
+    print(character_dict)
+    print(character_index_dict)
+    print(invert_character_index_dict)
+    # real_prompts = prompts[id_length:]
+    if device == "cuda":
+        torch.cuda.empty_cache()
+    write = True
+    cur_step = 0
+
+    attn_count = 0
+    # id_prompts, negative_prompt = apply_style(style_name, id_prompts, negative_prompt)
+    # print(id_prompts)
+    setup_seed(seed_)
+    total_results = []
+    id_images = []
+    results_dict = {}
+    global cur_character
+    if not load_chars:
+        for character_key in character_dict.keys():
+            cur_character = [character_key]
+            ref_indexs = ref_indexs_dict[character_key]
+            print(character_key, ref_indexs)
+            current_prompts = [replace_prompts[ref_ind] for ref_ind in ref_indexs]
+            print(current_prompts)
+            setup_seed(seed_)
+            generator = torch.Generator(device=device).manual_seed(seed_)
+            cur_step = 0
+            cur_positive_prompts, negative_prompt = apply_style(
+                style_name, current_prompts, negative_prompt
+            )
+            if _model_type == "original":
+                id_images = pipe(
+                    cur_positive_prompts,
+                    num_inference_steps=_num_steps,
+                    guidance_scale=guidance_scale,
+                    height=height,
+                    width=width,
+                    negative_prompt=negative_prompt,
+                    generator=generator,
+                ).images
+            elif _model_type == "Photomaker":
+                id_images = pipe(
+                    cur_positive_prompts,
+                    input_id_images=input_id_images_dict[character_key],
+                    num_inference_steps=_num_steps,
+                    guidance_scale=guidance_scale,
+                    start_merge_step=start_merge_step,
+                    height=height,
+                    width=width,
+                    negative_prompt=negative_prompt,
+                    generator=generator,
+                ).images
+            else:
+                raise NotImplementedError(
+                    "You should choice between original and Photomaker!",
+                    f"But you choice {_model_type}",
+                )
+
+            # total_results = id_images + total_results
+            # yield total_results
+            print(id_images)
+            for ind, img in enumerate(id_images):
+                print(ref_indexs[ind])
+                results_dict[ref_indexs[ind]] = img
+            # real_images = []
+            yield [results_dict[ind] for ind in results_dict.keys()]
+    write = False
+    if not load_chars:
+        real_prompts_inds = [
+            ind for ind in range(len(prompts)) if ind not in ref_totals
+        ]
+    else:
+        real_prompts_inds = [ind for ind in range(len(prompts))]
+    print(real_prompts_inds)
+
+    for real_prompts_ind in real_prompts_inds:
+        real_prompt = replace_prompts[real_prompts_ind]
+        cur_character = get_ref_character(prompts[real_prompts_ind], character_dict)
+        print(cur_character, real_prompt)
+        setup_seed(seed_)
+        if len(cur_character) > 1 and _model_type == "Photomaker":
+            raise gr.Error(
+                "Temporarily Not Support Multiple character in Ref Image Mode!"
+            )
+        generator = torch.Generator(device=device).manual_seed(seed_)
+        cur_step = 0
+        real_prompt = apply_style_positive(style_name, real_prompt)
+        if _model_type == "original":
+            results_dict[real_prompts_ind] = pipe(
+                real_prompt,
+                num_inference_steps=_num_steps,
+                guidance_scale=guidance_scale,
+                height=height,
+                width=width,
+                negative_prompt=negative_prompt,
+                generator=generator,
+            ).images[0]
+        elif _model_type == "Photomaker":
+            results_dict[real_prompts_ind] = pipe(
+                real_prompt,
+                input_id_images=(
+                    input_id_images_dict[cur_character[0]]
+                    if real_prompts_ind not in nc_indexs
+                    else input_id_images_dict[character_list[0]]
+                ),
+                num_inference_steps=_num_steps,
+                guidance_scale=guidance_scale,
+                start_merge_step=start_merge_step,
+                height=height,
+                width=width,
+                negative_prompt=negative_prompt,
+                generator=generator,
+                nc_flag=True if real_prompts_ind in nc_indexs else False,
+            ).images[0]
+        else:
+            raise NotImplementedError(
+                "You should choice between original and Photomaker!",
+                f"But you choice {_model_type}",
+            )
+        yield [results_dict[ind] for ind in results_dict.keys()]
+    total_results = [results_dict[ind] for ind in range(len(prompts))]
+    if _comic_type != "No typesetting (default)":
+        captions = prompt_array.splitlines()
+        captions = [caption.replace("[NC]", "") for caption in captions]
+        captions = [
+            caption.split("#")[-1] if "#" in caption else caption
+            for caption in captions
+        ]
+        font_path = os.path.join("fonts", font_choice)
+        font = ImageFont.truetype(font_path, int(45))
+        total_results = (
+            get_comic(total_results, _comic_type, captions=captions, font=font)
+            + total_results
+        )
+    save_results(pipe.unet, total_results)
+
+    yield total_results
+
+
+def array2string(arr):
+    stringtmp = ""
+    for i, part in enumerate(arr):
+        if i != len(arr) - 1:
+            stringtmp += part + "\n"
+        else:
+            stringtmp += part
+
+    return stringtmp
+
+
+#################################################
+#################################################
+### define the interface
+
+with gr.Blocks(css=css) as demo:
+    binary_matrixes = gr.State([])
+    color_layout = gr.State([])
+
+    # gr.Markdown(logo)
+    gr.Markdown(title)
+    gr.Markdown(description)
+
+    with gr.Row():
+        with gr.Group(elem_id="main-image"):
+
+            prompts = []
+            colors = []
+
+            with gr.Column(visible=True) as gen_prompt_vis:
+                sd_type = gr.Dropdown(
+                    choices=list(models_dict.keys()),
+                    value="Unstable",
+                    label="sd_type",
+                    info="Select pretrained model",
+                )
+                model_type = gr.Radio(
+                    ["Only Using Textual Description", "Using Ref Images"],
+                    label="model_type",
+                    value="Only Using Textual Description",
+                    info="Control type of the Character",
+                )
+                with gr.Group(visible=False) as control_image_input:
+                    files = gr.Files(
+                        label="Drag (Select) 1 or more photos of your face",
+                        file_types=["image"],
+                    )
+                    uploaded_files = gr.Gallery(
+                        label="Your images",
+                        visible=False,
+                        columns=5,
+                        rows=1,
+                        height=200,
+                    )
+                    with gr.Column(visible=False) as clear_button:
+                        remove_and_reupload = gr.ClearButton(
+                            value="Remove and upload new ones",
+                            components=files,
+                            size="sm",
+                        )
+                general_prompt = gr.Textbox(
+                    value="",
+                    lines=2,
+                    label="(1) Textual Description for Character",
+                    interactive=True,
+                )
+                negative_prompt = gr.Textbox(
+                    value="", label="(2) Negative_prompt", interactive=True
+                )
+                style = gr.Dropdown(
+                    label="Style template",
+                    choices=STYLE_NAMES,
+                    value=DEFAULT_STYLE_NAME,
+                )
+                prompt_array = gr.Textbox(
+                    lines=3,
+                    value="",
+                    label="(3) Comic Description (each line corresponds to a frame).",
+                    interactive=True,
+                )
+                char_path = gr.Textbox(
+                    lines=2,
+                    value="",
+                    visible=False,
+                    label="(Optional) Character files",
+                    interactive=True,
+                )
+                char_btn = gr.Button("Load Character files", visible=False)
+                with gr.Accordion("(4) Tune the hyperparameters", open=True):
+                    font_choice = gr.Dropdown(
+                        label="Select Font",
+                        choices=[
+                            f for f in os.listdir("./fonts") if f.endswith(".ttf")
+                        ],
+                        value="Inkfree.ttf",
+                        info="Select font for the final slide.",
+                        interactive=True,
+                    )
+                    sa32_ = gr.Slider(
+                        label=" (The degree of Paired Attention at 32 x 32 self-attention layers) ",
+                        minimum=0,
+                        maximum=1.0,
+                        value=0.5,
+                        step=0.1,
+                    )
+                    sa64_ = gr.Slider(
+                        label=" (The degree of Paired Attention at 64 x 64 self-attention layers) ",
+                        minimum=0,
+                        maximum=1.0,
+                        value=0.5,
+                        step=0.1,
+                    )
+                    id_length_ = gr.Slider(
+                        label="Number of id images in total images",
+                        minimum=1,
+                        maximum=4,
+                        value=1,
+                        step=1,
+                    )
+                    with gr.Row():
+                        seed_ = gr.Slider(
+                            label="Seed", minimum=-1, maximum=MAX_SEED, value=0, step=1
+                        )
+                        randomize_seed_btn = gr.Button("🎲", size="sm")
+                    num_steps = gr.Slider(
+                        label="Number of sample steps",
+                        minimum=20,
+                        maximum=100,
+                        step=1,
+                        value=35,
+                    )
+                    G_height = gr.Slider(
+                        label="height",
+                        minimum=256,
+                        maximum=1024,
+                        step=32,
+                        value=768,
+                    )
+                    G_width = gr.Slider(
+                        label="width",
+                        minimum=256,
+                        maximum=1024,
+                        step=32,
+                        value=768,
+                    )
+                    comic_type = gr.Radio(
+                        [
+                            "No typesetting (default)",
+                            "Four Pannel",
+                            "Classic Comic Style",
+                        ],
+                        value="Classic Comic Style",
+                        label="Typesetting Style",
+                        info="Select the typesetting style ",
+                    )
+                    guidance_scale = gr.Slider(
+                        label="Guidance scale",
+                        minimum=0.1,
+                        maximum=10.0,
+                        step=0.1,
+                        value=5,
+                    )
+                    style_strength_ratio = gr.Slider(
+                        label="Style strength of Ref Image (%)",
+                        minimum=15,
+                        maximum=50,
+                        step=1,
+                        value=20,
+                        visible=False,
+                    )
+                    Ip_Adapter_Strength = gr.Slider(
+                        label="Ip_Adapter_Strength",
+                        minimum=0,
+                        maximum=1,
+                        step=0.1,
+                        value=0.5,
+                        visible=False,
+                    )
+                final_run_btn = gr.Button("Generate ! 😺")
+
+        with gr.Column():
+            out_image = gr.Gallery(label="Result", columns=2, height="auto")
+            generated_information = gr.Markdown(
+                label="Generation Details", value="", visible=False
+            )
+            gr.Markdown(version)
+    model_type.change(
+        fn=change_visiale_by_model_type,
+        inputs=model_type,
+        outputs=[control_image_input, style_strength_ratio, Ip_Adapter_Strength],
+    )
+    files.upload(
+        fn=swap_to_gallery, inputs=files, outputs=[uploaded_files, clear_button, files]
+    )
+    remove_and_reupload.click(
+        fn=remove_back_to_files, outputs=[uploaded_files, clear_button, files]
+    )
+    char_btn.click(fn=load_character_files, inputs=char_path, outputs=[general_prompt])
+
+    randomize_seed_btn.click(
+        fn=lambda: random.randint(-1, MAX_SEED),
+        inputs=[],
+        outputs=seed_,
+    )
+
+    final_run_btn.click(fn=set_text_unfinished, outputs=generated_information).then(
+        process_generation,
+        inputs=[
+            sd_type,
+            model_type,
+            files,
+            num_steps,
+            style,
+            Ip_Adapter_Strength,
+            style_strength_ratio,
+            guidance_scale,
+            seed_,
+            sa32_,
+            sa64_,
+            id_length_,
+            general_prompt,
+            negative_prompt,
+            prompt_array,
+            G_height,
+            G_width,
+            comic_type,
+            font_choice,
+            char_path,
+        ],
+        outputs=out_image,
+    ).then(fn=set_text_finished, outputs=generated_information)
+
+    gr.Examples(
+        examples=[
+            [
+                0,
+                0.5,
+                0.5,
+                2,
+                "[Bob] A man, wearing a black suit\n[Alice]a woman, wearing a white shirt",
+                "bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs",
+                array2string(
+                    [
+                        "[Bob] at home, read new paper #at home, The newspaper says there is a treasure house in the forest.",
+                        "[Bob] on the road, near the forest",
+                        "[Alice] is make a call at home # [Bob] invited [Alice] to join him on an adventure.",
+                        "[NC]A tiger appeared in the forest, at night ",
+                        "[NC] The car on the road, near the forest #They drives to the forest in search of treasure.",
+                        "[Bob] very frightened, open mouth, in the forest, at night",
+                        "[Alice] very frightened, open mouth, in the forest, at night",
+                        "[Bob]  and [Alice] running very fast, in the forest, at night",
+                        "[NC] A house in the forest, at night #Suddenly, They discovers the treasure house!",
+                        "[Bob]  and [Alice]  in the house filled with  treasure, laughing, at night #He is overjoyed inside the house.",
+                    ]
+                ),
+                "Comic book",
+                "Only Using Textual Description",
+                get_image_path_list("./examples/taylor"),
+                768,
+                768,
+            ],
+            [
+                0,
+                0.5,
+                0.5,
+                2,
+                "[Bob] A man img, wearing a black suit\n[Alice]a woman img, wearing a white shirt",
+                "bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs",
+                array2string(
+                    [
+                        "[Bob] at home, read new paper #at home, The newspaper says there is a treasure house in the forest.",
+                        "[Bob] on the road, near the forest",
+                        "[Alice] is make a call at home # [Bob] invited [Alice] to join him on an adventure.",
+                        "[NC] The car on the road, near the forest #They drives to the forest in search of treasure.",
+                        "[NC]A tiger appeared in the forest, at night ",
+                        "[Bob] very frightened, open mouth, in the forest, at night",
+                        "[Alice] very frightened, open mouth, in the forest, at night",
+                        "[Bob]  running very fast, in the forest, at night",
+                        "[NC] A house in the forest, at night #Suddenly, They discovers the treasure house!",
+                        "[Bob]  in the house filled with  treasure, laughing, at night #They are overjoyed inside the house.",
+                    ]
+                ),
+                "Comic book",
+                "Using Ref Images",
+                get_image_path_list("./examples/twoperson"),
+                1024,
+                1024,
+            ],
+            [
+                1,
+                0.5,
+                0.5,
+                3,
+                "[Taylor]a woman img, wearing a white T-shirt, blue loose hair",
+                "bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs",
+                array2string(
+                    [
+                        "[Taylor]wake up in the bed",
+                        "[Taylor]have breakfast",
+                        "[Taylor]is on the road, go to company",
+                        "[Taylor]work in the company",
+                        "[Taylor]Take a walk next to the company at noon",
+                        "[Taylor]lying in bed at night",
+                    ]
+                ),
+                "Japanese Anime",
+                "Using Ref Images",
+                get_image_path_list("./examples/taylor"),
+                768,
+                768,
+            ],
+            [
+                0,
+                0.5,
+                0.5,
+                3,
+                "[Bob]a man, wearing black jacket",
+                "bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs",
+                array2string(
+                    [
+                        "[Bob]wake up in the bed",
+                        "[Bob]have breakfast",
+                        "[Bob]is on the road, go to the company,  close look",
+                        "[Bob]work in the company",
+                        "[Bob]laughing happily",
+                        "[Bob]lying in bed at night",
+                    ]
+                ),
+                "Japanese Anime",
+                "Only Using Textual Description",
+                get_image_path_list("./examples/taylor"),
+                768,
+                768,
+            ],
+            [
+                0,
+                0.3,
+                0.5,
+                3,
+                "[Kitty]a girl, wearing white shirt, black skirt, black tie, yellow hair",
+                "bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs",
+                array2string(
+                    [
+                        "[Kitty]at home #at home, began to go to drawing",
+                        "[Kitty]sitting alone on a park bench.",
+                        "[Kitty]reading a book on a park bench.",
+                        "[NC]A squirrel approaches, peeking over the bench. ",
+                        "[Kitty]look around in the park. # She looks around and enjoys the beauty of nature.",
+                        "[NC]leaf falls from the tree, landing on the sketchbook.",
+                        "[Kitty]picks up the leaf, examining its details closely.",
+                        "[NC]The brown squirrel appear.",
+                        "[Kitty]is very happy # She is very happy to see the squirrel again",
+                        "[NC]The brown squirrel takes the cracker and scampers up a tree. # She gives the squirrel cracker",
+                    ]
+                ),
+                "Japanese Anime",
+                "Only Using Textual Description",
+                get_image_path_list("./examples/taylor"),
+                768,
+                768,
+            ],
+        ],
+        inputs=[
+            seed_,
+            sa32_,
+            sa64_,
+            id_length_,
+            general_prompt,
+            negative_prompt,
+            prompt_array,
+            style,
+            model_type,
+            files,
+            G_height,
+            G_width,
+        ],
+        # outputs=[post_sketch, binary_matrixes, *color_row, *colors, *prompts, gen_prompt_vis, general_prompt, seed_],
+        # run_on_click=True,
+        label="😺 Examples 😺",
+    )
+    gr.Markdown(article)
+
+
+demo.launch(server_name="0.0.0.0", share=True)

myenv/Lib/site-packages/MarkupSafe-2.1.5.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

myenv/Lib/site-packages/MarkupSafe-2.1.5.dist-info/LICENSE.rst

@@ -0,0 +1,28 @@
+Copyright 2010 Pallets
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+1.  Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+
+2.  Redistributions in binary form must reproduce the above copyright
+    notice, this list of conditions and the following disclaimer in the
+    documentation and/or other materials provided with the distribution.
+
+3.  Neither the name of the copyright holder nor the names of its
+    contributors may be used to endorse or promote products derived from
+    this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
+PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

myenv/Lib/site-packages/MarkupSafe-2.1.5.dist-info/METADATA

@@ -0,0 +1,93 @@
+Metadata-Version: 2.1
+Name: MarkupSafe
+Version: 2.1.5
+Summary: Safely add untrusted strings to HTML/XML markup.
+Home-page: https://palletsprojects.com/p/markupsafe/
+Maintainer: Pallets
+Maintainer-email: contact@palletsprojects.com
+License: BSD-3-Clause
+Project-URL: Donate, https://palletsprojects.com/donate
+Project-URL: Documentation, https://markupsafe.palletsprojects.com/
+Project-URL: Changes, https://markupsafe.palletsprojects.com/changes/
+Project-URL: Source Code, https://github.com/pallets/markupsafe/
+Project-URL: Issue Tracker, https://github.com/pallets/markupsafe/issues/
+Project-URL: Chat, https://discord.gg/pallets
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Environment :: Web Environment
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Topic :: Internet :: WWW/HTTP :: Dynamic Content
+Classifier: Topic :: Text Processing :: Markup :: HTML
+Requires-Python: >=3.7
+Description-Content-Type: text/x-rst
+License-File: LICENSE.rst
+
+MarkupSafe
+==========
+
+MarkupSafe implements a text object that escapes characters so it is
+safe to use in HTML and XML. Characters that have special meanings are
+replaced so that they display as the actual characters. This mitigates
+injection attacks, meaning untrusted user input can safely be displayed
+on a page.
+
+
+Installing
+----------
+
+Install and update using `pip`_:
+
+.. code-block:: text
+
+    pip install -U MarkupSafe
+
+.. _pip: https://pip.pypa.io/en/stable/getting-started/
+
+
+Examples
+--------
+
+.. code-block:: pycon
+
+    >>> from markupsafe import Markup, escape
+
+    >>> # escape replaces special characters and wraps in Markup
+    >>> escape("<script>alert(document.cookie);</script>")
+    Markup('&lt;script&gt;alert(document.cookie);&lt;/script&gt;')
+
+    >>> # wrap in Markup to mark text "safe" and prevent escaping
+    >>> Markup("<strong>Hello</strong>")
+    Markup('<strong>hello</strong>')
+
+    >>> escape(Markup("<strong>Hello</strong>"))
+    Markup('<strong>hello</strong>')
+
+    >>> # Markup is a str subclass
+    >>> # methods and operators escape their arguments
+    >>> template = Markup("Hello <em>{name}</em>")
+    >>> template.format(name='"World"')
+    Markup('Hello <em>&#34;World&#34;</em>')
+
+
+Donate
+------
+
+The Pallets organization develops and supports MarkupSafe and other
+popular packages. In order to grow the community of contributors and
+users, and allow the maintainers to devote more time to the projects,
+`please donate today`_.
+
+.. _please donate today: https://palletsprojects.com/donate
+
+
+Links
+-----
+
+-   Documentation: https://markupsafe.palletsprojects.com/
+-   Changes: https://markupsafe.palletsprojects.com/changes/
+-   PyPI Releases: https://pypi.org/project/MarkupSafe/
+-   Source Code: https://github.com/pallets/markupsafe/
+-   Issue Tracker: https://github.com/pallets/markupsafe/issues/
+-   Chat: https://discord.gg/pallets

myenv/Lib/site-packages/MarkupSafe-2.1.5.dist-info/RECORD

@@ -0,0 +1,14 @@
+MarkupSafe-2.1.5.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+MarkupSafe-2.1.5.dist-info/LICENSE.rst,sha256=RjHsDbX9kKVH4zaBcmTGeYIUM4FG-KyUtKV_lu6MnsQ,1503
+MarkupSafe-2.1.5.dist-info/METADATA,sha256=icNlaniV7YIQZ1BScCVqNaRtm7MAgfw8d3OBmoSVyAY,3096
+MarkupSafe-2.1.5.dist-info/RECORD,,
+MarkupSafe-2.1.5.dist-info/WHEEL,sha256=ircjsfhzblqgSzO8ow7-0pXK-RVqDqNRGQ8F650AUNM,102
+MarkupSafe-2.1.5.dist-info/top_level.txt,sha256=qy0Plje5IJuvsCBjejJyhDCjEAdcDLK_2agVcex8Z6U,11
+markupsafe/__init__.py,sha256=m1ysNeqf55zbEoJtaovca40ivrkEFolPlw5bGoC5Gi4,11290
+markupsafe/__pycache__/__init__.cpython-311.pyc,,
+markupsafe/__pycache__/_native.cpython-311.pyc,,
+markupsafe/_native.py,sha256=_Q7UsXCOvgdonCgqG3l5asANI6eo50EKnDM-mlwEC5M,1776
+markupsafe/_speedups.c,sha256=n3jzzaJwXcoN8nTFyA53f3vSqsWK2vujI-v6QYifjhQ,7403
+markupsafe/_speedups.cp311-win_amd64.pyd,sha256=MEqnkyBOHmstwQr50hKitovHjrHhMJ0gYmya4Fu1DK0,15872
+markupsafe/_speedups.pyi,sha256=f5QtwIOP0eLrxh2v5p6SmaYmlcHIGIfmz0DovaqL0OU,238
+markupsafe/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0

myenv/Lib/site-packages/MarkupSafe-2.1.5.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.42.0)
+Root-Is-Purelib: false
+Tag: cp311-cp311-win_amd64
+

myenv/Lib/site-packages/MarkupSafe-2.1.5.dist-info/top_level.txt

@@ -0,0 +1 @@
+markupsafe

myenv/Lib/site-packages/PIL/BdfFontFile.py

@@ -0,0 +1,133 @@
+#
+# The Python Imaging Library
+# $Id$
+#
+# bitmap distribution font (bdf) file parser
+#
+# history:
+# 1996-05-16 fl   created (as bdf2pil)
+# 1997-08-25 fl   converted to FontFile driver
+# 2001-05-25 fl   removed bogus __init__ call
+# 2002-11-20 fl   robustification (from Kevin Cazabon, Dmitry Vasiliev)
+# 2003-04-22 fl   more robustification (from Graham Dumpleton)
+#
+# Copyright (c) 1997-2003 by Secret Labs AB.
+# Copyright (c) 1997-2003 by Fredrik Lundh.
+#
+# See the README file for information on usage and redistribution.
+#
+
+"""
+Parse X Bitmap Distribution Format (BDF)
+"""
+from __future__ import annotations
+
+from typing import BinaryIO
+
+from . import FontFile, Image
+
+bdf_slant = {
+    "R": "Roman",
+    "I": "Italic",
+    "O": "Oblique",
+    "RI": "Reverse Italic",
+    "RO": "Reverse Oblique",
+    "OT": "Other",
+}
+
+bdf_spacing = {"P": "Proportional", "M": "Monospaced", "C": "Cell"}
+
+
+def bdf_char(
+    f: BinaryIO,
+) -> (
+    tuple[
+        str,
+        int,
+        tuple[tuple[int, int], tuple[int, int, int, int], tuple[int, int, int, int]],
+        Image.Image,
+    ]
+    | None
+):
+    # skip to STARTCHAR
+    while True:
+        s = f.readline()
+        if not s:
+            return None
+        if s[:9] == b"STARTCHAR":
+            break
+    id = s[9:].strip().decode("ascii")
+
+    # load symbol properties
+    props = {}
+    while True:
+        s = f.readline()
+        if not s or s[:6] == b"BITMAP":
+            break
+        i = s.find(b" ")
+        props[s[:i].decode("ascii")] = s[i + 1 : -1].decode("ascii")
+
+    # load bitmap
+    bitmap = bytearray()
+    while True:
+        s = f.readline()
+        if not s or s[:7] == b"ENDCHAR":
+            break
+        bitmap += s[:-1]
+
+    # The word BBX
+    # followed by the width in x (BBw), height in y (BBh),
+    # and x and y displacement (BBxoff0, BByoff0)
+    # of the lower left corner from the origin of the character.
+    width, height, x_disp, y_disp = (int(p) for p in props["BBX"].split())
+
+    # The word DWIDTH
+    # followed by the width in x and y of the character in device pixels.
+    dwx, dwy = (int(p) for p in props["DWIDTH"].split())
+
+    bbox = (
+        (dwx, dwy),
+        (x_disp, -y_disp - height, width + x_disp, -y_disp),
+        (0, 0, width, height),
+    )
+
+    try:
+        im = Image.frombytes("1", (width, height), bitmap, "hex", "1")
+    except ValueError:
+        # deal with zero-width characters
+        im = Image.new("1", (width, height))
+
+    return id, int(props["ENCODING"]), bbox, im
+
+
+class BdfFontFile(FontFile.FontFile):
+    """Font file plugin for the X11 BDF format."""
+
+    def __init__(self, fp: BinaryIO) -> None:
+        super().__init__()
+
+        s = fp.readline()
+        if s[:13] != b"STARTFONT 2.1":
+            msg = "not a valid BDF file"
+            raise SyntaxError(msg)
+
+        props = {}
+        comments = []
+
+        while True:
+            s = fp.readline()
+            if not s or s[:13] == b"ENDPROPERTIES":
+                break
+            i = s.find(b" ")
+            props[s[:i].decode("ascii")] = s[i + 1 : -1].decode("ascii")
+            if s[:i] in [b"COMMENT", b"COPYRIGHT"]:
+                if s.find(b"LogicalFontDescription") < 0:
+                    comments.append(s[i + 1 : -1].decode("ascii"))
+
+        while True:
+            c = bdf_char(fp)
+            if not c:
+                break
+            id, ch, (xy, dst, src), im = c
+            if 0 <= ch < len(self.glyph):
+                self.glyph[ch] = xy, dst, src, im

myenv/Lib/site-packages/PIL/BlpImagePlugin.py

@@ -0,0 +1,488 @@
+"""
+Blizzard Mipmap Format (.blp)
+Jerome Leclanche <jerome@leclan.ch>
+
+The contents of this file are hereby released in the public domain (CC0)
+Full text of the CC0 license:
+  https://creativecommons.org/publicdomain/zero/1.0/
+
+BLP1 files, used mostly in Warcraft III, are not fully supported.
+All types of BLP2 files used in World of Warcraft are supported.
+
+The BLP file structure consists of a header, up to 16 mipmaps of the
+texture
+
+Texture sizes must be powers of two, though the two dimensions do
+not have to be equal; 512x256 is valid, but 512x200 is not.
+The first mipmap (mipmap #0) is the full size image; each subsequent
+mipmap halves both dimensions. The final mipmap should be 1x1.
+
+BLP files come in many different flavours:
+* JPEG-compressed (type == 0) - only supported for BLP1.
+* RAW images (type == 1, encoding == 1). Each mipmap is stored as an
+  array of 8-bit values, one per pixel, left to right, top to bottom.
+  Each value is an index to the palette.
+* DXT-compressed (type == 1, encoding == 2):
+- DXT1 compression is used if alpha_encoding == 0.
+  - An additional alpha bit is used if alpha_depth == 1.
+  - DXT3 compression is used if alpha_encoding == 1.
+  - DXT5 compression is used if alpha_encoding == 7.
+"""
+
+from __future__ import annotations
+
+import abc
+import os
+import struct
+from enum import IntEnum
+from io import BytesIO
+from typing import IO
+
+from . import Image, ImageFile
+
+
+class Format(IntEnum):
+    JPEG = 0
+
+
+class Encoding(IntEnum):
+    UNCOMPRESSED = 1
+    DXT = 2
+    UNCOMPRESSED_RAW_BGRA = 3
+
+
+class AlphaEncoding(IntEnum):
+    DXT1 = 0
+    DXT3 = 1
+    DXT5 = 7
+
+
+def unpack_565(i: int) -> tuple[int, int, int]:
+    return ((i >> 11) & 0x1F) << 3, ((i >> 5) & 0x3F) << 2, (i & 0x1F) << 3
+
+
+def decode_dxt1(
+    data: bytes, alpha: bool = False
+) -> tuple[bytearray, bytearray, bytearray, bytearray]:
+    """
+    input: one "row" of data (i.e. will produce 4*width pixels)
+    """
+
+    blocks = len(data) // 8  # number of blocks in row
+    ret = (bytearray(), bytearray(), bytearray(), bytearray())
+
+    for block_index in range(blocks):
+        # Decode next 8-byte block.
+        idx = block_index * 8
+        color0, color1, bits = struct.unpack_from("<HHI", data, idx)
+
+        r0, g0, b0 = unpack_565(color0)
+        r1, g1, b1 = unpack_565(color1)
+
+        # Decode this block into 4x4 pixels
+        # Accumulate the results onto our 4 row accumulators
+        for j in range(4):
+            for i in range(4):
+                # get next control op and generate a pixel
+
+                control = bits & 3
+                bits = bits >> 2
+
+                a = 0xFF
+                if control == 0:
+                    r, g, b = r0, g0, b0
+                elif control == 1:
+                    r, g, b = r1, g1, b1
+                elif control == 2:
+                    if color0 > color1:
+                        r = (2 * r0 + r1) // 3
+                        g = (2 * g0 + g1) // 3
+                        b = (2 * b0 + b1) // 3
+                    else:
+                        r = (r0 + r1) // 2
+                        g = (g0 + g1) // 2
+                        b = (b0 + b1) // 2
+                elif control == 3:
+                    if color0 > color1:
+                        r = (2 * r1 + r0) // 3
+                        g = (2 * g1 + g0) // 3
+                        b = (2 * b1 + b0) // 3
+                    else:
+                        r, g, b, a = 0, 0, 0, 0
+
+                if alpha:
+                    ret[j].extend([r, g, b, a])
+                else:
+                    ret[j].extend([r, g, b])
+
+    return ret
+
+
+def decode_dxt3(data: bytes) -> tuple[bytearray, bytearray, bytearray, bytearray]:
+    """
+    input: one "row" of data (i.e. will produce 4*width pixels)
+    """
+
+    blocks = len(data) // 16  # number of blocks in row
+    ret = (bytearray(), bytearray(), bytearray(), bytearray())
+
+    for block_index in range(blocks):
+        idx = block_index * 16
+        block = data[idx : idx + 16]
+        # Decode next 16-byte block.
+        bits = struct.unpack_from("<8B", block)
+        color0, color1 = struct.unpack_from("<HH", block, 8)
+
+        (code,) = struct.unpack_from("<I", block, 12)
+
+        r0, g0, b0 = unpack_565(color0)
+        r1, g1, b1 = unpack_565(color1)
+
+        for j in range(4):
+            high = False  # Do we want the higher bits?
+            for i in range(4):
+                alphacode_index = (4 * j + i) // 2
+                a = bits[alphacode_index]
+                if high:
+                    high = False
+                    a >>= 4
+                else:
+                    high = True
+                    a &= 0xF
+                a *= 17  # We get a value between 0 and 15
+
+                color_code = (code >> 2 * (4 * j + i)) & 0x03
+
+                if color_code == 0:
+                    r, g, b = r0, g0, b0
+                elif color_code == 1:
+                    r, g, b = r1, g1, b1
+                elif color_code == 2:
+                    r = (2 * r0 + r1) // 3
+                    g = (2 * g0 + g1) // 3
+                    b = (2 * b0 + b1) // 3
+                elif color_code == 3:
+                    r = (2 * r1 + r0) // 3
+                    g = (2 * g1 + g0) // 3
+                    b = (2 * b1 + b0) // 3
+
+                ret[j].extend([r, g, b, a])
+
+    return ret
+
+
+def decode_dxt5(data: bytes) -> tuple[bytearray, bytearray, bytearray, bytearray]:
+    """
+    input: one "row" of data (i.e. will produce 4 * width pixels)
+    """
+
+    blocks = len(data) // 16  # number of blocks in row
+    ret = (bytearray(), bytearray(), bytearray(), bytearray())
+
+    for block_index in range(blocks):
+        idx = block_index * 16
+        block = data[idx : idx + 16]
+        # Decode next 16-byte block.
+        a0, a1 = struct.unpack_from("<BB", block)
+
+        bits = struct.unpack_from("<6B", block, 2)
+        alphacode1 = bits[2] | (bits[3] << 8) | (bits[4] << 16) | (bits[5] << 24)
+        alphacode2 = bits[0] | (bits[1] << 8)
+
+        color0, color1 = struct.unpack_from("<HH", block, 8)
+
+        (code,) = struct.unpack_from("<I", block, 12)
+
+        r0, g0, b0 = unpack_565(color0)
+        r1, g1, b1 = unpack_565(color1)
+
+        for j in range(4):
+            for i in range(4):
+                # get next control op and generate a pixel
+                alphacode_index = 3 * (4 * j + i)
+
+                if alphacode_index <= 12:
+                    alphacode = (alphacode2 >> alphacode_index) & 0x07
+                elif alphacode_index == 15:
+                    alphacode = (alphacode2 >> 15) | ((alphacode1 << 1) & 0x06)
+                else:  # alphacode_index >= 18 and alphacode_index <= 45
+                    alphacode = (alphacode1 >> (alphacode_index - 16)) & 0x07
+
+                if alphacode == 0:
+                    a = a0
+                elif alphacode == 1:
+                    a = a1
+                elif a0 > a1:
+                    a = ((8 - alphacode) * a0 + (alphacode - 1) * a1) // 7
+                elif alphacode == 6:
+                    a = 0
+                elif alphacode == 7:
+                    a = 255
+                else:
+                    a = ((6 - alphacode) * a0 + (alphacode - 1) * a1) // 5
+
+                color_code = (code >> 2 * (4 * j + i)) & 0x03
+
+                if color_code == 0:
+                    r, g, b = r0, g0, b0
+                elif color_code == 1:
+                    r, g, b = r1, g1, b1
+                elif color_code == 2:
+                    r = (2 * r0 + r1) // 3
+                    g = (2 * g0 + g1) // 3
+                    b = (2 * b0 + b1) // 3
+                elif color_code == 3:
+                    r = (2 * r1 + r0) // 3
+                    g = (2 * g1 + g0) // 3
+                    b = (2 * b1 + b0) // 3
+
+                ret[j].extend([r, g, b, a])
+
+    return ret
+
+
+class BLPFormatError(NotImplementedError):
+    pass
+
+
+def _accept(prefix: bytes) -> bool:
+    return prefix[:4] in (b"BLP1", b"BLP2")
+
+
+class BlpImageFile(ImageFile.ImageFile):
+    """
+    Blizzard Mipmap Format
+    """
+
+    format = "BLP"
+    format_description = "Blizzard Mipmap Format"
+
+    def _open(self) -> None:
+        self.magic = self.fp.read(4)
+
+        self.fp.seek(5, os.SEEK_CUR)
+        (self._blp_alpha_depth,) = struct.unpack("<b", self.fp.read(1))
+
+        self.fp.seek(2, os.SEEK_CUR)
+        self._size = struct.unpack("<II", self.fp.read(8))
+
+        if self.magic in (b"BLP1", b"BLP2"):
+            decoder = self.magic.decode()
+        else:
+            msg = f"Bad BLP magic {repr(self.magic)}"
+            raise BLPFormatError(msg)
+
+        self._mode = "RGBA" if self._blp_alpha_depth else "RGB"
+        self.tile = [(decoder, (0, 0) + self.size, 0, (self.mode, 0, 1))]
+
+
+class _BLPBaseDecoder(ImageFile.PyDecoder):
+    _pulls_fd = True
+
+    def decode(self, buffer: bytes) -> tuple[int, int]:
+        try:
+            self._read_blp_header()
+            self._load()
+        except struct.error as e:
+            msg = "Truncated BLP file"
+            raise OSError(msg) from e
+        return -1, 0
+
+    @abc.abstractmethod
+    def _load(self) -> None:
+        pass
+
+    def _read_blp_header(self) -> None:
+        assert self.fd is not None
+        self.fd.seek(4)
+        (self._blp_compression,) = struct.unpack("<i", self._safe_read(4))
+
+        (self._blp_encoding,) = struct.unpack("<b", self._safe_read(1))
+        (self._blp_alpha_depth,) = struct.unpack("<b", self._safe_read(1))
+        (self._blp_alpha_encoding,) = struct.unpack("<b", self._safe_read(1))
+        self.fd.seek(1, os.SEEK_CUR)  # mips
+
+        self.size = struct.unpack("<II", self._safe_read(8))
+
+        if isinstance(self, BLP1Decoder):
+            # Only present for BLP1
+            (self._blp_encoding,) = struct.unpack("<i", self._safe_read(4))
+            self.fd.seek(4, os.SEEK_CUR)  # subtype
+
+        self._blp_offsets = struct.unpack("<16I", self._safe_read(16 * 4))
+        self._blp_lengths = struct.unpack("<16I", self._safe_read(16 * 4))
+
+    def _safe_read(self, length: int) -> bytes:
+        return ImageFile._safe_read(self.fd, length)
+
+    def _read_palette(self) -> list[tuple[int, int, int, int]]:
+        ret = []
+        for i in range(256):
+            try:
+                b, g, r, a = struct.unpack("<4B", self._safe_read(4))
+            except struct.error:
+                break
+            ret.append((b, g, r, a))
+        return ret
+
+    def _read_bgra(self, palette: list[tuple[int, int, int, int]]) -> bytearray:
+        data = bytearray()
+        _data = BytesIO(self._safe_read(self._blp_lengths[0]))
+        while True:
+            try:
+                (offset,) = struct.unpack("<B", _data.read(1))
+            except struct.error:
+                break
+            b, g, r, a = palette[offset]
+            d: tuple[int, ...] = (r, g, b)
+            if self._blp_alpha_depth:
+                d += (a,)
+            data.extend(d)
+        return data
+
+
+class BLP1Decoder(_BLPBaseDecoder):
+    def _load(self) -> None:
+        if self._blp_compression == Format.JPEG:
+            self._decode_jpeg_stream()
+
+        elif self._blp_compression == 1:
+            if self._blp_encoding in (4, 5):
+                palette = self._read_palette()
+                data = self._read_bgra(palette)
+                self.set_as_raw(data)
+            else:
+                msg = f"Unsupported BLP encoding {repr(self._blp_encoding)}"
+                raise BLPFormatError(msg)
+        else:
+            msg = f"Unsupported BLP compression {repr(self._blp_encoding)}"
+            raise BLPFormatError(msg)
+
+    def _decode_jpeg_stream(self) -> None:
+        from .JpegImagePlugin import JpegImageFile
+
+        (jpeg_header_size,) = struct.unpack("<I", self._safe_read(4))
+        jpeg_header = self._safe_read(jpeg_header_size)
+        assert self.fd is not None
+        self._safe_read(self._blp_offsets[0] - self.fd.tell())  # What IS this?
+        data = self._safe_read(self._blp_lengths[0])
+        data = jpeg_header + data
+        image = JpegImageFile(BytesIO(data))
+        Image._decompression_bomb_check(image.size)
+        if image.mode == "CMYK":
+            decoder_name, extents, offset, args = image.tile[0]
+            image.tile = [(decoder_name, extents, offset, (args[0], "CMYK"))]
+        r, g, b = image.convert("RGB").split()
+        reversed_image = Image.merge("RGB", (b, g, r))
+        self.set_as_raw(reversed_image.tobytes())
+
+
+class BLP2Decoder(_BLPBaseDecoder):
+    def _load(self) -> None:
+        palette = self._read_palette()
+
+        assert self.fd is not None
+        self.fd.seek(self._blp_offsets[0])
+
+        if self._blp_compression == 1:
+            # Uncompressed or DirectX compression
+
+            if self._blp_encoding == Encoding.UNCOMPRESSED:
+                data = self._read_bgra(palette)
+
+            elif self._blp_encoding == Encoding.DXT:
+                data = bytearray()
+                if self._blp_alpha_encoding == AlphaEncoding.DXT1:
+                    linesize = (self.size[0] + 3) // 4 * 8
+                    for yb in range((self.size[1] + 3) // 4):
+                        for d in decode_dxt1(
+                            self._safe_read(linesize), alpha=bool(self._blp_alpha_depth)
+                        ):
+                            data += d
+
+                elif self._blp_alpha_encoding == AlphaEncoding.DXT3:
+                    linesize = (self.size[0] + 3) // 4 * 16
+                    for yb in range((self.size[1] + 3) // 4):
+                        for d in decode_dxt3(self._safe_read(linesize)):
+                            data += d
+
+                elif self._blp_alpha_encoding == AlphaEncoding.DXT5:
+                    linesize = (self.size[0] + 3) // 4 * 16
+                    for yb in range((self.size[1] + 3) // 4):
+                        for d in decode_dxt5(self._safe_read(linesize)):
+                            data += d
+                else:
+                    msg = f"Unsupported alpha encoding {repr(self._blp_alpha_encoding)}"
+                    raise BLPFormatError(msg)
+            else:
+                msg = f"Unknown BLP encoding {repr(self._blp_encoding)}"
+                raise BLPFormatError(msg)
+
+        else:
+            msg = f"Unknown BLP compression {repr(self._blp_compression)}"
+            raise BLPFormatError(msg)
+
+        self.set_as_raw(data)
+
+
+class BLPEncoder(ImageFile.PyEncoder):
+    _pushes_fd = True
+
+    def _write_palette(self) -> bytes:
+        data = b""
+        assert self.im is not None
+        palette = self.im.getpalette("RGBA", "RGBA")
+        for i in range(len(palette) // 4):
+            r, g, b, a = palette[i * 4 : (i + 1) * 4]
+            data += struct.pack("<4B", b, g, r, a)
+        while len(data) < 256 * 4:
+            data += b"\x00" * 4
+        return data
+
+    def encode(self, bufsize: int) -> tuple[int, int, bytes]:
+        palette_data = self._write_palette()
+
+        offset = 20 + 16 * 4 * 2 + len(palette_data)
+        data = struct.pack("<16I", offset, *((0,) * 15))
+
+        assert self.im is not None
+        w, h = self.im.size
+        data += struct.pack("<16I", w * h, *((0,) * 15))
+
+        data += palette_data
+
+        for y in range(h):
+            for x in range(w):
+                data += struct.pack("<B", self.im.getpixel((x, y)))
+
+        return len(data), 0, data
+
+
+def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None:
+    if im.mode != "P":
+        msg = "Unsupported BLP image mode"
+        raise ValueError(msg)
+
+    magic = b"BLP1" if im.encoderinfo.get("blp_version") == "BLP1" else b"BLP2"
+    fp.write(magic)
+
+    fp.write(struct.pack("<i", 1))  # Uncompressed or DirectX compression
+    fp.write(struct.pack("<b", Encoding.UNCOMPRESSED))
+    fp.write(struct.pack("<b", 1 if im.palette.mode == "RGBA" else 0))
+    fp.write(struct.pack("<b", 0))  # alpha encoding
+    fp.write(struct.pack("<b", 0))  # mips
+    fp.write(struct.pack("<II", *im.size))
+    if magic == b"BLP1":
+        fp.write(struct.pack("<i", 5))
+        fp.write(struct.pack("<i", 0))
+
+    ImageFile._save(im, fp, [("BLP", (0, 0) + im.size, 0, im.mode)])
+
+
+Image.register_open(BlpImageFile.format, BlpImageFile, _accept)
+Image.register_extension(BlpImageFile.format, ".blp")
+Image.register_decoder("BLP1", BLP1Decoder)
+Image.register_decoder("BLP2", BLP2Decoder)
+
+Image.register_save(BlpImageFile.format, _save)
+Image.register_encoder("BLP", BLPEncoder)

myenv/Lib/site-packages/PIL/BmpImagePlugin.py

@@ -0,0 +1,489 @@
+#
+# The Python Imaging Library.
+# $Id$
+#
+# BMP file handler
+#
+# Windows (and OS/2) native bitmap storage format.
+#
+# history:
+# 1995-09-01 fl   Created
+# 1996-04-30 fl   Added save
+# 1997-08-27 fl   Fixed save of 1-bit images
+# 1998-03-06 fl   Load P images as L where possible
+# 1998-07-03 fl   Load P images as 1 where possible
+# 1998-12-29 fl   Handle small palettes
+# 2002-12-30 fl   Fixed load of 1-bit palette images
+# 2003-04-21 fl   Fixed load of 1-bit monochrome images
+# 2003-04-23 fl   Added limited support for BI_BITFIELDS compression
+#
+# Copyright (c) 1997-2003 by Secret Labs AB
+# Copyright (c) 1995-2003 by Fredrik Lundh
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+import os
+from typing import IO
+
+from . import Image, ImageFile, ImagePalette
+from ._binary import i16le as i16
+from ._binary import i32le as i32
+from ._binary import o8
+from ._binary import o16le as o16
+from ._binary import o32le as o32
+
+#
+# --------------------------------------------------------------------
+# Read BMP file
+
+BIT2MODE = {
+    # bits => mode, rawmode
+    1: ("P", "P;1"),
+    4: ("P", "P;4"),
+    8: ("P", "P"),
+    16: ("RGB", "BGR;15"),
+    24: ("RGB", "BGR"),
+    32: ("RGB", "BGRX"),
+}
+
+
+def _accept(prefix: bytes) -> bool:
+    return prefix[:2] == b"BM"
+
+
+def _dib_accept(prefix: bytes) -> bool:
+    return i32(prefix) in [12, 40, 52, 56, 64, 108, 124]
+
+
+# =============================================================================
+# Image plugin for the Windows BMP format.
+# =============================================================================
+class BmpImageFile(ImageFile.ImageFile):
+    """Image plugin for the Windows Bitmap format (BMP)"""
+
+    # ------------------------------------------------------------- Description
+    format_description = "Windows Bitmap"
+    format = "BMP"
+
+    # -------------------------------------------------- BMP Compression values
+    COMPRESSIONS = {"RAW": 0, "RLE8": 1, "RLE4": 2, "BITFIELDS": 3, "JPEG": 4, "PNG": 5}
+    for k, v in COMPRESSIONS.items():
+        vars()[k] = v
+
+    def _bitmap(self, header=0, offset=0):
+        """Read relevant info about the BMP"""
+        read, seek = self.fp.read, self.fp.seek
+        if header:
+            seek(header)
+        # read bmp header size @offset 14 (this is part of the header size)
+        file_info = {"header_size": i32(read(4)), "direction": -1}
+
+        # -------------------- If requested, read header at a specific position
+        # read the rest of the bmp header, without its size
+        header_data = ImageFile._safe_read(self.fp, file_info["header_size"] - 4)
+
+        # ------------------------------- Windows Bitmap v2, IBM OS/2 Bitmap v1
+        # ----- This format has different offsets because of width/height types
+        # 12: BITMAPCOREHEADER/OS21XBITMAPHEADER
+        if file_info["header_size"] == 12:
+            file_info["width"] = i16(header_data, 0)
+            file_info["height"] = i16(header_data, 2)
+            file_info["planes"] = i16(header_data, 4)
+            file_info["bits"] = i16(header_data, 6)
+            file_info["compression"] = self.RAW
+            file_info["palette_padding"] = 3
+
+        # --------------------------------------------- Windows Bitmap v3 to v5
+        #  40: BITMAPINFOHEADER
+        #  52: BITMAPV2HEADER
+        #  56: BITMAPV3HEADER
+        #  64: BITMAPCOREHEADER2/OS22XBITMAPHEADER
+        # 108: BITMAPV4HEADER
+        # 124: BITMAPV5HEADER
+        elif file_info["header_size"] in (40, 52, 56, 64, 108, 124):
+            file_info["y_flip"] = header_data[7] == 0xFF
+            file_info["direction"] = 1 if file_info["y_flip"] else -1
+            file_info["width"] = i32(header_data, 0)
+            file_info["height"] = (
+                i32(header_data, 4)
+                if not file_info["y_flip"]
+                else 2**32 - i32(header_data, 4)
+            )
+            file_info["planes"] = i16(header_data, 8)
+            file_info["bits"] = i16(header_data, 10)
+            file_info["compression"] = i32(header_data, 12)
+            # byte size of pixel data
+            file_info["data_size"] = i32(header_data, 16)
+            file_info["pixels_per_meter"] = (
+                i32(header_data, 20),
+                i32(header_data, 24),
+            )
+            file_info["colors"] = i32(header_data, 28)
+            file_info["palette_padding"] = 4
+            self.info["dpi"] = tuple(x / 39.3701 for x in file_info["pixels_per_meter"])
+            if file_info["compression"] == self.BITFIELDS:
+                masks = ["r_mask", "g_mask", "b_mask"]
+                if len(header_data) >= 48:
+                    if len(header_data) >= 52:
+                        masks.append("a_mask")
+                    else:
+                        file_info["a_mask"] = 0x0
+                    for idx, mask in enumerate(masks):
+                        file_info[mask] = i32(header_data, 36 + idx * 4)
+                else:
+                    # 40 byte headers only have the three components in the
+                    # bitfields masks, ref:
+                    # https://msdn.microsoft.com/en-us/library/windows/desktop/dd183376(v=vs.85).aspx
+                    # See also
+                    # https://github.com/python-pillow/Pillow/issues/1293
+                    # There is a 4th component in the RGBQuad, in the alpha
+                    # location, but it is listed as a reserved component,
+                    # and it is not generally an alpha channel
+                    file_info["a_mask"] = 0x0
+                    for mask in masks:
+                        file_info[mask] = i32(read(4))
+                file_info["rgb_mask"] = (
+                    file_info["r_mask"],
+                    file_info["g_mask"],
+                    file_info["b_mask"],
+                )
+                file_info["rgba_mask"] = (
+                    file_info["r_mask"],
+                    file_info["g_mask"],
+                    file_info["b_mask"],
+                    file_info["a_mask"],
+                )
+        else:
+            msg = f"Unsupported BMP header type ({file_info['header_size']})"
+            raise OSError(msg)
+
+        # ------------------ Special case : header is reported 40, which
+        # ---------------------- is shorter than real size for bpp >= 16
+        self._size = file_info["width"], file_info["height"]
+
+        # ------- If color count was not found in the header, compute from bits
+        file_info["colors"] = (
+            file_info["colors"]
+            if file_info.get("colors", 0)
+            else (1 << file_info["bits"])
+        )
+        if offset == 14 + file_info["header_size"] and file_info["bits"] <= 8:
+            offset += 4 * file_info["colors"]
+
+        # ---------------------- Check bit depth for unusual unsupported values
+        self._mode, raw_mode = BIT2MODE.get(file_info["bits"], (None, None))
+        if self.mode is None:
+            msg = f"Unsupported BMP pixel depth ({file_info['bits']})"
+            raise OSError(msg)
+
+        # ---------------- Process BMP with Bitfields compression (not palette)
+        decoder_name = "raw"
+        if file_info["compression"] == self.BITFIELDS:
+            SUPPORTED = {
+                32: [
+                    (0xFF0000, 0xFF00, 0xFF, 0x0),
+                    (0xFF000000, 0xFF0000, 0xFF00, 0x0),
+                    (0xFF000000, 0xFF00, 0xFF, 0x0),
+                    (0xFF000000, 0xFF0000, 0xFF00, 0xFF),
+                    (0xFF, 0xFF00, 0xFF0000, 0xFF000000),
+                    (0xFF0000, 0xFF00, 0xFF, 0xFF000000),
+                    (0xFF000000, 0xFF00, 0xFF, 0xFF0000),
+                    (0x0, 0x0, 0x0, 0x0),
+                ],
+                24: [(0xFF0000, 0xFF00, 0xFF)],
+                16: [(0xF800, 0x7E0, 0x1F), (0x7C00, 0x3E0, 0x1F)],
+            }
+            MASK_MODES = {
+                (32, (0xFF0000, 0xFF00, 0xFF, 0x0)): "BGRX",
+                (32, (0xFF000000, 0xFF0000, 0xFF00, 0x0)): "XBGR",
+                (32, (0xFF000000, 0xFF00, 0xFF, 0x0)): "BGXR",
+                (32, (0xFF000000, 0xFF0000, 0xFF00, 0xFF)): "ABGR",
+                (32, (0xFF, 0xFF00, 0xFF0000, 0xFF000000)): "RGBA",
+                (32, (0xFF0000, 0xFF00, 0xFF, 0xFF000000)): "BGRA",
+                (32, (0xFF000000, 0xFF00, 0xFF, 0xFF0000)): "BGAR",
+                (32, (0x0, 0x0, 0x0, 0x0)): "BGRA",
+                (24, (0xFF0000, 0xFF00, 0xFF)): "BGR",
+                (16, (0xF800, 0x7E0, 0x1F)): "BGR;16",
+                (16, (0x7C00, 0x3E0, 0x1F)): "BGR;15",
+            }
+            if file_info["bits"] in SUPPORTED:
+                if (
+                    file_info["bits"] == 32
+                    and file_info["rgba_mask"] in SUPPORTED[file_info["bits"]]
+                ):
+                    raw_mode = MASK_MODES[(file_info["bits"], file_info["rgba_mask"])]
+                    self._mode = "RGBA" if "A" in raw_mode else self.mode
+                elif (
+                    file_info["bits"] in (24, 16)
+                    and file_info["rgb_mask"] in SUPPORTED[file_info["bits"]]
+                ):
+                    raw_mode = MASK_MODES[(file_info["bits"], file_info["rgb_mask"])]
+                else:
+                    msg = "Unsupported BMP bitfields layout"
+                    raise OSError(msg)
+            else:
+                msg = "Unsupported BMP bitfields layout"
+                raise OSError(msg)
+        elif file_info["compression"] == self.RAW:
+            if file_info["bits"] == 32 and header == 22:  # 32-bit .cur offset
+                raw_mode, self._mode = "BGRA", "RGBA"
+        elif file_info["compression"] in (self.RLE8, self.RLE4):
+            decoder_name = "bmp_rle"
+        else:
+            msg = f"Unsupported BMP compression ({file_info['compression']})"
+            raise OSError(msg)
+
+        # --------------- Once the header is processed, process the palette/LUT
+        if self.mode == "P":  # Paletted for 1, 4 and 8 bit images
+            # ---------------------------------------------------- 1-bit images
+            if not (0 < file_info["colors"] <= 65536):
+                msg = f"Unsupported BMP Palette size ({file_info['colors']})"
+                raise OSError(msg)
+            else:
+                padding = file_info["palette_padding"]
+                palette = read(padding * file_info["colors"])
+                grayscale = True
+                indices = (
+                    (0, 255)
+                    if file_info["colors"] == 2
+                    else list(range(file_info["colors"]))
+                )
+
+                # ----------------- Check if grayscale and ignore palette if so
+                for ind, val in enumerate(indices):
+                    rgb = palette[ind * padding : ind * padding + 3]
+                    if rgb != o8(val) * 3:
+                        grayscale = False
+
+                # ------- If all colors are gray, white or black, ditch palette
+                if grayscale:
+                    self._mode = "1" if file_info["colors"] == 2 else "L"
+                    raw_mode = self.mode
+                else:
+                    self._mode = "P"
+                    self.palette = ImagePalette.raw(
+                        "BGRX" if padding == 4 else "BGR", palette
+                    )
+
+        # ---------------------------- Finally set the tile data for the plugin
+        self.info["compression"] = file_info["compression"]
+        args = [raw_mode]
+        if decoder_name == "bmp_rle":
+            args.append(file_info["compression"] == self.RLE4)
+        else:
+            args.append(((file_info["width"] * file_info["bits"] + 31) >> 3) & (~3))
+        args.append(file_info["direction"])
+        self.tile = [
+            (
+                decoder_name,
+                (0, 0, file_info["width"], file_info["height"]),
+                offset or self.fp.tell(),
+                tuple(args),
+            )
+        ]
+
+    def _open(self) -> None:
+        """Open file, check magic number and read header"""
+        # read 14 bytes: magic number, filesize, reserved, header final offset
+        head_data = self.fp.read(14)
+        # choke if the file does not have the required magic bytes
+        if not _accept(head_data):
+            msg = "Not a BMP file"
+            raise SyntaxError(msg)
+        # read the start position of the BMP image data (u32)
+        offset = i32(head_data, 10)
+        # load bitmap information (offset=raster info)
+        self._bitmap(offset=offset)
+
+
+class BmpRleDecoder(ImageFile.PyDecoder):
+    _pulls_fd = True
+
+    def decode(self, buffer: bytes) -> tuple[int, int]:
+        assert self.fd is not None
+        rle4 = self.args[1]
+        data = bytearray()
+        x = 0
+        dest_length = self.state.xsize * self.state.ysize
+        while len(data) < dest_length:
+            pixels = self.fd.read(1)
+            byte = self.fd.read(1)
+            if not pixels or not byte:
+                break
+            num_pixels = pixels[0]
+            if num_pixels:
+                # encoded mode
+                if x + num_pixels > self.state.xsize:
+                    # Too much data for row
+                    num_pixels = max(0, self.state.xsize - x)
+                if rle4:
+                    first_pixel = o8(byte[0] >> 4)
+                    second_pixel = o8(byte[0] & 0x0F)
+                    for index in range(num_pixels):
+                        if index % 2 == 0:
+                            data += first_pixel
+                        else:
+                            data += second_pixel
+                else:
+                    data += byte * num_pixels
+                x += num_pixels
+            else:
+                if byte[0] == 0:
+                    # end of line
+                    while len(data) % self.state.xsize != 0:
+                        data += b"\x00"
+                    x = 0
+                elif byte[0] == 1:
+                    # end of bitmap
+                    break
+                elif byte[0] == 2:
+                    # delta
+                    bytes_read = self.fd.read(2)
+                    if len(bytes_read) < 2:
+                        break
+                    right, up = self.fd.read(2)
+                    data += b"\x00" * (right + up * self.state.xsize)
+                    x = len(data) % self.state.xsize
+                else:
+                    # absolute mode
+                    if rle4:
+                        # 2 pixels per byte
+                        byte_count = byte[0] // 2
+                        bytes_read = self.fd.read(byte_count)
+                        for byte_read in bytes_read:
+                            data += o8(byte_read >> 4)
+                            data += o8(byte_read & 0x0F)
+                    else:
+                        byte_count = byte[0]
+                        bytes_read = self.fd.read(byte_count)
+                        data += bytes_read
+                    if len(bytes_read) < byte_count:
+                        break
+                    x += byte[0]
+
+                    # align to 16-bit word boundary
+                    if self.fd.tell() % 2 != 0:
+                        self.fd.seek(1, os.SEEK_CUR)
+        rawmode = "L" if self.mode == "L" else "P"
+        self.set_as_raw(bytes(data), (rawmode, 0, self.args[-1]))
+        return -1, 0
+
+
+# =============================================================================
+# Image plugin for the DIB format (BMP alias)
+# =============================================================================
+class DibImageFile(BmpImageFile):
+    format = "DIB"
+    format_description = "Windows Bitmap"
+
+    def _open(self) -> None:
+        self._bitmap()
+
+
+#
+# --------------------------------------------------------------------
+# Write BMP file
+
+
+SAVE = {
+    "1": ("1", 1, 2),
+    "L": ("L", 8, 256),
+    "P": ("P", 8, 256),
+    "RGB": ("BGR", 24, 0),
+    "RGBA": ("BGRA", 32, 0),
+}
+
+
+def _dib_save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None:
+    _save(im, fp, filename, False)
+
+
+def _save(
+    im: Image.Image, fp: IO[bytes], filename: str | bytes, bitmap_header: bool = True
+) -> None:
+    try:
+        rawmode, bits, colors = SAVE[im.mode]
+    except KeyError as e:
+        msg = f"cannot write mode {im.mode} as BMP"
+        raise OSError(msg) from e
+
+    info = im.encoderinfo
+
+    dpi = info.get("dpi", (96, 96))
+
+    # 1 meter == 39.3701 inches
+    ppm = tuple(int(x * 39.3701 + 0.5) for x in dpi)
+
+    stride = ((im.size[0] * bits + 7) // 8 + 3) & (~3)
+    header = 40  # or 64 for OS/2 version 2
+    image = stride * im.size[1]
+
+    if im.mode == "1":
+        palette = b"".join(o8(i) * 4 for i in (0, 255))
+    elif im.mode == "L":
+        palette = b"".join(o8(i) * 4 for i in range(256))
+    elif im.mode == "P":
+        palette = im.im.getpalette("RGB", "BGRX")
+        colors = len(palette) // 4
+    else:
+        palette = None
+
+    # bitmap header
+    if bitmap_header:
+        offset = 14 + header + colors * 4
+        file_size = offset + image
+        if file_size > 2**32 - 1:
+            msg = "File size is too large for the BMP format"
+            raise ValueError(msg)
+        fp.write(
+            b"BM"  # file type (magic)
+            + o32(file_size)  # file size
+            + o32(0)  # reserved
+            + o32(offset)  # image data offset
+        )
+
+    # bitmap info header
+    fp.write(
+        o32(header)  # info header size
+        + o32(im.size[0])  # width
+        + o32(im.size[1])  # height
+        + o16(1)  # planes
+        + o16(bits)  # depth
+        + o32(0)  # compression (0=uncompressed)
+        + o32(image)  # size of bitmap
+        + o32(ppm[0])  # resolution
+        + o32(ppm[1])  # resolution
+        + o32(colors)  # colors used
+        + o32(colors)  # colors important
+    )
+
+    fp.write(b"\0" * (header - 40))  # padding (for OS/2 format)
+
+    if palette:
+        fp.write(palette)
+
+    ImageFile._save(im, fp, [("raw", (0, 0) + im.size, 0, (rawmode, stride, -1))])
+
+
+#
+# --------------------------------------------------------------------
+# Registry
+
+
+Image.register_open(BmpImageFile.format, BmpImageFile, _accept)
+Image.register_save(BmpImageFile.format, _save)
+
+Image.register_extension(BmpImageFile.format, ".bmp")
+
+Image.register_mime(BmpImageFile.format, "image/bmp")
+
+Image.register_decoder("bmp_rle", BmpRleDecoder)
+
+Image.register_open(DibImageFile.format, DibImageFile, _dib_accept)
+Image.register_save(DibImageFile.format, _dib_save)
+
+Image.register_extension(DibImageFile.format, ".dib")
+
+Image.register_mime(DibImageFile.format, "image/bmp")

myenv/Lib/site-packages/PIL/BufrStubImagePlugin.py

@@ -0,0 +1,76 @@
+#
+# The Python Imaging Library
+# $Id$
+#
+# BUFR stub adapter
+#
+# Copyright (c) 1996-2003 by Fredrik Lundh
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+from typing import IO
+
+from . import Image, ImageFile
+
+_handler = None
+
+
+def register_handler(handler: ImageFile.StubHandler | None) -> None:
+    """
+    Install application-specific BUFR image handler.
+
+    :param handler: Handler object.
+    """
+    global _handler
+    _handler = handler
+
+
+# --------------------------------------------------------------------
+# Image adapter
+
+
+def _accept(prefix: bytes) -> bool:
+    return prefix[:4] == b"BUFR" or prefix[:4] == b"ZCZC"
+
+
+class BufrStubImageFile(ImageFile.StubImageFile):
+    format = "BUFR"
+    format_description = "BUFR"
+
+    def _open(self) -> None:
+        offset = self.fp.tell()
+
+        if not _accept(self.fp.read(4)):
+            msg = "Not a BUFR file"
+            raise SyntaxError(msg)
+
+        self.fp.seek(offset)
+
+        # make something up
+        self._mode = "F"
+        self._size = 1, 1
+
+        loader = self._load()
+        if loader:
+            loader.open(self)
+
+    def _load(self) -> ImageFile.StubHandler | None:
+        return _handler
+
+
+def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None:
+    if _handler is None or not hasattr(_handler, "save"):
+        msg = "BUFR save handler not installed"
+        raise OSError(msg)
+    _handler.save(im, fp, filename)
+
+
+# --------------------------------------------------------------------
+# Registry
+
+Image.register_open(BufrStubImageFile.format, BufrStubImageFile, _accept)
+Image.register_save(BufrStubImageFile.format, _save)
+
+Image.register_extension(BufrStubImageFile.format, ".bufr")

myenv/Lib/site-packages/PIL/ContainerIO.py

@@ -0,0 +1,121 @@
+#
+# The Python Imaging Library.
+# $Id$
+#
+# a class to read from a container file
+#
+# History:
+# 1995-06-18 fl     Created
+# 1995-09-07 fl     Added readline(), readlines()
+#
+# Copyright (c) 1997-2001 by Secret Labs AB
+# Copyright (c) 1995 by Fredrik Lundh
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+import io
+from typing import IO, AnyStr, Generic, Literal
+
+
+class ContainerIO(Generic[AnyStr]):
+    """
+    A file object that provides read access to a part of an existing
+    file (for example a TAR file).
+    """
+
+    def __init__(self, file: IO[AnyStr], offset: int, length: int) -> None:
+        """
+        Create file object.
+
+        :param file: Existing file.
+        :param offset: Start of region, in bytes.
+        :param length: Size of region, in bytes.
+        """
+        self.fh: IO[AnyStr] = file
+        self.pos = 0
+        self.offset = offset
+        self.length = length
+        self.fh.seek(offset)
+
+    ##
+    # Always false.
+
+    def isatty(self) -> bool:
+        return False
+
+    def seek(self, offset: int, mode: Literal[0, 1, 2] = io.SEEK_SET) -> None:
+        """
+        Move file pointer.
+
+        :param offset: Offset in bytes.
+        :param mode: Starting position. Use 0 for beginning of region, 1
+           for current offset, and 2 for end of region.  You cannot move
+           the pointer outside the defined region.
+        """
+        if mode == 1:
+            self.pos = self.pos + offset
+        elif mode == 2:
+            self.pos = self.length + offset
+        else:
+            self.pos = offset
+        # clamp
+        self.pos = max(0, min(self.pos, self.length))
+        self.fh.seek(self.offset + self.pos)
+
+    def tell(self) -> int:
+        """
+        Get current file pointer.
+
+        :returns: Offset from start of region, in bytes.
+        """
+        return self.pos
+
+    def read(self, n: int = 0) -> AnyStr:
+        """
+        Read data.
+
+        :param n: Number of bytes to read. If omitted or zero,
+            read until end of region.
+        :returns: An 8-bit string.
+        """
+        if n:
+            n = min(n, self.length - self.pos)
+        else:
+            n = self.length - self.pos
+        if not n:  # EOF
+            return b"" if "b" in self.fh.mode else ""  # type: ignore[return-value]
+        self.pos = self.pos + n
+        return self.fh.read(n)
+
+    def readline(self) -> AnyStr:
+        """
+        Read a line of text.
+
+        :returns: An 8-bit string.
+        """
+        s: AnyStr = b"" if "b" in self.fh.mode else ""  # type: ignore[assignment]
+        newline_character = b"\n" if "b" in self.fh.mode else "\n"
+        while True:
+            c = self.read(1)
+            if not c:
+                break
+            s = s + c
+            if c == newline_character:
+                break
+        return s
+
+    def readlines(self) -> list[AnyStr]:
+        """
+        Read multiple lines of text.
+
+        :returns: A list of 8-bit strings.
+        """
+        lines = []
+        while True:
+            s = self.readline()
+            if not s:
+                break
+            lines.append(s)
+        return lines

myenv/Lib/site-packages/PIL/CurImagePlugin.py

@@ -0,0 +1,75 @@
+#
+# The Python Imaging Library.
+# $Id$
+#
+# Windows Cursor support for PIL
+#
+# notes:
+#       uses BmpImagePlugin.py to read the bitmap data.
+#
+# history:
+#       96-05-27 fl     Created
+#
+# Copyright (c) Secret Labs AB 1997.
+# Copyright (c) Fredrik Lundh 1996.
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+from . import BmpImagePlugin, Image
+from ._binary import i16le as i16
+from ._binary import i32le as i32
+
+#
+# --------------------------------------------------------------------
+
+
+def _accept(prefix: bytes) -> bool:
+    return prefix[:4] == b"\0\0\2\0"
+
+
+##
+# Image plugin for Windows Cursor files.
+
+
+class CurImageFile(BmpImagePlugin.BmpImageFile):
+    format = "CUR"
+    format_description = "Windows Cursor"
+
+    def _open(self) -> None:
+        offset = self.fp.tell()
+
+        # check magic
+        s = self.fp.read(6)
+        if not _accept(s):
+            msg = "not a CUR file"
+            raise SyntaxError(msg)
+
+        # pick the largest cursor in the file
+        m = b""
+        for i in range(i16(s, 4)):
+            s = self.fp.read(16)
+            if not m:
+                m = s
+            elif s[0] > m[0] and s[1] > m[1]:
+                m = s
+        if not m:
+            msg = "No cursors were found"
+            raise TypeError(msg)
+
+        # load as bitmap
+        self._bitmap(i32(m, 12) + offset)
+
+        # patch up the bitmap height
+        self._size = self.size[0], self.size[1] // 2
+        d, e, o, a = self.tile[0]
+        self.tile[0] = d, (0, 0) + self.size, o, a
+
+
+#
+# --------------------------------------------------------------------
+
+Image.register_open(CurImageFile.format, CurImageFile, _accept)
+
+Image.register_extension(CurImageFile.format, ".cur")

myenv/Lib/site-packages/PIL/DcxImagePlugin.py

@@ -0,0 +1,80 @@
+#
+# The Python Imaging Library.
+# $Id$
+#
+# DCX file handling
+#
+# DCX is a container file format defined by Intel, commonly used
+# for fax applications.  Each DCX file consists of a directory
+# (a list of file offsets) followed by a set of (usually 1-bit)
+# PCX files.
+#
+# History:
+# 1995-09-09 fl   Created
+# 1996-03-20 fl   Properly derived from PcxImageFile.
+# 1998-07-15 fl   Renamed offset attribute to avoid name clash
+# 2002-07-30 fl   Fixed file handling
+#
+# Copyright (c) 1997-98 by Secret Labs AB.
+# Copyright (c) 1995-96 by Fredrik Lundh.
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+from . import Image
+from ._binary import i32le as i32
+from .PcxImagePlugin import PcxImageFile
+
+MAGIC = 0x3ADE68B1  # QUIZ: what's this value, then?
+
+
+def _accept(prefix: bytes) -> bool:
+    return len(prefix) >= 4 and i32(prefix) == MAGIC
+
+
+##
+# Image plugin for the Intel DCX format.
+
+
+class DcxImageFile(PcxImageFile):
+    format = "DCX"
+    format_description = "Intel DCX"
+    _close_exclusive_fp_after_loading = False
+
+    def _open(self) -> None:
+        # Header
+        s = self.fp.read(4)
+        if not _accept(s):
+            msg = "not a DCX file"
+            raise SyntaxError(msg)
+
+        # Component directory
+        self._offset = []
+        for i in range(1024):
+            offset = i32(self.fp.read(4))
+            if not offset:
+                break
+            self._offset.append(offset)
+
+        self._fp = self.fp
+        self.frame = -1
+        self.n_frames = len(self._offset)
+        self.is_animated = self.n_frames > 1
+        self.seek(0)
+
+    def seek(self, frame: int) -> None:
+        if not self._seek_check(frame):
+            return
+        self.frame = frame
+        self.fp = self._fp
+        self.fp.seek(self._offset[frame])
+        PcxImageFile._open(self)
+
+    def tell(self) -> int:
+        return self.frame
+
+
+Image.register_open(DcxImageFile.format, DcxImageFile, _accept)
+
+Image.register_extension(DcxImageFile.format, ".dcx")

myenv/Lib/site-packages/PIL/DdsImagePlugin.py

@@ -0,0 +1,575 @@
+"""
+A Pillow loader for .dds files (S3TC-compressed aka DXTC)
+Jerome Leclanche <jerome@leclan.ch>
+
+Documentation:
+https://web.archive.org/web/20170802060935/http://oss.sgi.com/projects/ogl-sample/registry/EXT/texture_compression_s3tc.txt
+
+The contents of this file are hereby released in the public domain (CC0)
+Full text of the CC0 license:
+https://creativecommons.org/publicdomain/zero/1.0/
+"""
+
+from __future__ import annotations
+
+import io
+import struct
+import sys
+from enum import IntEnum, IntFlag
+from typing import IO
+
+from . import Image, ImageFile, ImagePalette
+from ._binary import i32le as i32
+from ._binary import o8
+from ._binary import o32le as o32
+
+# Magic ("DDS ")
+DDS_MAGIC = 0x20534444
+
+
+# DDS flags
+class DDSD(IntFlag):
+    CAPS = 0x1
+    HEIGHT = 0x2
+    WIDTH = 0x4
+    PITCH = 0x8
+    PIXELFORMAT = 0x1000
+    MIPMAPCOUNT = 0x20000
+    LINEARSIZE = 0x80000
+    DEPTH = 0x800000
+
+
+# DDS caps
+class DDSCAPS(IntFlag):
+    COMPLEX = 0x8
+    TEXTURE = 0x1000
+    MIPMAP = 0x400000
+
+
+class DDSCAPS2(IntFlag):
+    CUBEMAP = 0x200
+    CUBEMAP_POSITIVEX = 0x400
+    CUBEMAP_NEGATIVEX = 0x800
+    CUBEMAP_POSITIVEY = 0x1000
+    CUBEMAP_NEGATIVEY = 0x2000
+    CUBEMAP_POSITIVEZ = 0x4000
+    CUBEMAP_NEGATIVEZ = 0x8000
+    VOLUME = 0x200000
+
+
+# Pixel Format
+class DDPF(IntFlag):
+    ALPHAPIXELS = 0x1
+    ALPHA = 0x2
+    FOURCC = 0x4
+    PALETTEINDEXED8 = 0x20
+    RGB = 0x40
+    LUMINANCE = 0x20000
+
+
+# dxgiformat.h
+class DXGI_FORMAT(IntEnum):
+    UNKNOWN = 0
+    R32G32B32A32_TYPELESS = 1
+    R32G32B32A32_FLOAT = 2
+    R32G32B32A32_UINT = 3
+    R32G32B32A32_SINT = 4
+    R32G32B32_TYPELESS = 5
+    R32G32B32_FLOAT = 6
+    R32G32B32_UINT = 7
+    R32G32B32_SINT = 8
+    R16G16B16A16_TYPELESS = 9
+    R16G16B16A16_FLOAT = 10
+    R16G16B16A16_UNORM = 11
+    R16G16B16A16_UINT = 12
+    R16G16B16A16_SNORM = 13
+    R16G16B16A16_SINT = 14
+    R32G32_TYPELESS = 15
+    R32G32_FLOAT = 16
+    R32G32_UINT = 17
+    R32G32_SINT = 18
+    R32G8X24_TYPELESS = 19
+    D32_FLOAT_S8X24_UINT = 20
+    R32_FLOAT_X8X24_TYPELESS = 21
+    X32_TYPELESS_G8X24_UINT = 22
+    R10G10B10A2_TYPELESS = 23
+    R10G10B10A2_UNORM = 24
+    R10G10B10A2_UINT = 25
+    R11G11B10_FLOAT = 26
+    R8G8B8A8_TYPELESS = 27
+    R8G8B8A8_UNORM = 28
+    R8G8B8A8_UNORM_SRGB = 29
+    R8G8B8A8_UINT = 30
+    R8G8B8A8_SNORM = 31
+    R8G8B8A8_SINT = 32
+    R16G16_TYPELESS = 33
+    R16G16_FLOAT = 34
+    R16G16_UNORM = 35
+    R16G16_UINT = 36
+    R16G16_SNORM = 37
+    R16G16_SINT = 38
+    R32_TYPELESS = 39
+    D32_FLOAT = 40
+    R32_FLOAT = 41
+    R32_UINT = 42
+    R32_SINT = 43
+    R24G8_TYPELESS = 44
+    D24_UNORM_S8_UINT = 45
+    R24_UNORM_X8_TYPELESS = 46
+    X24_TYPELESS_G8_UINT = 47
+    R8G8_TYPELESS = 48
+    R8G8_UNORM = 49
+    R8G8_UINT = 50
+    R8G8_SNORM = 51
+    R8G8_SINT = 52
+    R16_TYPELESS = 53
+    R16_FLOAT = 54
+    D16_UNORM = 55
+    R16_UNORM = 56
+    R16_UINT = 57
+    R16_SNORM = 58
+    R16_SINT = 59
+    R8_TYPELESS = 60
+    R8_UNORM = 61
+    R8_UINT = 62
+    R8_SNORM = 63
+    R8_SINT = 64
+    A8_UNORM = 65
+    R1_UNORM = 66
+    R9G9B9E5_SHAREDEXP = 67
+    R8G8_B8G8_UNORM = 68
+    G8R8_G8B8_UNORM = 69
+    BC1_TYPELESS = 70
+    BC1_UNORM = 71
+    BC1_UNORM_SRGB = 72
+    BC2_TYPELESS = 73
+    BC2_UNORM = 74
+    BC2_UNORM_SRGB = 75
+    BC3_TYPELESS = 76
+    BC3_UNORM = 77
+    BC3_UNORM_SRGB = 78
+    BC4_TYPELESS = 79
+    BC4_UNORM = 80
+    BC4_SNORM = 81
+    BC5_TYPELESS = 82
+    BC5_UNORM = 83
+    BC5_SNORM = 84
+    B5G6R5_UNORM = 85
+    B5G5R5A1_UNORM = 86
+    B8G8R8A8_UNORM = 87
+    B8G8R8X8_UNORM = 88
+    R10G10B10_XR_BIAS_A2_UNORM = 89
+    B8G8R8A8_TYPELESS = 90
+    B8G8R8A8_UNORM_SRGB = 91
+    B8G8R8X8_TYPELESS = 92
+    B8G8R8X8_UNORM_SRGB = 93
+    BC6H_TYPELESS = 94
+    BC6H_UF16 = 95
+    BC6H_SF16 = 96
+    BC7_TYPELESS = 97
+    BC7_UNORM = 98
+    BC7_UNORM_SRGB = 99
+    AYUV = 100
+    Y410 = 101
+    Y416 = 102
+    NV12 = 103
+    P010 = 104
+    P016 = 105
+    OPAQUE_420 = 106
+    YUY2 = 107
+    Y210 = 108
+    Y216 = 109
+    NV11 = 110
+    AI44 = 111
+    IA44 = 112
+    P8 = 113
+    A8P8 = 114
+    B4G4R4A4_UNORM = 115
+    P208 = 130
+    V208 = 131
+    V408 = 132
+    SAMPLER_FEEDBACK_MIN_MIP_OPAQUE = 189
+    SAMPLER_FEEDBACK_MIP_REGION_USED_OPAQUE = 190
+
+
+class D3DFMT(IntEnum):
+    UNKNOWN = 0
+    R8G8B8 = 20
+    A8R8G8B8 = 21
+    X8R8G8B8 = 22
+    R5G6B5 = 23
+    X1R5G5B5 = 24
+    A1R5G5B5 = 25
+    A4R4G4B4 = 26
+    R3G3B2 = 27
+    A8 = 28
+    A8R3G3B2 = 29
+    X4R4G4B4 = 30
+    A2B10G10R10 = 31
+    A8B8G8R8 = 32
+    X8B8G8R8 = 33
+    G16R16 = 34
+    A2R10G10B10 = 35
+    A16B16G16R16 = 36
+    A8P8 = 40
+    P8 = 41
+    L8 = 50
+    A8L8 = 51
+    A4L4 = 52
+    V8U8 = 60
+    L6V5U5 = 61
+    X8L8V8U8 = 62
+    Q8W8V8U8 = 63
+    V16U16 = 64
+    A2W10V10U10 = 67
+    D16_LOCKABLE = 70
+    D32 = 71
+    D15S1 = 73
+    D24S8 = 75
+    D24X8 = 77
+    D24X4S4 = 79
+    D16 = 80
+    D32F_LOCKABLE = 82
+    D24FS8 = 83
+    D32_LOCKABLE = 84
+    S8_LOCKABLE = 85
+    L16 = 81
+    VERTEXDATA = 100
+    INDEX16 = 101
+    INDEX32 = 102
+    Q16W16V16U16 = 110
+    R16F = 111
+    G16R16F = 112
+    A16B16G16R16F = 113
+    R32F = 114
+    G32R32F = 115
+    A32B32G32R32F = 116
+    CxV8U8 = 117
+    A1 = 118
+    A2B10G10R10_XR_BIAS = 119
+    BINARYBUFFER = 199
+
+    UYVY = i32(b"UYVY")
+    R8G8_B8G8 = i32(b"RGBG")
+    YUY2 = i32(b"YUY2")
+    G8R8_G8B8 = i32(b"GRGB")
+    DXT1 = i32(b"DXT1")
+    DXT2 = i32(b"DXT2")
+    DXT3 = i32(b"DXT3")
+    DXT4 = i32(b"DXT4")
+    DXT5 = i32(b"DXT5")
+    DX10 = i32(b"DX10")
+    BC4S = i32(b"BC4S")
+    BC4U = i32(b"BC4U")
+    BC5S = i32(b"BC5S")
+    BC5U = i32(b"BC5U")
+    ATI1 = i32(b"ATI1")
+    ATI2 = i32(b"ATI2")
+    MULTI2_ARGB8 = i32(b"MET1")
+
+
+# Backward compatibility layer
+module = sys.modules[__name__]
+for item in DDSD:
+    assert item.name is not None
+    setattr(module, f"DDSD_{item.name}", item.value)
+for item1 in DDSCAPS:
+    assert item1.name is not None
+    setattr(module, f"DDSCAPS_{item1.name}", item1.value)
+for item2 in DDSCAPS2:
+    assert item2.name is not None
+    setattr(module, f"DDSCAPS2_{item2.name}", item2.value)
+for item3 in DDPF:
+    assert item3.name is not None
+    setattr(module, f"DDPF_{item3.name}", item3.value)
+
+DDS_FOURCC = DDPF.FOURCC
+DDS_RGB = DDPF.RGB
+DDS_RGBA = DDPF.RGB | DDPF.ALPHAPIXELS
+DDS_LUMINANCE = DDPF.LUMINANCE
+DDS_LUMINANCEA = DDPF.LUMINANCE | DDPF.ALPHAPIXELS
+DDS_ALPHA = DDPF.ALPHA
+DDS_PAL8 = DDPF.PALETTEINDEXED8
+
+DDS_HEADER_FLAGS_TEXTURE = DDSD.CAPS | DDSD.HEIGHT | DDSD.WIDTH | DDSD.PIXELFORMAT
+DDS_HEADER_FLAGS_MIPMAP = DDSD.MIPMAPCOUNT
+DDS_HEADER_FLAGS_VOLUME = DDSD.DEPTH
+DDS_HEADER_FLAGS_PITCH = DDSD.PITCH
+DDS_HEADER_FLAGS_LINEARSIZE = DDSD.LINEARSIZE
+
+DDS_HEIGHT = DDSD.HEIGHT
+DDS_WIDTH = DDSD.WIDTH
+
+DDS_SURFACE_FLAGS_TEXTURE = DDSCAPS.TEXTURE
+DDS_SURFACE_FLAGS_MIPMAP = DDSCAPS.COMPLEX | DDSCAPS.MIPMAP
+DDS_SURFACE_FLAGS_CUBEMAP = DDSCAPS.COMPLEX
+
+DDS_CUBEMAP_POSITIVEX = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_POSITIVEX
+DDS_CUBEMAP_NEGATIVEX = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_NEGATIVEX
+DDS_CUBEMAP_POSITIVEY = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_POSITIVEY
+DDS_CUBEMAP_NEGATIVEY = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_NEGATIVEY
+DDS_CUBEMAP_POSITIVEZ = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_POSITIVEZ
+DDS_CUBEMAP_NEGATIVEZ = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_NEGATIVEZ
+
+DXT1_FOURCC = D3DFMT.DXT1
+DXT3_FOURCC = D3DFMT.DXT3
+DXT5_FOURCC = D3DFMT.DXT5
+
+DXGI_FORMAT_R8G8B8A8_TYPELESS = DXGI_FORMAT.R8G8B8A8_TYPELESS
+DXGI_FORMAT_R8G8B8A8_UNORM = DXGI_FORMAT.R8G8B8A8_UNORM
+DXGI_FORMAT_R8G8B8A8_UNORM_SRGB = DXGI_FORMAT.R8G8B8A8_UNORM_SRGB
+DXGI_FORMAT_BC5_TYPELESS = DXGI_FORMAT.BC5_TYPELESS
+DXGI_FORMAT_BC5_UNORM = DXGI_FORMAT.BC5_UNORM
+DXGI_FORMAT_BC5_SNORM = DXGI_FORMAT.BC5_SNORM
+DXGI_FORMAT_BC6H_UF16 = DXGI_FORMAT.BC6H_UF16
+DXGI_FORMAT_BC6H_SF16 = DXGI_FORMAT.BC6H_SF16
+DXGI_FORMAT_BC7_TYPELESS = DXGI_FORMAT.BC7_TYPELESS
+DXGI_FORMAT_BC7_UNORM = DXGI_FORMAT.BC7_UNORM
+DXGI_FORMAT_BC7_UNORM_SRGB = DXGI_FORMAT.BC7_UNORM_SRGB
+
+
+class DdsImageFile(ImageFile.ImageFile):
+    format = "DDS"
+    format_description = "DirectDraw Surface"
+
+    def _open(self) -> None:
+        if not _accept(self.fp.read(4)):
+            msg = "not a DDS file"
+            raise SyntaxError(msg)
+        (header_size,) = struct.unpack("<I", self.fp.read(4))
+        if header_size != 124:
+            msg = f"Unsupported header size {repr(header_size)}"
+            raise OSError(msg)
+        header_bytes = self.fp.read(header_size - 4)
+        if len(header_bytes) != 120:
+            msg = f"Incomplete header: {len(header_bytes)} bytes"
+            raise OSError(msg)
+        header = io.BytesIO(header_bytes)
+
+        flags, height, width = struct.unpack("<3I", header.read(12))
+        self._size = (width, height)
+        extents = (0, 0) + self.size
+
+        pitch, depth, mipmaps = struct.unpack("<3I", header.read(12))
+        struct.unpack("<11I", header.read(44))  # reserved
+
+        # pixel format
+        pfsize, pfflags, fourcc, bitcount = struct.unpack("<4I", header.read(16))
+        n = 0
+        rawmode = None
+        if pfflags & DDPF.RGB:
+            # Texture contains uncompressed RGB data
+            if pfflags & DDPF.ALPHAPIXELS:
+                self._mode = "RGBA"
+                mask_count = 4
+            else:
+                self._mode = "RGB"
+                mask_count = 3
+
+            masks = struct.unpack(f"<{mask_count}I", header.read(mask_count * 4))
+            self.tile = [("dds_rgb", extents, 0, (bitcount, masks))]
+            return
+        elif pfflags & DDPF.LUMINANCE:
+            if bitcount == 8:
+                self._mode = "L"
+            elif bitcount == 16 and pfflags & DDPF.ALPHAPIXELS:
+                self._mode = "LA"
+            else:
+                msg = f"Unsupported bitcount {bitcount} for {pfflags}"
+                raise OSError(msg)
+        elif pfflags & DDPF.PALETTEINDEXED8:
+            self._mode = "P"
+            self.palette = ImagePalette.raw("RGBA", self.fp.read(1024))
+            self.palette.mode = "RGBA"
+        elif pfflags & DDPF.FOURCC:
+            offset = header_size + 4
+            if fourcc == D3DFMT.DXT1:
+                self._mode = "RGBA"
+                self.pixel_format = "DXT1"
+                n = 1
+            elif fourcc == D3DFMT.DXT3:
+                self._mode = "RGBA"
+                self.pixel_format = "DXT3"
+                n = 2
+            elif fourcc == D3DFMT.DXT5:
+                self._mode = "RGBA"
+                self.pixel_format = "DXT5"
+                n = 3
+            elif fourcc in (D3DFMT.BC4U, D3DFMT.ATI1):
+                self._mode = "L"
+                self.pixel_format = "BC4"
+                n = 4
+            elif fourcc == D3DFMT.BC5S:
+                self._mode = "RGB"
+                self.pixel_format = "BC5S"
+                n = 5
+            elif fourcc in (D3DFMT.BC5U, D3DFMT.ATI2):
+                self._mode = "RGB"
+                self.pixel_format = "BC5"
+                n = 5
+            elif fourcc == D3DFMT.DX10:
+                offset += 20
+                # ignoring flags which pertain to volume textures and cubemaps
+                (dxgi_format,) = struct.unpack("<I", self.fp.read(4))
+                self.fp.read(16)
+                if dxgi_format in (
+                    DXGI_FORMAT.BC1_UNORM,
+                    DXGI_FORMAT.BC1_TYPELESS,
+                ):
+                    self._mode = "RGBA"
+                    self.pixel_format = "BC1"
+                    n = 1
+                elif dxgi_format in (DXGI_FORMAT.BC4_TYPELESS, DXGI_FORMAT.BC4_UNORM):
+                    self._mode = "L"
+                    self.pixel_format = "BC4"
+                    n = 4
+                elif dxgi_format in (DXGI_FORMAT.BC5_TYPELESS, DXGI_FORMAT.BC5_UNORM):
+                    self._mode = "RGB"
+                    self.pixel_format = "BC5"
+                    n = 5
+                elif dxgi_format == DXGI_FORMAT.BC5_SNORM:
+                    self._mode = "RGB"
+                    self.pixel_format = "BC5S"
+                    n = 5
+                elif dxgi_format == DXGI_FORMAT.BC6H_UF16:
+                    self._mode = "RGB"
+                    self.pixel_format = "BC6H"
+                    n = 6
+                elif dxgi_format == DXGI_FORMAT.BC6H_SF16:
+                    self._mode = "RGB"
+                    self.pixel_format = "BC6HS"
+                    n = 6
+                elif dxgi_format in (
+                    DXGI_FORMAT.BC7_TYPELESS,
+                    DXGI_FORMAT.BC7_UNORM,
+                    DXGI_FORMAT.BC7_UNORM_SRGB,
+                ):
+                    self._mode = "RGBA"
+                    self.pixel_format = "BC7"
+                    n = 7
+                    if dxgi_format == DXGI_FORMAT.BC7_UNORM_SRGB:
+                        self.info["gamma"] = 1 / 2.2
+                elif dxgi_format in (
+                    DXGI_FORMAT.R8G8B8A8_TYPELESS,
+                    DXGI_FORMAT.R8G8B8A8_UNORM,
+                    DXGI_FORMAT.R8G8B8A8_UNORM_SRGB,
+                ):
+                    self._mode = "RGBA"
+                    if dxgi_format == DXGI_FORMAT.R8G8B8A8_UNORM_SRGB:
+                        self.info["gamma"] = 1 / 2.2
+                else:
+                    msg = f"Unimplemented DXGI format {dxgi_format}"
+                    raise NotImplementedError(msg)
+            else:
+                msg = f"Unimplemented pixel format {repr(fourcc)}"
+                raise NotImplementedError(msg)
+        else:
+            msg = f"Unknown pixel format flags {pfflags}"
+            raise NotImplementedError(msg)
+
+        if n:
+            self.tile = [
+                ImageFile._Tile("bcn", extents, offset, (n, self.pixel_format))
+            ]
+        else:
+            self.tile = [ImageFile._Tile("raw", extents, 0, rawmode or self.mode)]
+
+    def load_seek(self, pos: int) -> None:
+        pass
+
+
+class DdsRgbDecoder(ImageFile.PyDecoder):
+    _pulls_fd = True
+
+    def decode(self, buffer: bytes) -> tuple[int, int]:
+        assert self.fd is not None
+        bitcount, masks = self.args
+
+        # Some masks will be padded with zeros, e.g. R 0b11 G 0b1100
+        # Calculate how many zeros each mask is padded with
+        mask_offsets = []
+        # And the maximum value of each channel without the padding
+        mask_totals = []
+        for mask in masks:
+            offset = 0
+            if mask != 0:
+                while mask >> (offset + 1) << (offset + 1) == mask:
+                    offset += 1
+            mask_offsets.append(offset)
+            mask_totals.append(mask >> offset)
+
+        data = bytearray()
+        bytecount = bitcount // 8
+        dest_length = self.state.xsize * self.state.ysize * len(masks)
+        while len(data) < dest_length:
+            value = int.from_bytes(self.fd.read(bytecount), "little")
+            for i, mask in enumerate(masks):
+                masked_value = value & mask
+                # Remove the zero padding, and scale it to 8 bits
+                data += o8(
+                    int(((masked_value >> mask_offsets[i]) / mask_totals[i]) * 255)
+                )
+        self.set_as_raw(data)
+        return -1, 0
+
+
+def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None:
+    if im.mode not in ("RGB", "RGBA", "L", "LA"):
+        msg = f"cannot write mode {im.mode} as DDS"
+        raise OSError(msg)
+
+    alpha = im.mode[-1] == "A"
+    if im.mode[0] == "L":
+        pixel_flags = DDPF.LUMINANCE
+        rawmode = im.mode
+        if alpha:
+            rgba_mask = [0x000000FF, 0x000000FF, 0x000000FF]
+        else:
+            rgba_mask = [0xFF000000, 0xFF000000, 0xFF000000]
+    else:
+        pixel_flags = DDPF.RGB
+        rawmode = im.mode[::-1]
+        rgba_mask = [0x00FF0000, 0x0000FF00, 0x000000FF]
+
+        if alpha:
+            r, g, b, a = im.split()
+            im = Image.merge("RGBA", (a, r, g, b))
+    if alpha:
+        pixel_flags |= DDPF.ALPHAPIXELS
+    rgba_mask.append(0xFF000000 if alpha else 0)
+
+    flags = DDSD.CAPS | DDSD.HEIGHT | DDSD.WIDTH | DDSD.PITCH | DDSD.PIXELFORMAT
+    bitcount = len(im.getbands()) * 8
+    pitch = (im.width * bitcount + 7) // 8
+
+    fp.write(
+        o32(DDS_MAGIC)
+        + struct.pack(
+            "<7I",
+            124,  # header size
+            flags,  # flags
+            im.height,
+            im.width,
+            pitch,
+            0,  # depth
+            0,  # mipmaps
+        )
+        + struct.pack("11I", *((0,) * 11))  # reserved
+        # pfsize, pfflags, fourcc, bitcount
+        + struct.pack("<4I", 32, pixel_flags, 0, bitcount)
+        + struct.pack("<4I", *rgba_mask)  # dwRGBABitMask
+        + struct.pack("<5I", DDSCAPS.TEXTURE, 0, 0, 0, 0)
+    )
+    ImageFile._save(
+        im, fp, [ImageFile._Tile("raw", (0, 0) + im.size, 0, (rawmode, 0, 1))]
+    )
+
+
+def _accept(prefix: bytes) -> bool:
+    return prefix[:4] == b"DDS "
+
+
+Image.register_open(DdsImageFile.format, DdsImageFile, _accept)
+Image.register_decoder("dds_rgb", DdsRgbDecoder)
+Image.register_save(DdsImageFile.format, _save)
+Image.register_extension(DdsImageFile.format, ".dds")

myenv/Lib/site-packages/PIL/EpsImagePlugin.py

@@ -0,0 +1,478 @@
+#
+# The Python Imaging Library.
+# $Id$
+#
+# EPS file handling
+#
+# History:
+# 1995-09-01 fl   Created (0.1)
+# 1996-05-18 fl   Don't choke on "atend" fields, Ghostscript interface (0.2)
+# 1996-08-22 fl   Don't choke on floating point BoundingBox values
+# 1996-08-23 fl   Handle files from Macintosh (0.3)
+# 2001-02-17 fl   Use 're' instead of 'regex' (Python 2.1) (0.4)
+# 2003-09-07 fl   Check gs.close status (from Federico Di Gregorio) (0.5)
+# 2014-05-07 e    Handling of EPS with binary preview and fixed resolution
+#                 resizing
+#
+# Copyright (c) 1997-2003 by Secret Labs AB.
+# Copyright (c) 1995-2003 by Fredrik Lundh
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+import io
+import os
+import re
+import subprocess
+import sys
+import tempfile
+from typing import IO
+
+from . import Image, ImageFile
+from ._binary import i32le as i32
+from ._deprecate import deprecate
+
+# --------------------------------------------------------------------
+
+
+split = re.compile(r"^%%([^:]*):[ \t]*(.*)[ \t]*$")
+field = re.compile(r"^%[%!\w]([^:]*)[ \t]*$")
+
+gs_binary: str | bool | None = None
+gs_windows_binary = None
+
+
+def has_ghostscript() -> bool:
+    global gs_binary, gs_windows_binary
+    if gs_binary is None:
+        if sys.platform.startswith("win"):
+            if gs_windows_binary is None:
+                import shutil
+
+                for binary in ("gswin32c", "gswin64c", "gs"):
+                    if shutil.which(binary) is not None:
+                        gs_windows_binary = binary
+                        break
+                else:
+                    gs_windows_binary = False
+            gs_binary = gs_windows_binary
+        else:
+            try:
+                subprocess.check_call(["gs", "--version"], stdout=subprocess.DEVNULL)
+                gs_binary = "gs"
+            except OSError:
+                gs_binary = False
+    return gs_binary is not False
+
+
+def Ghostscript(tile, size, fp, scale=1, transparency=False):
+    """Render an image using Ghostscript"""
+    global gs_binary
+    if not has_ghostscript():
+        msg = "Unable to locate Ghostscript on paths"
+        raise OSError(msg)
+
+    # Unpack decoder tile
+    decoder, tile, offset, data = tile[0]
+    length, bbox = data
+
+    # Hack to support hi-res rendering
+    scale = int(scale) or 1
+    width = size[0] * scale
+    height = size[1] * scale
+    # resolution is dependent on bbox and size
+    res_x = 72.0 * width / (bbox[2] - bbox[0])
+    res_y = 72.0 * height / (bbox[3] - bbox[1])
+
+    out_fd, outfile = tempfile.mkstemp()
+    os.close(out_fd)
+
+    infile_temp = None
+    if hasattr(fp, "name") and os.path.exists(fp.name):
+        infile = fp.name
+    else:
+        in_fd, infile_temp = tempfile.mkstemp()
+        os.close(in_fd)
+        infile = infile_temp
+
+        # Ignore length and offset!
+        # Ghostscript can read it
+        # Copy whole file to read in Ghostscript
+        with open(infile_temp, "wb") as f:
+            # fetch length of fp
+            fp.seek(0, io.SEEK_END)
+            fsize = fp.tell()
+            # ensure start position
+            # go back
+            fp.seek(0)
+            lengthfile = fsize
+            while lengthfile > 0:
+                s = fp.read(min(lengthfile, 100 * 1024))
+                if not s:
+                    break
+                lengthfile -= len(s)
+                f.write(s)
+
+    device = "pngalpha" if transparency else "ppmraw"
+
+    # Build Ghostscript command
+    command = [
+        gs_binary,
+        "-q",  # quiet mode
+        f"-g{width:d}x{height:d}",  # set output geometry (pixels)
+        f"-r{res_x:f}x{res_y:f}",  # set input DPI (dots per inch)
+        "-dBATCH",  # exit after processing
+        "-dNOPAUSE",  # don't pause between pages
+        "-dSAFER",  # safe mode
+        f"-sDEVICE={device}",
+        f"-sOutputFile={outfile}",  # output file
+        # adjust for image origin
+        "-c",
+        f"{-bbox[0]} {-bbox[1]} translate",
+        "-f",
+        infile,  # input file
+        # showpage (see https://bugs.ghostscript.com/show_bug.cgi?id=698272)
+        "-c",
+        "showpage",
+    ]
+
+    # push data through Ghostscript
+    try:
+        startupinfo = None
+        if sys.platform.startswith("win"):
+            startupinfo = subprocess.STARTUPINFO()
+            startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
+        subprocess.check_call(command, startupinfo=startupinfo)
+        out_im = Image.open(outfile)
+        out_im.load()
+    finally:
+        try:
+            os.unlink(outfile)
+            if infile_temp:
+                os.unlink(infile_temp)
+        except OSError:
+            pass
+
+    im = out_im.im.copy()
+    out_im.close()
+    return im
+
+
+class PSFile:
+    """
+    Wrapper for bytesio object that treats either CR or LF as end of line.
+    This class is no longer used internally, but kept for backwards compatibility.
+    """
+
+    def __init__(self, fp):
+        deprecate(
+            "PSFile",
+            11,
+            action="If you need the functionality of this class "
+            "you will need to implement it yourself.",
+        )
+        self.fp = fp
+        self.char = None
+
+    def seek(self, offset, whence=io.SEEK_SET):
+        self.char = None
+        self.fp.seek(offset, whence)
+
+    def readline(self) -> str:
+        s = [self.char or b""]
+        self.char = None
+
+        c = self.fp.read(1)
+        while (c not in b"\r\n") and len(c):
+            s.append(c)
+            c = self.fp.read(1)
+
+        self.char = self.fp.read(1)
+        # line endings can be 1 or 2 of \r \n, in either order
+        if self.char in b"\r\n":
+            self.char = None
+
+        return b"".join(s).decode("latin-1")
+
+
+def _accept(prefix: bytes) -> bool:
+    return prefix[:4] == b"%!PS" or (len(prefix) >= 4 and i32(prefix) == 0xC6D3D0C5)
+
+
+##
+# Image plugin for Encapsulated PostScript. This plugin supports only
+# a few variants of this format.
+
+
+class EpsImageFile(ImageFile.ImageFile):
+    """EPS File Parser for the Python Imaging Library"""
+
+    format = "EPS"
+    format_description = "Encapsulated Postscript"
+
+    mode_map = {1: "L", 2: "LAB", 3: "RGB", 4: "CMYK"}
+
+    def _open(self) -> None:
+        (length, offset) = self._find_offset(self.fp)
+
+        # go to offset - start of "%!PS"
+        self.fp.seek(offset)
+
+        self._mode = "RGB"
+        self._size = None
+
+        byte_arr = bytearray(255)
+        bytes_mv = memoryview(byte_arr)
+        bytes_read = 0
+        reading_header_comments = True
+        reading_trailer_comments = False
+        trailer_reached = False
+
+        def check_required_header_comments() -> None:
+            """
+            The EPS specification requires that some headers exist.
+            This should be checked when the header comments formally end,
+            when image data starts, or when the file ends, whichever comes first.
+            """
+            if "PS-Adobe" not in self.info:
+                msg = 'EPS header missing "%!PS-Adobe" comment'
+                raise SyntaxError(msg)
+            if "BoundingBox" not in self.info:
+                msg = 'EPS header missing "%%BoundingBox" comment'
+                raise SyntaxError(msg)
+
+        def _read_comment(s: str) -> bool:
+            nonlocal reading_trailer_comments
+            try:
+                m = split.match(s)
+            except re.error as e:
+                msg = "not an EPS file"
+                raise SyntaxError(msg) from e
+
+            if not m:
+                return False
+
+            k, v = m.group(1, 2)
+            self.info[k] = v
+            if k == "BoundingBox":
+                if v == "(atend)":
+                    reading_trailer_comments = True
+                elif not self._size or (trailer_reached and reading_trailer_comments):
+                    try:
+                        # Note: The DSC spec says that BoundingBox
+                        # fields should be integers, but some drivers
+                        # put floating point values there anyway.
+                        box = [int(float(i)) for i in v.split()]
+                        self._size = box[2] - box[0], box[3] - box[1]
+                        self.tile = [("eps", (0, 0) + self.size, offset, (length, box))]
+                    except Exception:
+                        pass
+            return True
+
+        while True:
+            byte = self.fp.read(1)
+            if byte == b"":
+                # if we didn't read a byte we must be at the end of the file
+                if bytes_read == 0:
+                    if reading_header_comments:
+                        check_required_header_comments()
+                    break
+            elif byte in b"\r\n":
+                # if we read a line ending character, ignore it and parse what
+                # we have already read. if we haven't read any other characters,
+                # continue reading
+                if bytes_read == 0:
+                    continue
+            else:
+                # ASCII/hexadecimal lines in an EPS file must not exceed
+                # 255 characters, not including line ending characters
+                if bytes_read >= 255:
+                    # only enforce this for lines starting with a "%",
+                    # otherwise assume it's binary data
+                    if byte_arr[0] == ord("%"):
+                        msg = "not an EPS file"
+                        raise SyntaxError(msg)
+                    else:
+                        if reading_header_comments:
+                            check_required_header_comments()
+                            reading_header_comments = False
+                        # reset bytes_read so we can keep reading
+                        # data until the end of the line
+                        bytes_read = 0
+                byte_arr[bytes_read] = byte[0]
+                bytes_read += 1
+                continue
+
+            if reading_header_comments:
+                # Load EPS header
+
+                # if this line doesn't start with a "%",
+                # or does start with "%%EndComments",
+                # then we've reached the end of the header/comments
+                if byte_arr[0] != ord("%") or bytes_mv[:13] == b"%%EndComments":
+                    check_required_header_comments()
+                    reading_header_comments = False
+                    continue
+
+                s = str(bytes_mv[:bytes_read], "latin-1")
+                if not _read_comment(s):
+                    m = field.match(s)
+                    if m:
+                        k = m.group(1)
+                        if k[:8] == "PS-Adobe":
+                            self.info["PS-Adobe"] = k[9:]
+                        else:
+                            self.info[k] = ""
+                    elif s[0] == "%":
+                        # handle non-DSC PostScript comments that some
+                        # tools mistakenly put in the Comments section
+                        pass
+                    else:
+                        msg = "bad EPS header"
+                        raise OSError(msg)
+            elif bytes_mv[:11] == b"%ImageData:":
+                # Check for an "ImageData" descriptor
+                # https://www.adobe.com/devnet-apps/photoshop/fileformatashtml/#50577413_pgfId-1035096
+
+                # Values:
+                # columns
+                # rows
+                # bit depth (1 or 8)
+                # mode (1: L, 2: LAB, 3: RGB, 4: CMYK)
+                # number of padding channels
+                # block size (number of bytes per row per channel)
+                # binary/ascii (1: binary, 2: ascii)
+                # data start identifier (the image data follows after a single line
+                #   consisting only of this quoted value)
+                image_data_values = byte_arr[11:bytes_read].split(None, 7)
+                columns, rows, bit_depth, mode_id = (
+                    int(value) for value in image_data_values[:4]
+                )
+
+                if bit_depth == 1:
+                    self._mode = "1"
+                elif bit_depth == 8:
+                    try:
+                        self._mode = self.mode_map[mode_id]
+                    except ValueError:
+                        break
+                else:
+                    break
+
+                self._size = columns, rows
+                return
+            elif bytes_mv[:5] == b"%%EOF":
+                break
+            elif trailer_reached and reading_trailer_comments:
+                # Load EPS trailer
+                s = str(bytes_mv[:bytes_read], "latin-1")
+                _read_comment(s)
+            elif bytes_mv[:9] == b"%%Trailer":
+                trailer_reached = True
+            bytes_read = 0
+
+        if not self._size:
+            msg = "cannot determine EPS bounding box"
+            raise OSError(msg)
+
+    def _find_offset(self, fp):
+        s = fp.read(4)
+
+        if s == b"%!PS":
+            # for HEAD without binary preview
+            fp.seek(0, io.SEEK_END)
+            length = fp.tell()
+            offset = 0
+        elif i32(s) == 0xC6D3D0C5:
+            # FIX for: Some EPS file not handled correctly / issue #302
+            # EPS can contain binary data
+            # or start directly with latin coding
+            # more info see:
+            # https://web.archive.org/web/20160528181353/http://partners.adobe.com/public/developer/en/ps/5002.EPSF_Spec.pdf
+            s = fp.read(8)
+            offset = i32(s)
+            length = i32(s, 4)
+        else:
+            msg = "not an EPS file"
+            raise SyntaxError(msg)
+
+        return length, offset
+
+    def load(self, scale=1, transparency=False):
+        # Load EPS via Ghostscript
+        if self.tile:
+            self.im = Ghostscript(self.tile, self.size, self.fp, scale, transparency)
+            self._mode = self.im.mode
+            self._size = self.im.size
+            self.tile = []
+        return Image.Image.load(self)
+
+    def load_seek(self, pos: int) -> None:
+        # we can't incrementally load, so force ImageFile.parser to
+        # use our custom load method by defining this method.
+        pass
+
+
+# --------------------------------------------------------------------
+
+
+def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes, eps: int = 1) -> None:
+    """EPS Writer for the Python Imaging Library."""
+
+    # make sure image data is available
+    im.load()
+
+    # determine PostScript image mode
+    if im.mode == "L":
+        operator = (8, 1, b"image")
+    elif im.mode == "RGB":
+        operator = (8, 3, b"false 3 colorimage")
+    elif im.mode == "CMYK":
+        operator = (8, 4, b"false 4 colorimage")
+    else:
+        msg = "image mode is not supported"
+        raise ValueError(msg)
+
+    if eps:
+        # write EPS header
+        fp.write(b"%!PS-Adobe-3.0 EPSF-3.0\n")
+        fp.write(b"%%Creator: PIL 0.1 EpsEncode\n")
+        # fp.write("%%CreationDate: %s"...)
+        fp.write(b"%%%%BoundingBox: 0 0 %d %d\n" % im.size)
+        fp.write(b"%%Pages: 1\n")
+        fp.write(b"%%EndComments\n")
+        fp.write(b"%%Page: 1 1\n")
+        fp.write(b"%%ImageData: %d %d " % im.size)
+        fp.write(b'%d %d 0 1 1 "%s"\n' % operator)
+
+    # image header
+    fp.write(b"gsave\n")
+    fp.write(b"10 dict begin\n")
+    fp.write(b"/buf %d string def\n" % (im.size[0] * operator[1]))
+    fp.write(b"%d %d scale\n" % im.size)
+    fp.write(b"%d %d 8\n" % im.size)  # <= bits
+    fp.write(b"[%d 0 0 -%d 0 %d]\n" % (im.size[0], im.size[1], im.size[1]))
+    fp.write(b"{ currentfile buf readhexstring pop } bind\n")
+    fp.write(operator[2] + b"\n")
+    if hasattr(fp, "flush"):
+        fp.flush()
+
+    ImageFile._save(im, fp, [("eps", (0, 0) + im.size, 0, None)])
+
+    fp.write(b"\n%%%%EndBinary\n")
+    fp.write(b"grestore end\n")
+    if hasattr(fp, "flush"):
+        fp.flush()
+
+
+# --------------------------------------------------------------------
+
+
+Image.register_open(EpsImageFile.format, EpsImageFile, _accept)
+
+Image.register_save(EpsImageFile.format, _save)
+
+Image.register_extensions(EpsImageFile.format, [".ps", ".eps"])
+
+Image.register_mime(EpsImageFile.format, "application/postscript")

myenv/Lib/site-packages/PIL/ExifTags.py

@@ -0,0 +1,381 @@
+#
+# The Python Imaging Library.
+# $Id$
+#
+# EXIF tags
+#
+# Copyright (c) 2003 by Secret Labs AB
+#
+# See the README file for information on usage and redistribution.
+#
+
+"""
+This module provides constants and clear-text names for various
+well-known EXIF tags.
+"""
+from __future__ import annotations
+
+from enum import IntEnum
+
+
+class Base(IntEnum):
+    # possibly incomplete
+    InteropIndex = 0x0001
+    ProcessingSoftware = 0x000B
+    NewSubfileType = 0x00FE
+    SubfileType = 0x00FF
+    ImageWidth = 0x0100
+    ImageLength = 0x0101
+    BitsPerSample = 0x0102
+    Compression = 0x0103
+    PhotometricInterpretation = 0x0106
+    Thresholding = 0x0107
+    CellWidth = 0x0108
+    CellLength = 0x0109
+    FillOrder = 0x010A
+    DocumentName = 0x010D
+    ImageDescription = 0x010E
+    Make = 0x010F
+    Model = 0x0110
+    StripOffsets = 0x0111
+    Orientation = 0x0112
+    SamplesPerPixel = 0x0115
+    RowsPerStrip = 0x0116
+    StripByteCounts = 0x0117
+    MinSampleValue = 0x0118
+    MaxSampleValue = 0x0119
+    XResolution = 0x011A
+    YResolution = 0x011B
+    PlanarConfiguration = 0x011C
+    PageName = 0x011D
+    FreeOffsets = 0x0120
+    FreeByteCounts = 0x0121
+    GrayResponseUnit = 0x0122
+    GrayResponseCurve = 0x0123
+    T4Options = 0x0124
+    T6Options = 0x0125
+    ResolutionUnit = 0x0128
+    PageNumber = 0x0129
+    TransferFunction = 0x012D
+    Software = 0x0131
+    DateTime = 0x0132
+    Artist = 0x013B
+    HostComputer = 0x013C
+    Predictor = 0x013D
+    WhitePoint = 0x013E
+    PrimaryChromaticities = 0x013F
+    ColorMap = 0x0140
+    HalftoneHints = 0x0141
+    TileWidth = 0x0142
+    TileLength = 0x0143
+    TileOffsets = 0x0144
+    TileByteCounts = 0x0145
+    SubIFDs = 0x014A
+    InkSet = 0x014C
+    InkNames = 0x014D
+    NumberOfInks = 0x014E
+    DotRange = 0x0150
+    TargetPrinter = 0x0151
+    ExtraSamples = 0x0152
+    SampleFormat = 0x0153
+    SMinSampleValue = 0x0154
+    SMaxSampleValue = 0x0155
+    TransferRange = 0x0156
+    ClipPath = 0x0157
+    XClipPathUnits = 0x0158
+    YClipPathUnits = 0x0159
+    Indexed = 0x015A
+    JPEGTables = 0x015B
+    OPIProxy = 0x015F
+    JPEGProc = 0x0200
+    JpegIFOffset = 0x0201
+    JpegIFByteCount = 0x0202
+    JpegRestartInterval = 0x0203
+    JpegLosslessPredictors = 0x0205
+    JpegPointTransforms = 0x0206
+    JpegQTables = 0x0207
+    JpegDCTables = 0x0208
+    JpegACTables = 0x0209
+    YCbCrCoefficients = 0x0211
+    YCbCrSubSampling = 0x0212
+    YCbCrPositioning = 0x0213
+    ReferenceBlackWhite = 0x0214
+    XMLPacket = 0x02BC
+    RelatedImageFileFormat = 0x1000
+    RelatedImageWidth = 0x1001
+    RelatedImageLength = 0x1002
+    Rating = 0x4746
+    RatingPercent = 0x4749
+    ImageID = 0x800D
+    CFARepeatPatternDim = 0x828D
+    BatteryLevel = 0x828F
+    Copyright = 0x8298
+    ExposureTime = 0x829A
+    FNumber = 0x829D
+    IPTCNAA = 0x83BB
+    ImageResources = 0x8649
+    ExifOffset = 0x8769
+    InterColorProfile = 0x8773
+    ExposureProgram = 0x8822
+    SpectralSensitivity = 0x8824
+    GPSInfo = 0x8825
+    ISOSpeedRatings = 0x8827
+    OECF = 0x8828
+    Interlace = 0x8829
+    TimeZoneOffset = 0x882A
+    SelfTimerMode = 0x882B
+    SensitivityType = 0x8830
+    StandardOutputSensitivity = 0x8831
+    RecommendedExposureIndex = 0x8832
+    ISOSpeed = 0x8833
+    ISOSpeedLatitudeyyy = 0x8834
+    ISOSpeedLatitudezzz = 0x8835
+    ExifVersion = 0x9000
+    DateTimeOriginal = 0x9003
+    DateTimeDigitized = 0x9004
+    OffsetTime = 0x9010
+    OffsetTimeOriginal = 0x9011
+    OffsetTimeDigitized = 0x9012
+    ComponentsConfiguration = 0x9101
+    CompressedBitsPerPixel = 0x9102
+    ShutterSpeedValue = 0x9201
+    ApertureValue = 0x9202
+    BrightnessValue = 0x9203
+    ExposureBiasValue = 0x9204
+    MaxApertureValue = 0x9205
+    SubjectDistance = 0x9206
+    MeteringMode = 0x9207
+    LightSource = 0x9208
+    Flash = 0x9209
+    FocalLength = 0x920A
+    Noise = 0x920D
+    ImageNumber = 0x9211
+    SecurityClassification = 0x9212
+    ImageHistory = 0x9213
+    TIFFEPStandardID = 0x9216
+    MakerNote = 0x927C
+    UserComment = 0x9286
+    SubsecTime = 0x9290
+    SubsecTimeOriginal = 0x9291
+    SubsecTimeDigitized = 0x9292
+    AmbientTemperature = 0x9400
+    Humidity = 0x9401
+    Pressure = 0x9402
+    WaterDepth = 0x9403
+    Acceleration = 0x9404
+    CameraElevationAngle = 0x9405
+    XPTitle = 0x9C9B
+    XPComment = 0x9C9C
+    XPAuthor = 0x9C9D
+    XPKeywords = 0x9C9E
+    XPSubject = 0x9C9F
+    FlashPixVersion = 0xA000
+    ColorSpace = 0xA001
+    ExifImageWidth = 0xA002
+    ExifImageHeight = 0xA003
+    RelatedSoundFile = 0xA004
+    ExifInteroperabilityOffset = 0xA005
+    FlashEnergy = 0xA20B
+    SpatialFrequencyResponse = 0xA20C
+    FocalPlaneXResolution = 0xA20E
+    FocalPlaneYResolution = 0xA20F
+    FocalPlaneResolutionUnit = 0xA210
+    SubjectLocation = 0xA214
+    ExposureIndex = 0xA215
+    SensingMethod = 0xA217
+    FileSource = 0xA300
+    SceneType = 0xA301
+    CFAPattern = 0xA302
+    CustomRendered = 0xA401
+    ExposureMode = 0xA402
+    WhiteBalance = 0xA403
+    DigitalZoomRatio = 0xA404
+    FocalLengthIn35mmFilm = 0xA405
+    SceneCaptureType = 0xA406
+    GainControl = 0xA407
+    Contrast = 0xA408
+    Saturation = 0xA409
+    Sharpness = 0xA40A
+    DeviceSettingDescription = 0xA40B
+    SubjectDistanceRange = 0xA40C
+    ImageUniqueID = 0xA420
+    CameraOwnerName = 0xA430
+    BodySerialNumber = 0xA431
+    LensSpecification = 0xA432
+    LensMake = 0xA433
+    LensModel = 0xA434
+    LensSerialNumber = 0xA435
+    CompositeImage = 0xA460
+    CompositeImageCount = 0xA461
+    CompositeImageExposureTimes = 0xA462
+    Gamma = 0xA500
+    PrintImageMatching = 0xC4A5
+    DNGVersion = 0xC612
+    DNGBackwardVersion = 0xC613
+    UniqueCameraModel = 0xC614
+    LocalizedCameraModel = 0xC615
+    CFAPlaneColor = 0xC616
+    CFALayout = 0xC617
+    LinearizationTable = 0xC618
+    BlackLevelRepeatDim = 0xC619
+    BlackLevel = 0xC61A
+    BlackLevelDeltaH = 0xC61B
+    BlackLevelDeltaV = 0xC61C
+    WhiteLevel = 0xC61D
+    DefaultScale = 0xC61E
+    DefaultCropOrigin = 0xC61F
+    DefaultCropSize = 0xC620
+    ColorMatrix1 = 0xC621
+    ColorMatrix2 = 0xC622
+    CameraCalibration1 = 0xC623
+    CameraCalibration2 = 0xC624
+    ReductionMatrix1 = 0xC625
+    ReductionMatrix2 = 0xC626
+    AnalogBalance = 0xC627
+    AsShotNeutral = 0xC628
+    AsShotWhiteXY = 0xC629
+    BaselineExposure = 0xC62A
+    BaselineNoise = 0xC62B
+    BaselineSharpness = 0xC62C
+    BayerGreenSplit = 0xC62D
+    LinearResponseLimit = 0xC62E
+    CameraSerialNumber = 0xC62F
+    LensInfo = 0xC630
+    ChromaBlurRadius = 0xC631
+    AntiAliasStrength = 0xC632
+    ShadowScale = 0xC633
+    DNGPrivateData = 0xC634
+    MakerNoteSafety = 0xC635
+    CalibrationIlluminant1 = 0xC65A
+    CalibrationIlluminant2 = 0xC65B
+    BestQualityScale = 0xC65C
+    RawDataUniqueID = 0xC65D
+    OriginalRawFileName = 0xC68B
+    OriginalRawFileData = 0xC68C
+    ActiveArea = 0xC68D
+    MaskedAreas = 0xC68E
+    AsShotICCProfile = 0xC68F
+    AsShotPreProfileMatrix = 0xC690
+    CurrentICCProfile = 0xC691
+    CurrentPreProfileMatrix = 0xC692
+    ColorimetricReference = 0xC6BF
+    CameraCalibrationSignature = 0xC6F3
+    ProfileCalibrationSignature = 0xC6F4
+    AsShotProfileName = 0xC6F6
+    NoiseReductionApplied = 0xC6F7
+    ProfileName = 0xC6F8
+    ProfileHueSatMapDims = 0xC6F9
+    ProfileHueSatMapData1 = 0xC6FA
+    ProfileHueSatMapData2 = 0xC6FB
+    ProfileToneCurve = 0xC6FC
+    ProfileEmbedPolicy = 0xC6FD
+    ProfileCopyright = 0xC6FE
+    ForwardMatrix1 = 0xC714
+    ForwardMatrix2 = 0xC715
+    PreviewApplicationName = 0xC716
+    PreviewApplicationVersion = 0xC717
+    PreviewSettingsName = 0xC718
+    PreviewSettingsDigest = 0xC719
+    PreviewColorSpace = 0xC71A
+    PreviewDateTime = 0xC71B
+    RawImageDigest = 0xC71C
+    OriginalRawFileDigest = 0xC71D
+    SubTileBlockSize = 0xC71E
+    RowInterleaveFactor = 0xC71F
+    ProfileLookTableDims = 0xC725
+    ProfileLookTableData = 0xC726
+    OpcodeList1 = 0xC740
+    OpcodeList2 = 0xC741
+    OpcodeList3 = 0xC74E
+    NoiseProfile = 0xC761
+
+
+"""Maps EXIF tags to tag names."""
+TAGS = {
+    **{i.value: i.name for i in Base},
+    0x920C: "SpatialFrequencyResponse",
+    0x9214: "SubjectLocation",
+    0x9215: "ExposureIndex",
+    0x828E: "CFAPattern",
+    0x920B: "FlashEnergy",
+    0x9216: "TIFF/EPStandardID",
+}
+
+
+class GPS(IntEnum):
+    GPSVersionID = 0
+    GPSLatitudeRef = 1
+    GPSLatitude = 2
+    GPSLongitudeRef = 3
+    GPSLongitude = 4
+    GPSAltitudeRef = 5
+    GPSAltitude = 6
+    GPSTimeStamp = 7
+    GPSSatellites = 8
+    GPSStatus = 9
+    GPSMeasureMode = 10
+    GPSDOP = 11
+    GPSSpeedRef = 12
+    GPSSpeed = 13
+    GPSTrackRef = 14
+    GPSTrack = 15
+    GPSImgDirectionRef = 16
+    GPSImgDirection = 17
+    GPSMapDatum = 18
+    GPSDestLatitudeRef = 19
+    GPSDestLatitude = 20
+    GPSDestLongitudeRef = 21
+    GPSDestLongitude = 22
+    GPSDestBearingRef = 23
+    GPSDestBearing = 24
+    GPSDestDistanceRef = 25
+    GPSDestDistance = 26
+    GPSProcessingMethod = 27
+    GPSAreaInformation = 28
+    GPSDateStamp = 29
+    GPSDifferential = 30
+    GPSHPositioningError = 31
+
+
+"""Maps EXIF GPS tags to tag names."""
+GPSTAGS = {i.value: i.name for i in GPS}
+
+
+class Interop(IntEnum):
+    InteropIndex = 1
+    InteropVersion = 2
+    RelatedImageFileFormat = 4096
+    RelatedImageWidth = 4097
+    RelatedImageHeight = 4098
+
+
+class IFD(IntEnum):
+    Exif = 34665
+    GPSInfo = 34853
+    Makernote = 37500
+    Interop = 40965
+    IFD1 = -1
+
+
+class LightSource(IntEnum):
+    Unknown = 0
+    Daylight = 1
+    Fluorescent = 2
+    Tungsten = 3
+    Flash = 4
+    Fine = 9
+    Cloudy = 10
+    Shade = 11
+    DaylightFluorescent = 12
+    DayWhiteFluorescent = 13
+    CoolWhiteFluorescent = 14
+    WhiteFluorescent = 15
+    StandardLightA = 17
+    StandardLightB = 18
+    StandardLightC = 19
+    D55 = 20
+    D65 = 21
+    D75 = 22
+    D50 = 23
+    ISO = 24
+    Other = 255

myenv/Lib/site-packages/PIL/FitsImagePlugin.py

@@ -0,0 +1,152 @@
+#
+# The Python Imaging Library
+# $Id$
+#
+# FITS file handling
+#
+# Copyright (c) 1998-2003 by Fredrik Lundh
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+import gzip
+import math
+
+from . import Image, ImageFile
+
+
+def _accept(prefix: bytes) -> bool:
+    return prefix[:6] == b"SIMPLE"
+
+
+class FitsImageFile(ImageFile.ImageFile):
+    format = "FITS"
+    format_description = "FITS"
+
+    def _open(self) -> None:
+        assert self.fp is not None
+
+        headers: dict[bytes, bytes] = {}
+        header_in_progress = False
+        decoder_name = ""
+        while True:
+            header = self.fp.read(80)
+            if not header:
+                msg = "Truncated FITS file"
+                raise OSError(msg)
+            keyword = header[:8].strip()
+            if keyword in (b"SIMPLE", b"XTENSION"):
+                header_in_progress = True
+            elif headers and not header_in_progress:
+                # This is now a data unit
+                break
+            elif keyword == b"END":
+                # Seek to the end of the header unit
+                self.fp.seek(math.ceil(self.fp.tell() / 2880) * 2880)
+                if not decoder_name:
+                    decoder_name, offset, args = self._parse_headers(headers)
+
+                header_in_progress = False
+                continue
+
+            if decoder_name:
+                # Keep going to read past the headers
+                continue
+
+            value = header[8:].split(b"/")[0].strip()
+            if value.startswith(b"="):
+                value = value[1:].strip()
+            if not headers and (not _accept(keyword) or value != b"T"):
+                msg = "Not a FITS file"
+                raise SyntaxError(msg)
+            headers[keyword] = value
+
+        if not decoder_name:
+            msg = "No image data"
+            raise ValueError(msg)
+
+        offset += self.fp.tell() - 80
+        self.tile = [(decoder_name, (0, 0) + self.size, offset, args)]
+
+    def _get_size(
+        self, headers: dict[bytes, bytes], prefix: bytes
+    ) -> tuple[int, int] | None:
+        naxis = int(headers[prefix + b"NAXIS"])
+        if naxis == 0:
+            return None
+
+        if naxis == 1:
+            return 1, int(headers[prefix + b"NAXIS1"])
+        else:
+            return int(headers[prefix + b"NAXIS1"]), int(headers[prefix + b"NAXIS2"])
+
+    def _parse_headers(
+        self, headers: dict[bytes, bytes]
+    ) -> tuple[str, int, tuple[str | int, ...]]:
+        prefix = b""
+        decoder_name = "raw"
+        offset = 0
+        if (
+            headers.get(b"XTENSION") == b"'BINTABLE'"
+            and headers.get(b"ZIMAGE") == b"T"
+            and headers[b"ZCMPTYPE"] == b"'GZIP_1  '"
+        ):
+            no_prefix_size = self._get_size(headers, prefix) or (0, 0)
+            number_of_bits = int(headers[b"BITPIX"])
+            offset = no_prefix_size[0] * no_prefix_size[1] * (number_of_bits // 8)
+
+            prefix = b"Z"
+            decoder_name = "fits_gzip"
+
+        size = self._get_size(headers, prefix)
+        if not size:
+            return "", 0, ()
+
+        self._size = size
+
+        number_of_bits = int(headers[prefix + b"BITPIX"])
+        if number_of_bits == 8:
+            self._mode = "L"
+        elif number_of_bits == 16:
+            self._mode = "I;16"
+        elif number_of_bits == 32:
+            self._mode = "I"
+        elif number_of_bits in (-32, -64):
+            self._mode = "F"
+
+        args: tuple[str | int, ...]
+        if decoder_name == "raw":
+            args = (self.mode, 0, -1)
+        else:
+            args = (number_of_bits,)
+        return decoder_name, offset, args
+
+
+class FitsGzipDecoder(ImageFile.PyDecoder):
+    _pulls_fd = True
+
+    def decode(self, buffer: bytes) -> tuple[int, int]:
+        assert self.fd is not None
+        value = gzip.decompress(self.fd.read())
+
+        rows = []
+        offset = 0
+        number_of_bits = min(self.args[0] // 8, 4)
+        for y in range(self.state.ysize):
+            row = bytearray()
+            for x in range(self.state.xsize):
+                row += value[offset + (4 - number_of_bits) : offset + 4]
+                offset += 4
+            rows.append(row)
+        self.set_as_raw(bytes([pixel for row in rows[::-1] for pixel in row]))
+        return -1, 0
+
+
+# --------------------------------------------------------------------
+# Registry
+
+Image.register_open(FitsImageFile.format, FitsImageFile, _accept)
+Image.register_decoder("fits_gzip", FitsGzipDecoder)
+
+Image.register_extensions(FitsImageFile.format, [".fit", ".fits"])

myenv/Lib/site-packages/PIL/FliImagePlugin.py

@@ -0,0 +1,174 @@
+#
+# The Python Imaging Library.
+# $Id$
+#
+# FLI/FLC file handling.
+#
+# History:
+#       95-09-01 fl     Created
+#       97-01-03 fl     Fixed parser, setup decoder tile
+#       98-07-15 fl     Renamed offset attribute to avoid name clash
+#
+# Copyright (c) Secret Labs AB 1997-98.
+# Copyright (c) Fredrik Lundh 1995-97.
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+import os
+
+from . import Image, ImageFile, ImagePalette
+from ._binary import i16le as i16
+from ._binary import i32le as i32
+from ._binary import o8
+
+#
+# decoder
+
+
+def _accept(prefix: bytes) -> bool:
+    return (
+        len(prefix) >= 6
+        and i16(prefix, 4) in [0xAF11, 0xAF12]
+        and i16(prefix, 14) in [0, 3]  # flags
+    )
+
+
+##
+# Image plugin for the FLI/FLC animation format.  Use the <b>seek</b>
+# method to load individual frames.
+
+
+class FliImageFile(ImageFile.ImageFile):
+    format = "FLI"
+    format_description = "Autodesk FLI/FLC Animation"
+    _close_exclusive_fp_after_loading = False
+
+    def _open(self):
+        # HEAD
+        s = self.fp.read(128)
+        if not (_accept(s) and s[20:22] == b"\x00\x00"):
+            msg = "not an FLI/FLC file"
+            raise SyntaxError(msg)
+
+        # frames
+        self.n_frames = i16(s, 6)
+        self.is_animated = self.n_frames > 1
+
+        # image characteristics
+        self._mode = "P"
+        self._size = i16(s, 8), i16(s, 10)
+
+        # animation speed
+        duration = i32(s, 16)
+        magic = i16(s, 4)
+        if magic == 0xAF11:
+            duration = (duration * 1000) // 70
+        self.info["duration"] = duration
+
+        # look for palette
+        palette = [(a, a, a) for a in range(256)]
+
+        s = self.fp.read(16)
+
+        self.__offset = 128
+
+        if i16(s, 4) == 0xF100:
+            # prefix chunk; ignore it
+            self.__offset = self.__offset + i32(s)
+            self.fp.seek(self.__offset)
+            s = self.fp.read(16)
+
+        if i16(s, 4) == 0xF1FA:
+            # look for palette chunk
+            number_of_subchunks = i16(s, 6)
+            chunk_size = None
+            for _ in range(number_of_subchunks):
+                if chunk_size is not None:
+                    self.fp.seek(chunk_size - 6, os.SEEK_CUR)
+                s = self.fp.read(6)
+                chunk_type = i16(s, 4)
+                if chunk_type in (4, 11):
+                    self._palette(palette, 2 if chunk_type == 11 else 0)
+                    break
+                chunk_size = i32(s)
+                if not chunk_size:
+                    break
+
+        palette = [o8(r) + o8(g) + o8(b) for (r, g, b) in palette]
+        self.palette = ImagePalette.raw("RGB", b"".join(palette))
+
+        # set things up to decode first frame
+        self.__frame = -1
+        self._fp = self.fp
+        self.__rewind = self.fp.tell()
+        self.seek(0)
+
+    def _palette(self, palette, shift):
+        # load palette
+
+        i = 0
+        for e in range(i16(self.fp.read(2))):
+            s = self.fp.read(2)
+            i = i + s[0]
+            n = s[1]
+            if n == 0:
+                n = 256
+            s = self.fp.read(n * 3)
+            for n in range(0, len(s), 3):
+                r = s[n] << shift
+                g = s[n + 1] << shift
+                b = s[n + 2] << shift
+                palette[i] = (r, g, b)
+                i += 1
+
+    def seek(self, frame: int) -> None:
+        if not self._seek_check(frame):
+            return
+        if frame < self.__frame:
+            self._seek(0)
+
+        for f in range(self.__frame + 1, frame + 1):
+            self._seek(f)
+
+    def _seek(self, frame: int) -> None:
+        if frame == 0:
+            self.__frame = -1
+            self._fp.seek(self.__rewind)
+            self.__offset = 128
+        else:
+            # ensure that the previous frame was loaded
+            self.load()
+
+        if frame != self.__frame + 1:
+            msg = f"cannot seek to frame {frame}"
+            raise ValueError(msg)
+        self.__frame = frame
+
+        # move to next frame
+        self.fp = self._fp
+        self.fp.seek(self.__offset)
+
+        s = self.fp.read(4)
+        if not s:
+            msg = "missing frame size"
+            raise EOFError(msg)
+
+        framesize = i32(s)
+
+        self.decodermaxblock = framesize
+        self.tile = [("fli", (0, 0) + self.size, self.__offset, None)]
+
+        self.__offset += framesize
+
+    def tell(self) -> int:
+        return self.__frame
+
+
+#
+# registry
+
+Image.register_open(FliImageFile.format, FliImageFile, _accept)
+
+Image.register_extensions(FliImageFile.format, [".fli", ".flc"])

myenv/Lib/site-packages/PIL/FontFile.py

@@ -0,0 +1,134 @@
+#
+# The Python Imaging Library
+# $Id$
+#
+# base class for raster font file parsers
+#
+# history:
+# 1997-06-05 fl   created
+# 1997-08-19 fl   restrict image width
+#
+# Copyright (c) 1997-1998 by Secret Labs AB
+# Copyright (c) 1997-1998 by Fredrik Lundh
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+import os
+from typing import BinaryIO
+
+from . import Image, _binary
+
+WIDTH = 800
+
+
+def puti16(
+    fp: BinaryIO, values: tuple[int, int, int, int, int, int, int, int, int, int]
+) -> None:
+    """Write network order (big-endian) 16-bit sequence"""
+    for v in values:
+        if v < 0:
+            v += 65536
+        fp.write(_binary.o16be(v))
+
+
+class FontFile:
+    """Base class for raster font file handlers."""
+
+    bitmap: Image.Image | None = None
+
+    def __init__(self) -> None:
+        self.info: dict[bytes, bytes | int] = {}
+        self.glyph: list[
+            tuple[
+                tuple[int, int],
+                tuple[int, int, int, int],
+                tuple[int, int, int, int],
+                Image.Image,
+            ]
+            | None
+        ] = [None] * 256
+
+    def __getitem__(self, ix: int) -> (
+        tuple[
+            tuple[int, int],
+            tuple[int, int, int, int],
+            tuple[int, int, int, int],
+            Image.Image,
+        ]
+        | None
+    ):
+        return self.glyph[ix]
+
+    def compile(self) -> None:
+        """Create metrics and bitmap"""
+
+        if self.bitmap:
+            return
+
+        # create bitmap large enough to hold all data
+        h = w = maxwidth = 0
+        lines = 1
+        for glyph in self.glyph:
+            if glyph:
+                d, dst, src, im = glyph
+                h = max(h, src[3] - src[1])
+                w = w + (src[2] - src[0])
+                if w > WIDTH:
+                    lines += 1
+                    w = src[2] - src[0]
+                maxwidth = max(maxwidth, w)
+
+        xsize = maxwidth
+        ysize = lines * h
+
+        if xsize == 0 and ysize == 0:
+            return
+
+        self.ysize = h
+
+        # paste glyphs into bitmap
+        self.bitmap = Image.new("1", (xsize, ysize))
+        self.metrics: list[
+            tuple[tuple[int, int], tuple[int, int, int, int], tuple[int, int, int, int]]
+            | None
+        ] = [None] * 256
+        x = y = 0
+        for i in range(256):
+            glyph = self[i]
+            if glyph:
+                d, dst, src, im = glyph
+                xx = src[2] - src[0]
+                x0, y0 = x, y
+                x = x + xx
+                if x > WIDTH:
+                    x, y = 0, y + h
+                    x0, y0 = x, y
+                    x = xx
+                s = src[0] + x0, src[1] + y0, src[2] + x0, src[3] + y0
+                self.bitmap.paste(im.crop(src), s)
+                self.metrics[i] = d, dst, s
+
+    def save(self, filename: str) -> None:
+        """Save font"""
+
+        self.compile()
+
+        # font data
+        if not self.bitmap:
+            msg = "No bitmap created"
+            raise ValueError(msg)
+        self.bitmap.save(os.path.splitext(filename)[0] + ".pbm", "PNG")
+
+        # font metrics
+        with open(os.path.splitext(filename)[0] + ".pil", "wb") as fp:
+            fp.write(b"PILfont\n")
+            fp.write(f";;;;;;{self.ysize};\n".encode("ascii"))  # HACK!!!
+            fp.write(b"DATA\n")
+            for id in range(256):
+                m = self.metrics[id]
+                if not m:
+                    puti16(fp, (0,) * 10)
+                else:
+                    puti16(fp, m[0] + m[1] + m[2])

myenv/Lib/site-packages/PIL/FpxImagePlugin.py

@@ -0,0 +1,255 @@
+#
+# THIS IS WORK IN PROGRESS
+#
+# The Python Imaging Library.
+# $Id$
+#
+# FlashPix support for PIL
+#
+# History:
+# 97-01-25 fl   Created (reads uncompressed RGB images only)
+#
+# Copyright (c) Secret Labs AB 1997.
+# Copyright (c) Fredrik Lundh 1997.
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+import olefile
+
+from . import Image, ImageFile
+from ._binary import i32le as i32
+
+# we map from colour field tuples to (mode, rawmode) descriptors
+MODES = {
+    # opacity
+    (0x00007FFE,): ("A", "L"),
+    # monochrome
+    (0x00010000,): ("L", "L"),
+    (0x00018000, 0x00017FFE): ("RGBA", "LA"),
+    # photo YCC
+    (0x00020000, 0x00020001, 0x00020002): ("RGB", "YCC;P"),
+    (0x00028000, 0x00028001, 0x00028002, 0x00027FFE): ("RGBA", "YCCA;P"),
+    # standard RGB (NIFRGB)
+    (0x00030000, 0x00030001, 0x00030002): ("RGB", "RGB"),
+    (0x00038000, 0x00038001, 0x00038002, 0x00037FFE): ("RGBA", "RGBA"),
+}
+
+
+#
+# --------------------------------------------------------------------
+
+
+def _accept(prefix: bytes) -> bool:
+    return prefix[:8] == olefile.MAGIC
+
+
+##
+# Image plugin for the FlashPix images.
+
+
+class FpxImageFile(ImageFile.ImageFile):
+    format = "FPX"
+    format_description = "FlashPix"
+
+    def _open(self):
+        #
+        # read the OLE directory and see if this is a likely
+        # to be a FlashPix file
+
+        try:
+            self.ole = olefile.OleFileIO(self.fp)
+        except OSError as e:
+            msg = "not an FPX file; invalid OLE file"
+            raise SyntaxError(msg) from e
+
+        if self.ole.root.clsid != "56616700-C154-11CE-8553-00AA00A1F95B":
+            msg = "not an FPX file; bad root CLSID"
+            raise SyntaxError(msg)
+
+        self._open_index(1)
+
+    def _open_index(self, index: int = 1) -> None:
+        #
+        # get the Image Contents Property Set
+
+        prop = self.ole.getproperties(
+            [f"Data Object Store {index:06d}", "\005Image Contents"]
+        )
+
+        # size (highest resolution)
+
+        self._size = prop[0x1000002], prop[0x1000003]
+
+        size = max(self.size)
+        i = 1
+        while size > 64:
+            size = size // 2
+            i += 1
+        self.maxid = i - 1
+
+        # mode.  instead of using a single field for this, flashpix
+        # requires you to specify the mode for each channel in each
+        # resolution subimage, and leaves it to the decoder to make
+        # sure that they all match.  for now, we'll cheat and assume
+        # that this is always the case.
+
+        id = self.maxid << 16
+
+        s = prop[0x2000002 | id]
+
+        bands = i32(s, 4)
+        if bands > 4:
+            msg = "Invalid number of bands"
+            raise OSError(msg)
+
+        # note: for now, we ignore the "uncalibrated" flag
+        colors = tuple(i32(s, 8 + i * 4) & 0x7FFFFFFF for i in range(bands))
+
+        self._mode, self.rawmode = MODES[colors]
+
+        # load JPEG tables, if any
+        self.jpeg = {}
+        for i in range(256):
+            id = 0x3000001 | (i << 16)
+            if id in prop:
+                self.jpeg[i] = prop[id]
+
+        self._open_subimage(1, self.maxid)
+
+    def _open_subimage(self, index: int = 1, subimage: int = 0) -> None:
+        #
+        # setup tile descriptors for a given subimage
+
+        stream = [
+            f"Data Object Store {index:06d}",
+            f"Resolution {subimage:04d}",
+            "Subimage 0000 Header",
+        ]
+
+        fp = self.ole.openstream(stream)
+
+        # skip prefix
+        fp.read(28)
+
+        # header stream
+        s = fp.read(36)
+
+        size = i32(s, 4), i32(s, 8)
+        # tilecount = i32(s, 12)
+        tilesize = i32(s, 16), i32(s, 20)
+        # channels = i32(s, 24)
+        offset = i32(s, 28)
+        length = i32(s, 32)
+
+        if size != self.size:
+            msg = "subimage mismatch"
+            raise OSError(msg)
+
+        # get tile descriptors
+        fp.seek(28 + offset)
+        s = fp.read(i32(s, 12) * length)
+
+        x = y = 0
+        xsize, ysize = size
+        xtile, ytile = tilesize
+        self.tile = []
+
+        for i in range(0, len(s), length):
+            x1 = min(xsize, x + xtile)
+            y1 = min(ysize, y + ytile)
+
+            compression = i32(s, i + 8)
+
+            if compression == 0:
+                self.tile.append(
+                    (
+                        "raw",
+                        (x, y, x1, y1),
+                        i32(s, i) + 28,
+                        (self.rawmode,),
+                    )
+                )
+
+            elif compression == 1:
+                # FIXME: the fill decoder is not implemented
+                self.tile.append(
+                    (
+                        "fill",
+                        (x, y, x1, y1),
+                        i32(s, i) + 28,
+                        (self.rawmode, s[12:16]),
+                    )
+                )
+
+            elif compression == 2:
+                internal_color_conversion = s[14]
+                jpeg_tables = s[15]
+                rawmode = self.rawmode
+
+                if internal_color_conversion:
+                    # The image is stored as usual (usually YCbCr).
+                    if rawmode == "RGBA":
+                        # For "RGBA", data is stored as YCbCrA based on
+                        # negative RGB. The following trick works around
+                        # this problem :
+                        jpegmode, rawmode = "YCbCrK", "CMYK"
+                    else:
+                        jpegmode = None  # let the decoder decide
+
+                else:
+                    # The image is stored as defined by rawmode
+                    jpegmode = rawmode
+
+                self.tile.append(
+                    (
+                        "jpeg",
+                        (x, y, x1, y1),
+                        i32(s, i) + 28,
+                        (rawmode, jpegmode),
+                    )
+                )
+
+                # FIXME: jpeg tables are tile dependent; the prefix
+                # data must be placed in the tile descriptor itself!
+
+                if jpeg_tables:
+                    self.tile_prefix = self.jpeg[jpeg_tables]
+
+            else:
+                msg = "unknown/invalid compression"
+                raise OSError(msg)
+
+            x = x + xtile
+            if x >= xsize:
+                x, y = 0, y + ytile
+                if y >= ysize:
+                    break  # isn't really required
+
+        self.stream = stream
+        self._fp = self.fp
+        self.fp = None
+
+    def load(self):
+        if not self.fp:
+            self.fp = self.ole.openstream(self.stream[:2] + ["Subimage 0000 Data"])
+
+        return ImageFile.ImageFile.load(self)
+
+    def close(self) -> None:
+        self.ole.close()
+        super().close()
+
+    def __exit__(self, *args: object) -> None:
+        self.ole.close()
+        super().__exit__()
+
+
+#
+# --------------------------------------------------------------------
+
+
+Image.register_open(FpxImageFile.format, FpxImageFile, _accept)
+
+Image.register_extension(FpxImageFile.format, ".fpx")

myenv/Lib/site-packages/PIL/FtexImagePlugin.py

@@ -0,0 +1,115 @@
+"""
+A Pillow loader for .ftc and .ftu files (FTEX)
+Jerome Leclanche <jerome@leclan.ch>
+
+The contents of this file are hereby released in the public domain (CC0)
+Full text of the CC0 license:
+  https://creativecommons.org/publicdomain/zero/1.0/
+
+Independence War 2: Edge Of Chaos - Texture File Format - 16 October 2001
+
+The textures used for 3D objects in Independence War 2: Edge Of Chaos are in a
+packed custom format called FTEX. This file format uses file extensions FTC
+and FTU.
+* FTC files are compressed textures (using standard texture compression).
+* FTU files are not compressed.
+Texture File Format
+The FTC and FTU texture files both use the same format. This
+has the following structure:
+{header}
+{format_directory}
+{data}
+Where:
+{header} = {
+    u32:magic,
+    u32:version,
+    u32:width,
+    u32:height,
+    u32:mipmap_count,
+    u32:format_count
+}
+
+* The "magic" number is "FTEX".
+* "width" and "height" are the dimensions of the texture.
+* "mipmap_count" is the number of mipmaps in the texture.
+* "format_count" is the number of texture formats (different versions of the
+same texture) in this file.
+
+{format_directory} = format_count * { u32:format, u32:where }
+
+The format value is 0 for DXT1 compressed textures and 1 for 24-bit RGB
+uncompressed textures.
+The texture data for a format starts at the position "where" in the file.
+
+Each set of texture data in the file has the following structure:
+{data} = format_count * { u32:mipmap_size, mipmap_size * { u8 } }
+* "mipmap_size" is the number of bytes in that mip level. For compressed
+textures this is the size of the texture data compressed with DXT1. For 24 bit
+uncompressed textures, this is 3 * width * height. Following this are the image
+bytes for that mipmap level.
+
+Note: All data is stored in little-Endian (Intel) byte order.
+"""
+
+from __future__ import annotations
+
+import struct
+from enum import IntEnum
+from io import BytesIO
+
+from . import Image, ImageFile
+
+MAGIC = b"FTEX"
+
+
+class Format(IntEnum):
+    DXT1 = 0
+    UNCOMPRESSED = 1
+
+
+class FtexImageFile(ImageFile.ImageFile):
+    format = "FTEX"
+    format_description = "Texture File Format (IW2:EOC)"
+
+    def _open(self) -> None:
+        if not _accept(self.fp.read(4)):
+            msg = "not an FTEX file"
+            raise SyntaxError(msg)
+        struct.unpack("<i", self.fp.read(4))  # version
+        self._size = struct.unpack("<2i", self.fp.read(8))
+        mipmap_count, format_count = struct.unpack("<2i", self.fp.read(8))
+
+        self._mode = "RGB"
+
+        # Only support single-format files.
+        # I don't know of any multi-format file.
+        assert format_count == 1
+
+        format, where = struct.unpack("<2i", self.fp.read(8))
+        self.fp.seek(where)
+        (mipmap_size,) = struct.unpack("<i", self.fp.read(4))
+
+        data = self.fp.read(mipmap_size)
+
+        if format == Format.DXT1:
+            self._mode = "RGBA"
+            self.tile = [("bcn", (0, 0) + self.size, 0, 1)]
+        elif format == Format.UNCOMPRESSED:
+            self.tile = [("raw", (0, 0) + self.size, 0, ("RGB", 0, 1))]
+        else:
+            msg = f"Invalid texture compression format: {repr(format)}"
+            raise ValueError(msg)
+
+        self.fp.close()
+        self.fp = BytesIO(data)
+
+    def load_seek(self, pos: int) -> None:
+        pass
+
+
+def _accept(prefix: bytes) -> bool:
+    return prefix[:4] == MAGIC
+
+
+Image.register_open(FtexImageFile.format, FtexImageFile, _accept)
+Image.register_extensions(FtexImageFile.format, [".ftc", ".ftu"])

myenv/Lib/site-packages/PIL/GbrImagePlugin.py

@@ -0,0 +1,103 @@
+#
+# The Python Imaging Library
+#
+# load a GIMP brush file
+#
+# History:
+#       96-03-14 fl     Created
+#       16-01-08 es     Version 2
+#
+# Copyright (c) Secret Labs AB 1997.
+# Copyright (c) Fredrik Lundh 1996.
+# Copyright (c) Eric Soroos 2016.
+#
+# See the README file for information on usage and redistribution.
+#
+#
+# See https://github.com/GNOME/gimp/blob/mainline/devel-docs/gbr.txt for
+# format documentation.
+#
+# This code Interprets version 1 and 2 .gbr files.
+# Version 1 files are obsolete, and should not be used for new
+#   brushes.
+# Version 2 files are saved by GIMP v2.8 (at least)
+# Version 3 files have a format specifier of 18 for 16bit floats in
+#   the color depth field. This is currently unsupported by Pillow.
+from __future__ import annotations
+
+from . import Image, ImageFile
+from ._binary import i32be as i32
+
+
+def _accept(prefix: bytes) -> bool:
+    return len(prefix) >= 8 and i32(prefix, 0) >= 20 and i32(prefix, 4) in (1, 2)
+
+
+##
+# Image plugin for the GIMP brush format.
+
+
+class GbrImageFile(ImageFile.ImageFile):
+    format = "GBR"
+    format_description = "GIMP brush file"
+
+    def _open(self) -> None:
+        header_size = i32(self.fp.read(4))
+        if header_size < 20:
+            msg = "not a GIMP brush"
+            raise SyntaxError(msg)
+        version = i32(self.fp.read(4))
+        if version not in (1, 2):
+            msg = f"Unsupported GIMP brush version: {version}"
+            raise SyntaxError(msg)
+
+        width = i32(self.fp.read(4))
+        height = i32(self.fp.read(4))
+        color_depth = i32(self.fp.read(4))
+        if width <= 0 or height <= 0:
+            msg = "not a GIMP brush"
+            raise SyntaxError(msg)
+        if color_depth not in (1, 4):
+            msg = f"Unsupported GIMP brush color depth: {color_depth}"
+            raise SyntaxError(msg)
+
+        if version == 1:
+            comment_length = header_size - 20
+        else:
+            comment_length = header_size - 28
+            magic_number = self.fp.read(4)
+            if magic_number != b"GIMP":
+                msg = "not a GIMP brush, bad magic number"
+                raise SyntaxError(msg)
+            self.info["spacing"] = i32(self.fp.read(4))
+
+        comment = self.fp.read(comment_length)[:-1]
+
+        if color_depth == 1:
+            self._mode = "L"
+        else:
+            self._mode = "RGBA"
+
+        self._size = width, height
+
+        self.info["comment"] = comment
+
+        # Image might not be small
+        Image._decompression_bomb_check(self.size)
+
+        # Data is an uncompressed block of w * h * bytes/pixel
+        self._data_size = width * height * color_depth
+
+    def load(self):
+        if not self.im:
+            self.im = Image.core.new(self.mode, self.size)
+            self.frombytes(self.fp.read(self._data_size))
+        return Image.Image.load(self)
+
+
+#
+# registry
+
+
+Image.register_open(GbrImageFile.format, GbrImageFile, _accept)
+Image.register_extension(GbrImageFile.format, ".gbr")

myenv/Lib/site-packages/PIL/GdImageFile.py

@@ -0,0 +1,102 @@
+#
+# The Python Imaging Library.
+# $Id$
+#
+# GD file handling
+#
+# History:
+# 1996-04-12 fl   Created
+#
+# Copyright (c) 1997 by Secret Labs AB.
+# Copyright (c) 1996 by Fredrik Lundh.
+#
+# See the README file for information on usage and redistribution.
+#
+
+
+"""
+.. note::
+    This format cannot be automatically recognized, so the
+    class is not registered for use with :py:func:`PIL.Image.open()`.  To open a
+    gd file, use the :py:func:`PIL.GdImageFile.open()` function instead.
+
+.. warning::
+    THE GD FORMAT IS NOT DESIGNED FOR DATA INTERCHANGE.  This
+    implementation is provided for convenience and demonstrational
+    purposes only.
+"""
+from __future__ import annotations
+
+from typing import IO
+
+from . import ImageFile, ImagePalette, UnidentifiedImageError
+from ._binary import i16be as i16
+from ._binary import i32be as i32
+from ._typing import StrOrBytesPath
+
+
+class GdImageFile(ImageFile.ImageFile):
+    """
+    Image plugin for the GD uncompressed format.  Note that this format
+    is not supported by the standard :py:func:`PIL.Image.open()` function.  To use
+    this plugin, you have to import the :py:mod:`PIL.GdImageFile` module and
+    use the :py:func:`PIL.GdImageFile.open()` function.
+    """
+
+    format = "GD"
+    format_description = "GD uncompressed images"
+
+    def _open(self) -> None:
+        # Header
+        assert self.fp is not None
+
+        s = self.fp.read(1037)
+
+        if i16(s) not in [65534, 65535]:
+            msg = "Not a valid GD 2.x .gd file"
+            raise SyntaxError(msg)
+
+        self._mode = "L"  # FIXME: "P"
+        self._size = i16(s, 2), i16(s, 4)
+
+        true_color = s[6]
+        true_color_offset = 2 if true_color else 0
+
+        # transparency index
+        tindex = i32(s, 7 + true_color_offset)
+        if tindex < 256:
+            self.info["transparency"] = tindex
+
+        self.palette = ImagePalette.raw(
+            "XBGR", s[7 + true_color_offset + 4 : 7 + true_color_offset + 4 + 256 * 4]
+        )
+
+        self.tile = [
+            (
+                "raw",
+                (0, 0) + self.size,
+                7 + true_color_offset + 4 + 256 * 4,
+                ("L", 0, 1),
+            )
+        ]
+
+
+def open(fp: StrOrBytesPath | IO[bytes], mode: str = "r") -> GdImageFile:
+    """
+    Load texture from a GD image file.
+
+    :param fp: GD file name, or an opened file handle.
+    :param mode: Optional mode.  In this version, if the mode argument
+        is given, it must be "r".
+    :returns: An image instance.
+    :raises OSError: If the image could not be read.
+    """
+    if mode != "r":
+        msg = "bad mode"
+        raise ValueError(msg)
+
+    try:
+        return GdImageFile(fp)
+    except SyntaxError as e:
+        msg = "cannot identify this image file"
+        raise UnidentifiedImageError(msg) from e

myenv/Lib/site-packages/PIL/GifImagePlugin.py

@@ -0,0 +1,1159 @@
+#
+# The Python Imaging Library.
+# $Id$
+#
+# GIF file handling
+#
+# History:
+# 1995-09-01 fl   Created
+# 1996-12-14 fl   Added interlace support
+# 1996-12-30 fl   Added animation support
+# 1997-01-05 fl   Added write support, fixed local colour map bug
+# 1997-02-23 fl   Make sure to load raster data in getdata()
+# 1997-07-05 fl   Support external decoder (0.4)
+# 1998-07-09 fl   Handle all modes when saving (0.5)
+# 1998-07-15 fl   Renamed offset attribute to avoid name clash
+# 2001-04-16 fl   Added rewind support (seek to frame 0) (0.6)
+# 2001-04-17 fl   Added palette optimization (0.7)
+# 2002-06-06 fl   Added transparency support for save (0.8)
+# 2004-02-24 fl   Disable interlacing for small images
+#
+# Copyright (c) 1997-2004 by Secret Labs AB
+# Copyright (c) 1995-2004 by Fredrik Lundh
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+import itertools
+import math
+import os
+import subprocess
+import sys
+from enum import IntEnum
+from functools import cached_property
+from typing import IO, TYPE_CHECKING, Any, List, Literal, NamedTuple, Union
+
+from . import (
+    Image,
+    ImageChops,
+    ImageFile,
+    ImageMath,
+    ImageOps,
+    ImagePalette,
+    ImageSequence,
+)
+from ._binary import i16le as i16
+from ._binary import o8
+from ._binary import o16le as o16
+
+if TYPE_CHECKING:
+    from . import _imaging
+
+
+class LoadingStrategy(IntEnum):
+    """.. versionadded:: 9.1.0"""
+
+    RGB_AFTER_FIRST = 0
+    RGB_AFTER_DIFFERENT_PALETTE_ONLY = 1
+    RGB_ALWAYS = 2
+
+
+#: .. versionadded:: 9.1.0
+LOADING_STRATEGY = LoadingStrategy.RGB_AFTER_FIRST
+
+# --------------------------------------------------------------------
+# Identify/read GIF files
+
+
+def _accept(prefix: bytes) -> bool:
+    return prefix[:6] in [b"GIF87a", b"GIF89a"]
+
+
+##
+# Image plugin for GIF images.  This plugin supports both GIF87 and
+# GIF89 images.
+
+
+class GifImageFile(ImageFile.ImageFile):
+    format = "GIF"
+    format_description = "Compuserve GIF"
+    _close_exclusive_fp_after_loading = False
+
+    global_palette = None
+
+    def data(self) -> bytes | None:
+        s = self.fp.read(1)
+        if s and s[0]:
+            return self.fp.read(s[0])
+        return None
+
+    def _is_palette_needed(self, p: bytes) -> bool:
+        for i in range(0, len(p), 3):
+            if not (i // 3 == p[i] == p[i + 1] == p[i + 2]):
+                return True
+        return False
+
+    def _open(self) -> None:
+        # Screen
+        s = self.fp.read(13)
+        if not _accept(s):
+            msg = "not a GIF file"
+            raise SyntaxError(msg)
+
+        self.info["version"] = s[:6]
+        self._size = i16(s, 6), i16(s, 8)
+        self.tile = []
+        flags = s[10]
+        bits = (flags & 7) + 1
+
+        if flags & 128:
+            # get global palette
+            self.info["background"] = s[11]
+            # check if palette contains colour indices
+            p = self.fp.read(3 << bits)
+            if self._is_palette_needed(p):
+                p = ImagePalette.raw("RGB", p)
+                self.global_palette = self.palette = p
+
+        self._fp = self.fp  # FIXME: hack
+        self.__rewind = self.fp.tell()
+        self._n_frames: int | None = None
+        self._seek(0)  # get ready to read first frame
+
+    @property
+    def n_frames(self) -> int:
+        if self._n_frames is None:
+            current = self.tell()
+            try:
+                while True:
+                    self._seek(self.tell() + 1, False)
+            except EOFError:
+                self._n_frames = self.tell() + 1
+            self.seek(current)
+        return self._n_frames
+
+    @cached_property
+    def is_animated(self) -> bool:
+        if self._n_frames is not None:
+            return self._n_frames != 1
+
+        current = self.tell()
+        if current:
+            return True
+
+        try:
+            self._seek(1, False)
+            is_animated = True
+        except EOFError:
+            is_animated = False
+
+        self.seek(current)
+        return is_animated
+
+    def seek(self, frame: int) -> None:
+        if not self._seek_check(frame):
+            return
+        if frame < self.__frame:
+            self.im = None
+            self._seek(0)
+
+        last_frame = self.__frame
+        for f in range(self.__frame + 1, frame + 1):
+            try:
+                self._seek(f)
+            except EOFError as e:
+                self.seek(last_frame)
+                msg = "no more images in GIF file"
+                raise EOFError(msg) from e
+
+    def _seek(self, frame: int, update_image: bool = True) -> None:
+        if frame == 0:
+            # rewind
+            self.__offset = 0
+            self.dispose: _imaging.ImagingCore | None = None
+            self.__frame = -1
+            self._fp.seek(self.__rewind)
+            self.disposal_method = 0
+            if "comment" in self.info:
+                del self.info["comment"]
+        else:
+            # ensure that the previous frame was loaded
+            if self.tile and update_image:
+                self.load()
+
+        if frame != self.__frame + 1:
+            msg = f"cannot seek to frame {frame}"
+            raise ValueError(msg)
+
+        self.fp = self._fp
+        if self.__offset:
+            # backup to last frame
+            self.fp.seek(self.__offset)
+            while self.data():
+                pass
+            self.__offset = 0
+
+        s = self.fp.read(1)
+        if not s or s == b";":
+            msg = "no more images in GIF file"
+            raise EOFError(msg)
+
+        palette: ImagePalette.ImagePalette | Literal[False] | None = None
+
+        info: dict[str, Any] = {}
+        frame_transparency = None
+        interlace = None
+        frame_dispose_extent = None
+        while True:
+            if not s:
+                s = self.fp.read(1)
+            if not s or s == b";":
+                break
+
+            elif s == b"!":
+                #
+                # extensions
+                #
+                s = self.fp.read(1)
+                block = self.data()
+                if s[0] == 249 and block is not None:
+                    #
+                    # graphic control extension
+                    #
+                    flags = block[0]
+                    if flags & 1:
+                        frame_transparency = block[3]
+                    info["duration"] = i16(block, 1) * 10
+
+                    # disposal method - find the value of bits 4 - 6
+                    dispose_bits = 0b00011100 & flags
+                    dispose_bits = dispose_bits >> 2
+                    if dispose_bits:
+                        # only set the dispose if it is not
+                        # unspecified. I'm not sure if this is
+                        # correct, but it seems to prevent the last
+                        # frame from looking odd for some animations
+                        self.disposal_method = dispose_bits
+                elif s[0] == 254:
+                    #
+                    # comment extension
+                    #
+                    comment = b""
+
+                    # Read this comment block
+                    while block:
+                        comment += block
+                        block = self.data()
+
+                    if "comment" in info:
+                        # If multiple comment blocks in frame, separate with \n
+                        info["comment"] += b"\n" + comment
+                    else:
+                        info["comment"] = comment
+                    s = None
+                    continue
+                elif s[0] == 255 and frame == 0 and block is not None:
+                    #
+                    # application extension
+                    #
+                    info["extension"] = block, self.fp.tell()
+                    if block[:11] == b"NETSCAPE2.0":
+                        block = self.data()
+                        if block and len(block) >= 3 and block[0] == 1:
+                            self.info["loop"] = i16(block, 1)
+                while self.data():
+                    pass
+
+            elif s == b",":
+                #
+                # local image
+                #
+                s = self.fp.read(9)
+
+                # extent
+                x0, y0 = i16(s, 0), i16(s, 2)
+                x1, y1 = x0 + i16(s, 4), y0 + i16(s, 6)
+                if (x1 > self.size[0] or y1 > self.size[1]) and update_image:
+                    self._size = max(x1, self.size[0]), max(y1, self.size[1])
+                    Image._decompression_bomb_check(self._size)
+                frame_dispose_extent = x0, y0, x1, y1
+                flags = s[8]
+
+                interlace = (flags & 64) != 0
+
+                if flags & 128:
+                    bits = (flags & 7) + 1
+                    p = self.fp.read(3 << bits)
+                    if self._is_palette_needed(p):
+                        palette = ImagePalette.raw("RGB", p)
+                    else:
+                        palette = False
+
+                # image data
+                bits = self.fp.read(1)[0]
+                self.__offset = self.fp.tell()
+                break
+            s = None
+
+        if interlace is None:
+            msg = "image not found in GIF frame"
+            raise EOFError(msg)
+
+        self.__frame = frame
+        if not update_image:
+            return
+
+        self.tile = []
+
+        if self.dispose:
+            self.im.paste(self.dispose, self.dispose_extent)
+
+        self._frame_palette = palette if palette is not None else self.global_palette
+        self._frame_transparency = frame_transparency
+        if frame == 0:
+            if self._frame_palette:
+                if LOADING_STRATEGY == LoadingStrategy.RGB_ALWAYS:
+                    self._mode = "RGBA" if frame_transparency is not None else "RGB"
+                else:
+                    self._mode = "P"
+            else:
+                self._mode = "L"
+
+            if not palette and self.global_palette:
+                from copy import copy
+
+                palette = copy(self.global_palette)
+            self.palette = palette
+        else:
+            if self.mode == "P":
+                if (
+                    LOADING_STRATEGY != LoadingStrategy.RGB_AFTER_DIFFERENT_PALETTE_ONLY
+                    or palette
+                ):
+                    self.pyaccess = None
+                    if "transparency" in self.info:
+                        self.im.putpalettealpha(self.info["transparency"], 0)
+                        self.im = self.im.convert("RGBA", Image.Dither.FLOYDSTEINBERG)
+                        self._mode = "RGBA"
+                        del self.info["transparency"]
+                    else:
+                        self._mode = "RGB"
+                        self.im = self.im.convert("RGB", Image.Dither.FLOYDSTEINBERG)
+
+        def _rgb(color: int) -> tuple[int, int, int]:
+            if self._frame_palette:
+                if color * 3 + 3 > len(self._frame_palette.palette):
+                    color = 0
+                return tuple(self._frame_palette.palette[color * 3 : color * 3 + 3])
+            else:
+                return (color, color, color)
+
+        self.dispose = None
+        self.dispose_extent = frame_dispose_extent
+        if self.dispose_extent and self.disposal_method >= 2:
+            try:
+                if self.disposal_method == 2:
+                    # replace with background colour
+
+                    # only dispose the extent in this frame
+                    x0, y0, x1, y1 = self.dispose_extent
+                    dispose_size = (x1 - x0, y1 - y0)
+
+                    Image._decompression_bomb_check(dispose_size)
+
+                    # by convention, attempt to use transparency first
+                    dispose_mode = "P"
+                    color = self.info.get("transparency", frame_transparency)
+                    if color is not None:
+                        if self.mode in ("RGB", "RGBA"):
+                            dispose_mode = "RGBA"
+                            color = _rgb(color) + (0,)
+                    else:
+                        color = self.info.get("background", 0)
+                        if self.mode in ("RGB", "RGBA"):
+                            dispose_mode = "RGB"
+                            color = _rgb(color)
+                    self.dispose = Image.core.fill(dispose_mode, dispose_size, color)
+                else:
+                    # replace with previous contents
+                    if self.im is not None:
+                        # only dispose the extent in this frame
+                        self.dispose = self._crop(self.im, self.dispose_extent)
+                    elif frame_transparency is not None:
+                        x0, y0, x1, y1 = self.dispose_extent
+                        dispose_size = (x1 - x0, y1 - y0)
+
+                        Image._decompression_bomb_check(dispose_size)
+                        dispose_mode = "P"
+                        color = frame_transparency
+                        if self.mode in ("RGB", "RGBA"):
+                            dispose_mode = "RGBA"
+                            color = _rgb(frame_transparency) + (0,)
+                        self.dispose = Image.core.fill(
+                            dispose_mode, dispose_size, color
+                        )
+            except AttributeError:
+                pass
+
+        if interlace is not None:
+            transparency = -1
+            if frame_transparency is not None:
+                if frame == 0:
+                    if LOADING_STRATEGY != LoadingStrategy.RGB_ALWAYS:
+                        self.info["transparency"] = frame_transparency
+                elif self.mode not in ("RGB", "RGBA"):
+                    transparency = frame_transparency
+            self.tile = [
+                (
+                    "gif",
+                    (x0, y0, x1, y1),
+                    self.__offset,
+                    (bits, interlace, transparency),
+                )
+            ]
+
+        if info.get("comment"):
+            self.info["comment"] = info["comment"]
+        for k in ["duration", "extension"]:
+            if k in info:
+                self.info[k] = info[k]
+            elif k in self.info:
+                del self.info[k]
+
+    def load_prepare(self) -> None:
+        temp_mode = "P" if self._frame_palette else "L"
+        self._prev_im = None
+        if self.__frame == 0:
+            if self._frame_transparency is not None:
+                self.im = Image.core.fill(
+                    temp_mode, self.size, self._frame_transparency
+                )
+        elif self.mode in ("RGB", "RGBA"):
+            self._prev_im = self.im
+            if self._frame_palette:
+                self.im = Image.core.fill("P", self.size, self._frame_transparency or 0)
+                self.im.putpalette("RGB", *self._frame_palette.getdata())
+            else:
+                self.im = None
+        self._mode = temp_mode
+        self._frame_palette = None
+
+        super().load_prepare()
+
+    def load_end(self) -> None:
+        if self.__frame == 0:
+            if self.mode == "P" and LOADING_STRATEGY == LoadingStrategy.RGB_ALWAYS:
+                if self._frame_transparency is not None:
+                    self.im.putpalettealpha(self._frame_transparency, 0)
+                    self._mode = "RGBA"
+                else:
+                    self._mode = "RGB"
+                self.im = self.im.convert(self.mode, Image.Dither.FLOYDSTEINBERG)
+            return
+        if not self._prev_im:
+            return
+        if self._frame_transparency is not None:
+            self.im.putpalettealpha(self._frame_transparency, 0)
+            frame_im = self.im.convert("RGBA")
+        else:
+            frame_im = self.im.convert("RGB")
+
+        assert self.dispose_extent is not None
+        frame_im = self._crop(frame_im, self.dispose_extent)
+
+        self.im = self._prev_im
+        self._mode = self.im.mode
+        if frame_im.mode == "RGBA":
+            self.im.paste(frame_im, self.dispose_extent, frame_im)
+        else:
+            self.im.paste(frame_im, self.dispose_extent)
+
+    def tell(self) -> int:
+        return self.__frame
+
+
+# --------------------------------------------------------------------
+# Write GIF files
+
+
+RAWMODE = {"1": "L", "L": "L", "P": "P"}
+
+
+def _normalize_mode(im: Image.Image) -> Image.Image:
+    """
+    Takes an image (or frame), returns an image in a mode that is appropriate
+    for saving in a Gif.
+
+    It may return the original image, or it may return an image converted to
+    palette or 'L' mode.
+
+    :param im: Image object
+    :returns: Image object
+    """
+    if im.mode in RAWMODE:
+        im.load()
+        return im
+    if Image.getmodebase(im.mode) == "RGB":
+        im = im.convert("P", palette=Image.Palette.ADAPTIVE)
+        if im.palette.mode == "RGBA":
+            for rgba in im.palette.colors:
+                if rgba[3] == 0:
+                    im.info["transparency"] = im.palette.colors[rgba]
+                    break
+        return im
+    return im.convert("L")
+
+
+_Palette = Union[bytes, bytearray, List[int], ImagePalette.ImagePalette]
+
+
+def _normalize_palette(
+    im: Image.Image, palette: _Palette | None, info: dict[str, Any]
+) -> Image.Image:
+    """
+    Normalizes the palette for image.
+      - Sets the palette to the incoming palette, if provided.
+      - Ensures that there's a palette for L mode images
+      - Optimizes the palette if necessary/desired.
+
+    :param im: Image object
+    :param palette: bytes object containing the source palette, or ....
+    :param info: encoderinfo
+    :returns: Image object
+    """
+    source_palette = None
+    if palette:
+        # a bytes palette
+        if isinstance(palette, (bytes, bytearray, list)):
+            source_palette = bytearray(palette[:768])
+        if isinstance(palette, ImagePalette.ImagePalette):
+            source_palette = bytearray(palette.palette)
+
+    if im.mode == "P":
+        if not source_palette:
+            source_palette = im.im.getpalette("RGB")[:768]
+    else:  # L-mode
+        if not source_palette:
+            source_palette = bytearray(i // 3 for i in range(768))
+        im.palette = ImagePalette.ImagePalette("RGB", palette=source_palette)
+
+    used_palette_colors: list[int] | None
+    if palette:
+        used_palette_colors = []
+        assert source_palette is not None
+        for i in range(0, len(source_palette), 3):
+            source_color = tuple(source_palette[i : i + 3])
+            index = im.palette.colors.get(source_color)
+            if index in used_palette_colors:
+                index = None
+            used_palette_colors.append(index)
+        for i, index in enumerate(used_palette_colors):
+            if index is None:
+                for j in range(len(used_palette_colors)):
+                    if j not in used_palette_colors:
+                        used_palette_colors[i] = j
+                        break
+        im = im.remap_palette(used_palette_colors)
+    else:
+        used_palette_colors = _get_optimize(im, info)
+        if used_palette_colors is not None:
+            im = im.remap_palette(used_palette_colors, source_palette)
+            if "transparency" in info:
+                try:
+                    info["transparency"] = used_palette_colors.index(
+                        info["transparency"]
+                    )
+                except ValueError:
+                    del info["transparency"]
+            return im
+
+    im.palette.palette = source_palette
+    return im
+
+
+def _write_single_frame(
+    im: Image.Image,
+    fp: IO[bytes],
+    palette: _Palette | None,
+) -> None:
+    im_out = _normalize_mode(im)
+    for k, v in im_out.info.items():
+        im.encoderinfo.setdefault(k, v)
+    im_out = _normalize_palette(im_out, palette, im.encoderinfo)
+
+    for s in _get_global_header(im_out, im.encoderinfo):
+        fp.write(s)
+
+    # local image header
+    flags = 0
+    if get_interlace(im):
+        flags = flags | 64
+    _write_local_header(fp, im, (0, 0), flags)
+
+    im_out.encoderconfig = (8, get_interlace(im))
+    ImageFile._save(im_out, fp, [("gif", (0, 0) + im.size, 0, RAWMODE[im_out.mode])])
+
+    fp.write(b"\0")  # end of image data
+
+
+def _getbbox(
+    base_im: Image.Image, im_frame: Image.Image
+) -> tuple[Image.Image, tuple[int, int, int, int] | None]:
+    if _get_palette_bytes(im_frame) != _get_palette_bytes(base_im):
+        im_frame = im_frame.convert("RGBA")
+        base_im = base_im.convert("RGBA")
+    delta = ImageChops.subtract_modulo(im_frame, base_im)
+    return delta, delta.getbbox(alpha_only=False)
+
+
+class _Frame(NamedTuple):
+    im: Image.Image
+    bbox: tuple[int, int, int, int] | None
+    encoderinfo: dict[str, Any]
+
+
+def _write_multiple_frames(
+    im: Image.Image, fp: IO[bytes], palette: _Palette | None
+) -> bool:
+    duration = im.encoderinfo.get("duration")
+    disposal = im.encoderinfo.get("disposal", im.info.get("disposal"))
+
+    im_frames: list[_Frame] = []
+    previous_im: Image.Image | None = None
+    frame_count = 0
+    background_im = None
+    for imSequence in itertools.chain([im], im.encoderinfo.get("append_images", [])):
+        for im_frame in ImageSequence.Iterator(imSequence):
+            # a copy is required here since seek can still mutate the image
+            im_frame = _normalize_mode(im_frame.copy())
+            if frame_count == 0:
+                for k, v in im_frame.info.items():
+                    if k == "transparency":
+                        continue
+                    im.encoderinfo.setdefault(k, v)
+
+            encoderinfo = im.encoderinfo.copy()
+            if "transparency" in im_frame.info:
+                encoderinfo.setdefault("transparency", im_frame.info["transparency"])
+            im_frame = _normalize_palette(im_frame, palette, encoderinfo)
+            if isinstance(duration, (list, tuple)):
+                encoderinfo["duration"] = duration[frame_count]
+            elif duration is None and "duration" in im_frame.info:
+                encoderinfo["duration"] = im_frame.info["duration"]
+            if isinstance(disposal, (list, tuple)):
+                encoderinfo["disposal"] = disposal[frame_count]
+            frame_count += 1
+
+            diff_frame = None
+            if im_frames and previous_im:
+                # delta frame
+                delta, bbox = _getbbox(previous_im, im_frame)
+                if not bbox:
+                    # This frame is identical to the previous frame
+                    if encoderinfo.get("duration"):
+                        im_frames[-1].encoderinfo["duration"] += encoderinfo["duration"]
+                    continue
+                if im_frames[-1].encoderinfo.get("disposal") == 2:
+                    if background_im is None:
+                        color = im.encoderinfo.get(
+                            "transparency", im.info.get("transparency", (0, 0, 0))
+                        )
+                        background = _get_background(im_frame, color)
+                        background_im = Image.new("P", im_frame.size, background)
+                        background_im.putpalette(im_frames[0].im.palette)
+                    bbox = _getbbox(background_im, im_frame)[1]
+                elif encoderinfo.get("optimize") and im_frame.mode != "1":
+                    if "transparency" not in encoderinfo:
+                        try:
+                            encoderinfo["transparency"] = (
+                                im_frame.palette._new_color_index(im_frame)
+                            )
+                        except ValueError:
+                            pass
+                    if "transparency" in encoderinfo:
+                        # When the delta is zero, fill the image with transparency
+                        diff_frame = im_frame.copy()
+                        fill = Image.new("P", delta.size, encoderinfo["transparency"])
+                        if delta.mode == "RGBA":
+                            r, g, b, a = delta.split()
+                            mask = ImageMath.lambda_eval(
+                                lambda args: args["convert"](
+                                    args["max"](
+                                        args["max"](
+                                            args["max"](args["r"], args["g"]), args["b"]
+                                        ),
+                                        args["a"],
+                                    )
+                                    * 255,
+                                    "1",
+                                ),
+                                r=r,
+                                g=g,
+                                b=b,
+                                a=a,
+                            )
+                        else:
+                            if delta.mode == "P":
+                                # Convert to L without considering palette
+                                delta_l = Image.new("L", delta.size)
+                                delta_l.putdata(delta.getdata())
+                                delta = delta_l
+                            mask = ImageMath.lambda_eval(
+                                lambda args: args["convert"](args["im"] * 255, "1"),
+                                im=delta,
+                            )
+                        diff_frame.paste(fill, mask=ImageOps.invert(mask))
+            else:
+                bbox = None
+            previous_im = im_frame
+            im_frames.append(_Frame(diff_frame or im_frame, bbox, encoderinfo))
+
+    if len(im_frames) == 1:
+        if "duration" in im.encoderinfo:
+            # Since multiple frames will not be written, use the combined duration
+            im.encoderinfo["duration"] = im_frames[0].encoderinfo["duration"]
+        return False
+
+    for frame_data in im_frames:
+        im_frame = frame_data.im
+        if not frame_data.bbox:
+            # global header
+            for s in _get_global_header(im_frame, frame_data.encoderinfo):
+                fp.write(s)
+            offset = (0, 0)
+        else:
+            # compress difference
+            if not palette:
+                frame_data.encoderinfo["include_color_table"] = True
+
+            im_frame = im_frame.crop(frame_data.bbox)
+            offset = frame_data.bbox[:2]
+        _write_frame_data(fp, im_frame, offset, frame_data.encoderinfo)
+    return True
+
+
+def _save_all(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None:
+    _save(im, fp, filename, save_all=True)
+
+
+def _save(
+    im: Image.Image, fp: IO[bytes], filename: str | bytes, save_all: bool = False
+) -> None:
+    # header
+    if "palette" in im.encoderinfo or "palette" in im.info:
+        palette = im.encoderinfo.get("palette", im.info.get("palette"))
+    else:
+        palette = None
+        im.encoderinfo.setdefault("optimize", True)
+
+    if not save_all or not _write_multiple_frames(im, fp, palette):
+        _write_single_frame(im, fp, palette)
+
+    fp.write(b";")  # end of file
+
+    if hasattr(fp, "flush"):
+        fp.flush()
+
+
+def get_interlace(im: Image.Image) -> int:
+    interlace = im.encoderinfo.get("interlace", 1)
+
+    # workaround for @PIL153
+    if min(im.size) < 16:
+        interlace = 0
+
+    return interlace
+
+
+def _write_local_header(
+    fp: IO[bytes], im: Image.Image, offset: tuple[int, int], flags: int
+) -> None:
+    try:
+        transparency = im.encoderinfo["transparency"]
+    except KeyError:
+        transparency = None
+
+    if "duration" in im.encoderinfo:
+        duration = int(im.encoderinfo["duration"] / 10)
+    else:
+        duration = 0
+
+    disposal = int(im.encoderinfo.get("disposal", 0))
+
+    if transparency is not None or duration != 0 or disposal:
+        packed_flag = 1 if transparency is not None else 0
+        packed_flag |= disposal << 2
+
+        fp.write(
+            b"!"
+            + o8(249)  # extension intro
+            + o8(4)  # length
+            + o8(packed_flag)  # packed fields
+            + o16(duration)  # duration
+            + o8(transparency or 0)  # transparency index
+            + o8(0)
+        )
+
+    include_color_table = im.encoderinfo.get("include_color_table")
+    if include_color_table:
+        palette_bytes = _get_palette_bytes(im)
+        color_table_size = _get_color_table_size(palette_bytes)
+        if color_table_size:
+            flags = flags | 128  # local color table flag
+            flags = flags | color_table_size
+
+    fp.write(
+        b","
+        + o16(offset[0])  # offset
+        + o16(offset[1])
+        + o16(im.size[0])  # size
+        + o16(im.size[1])
+        + o8(flags)  # flags
+    )
+    if include_color_table and color_table_size:
+        fp.write(_get_header_palette(palette_bytes))
+    fp.write(o8(8))  # bits
+
+
+def _save_netpbm(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None:
+    # Unused by default.
+    # To use, uncomment the register_save call at the end of the file.
+    #
+    # If you need real GIF compression and/or RGB quantization, you
+    # can use the external NETPBM/PBMPLUS utilities.  See comments
+    # below for information on how to enable this.
+    tempfile = im._dump()
+
+    try:
+        with open(filename, "wb") as f:
+            if im.mode != "RGB":
+                subprocess.check_call(
+                    ["ppmtogif", tempfile], stdout=f, stderr=subprocess.DEVNULL
+                )
+            else:
+                # Pipe ppmquant output into ppmtogif
+                # "ppmquant 256 %s | ppmtogif > %s" % (tempfile, filename)
+                quant_cmd = ["ppmquant", "256", tempfile]
+                togif_cmd = ["ppmtogif"]
+                quant_proc = subprocess.Popen(
+                    quant_cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL
+                )
+                togif_proc = subprocess.Popen(
+                    togif_cmd,
+                    stdin=quant_proc.stdout,
+                    stdout=f,
+                    stderr=subprocess.DEVNULL,
+                )
+
+                # Allow ppmquant to receive SIGPIPE if ppmtogif exits
+                assert quant_proc.stdout is not None
+                quant_proc.stdout.close()
+
+                retcode = quant_proc.wait()
+                if retcode:
+                    raise subprocess.CalledProcessError(retcode, quant_cmd)
+
+                retcode = togif_proc.wait()
+                if retcode:
+                    raise subprocess.CalledProcessError(retcode, togif_cmd)
+    finally:
+        try:
+            os.unlink(tempfile)
+        except OSError:
+            pass
+
+
+# Force optimization so that we can test performance against
+# cases where it took lots of memory and time previously.
+_FORCE_OPTIMIZE = False
+
+
+def _get_optimize(im: Image.Image, info: dict[str, Any]) -> list[int] | None:
+    """
+    Palette optimization is a potentially expensive operation.
+
+    This function determines if the palette should be optimized using
+    some heuristics, then returns the list of palette entries in use.
+
+    :param im: Image object
+    :param info: encoderinfo
+    :returns: list of indexes of palette entries in use, or None
+    """
+    if im.mode in ("P", "L") and info and info.get("optimize"):
+        # Potentially expensive operation.
+
+        # The palette saves 3 bytes per color not used, but palette
+        # lengths are restricted to 3*(2**N) bytes. Max saving would
+        # be 768 -> 6 bytes if we went all the way down to 2 colors.
+        # * If we're over 128 colors, we can't save any space.
+        # * If there aren't any holes, it's not worth collapsing.
+        # * If we have a 'large' image, the palette is in the noise.
+
+        # create the new palette if not every color is used
+        optimise = _FORCE_OPTIMIZE or im.mode == "L"
+        if optimise or im.width * im.height < 512 * 512:
+            # check which colors are used
+            used_palette_colors = []
+            for i, count in enumerate(im.histogram()):
+                if count:
+                    used_palette_colors.append(i)
+
+            if optimise or max(used_palette_colors) >= len(used_palette_colors):
+                return used_palette_colors
+
+            num_palette_colors = len(im.palette.palette) // Image.getmodebands(
+                im.palette.mode
+            )
+            current_palette_size = 1 << (num_palette_colors - 1).bit_length()
+            if (
+                # check that the palette would become smaller when saved
+                len(used_palette_colors) <= current_palette_size // 2
+                # check that the palette is not already the smallest possible size
+                and current_palette_size > 2
+            ):
+                return used_palette_colors
+    return None
+
+
+def _get_color_table_size(palette_bytes: bytes) -> int:
+    # calculate the palette size for the header
+    if not palette_bytes:
+        return 0
+    elif len(palette_bytes) < 9:
+        return 1
+    else:
+        return math.ceil(math.log(len(palette_bytes) // 3, 2)) - 1
+
+
+def _get_header_palette(palette_bytes: bytes) -> bytes:
+    """
+    Returns the palette, null padded to the next power of 2 (*3) bytes
+    suitable for direct inclusion in the GIF header
+
+    :param palette_bytes: Unpadded palette bytes, in RGBRGB form
+    :returns: Null padded palette
+    """
+    color_table_size = _get_color_table_size(palette_bytes)
+
+    # add the missing amount of bytes
+    # the palette has to be 2<<n in size
+    actual_target_size_diff = (2 << color_table_size) - len(palette_bytes) // 3
+    if actual_target_size_diff > 0:
+        palette_bytes += o8(0) * 3 * actual_target_size_diff
+    return palette_bytes
+
+
+def _get_palette_bytes(im: Image.Image) -> bytes:
+    """
+    Gets the palette for inclusion in the gif header
+
+    :param im: Image object
+    :returns: Bytes, len<=768 suitable for inclusion in gif header
+    """
+    return im.palette.palette if im.palette else b""
+
+
+def _get_background(
+    im: Image.Image,
+    info_background: int | tuple[int, int, int] | tuple[int, int, int, int] | None,
+) -> int:
+    background = 0
+    if info_background:
+        if isinstance(info_background, tuple):
+            # WebPImagePlugin stores an RGBA value in info["background"]
+            # So it must be converted to the same format as GifImagePlugin's
+            # info["background"] - a global color table index
+            try:
+                background = im.palette.getcolor(info_background, im)
+            except ValueError as e:
+                if str(e) not in (
+                    # If all 256 colors are in use,
+                    # then there is no need for the background color
+                    "cannot allocate more than 256 colors",
+                    # Ignore non-opaque WebP background
+                    "cannot add non-opaque RGBA color to RGB palette",
+                ):
+                    raise
+        else:
+            background = info_background
+    return background
+
+
+def _get_global_header(im: Image.Image, info: dict[str, Any]) -> list[bytes]:
+    """Return a list of strings representing a GIF header"""
+
+    # Header Block
+    # https://www.matthewflickinger.com/lab/whatsinagif/bits_and_bytes.asp
+
+    version = b"87a"
+    if im.info.get("version") == b"89a" or (
+        info
+        and (
+            "transparency" in info
+            or info.get("loop") is not None
+            or info.get("duration")
+            or info.get("comment")
+        )
+    ):
+        version = b"89a"
+
+    background = _get_background(im, info.get("background"))
+
+    palette_bytes = _get_palette_bytes(im)
+    color_table_size = _get_color_table_size(palette_bytes)
+
+    header = [
+        b"GIF"  # signature
+        + version  # version
+        + o16(im.size[0])  # canvas width
+        + o16(im.size[1]),  # canvas height
+        # Logical Screen Descriptor
+        # size of global color table + global color table flag
+        o8(color_table_size + 128),  # packed fields
+        # background + reserved/aspect
+        o8(background) + o8(0),
+        # Global Color Table
+        _get_header_palette(palette_bytes),
+    ]
+    if info.get("loop") is not None:
+        header.append(
+            b"!"
+            + o8(255)  # extension intro
+            + o8(11)
+            + b"NETSCAPE2.0"
+            + o8(3)
+            + o8(1)
+            + o16(info["loop"])  # number of loops
+            + o8(0)
+        )
+    if info.get("comment"):
+        comment_block = b"!" + o8(254)  # extension intro
+
+        comment = info["comment"]
+        if isinstance(comment, str):
+            comment = comment.encode()
+        for i in range(0, len(comment), 255):
+            subblock = comment[i : i + 255]
+            comment_block += o8(len(subblock)) + subblock
+
+        comment_block += o8(0)
+        header.append(comment_block)
+    return header
+
+
+def _write_frame_data(
+    fp: IO[bytes],
+    im_frame: Image.Image,
+    offset: tuple[int, int],
+    params: dict[str, Any],
+) -> None:
+    try:
+        im_frame.encoderinfo = params
+
+        # local image header
+        _write_local_header(fp, im_frame, offset, 0)
+
+        ImageFile._save(
+            im_frame, fp, [("gif", (0, 0) + im_frame.size, 0, RAWMODE[im_frame.mode])]
+        )
+
+        fp.write(b"\0")  # end of image data
+    finally:
+        del im_frame.encoderinfo
+
+
+# --------------------------------------------------------------------
+# Legacy GIF utilities
+
+
+def getheader(
+    im: Image.Image, palette: _Palette | None = None, info: dict[str, Any] | None = None
+) -> tuple[list[bytes], list[int] | None]:
+    """
+    Legacy Method to get Gif data from image.
+
+    Warning:: May modify image data.
+
+    :param im: Image object
+    :param palette: bytes object containing the source palette, or ....
+    :param info: encoderinfo
+    :returns: tuple of(list of header items, optimized palette)
+
+    """
+    if info is None:
+        info = {}
+
+    used_palette_colors = _get_optimize(im, info)
+
+    if "background" not in info and "background" in im.info:
+        info["background"] = im.info["background"]
+
+    im_mod = _normalize_palette(im, palette, info)
+    im.palette = im_mod.palette
+    im.im = im_mod.im
+    header = _get_global_header(im, info)
+
+    return header, used_palette_colors
+
+
+def getdata(
+    im: Image.Image, offset: tuple[int, int] = (0, 0), **params: Any
+) -> list[bytes]:
+    """
+    Legacy Method
+
+    Return a list of strings representing this image.
+    The first string is a local image header, the rest contains
+    encoded image data.
+
+    To specify duration, add the time in milliseconds,
+    e.g. ``getdata(im_frame, duration=1000)``
+
+    :param im: Image object
+    :param offset: Tuple of (x, y) pixels. Defaults to (0, 0)
+    :param \\**params: e.g. duration or other encoder info parameters
+    :returns: List of bytes containing GIF encoded frame data
+
+    """
+    from io import BytesIO
+
+    class Collector(BytesIO):
+        data = []
+
+        if sys.version_info >= (3, 12):
+            from collections.abc import Buffer
+
+            def write(self, data: Buffer) -> int:
+                self.data.append(data)
+                return len(data)
+
+        else:
+
+            def write(self, data: Any) -> int:
+                self.data.append(data)
+                return len(data)
+
+    im.load()  # make sure raster data is available
+
+    fp = Collector()
+
+    _write_frame_data(fp, im, offset, params)
+
+    return fp.data
+
+
+# --------------------------------------------------------------------
+# Registry
+
+Image.register_open(GifImageFile.format, GifImageFile, _accept)
+Image.register_save(GifImageFile.format, _save)
+Image.register_save_all(GifImageFile.format, _save_all)
+Image.register_extension(GifImageFile.format, ".gif")
+Image.register_mime(GifImageFile.format, "image/gif")
+
+#
+# Uncomment the following line if you wish to use NETPBM/PBMPLUS
+# instead of the built-in "uncompressed" GIF encoder
+
+# Image.register_save(GifImageFile.format, _save_netpbm)

myenv/Lib/site-packages/PIL/GimpGradientFile.py

@@ -0,0 +1,149 @@
+#
+# Python Imaging Library
+# $Id$
+#
+# stuff to read (and render) GIMP gradient files
+#
+# History:
+#       97-08-23 fl     Created
+#
+# Copyright (c) Secret Labs AB 1997.
+# Copyright (c) Fredrik Lundh 1997.
+#
+# See the README file for information on usage and redistribution.
+#
+
+"""
+Stuff to translate curve segments to palette values (derived from
+the corresponding code in GIMP, written by Federico Mena Quintero.
+See the GIMP distribution for more information.)
+"""
+from __future__ import annotations
+
+from math import log, pi, sin, sqrt
+from typing import IO, Callable
+
+from ._binary import o8
+
+EPSILON = 1e-10
+""""""  # Enable auto-doc for data member
+
+
+def linear(middle: float, pos: float) -> float:
+    if pos <= middle:
+        if middle < EPSILON:
+            return 0.0
+        else:
+            return 0.5 * pos / middle
+    else:
+        pos = pos - middle
+        middle = 1.0 - middle
+        if middle < EPSILON:
+            return 1.0
+        else:
+            return 0.5 + 0.5 * pos / middle
+
+
+def curved(middle: float, pos: float) -> float:
+    return pos ** (log(0.5) / log(max(middle, EPSILON)))
+
+
+def sine(middle: float, pos: float) -> float:
+    return (sin((-pi / 2.0) + pi * linear(middle, pos)) + 1.0) / 2.0
+
+
+def sphere_increasing(middle: float, pos: float) -> float:
+    return sqrt(1.0 - (linear(middle, pos) - 1.0) ** 2)
+
+
+def sphere_decreasing(middle: float, pos: float) -> float:
+    return 1.0 - sqrt(1.0 - linear(middle, pos) ** 2)
+
+
+SEGMENTS = [linear, curved, sine, sphere_increasing, sphere_decreasing]
+""""""  # Enable auto-doc for data member
+
+
+class GradientFile:
+    gradient: (
+        list[
+            tuple[
+                float,
+                float,
+                float,
+                list[float],
+                list[float],
+                Callable[[float, float], float],
+            ]
+        ]
+        | None
+    ) = None
+
+    def getpalette(self, entries: int = 256) -> tuple[bytes, str]:
+        assert self.gradient is not None
+        palette = []
+
+        ix = 0
+        x0, x1, xm, rgb0, rgb1, segment = self.gradient[ix]
+
+        for i in range(entries):
+            x = i / (entries - 1)
+
+            while x1 < x:
+                ix += 1
+                x0, x1, xm, rgb0, rgb1, segment = self.gradient[ix]
+
+            w = x1 - x0
+
+            if w < EPSILON:
+                scale = segment(0.5, 0.5)
+            else:
+                scale = segment((xm - x0) / w, (x - x0) / w)
+
+            # expand to RGBA
+            r = o8(int(255 * ((rgb1[0] - rgb0[0]) * scale + rgb0[0]) + 0.5))
+            g = o8(int(255 * ((rgb1[1] - rgb0[1]) * scale + rgb0[1]) + 0.5))
+            b = o8(int(255 * ((rgb1[2] - rgb0[2]) * scale + rgb0[2]) + 0.5))
+            a = o8(int(255 * ((rgb1[3] - rgb0[3]) * scale + rgb0[3]) + 0.5))
+
+            # add to palette
+            palette.append(r + g + b + a)
+
+        return b"".join(palette), "RGBA"
+
+
+class GimpGradientFile(GradientFile):
+    """File handler for GIMP's gradient format."""
+
+    def __init__(self, fp: IO[bytes]) -> None:
+        if fp.readline()[:13] != b"GIMP Gradient":
+            msg = "not a GIMP gradient file"
+            raise SyntaxError(msg)
+
+        line = fp.readline()
+
+        # GIMP 1.2 gradient files don't contain a name, but GIMP 1.3 files do
+        if line.startswith(b"Name: "):
+            line = fp.readline().strip()
+
+        count = int(line)
+
+        self.gradient = []
+
+        for i in range(count):
+            s = fp.readline().split()
+            w = [float(x) for x in s[:11]]
+
+            x0, x1 = w[0], w[2]
+            xm = w[1]
+            rgb0 = w[3:7]
+            rgb1 = w[7:11]
+
+            segment = SEGMENTS[int(s[11])]
+            cspace = int(s[12])
+
+            if cspace != 0:
+                msg = "cannot handle HSV colour space"
+                raise OSError(msg)
+
+            self.gradient.append((x0, x1, xm, rgb0, rgb1, segment))

myenv/Lib/site-packages/PIL/GimpPaletteFile.py

@@ -0,0 +1,58 @@
+#
+# Python Imaging Library
+# $Id$
+#
+# stuff to read GIMP palette files
+#
+# History:
+# 1997-08-23 fl     Created
+# 2004-09-07 fl     Support GIMP 2.0 palette files.
+#
+# Copyright (c) Secret Labs AB 1997-2004.  All rights reserved.
+# Copyright (c) Fredrik Lundh 1997-2004.
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+import re
+from typing import IO
+
+from ._binary import o8
+
+
+class GimpPaletteFile:
+    """File handler for GIMP's palette format."""
+
+    rawmode = "RGB"
+
+    def __init__(self, fp: IO[bytes]) -> None:
+        palette = [o8(i) * 3 for i in range(256)]
+
+        if fp.readline()[:12] != b"GIMP Palette":
+            msg = "not a GIMP palette file"
+            raise SyntaxError(msg)
+
+        for i in range(256):
+            s = fp.readline()
+            if not s:
+                break
+
+            # skip fields and comment lines
+            if re.match(rb"\w+:|#", s):
+                continue
+            if len(s) > 100:
+                msg = "bad palette file"
+                raise SyntaxError(msg)
+
+            v = tuple(map(int, s.split()[:3]))
+            if len(v) != 3:
+                msg = "bad palette entry"
+                raise ValueError(msg)
+
+            palette[i] = o8(v[0]) + o8(v[1]) + o8(v[2])
+
+        self.palette = b"".join(palette)
+
+    def getpalette(self) -> tuple[bytes, str]:
+        return self.palette, self.rawmode

myenv/Lib/site-packages/PIL/GribStubImagePlugin.py

@@ -0,0 +1,76 @@
+#
+# The Python Imaging Library
+# $Id$
+#
+# GRIB stub adapter
+#
+# Copyright (c) 1996-2003 by Fredrik Lundh
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+from typing import IO
+
+from . import Image, ImageFile
+
+_handler = None
+
+
+def register_handler(handler: ImageFile.StubHandler | None) -> None:
+    """
+    Install application-specific GRIB image handler.
+
+    :param handler: Handler object.
+    """
+    global _handler
+    _handler = handler
+
+
+# --------------------------------------------------------------------
+# Image adapter
+
+
+def _accept(prefix: bytes) -> bool:
+    return prefix[:4] == b"GRIB" and prefix[7] == 1
+
+
+class GribStubImageFile(ImageFile.StubImageFile):
+    format = "GRIB"
+    format_description = "GRIB"
+
+    def _open(self) -> None:
+        offset = self.fp.tell()
+
+        if not _accept(self.fp.read(8)):
+            msg = "Not a GRIB file"
+            raise SyntaxError(msg)
+
+        self.fp.seek(offset)
+
+        # make something up
+        self._mode = "F"
+        self._size = 1, 1
+
+        loader = self._load()
+        if loader:
+            loader.open(self)
+
+    def _load(self) -> ImageFile.StubHandler | None:
+        return _handler
+
+
+def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None:
+    if _handler is None or not hasattr(_handler, "save"):
+        msg = "GRIB save handler not installed"
+        raise OSError(msg)
+    _handler.save(im, fp, filename)
+
+
+# --------------------------------------------------------------------
+# Registry
+
+Image.register_open(GribStubImageFile.format, GribStubImageFile, _accept)
+Image.register_save(GribStubImageFile.format, _save)
+
+Image.register_extension(GribStubImageFile.format, ".grib")

myenv/Lib/site-packages/PIL/Hdf5StubImagePlugin.py

@@ -0,0 +1,76 @@
+#
+# The Python Imaging Library
+# $Id$
+#
+# HDF5 stub adapter
+#
+# Copyright (c) 2000-2003 by Fredrik Lundh
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+from typing import IO
+
+from . import Image, ImageFile
+
+_handler = None
+
+
+def register_handler(handler: ImageFile.StubHandler | None) -> None:
+    """
+    Install application-specific HDF5 image handler.
+
+    :param handler: Handler object.
+    """
+    global _handler
+    _handler = handler
+
+
+# --------------------------------------------------------------------
+# Image adapter
+
+
+def _accept(prefix: bytes) -> bool:
+    return prefix[:8] == b"\x89HDF\r\n\x1a\n"
+
+
+class HDF5StubImageFile(ImageFile.StubImageFile):
+    format = "HDF5"
+    format_description = "HDF5"
+
+    def _open(self) -> None:
+        offset = self.fp.tell()
+
+        if not _accept(self.fp.read(8)):
+            msg = "Not an HDF file"
+            raise SyntaxError(msg)
+
+        self.fp.seek(offset)
+
+        # make something up
+        self._mode = "F"
+        self._size = 1, 1
+
+        loader = self._load()
+        if loader:
+            loader.open(self)
+
+    def _load(self) -> ImageFile.StubHandler | None:
+        return _handler
+
+
+def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None:
+    if _handler is None or not hasattr(_handler, "save"):
+        msg = "HDF5 save handler not installed"
+        raise OSError(msg)
+    _handler.save(im, fp, filename)
+
+
+# --------------------------------------------------------------------
+# Registry
+
+Image.register_open(HDF5StubImageFile.format, HDF5StubImageFile, _accept)
+Image.register_save(HDF5StubImageFile.format, _save)
+
+Image.register_extensions(HDF5StubImageFile.format, [".h5", ".hdf"])