commit Florence2Ultra, Florence2Image2Prompt, LoadFlorence2Model nodes
This commit is contained in:
chflame163
@@ -98,6 +98,7 @@ When this error has occurred, please check the network environment.
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## Update
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<font size="4">**If the dependency package error after updating, please reinstall the relevant dependency packages. </font><br />
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* Commit [Florence2Ultra](#Florence2Ultra), [Florence2Image2Prompt](#Florence2Image2Prompt) and [LoadFlorence2Model](#LoadFlorence2Model) nodes.
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* [TransparentBackgroundUltra](#TransparentBackgroundUltra) node add new model support. Please download the model file according to the instructions.
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* Commit [SegformerUltraV2](#SegformerUltraV2), [SegfromerFashionPipeline](#SegfromerFashionPipeline) and [SegformerClothesPipeline](#SegformerClothesPipeline) nodes, used for segmentation of clothing. please download the model file according to the instructions.
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* Commit ```install_requirements.bat``` and ```install_requirements_aki.bat```, One click solution to install dependency packages.
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@@ -754,6 +755,22 @@ Node options:
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* token_limit: The maximum token limit for generating prompt words.
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* discribe: Enter a simple description here. supports Chinese text input.
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### <a id="table1">Florence2Image2Prompt</a>
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Use the Florence 2 model to infer prompt words. The code for this node section is from[yiwangsimple/florence_dw](https://github.com/yiwangsimple/florence_dw), thanks to the original author.
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*When using it for the first time, the model will be automatically downloaded.
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Node Options:
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* florence2_model: Florence2 model input.
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* image: Image input.
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* task: Select the task for florence2.
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* text_input: Text input for florence2.
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* max_new_tokens: The maximum number of tokens for generating text.
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* num_beams: The number of beam searches that generate text.
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* do_sample: Whether to use text generated sampling.
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* fill_mask: Whether to use text marker mask filling.
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### <a id="table1">ImageShift</a>
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Shift the image. this node supports the output of displacement seam masks, making it convenient to create continuous textures.
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@@ -1457,6 +1474,35 @@ On the basis of SegmentAnythingUltra, the following changes have been made:
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* device: Set whether the VitMatte to use cuda.
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* max_megapixels: Set the maximum size for VitMate operations.
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### <a id="table1">Florence2Ultra</a>
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Using the segmentation function of the Florence2 model, while also having ultra-high edge details.
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The code for this node section is from [spacepxl/ComfyUI-Florence-2](https://github.com/spacepxl/ComfyUI-Florence-2), thanks to the original author.
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Node Options:
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* florence2_model: Florence2 model input.
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* image: Image input.
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* task: Select the task for florence2.
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* text_input: Text input for florence2.
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* detail_method: Edge processing methods. provides VITMatte, VITMatte(local), PyMatting, GuidedFilter. If the model has been downloaded after the first use of VITMatte, you can use VITMatte (local) afterwards.
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* detail_erode: Mask the erosion range inward from the edge. the larger the value, the larger the range of inward repair.
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* detail_dilate: The edge of the mask expands outward. the larger the value, the wider the range of outward repair.
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* black_point: Edge black sampling threshold.
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* white_point: Edge white sampling threshold.
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* process_detail: Set to false here will skip edge processing to save runtime.
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* device: Set whether the VitMatte to use cuda.
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* max_megapixels: Set the maximum size for VitMate operations.
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### <a id="table1">LoadFlorence2Model</a>
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Florence2 model loader.
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*When using it for the first time, the model will be automatically downloaded.
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At present, there are base, base-ft, large, large-ft, DocVQA, SD3-Captioner and base-PromptGen models to choose from.
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### <a id="table1">RemBgUltra</a>
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Remove background. compared to the similar background removal nodes, this node has ultra-high edge details.
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@@ -99,6 +99,7 @@ git clone https://github.com/chflame163/ComfyUI_LayerStyle.git
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## 更新说明
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<font size="4">**如果本插件更新后出现依赖包错误，请重新安装相关依赖包。
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* 添加 [Florence2Ultra](#Florence2Ultra), [Florence2Image2Prompt](#Florence2Image2Prompt) 和 [LoadFlorence2Model](#LoadFlorence2Model) 节点。
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* [TransparentBackgroundUltra](#TransparentBackgroundUltra) 节点增加新模型支持。请按说明下载模型文件。
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* 添加 [SegformerUltraV2](#SegformerUltraV2), [SegfromerFashionPipeline](#SegfromerFashionPipeline) 和 [SegformerClothesPipeline](#SegformerClothesPipeline) 节点, 用于分割服饰。请按说明下载模型文件。
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* 添加 ```install_requirements.bat``` 和 ```install_requirements_aki.bat``` 文件, 一键解决安装依赖包问题。
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@@ -743,6 +744,21 @@ ImageScaleByAspectRatio的V2升级版
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* token_limit: 生成提示词的最大token限制。
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* discribe: 在这里输入简单的描述。支持中文。
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### <a id="table1">Florence2Image2Prompt</a>
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使用florence2模型反推提示词。本节点部分的代码来自[yiwangsimple/florence_dw](https://github.com/yiwangsimple/florence_dw)，感谢原作者。
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*首次使用时将自动下载模型，请在可以访问huggingface.co的网络环境下使用。
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节点选项说明:
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* florence2_model: Florence2模型输入。
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* image: 图片输入。
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* task: 选择florence2任务。
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* text_input: florence2任务文本输入。
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* max_new_tokens: 生成文本的最大token数量。
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* num_beams: 生成文本的beam search数量。
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* do_sample: 是否使用文本生成采样。
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* fill_mask: 是否使用文本标记掩码填充。
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### <a id="table1">ImageShift</a>
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使图片产生位移。此节点支持位移接缝遮罩的输出，方便制作连续贴图。
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@@ -1439,6 +1455,32 @@ SegmentAnythingUltra的V2升级版，增加了VITMatte边缘处理方法。
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* device: 设置是否使用cuda。
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* max_megapixels: 设置vitmatte运算的最大尺寸。
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### <a id="table1">Florence2Ultra</a>
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使用 Florence2 模型的分割功能，同时具有超高的边缘细节。
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本节点部分的代码来自[spacepxl/ComfyUI-Florence-2](https://github.com/spacepxl/ComfyUI-Florence-2)，感谢原作者。
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节点选项说明:
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* florence2_model: Florence2模型输入。
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* image: 图片输入。
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* task: 选择florence2任务。
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* text_input: florence2任务文本输入。
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* detail_method: 边缘处理方法。提供了VITMatte, VITMatte(local), PyMatting, GuidedFilter。如果首次使用VITMatte后模型已经下载，之后可以使用VITMatte(local)。
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* detail_erode: 遮罩边缘向内侵蚀范围。数值越大，向内修复的范围越大。
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* detail_dilate: 遮罩边缘向外扩张范围。数值越大，向外修复的范围越大。
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* black_point: 边缘黑色采样阈值。
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* white_point: 边缘黑色采样阈值。
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* process_detail: 此处设为False将跳过边缘处理以节省运行时间。
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* device: 设置是否使用cuda。
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* max_megapixels: 设置vitmatte运算的最大尺寸。
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### <a id="table1">LoadFlorence2Model</a>
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Florence2 模型加载器。
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目前有 base, base-ft, large, large-ft, DocVQA, SD3-Captioner 和 base-PromptGen模型可以选择。
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### <a id="table1">RemBgUltra</a>
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去除背景。与类似的背景移除节点相比，这个节点具有超高的边缘细节。
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本节点结合了spacepxl的[ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters)的Alpha Matte节点，以及ZHO-ZHO-ZHO的[ComfyUI-BRIA_AI-RMBG](https://github.com/ZHO-ZHO-ZHO/ComfyUI-BRIA_AI-RMBG)的功能，感谢原作者。
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@@ -7,6 +7,7 @@ from torchvision.transforms import ToPILImage
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import folder_paths
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from .imagefunc import files_for_uform_gen2_qwen, StopOnTokens, UformGen2QwenChat, clear_memory
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NODE_NAME = "QWenImage2Prompt"
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# Example of integrating UformGen2QwenChat into a node-like structure
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class QWenImage2Prompt:
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@@ -44,7 +45,9 @@ class QWenImage2Prompt:
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# Cleanup
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del chat_model
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clear_memory()
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return (response.split("assistant\n", 1)[1], )
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ret_text = response.split("assistant\n", 1)[1]
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log(f"{NODE_NAME} Processed, Question: {question}, Response: {ret_text} ", message_type='finish')
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return (ret_text, )
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NODE_CLASS_MAPPINGS = {
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"LayerUtility: QWenImage2Prompt": QWenImage2Prompt
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@@ -0,0 +1,488 @@
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import io
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from unittest.mock import patch
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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from transformers.dynamic_module_utils import get_imports
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import comfy.model_management
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from .imagefunc import *
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colormap = ['blue', 'orange', 'green', 'purple', 'brown', 'pink', 'gray', 'olive', 'cyan', 'red',
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'lime', 'indigo', 'violet', 'aqua', 'magenta', 'coral', 'gold', 'tan', 'skyblue']
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device = comfy.model_management.get_torch_device()
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model_repos = {
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"base": "microsoft/Florence-2-base",
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"base-ft": "microsoft/Florence-2-base-ft",
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"large": "microsoft/Florence-2-large",
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"large-ft": "microsoft/Florence-2-large-ft",
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"DocVQA": "HuggingFaceM4/Florence-2-DocVQA",
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"SD3-Captioner": "gokaygokay/Florence-2-SD3-Captioner",
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"base-PromptGen": "MiaoshouAI/Florence-2-base-PromptGen"
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}
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def fixed_get_imports(filename: str | os.PathLike) -> list[str]:
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"""Workaround for FlashAttention"""
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if os.path.basename(filename) != "modeling_florence2.py":
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return get_imports(filename)
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imports = get_imports(filename)
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imports.remove("flash_attn")
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return imports
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def load_model(version):
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florence_path = os.path.join(folder_paths.models_dir, "florence2")
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os.makedirs(florence_path, exist_ok=True)
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model_path = os.path.join(florence_path, version)
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if not os.path.exists(model_path):
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log(f"Downloading Florence2 {version} model...")
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repo_id = model_repos[version]
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from huggingface_hub import snapshot_download
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snapshot_download(repo_id=repo_id, local_dir=model_path, ignore_patterns=["*.md", "*.txt"])
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try:
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with patch("transformers.dynamic_module_utils.get_imports", fixed_get_imports):
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model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
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processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
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except Exception as e:
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log(f"Error loading model {version}: {str(e)}")
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log("Attempting to load tokenizer instead of processor...")
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try:
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model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
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processor = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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except Exception as e:
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log(f"Error loading model or tokenizer: {str(e)}")
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return (model.to(device), processor)
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def fig_to_pil(fig):
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buf = io.BytesIO()
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fig.savefig(buf, format='png', dpi=100, bbox_inches='tight', pad_inches=0)
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buf.seek(0)
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pil = Image.open(buf)
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plt.close()
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return pil
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def plot_bbox(image, data):
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fig, ax = plt.subplots()
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fig.set_size_inches(image.width / 100, image.height / 100)
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ax.imshow(image)
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for i, (bbox, label) in enumerate(zip(data['bboxes'], data['labels'])):
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x1, y1, x2, y2 = bbox
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rect = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=1, edgecolor='r', facecolor='none')
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ax.add_patch(rect)
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enum_label = f"{i}: {label}"
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plt.text(x1 + 7, y1 + 17, enum_label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5))
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ax.axis('off')
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return fig
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def draw_polygons(image, prediction, fill_mask=False):
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output_image = copy.deepcopy(image)
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draw = ImageDraw.Draw(output_image)
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scale = 1
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for polygons, label in zip(prediction['polygons'], prediction['labels']):
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color = random.choice(colormap)
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fill_color = color if fill_mask else None
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for _polygon in polygons:
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_polygon = np.array(_polygon).reshape(-1, 2)
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if len(_polygon) < 3:
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print('Invalid polygon:', _polygon)
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continue
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_polygon = (_polygon * scale).reshape(-1).tolist()
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if fill_mask:
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draw.polygon(_polygon, outline=color, fill=fill_color)
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else:
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draw.polygon(_polygon, outline=color)
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draw.text((_polygon[0] + 8, _polygon[1] + 2), label, fill=color)
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return output_image
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def convert_to_od_format(data):
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od_results = {
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'bboxes': data.get('bboxes', []),
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'labels': data.get('bboxes_labels', [])
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}
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return od_results
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def draw_ocr_bboxes(image, prediction):
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scale = 1
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output_image = copy.deepcopy(image)
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draw = ImageDraw.Draw(output_image)
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bboxes, labels = prediction['quad_boxes'], prediction['labels']
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for box, label in zip(bboxes, labels):
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color = random.choice(colormap)
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new_box = (np.array(box) * scale).tolist()
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draw.polygon(new_box, width=3, outline=color)
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draw.text((new_box[0] + 8, new_box[1] + 2),
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"{}".format(label),
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align="right",
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fill=color)
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return output_image
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def run_example(model, processor, task_prompt, image, max_new_tokens, num_beams, do_sample, text_input=None):
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if text_input is None:
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prompt = task_prompt
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else:
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prompt = task_prompt + text_input
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inputs = processor(text=prompt, images=image, return_tensors="pt").to(device)
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generated_ids = model.generate(
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input_ids=inputs["input_ids"],
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pixel_values=inputs["pixel_values"],
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max_new_tokens=max_new_tokens,
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early_stopping=False,
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do_sample=do_sample,
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num_beams=num_beams,
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)
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
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parsed_answer = processor.post_process_generation(
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generated_text,
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task=task_prompt,
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image_size=(image.width, image.height)
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)
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return parsed_answer
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def process_image(model, processor, image, task_prompt, max_new_tokens, num_beams, do_sample, fill_mask, text_input=None):
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if task_prompt == 'caption':
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task_prompt = '<CAPTION>'
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result = run_example(model, processor, task_prompt, image, max_new_tokens, num_beams, do_sample)
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return result[task_prompt], None
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elif task_prompt == 'detailed caption':
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task_prompt = '<DETAILED_CAPTION>'
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result = run_example(model, processor, task_prompt, image, max_new_tokens, num_beams, do_sample)
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return result[task_prompt], None
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elif task_prompt == 'more detailed caption':
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task_prompt = '<MORE_DETAILED_CAPTION>'
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result = run_example(model, processor, task_prompt, image, max_new_tokens, num_beams, do_sample)
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return result[task_prompt], None
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elif task_prompt == 'object detection':
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task_prompt = '<OD>'
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results = run_example(model, processor, task_prompt, image, max_new_tokens, num_beams, do_sample)
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fig = plot_bbox(image, results['<OD>'])
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return results[task_prompt], fig_to_pil(fig)
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elif task_prompt == 'dense region caption':
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task_prompt = '<DENSE_REGION_CAPTION>'
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results = run_example(model, processor, task_prompt, image, max_new_tokens, num_beams, do_sample)
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fig = plot_bbox(image, results['<DENSE_REGION_CAPTION>'])
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return results[task_prompt], fig_to_pil(fig)
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elif task_prompt == 'region proposal':
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task_prompt = '<REGION_PROPOSAL>'
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results = run_example(model, processor, task_prompt, image, max_new_tokens, num_beams, do_sample)
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fig = plot_bbox(image, results['<REGION_PROPOSAL>'])
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return results[task_prompt], fig_to_pil(fig)
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elif task_prompt == 'caption to phrase grounding':
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task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
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results = run_example(model, processor, task_prompt, image, max_new_tokens, num_beams, do_sample, text_input)
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fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
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return results[task_prompt], fig_to_pil(fig)
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elif task_prompt == 'referring expression segmentation':
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task_prompt = '<REFERRING_EXPRESSION_SEGMENTATION>'
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results = run_example(model, processor, task_prompt, image, max_new_tokens, num_beams, do_sample, text_input)
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output_image = draw_polygons(image, results['<REFERRING_EXPRESSION_SEGMENTATION>'], fill_mask)
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return results[task_prompt], output_image
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elif task_prompt == 'region to segmentation':
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task_prompt = '<REGION_TO_SEGMENTATION>'
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results = run_example(model, processor, task_prompt, image, max_new_tokens, num_beams, do_sample, text_input)
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output_image = draw_polygons(image, results['<REGION_TO_SEGMENTATION>'], fill_mask)
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return results[task_prompt], output_image
|
||||
elif task_prompt == 'open vocabulary detection':
|
||||
task_prompt = '<OPEN_VOCABULARY_DETECTION>'
|
||||
results = run_example(model, processor, task_prompt, image, max_new_tokens, num_beams, do_sample, text_input)
|
||||
bbox_results = convert_to_od_format(results['<OPEN_VOCABULARY_DETECTION>'])
|
||||
fig = plot_bbox(image, bbox_results)
|
||||
return bbox_results, fig_to_pil(fig)
|
||||
elif task_prompt == 'region to category':
|
||||
task_prompt = '<REGION_TO_CATEGORY>'
|
||||
results = run_example(model, processor, task_prompt, image, max_new_tokens, num_beams, do_sample, text_input)
|
||||
return results[task_prompt], None
|
||||
elif task_prompt == 'region to description':
|
||||
task_prompt = '<REGION_TO_DESCRIPTION>'
|
||||
results = run_example(model, processor, task_prompt, image, max_new_tokens, num_beams, do_sample, text_input)
|
||||
return results[task_prompt], None
|
||||
elif task_prompt == 'OCR':
|
||||
task_prompt = '<OCR>'
|
||||
result = run_example(model, processor, task_prompt, image, max_new_tokens, num_beams, do_sample)
|
||||
return result[task_prompt], None
|
||||
elif task_prompt == 'OCR with region':
|
||||
task_prompt = '<OCR_WITH_REGION>'
|
||||
results = run_example(model, processor, task_prompt, image, max_new_tokens, num_beams, do_sample)
|
||||
output_image = draw_ocr_bboxes(image, results['<OCR_WITH_REGION>'])
|
||||
output_results = {'bboxes': results[task_prompt].get('quad_boxes', []),
|
||||
'labels': results[task_prompt].get('labels', [])}
|
||||
return output_results, output_image
|
||||
else:
|
||||
return "", None # Return empty string and None for unknown task prompts
|
||||
|
||||
|
||||
def remove_angle_bracket_content(text):
|
||||
import re
|
||||
# 正则表达式匹配 "<>" 包围的内容，包括尖括号本身
|
||||
pattern = r'<[^>]*>'
|
||||
# 使用 re.sub 替换匹配的内容为空字符串
|
||||
cleaned_text = re.sub(pattern, '', text)
|
||||
return cleaned_text
|
||||
|
||||
|
||||
def decode_f_bboxes(F_BBOXES):
|
||||
if isinstance(F_BBOXES, str):
|
||||
return (torch.zeros(1, 512, 512, dtype=torch.float32), F_BBOXES)
|
||||
|
||||
width = F_BBOXES["width"]
|
||||
height = F_BBOXES["height"]
|
||||
mask = np.zeros((height, width), dtype=np.uint8)
|
||||
|
||||
x1_c = width
|
||||
y1_c = height
|
||||
x2_c = y2_c = 0
|
||||
label = ""
|
||||
if "bboxes" in F_BBOXES:
|
||||
for idx in range(len(F_BBOXES["bboxes"])):
|
||||
bbox = F_BBOXES["bboxes"][idx]
|
||||
|
||||
new_label = F_BBOXES["labels"][idx].removeprefix("</s>")
|
||||
if new_label not in label:
|
||||
if idx > 0:
|
||||
label = label + ", "
|
||||
label = label + new_label
|
||||
|
||||
if len(bbox) == 4:
|
||||
x1, y1, x2, y2 = int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3])
|
||||
elif len(bbox) == 8:
|
||||
x1 = int(min(bbox[0::2]))
|
||||
x2 = int(max(bbox[0::2]))
|
||||
y1 = int(min(bbox[1::2]))
|
||||
y2 = int(max(bbox[1::2]))
|
||||
else:
|
||||
continue
|
||||
|
||||
x1_c = min(x1_c, x1)
|
||||
y1_c = min(y1_c, y1)
|
||||
x2_c = max(x2_c, x2)
|
||||
y2_c = max(y2_c, y2)
|
||||
|
||||
mask[y1:y2, x1:x2] = 1
|
||||
|
||||
else:
|
||||
image = Image.new('RGB', (width, height), color='black')
|
||||
draw = ImageDraw.Draw(image)
|
||||
|
||||
x1_c = width
|
||||
y1_c = height
|
||||
x2_c = y2_c = 0
|
||||
|
||||
for polygon in F_BBOXES["polygons"][0]:
|
||||
_polygon = np.array(polygon).reshape(-1, 2)
|
||||
if len(_polygon) < 3:
|
||||
print('Invalid polygon:', _polygon)
|
||||
continue
|
||||
|
||||
draw.polygon(_polygon.flatten().tolist(), outline='white', fill='white')
|
||||
|
||||
x1_c = min(x1_c, int(min(polygon[0::2])))
|
||||
x2_c = max(x2_c, int(max(polygon[0::2])))
|
||||
y1_c = min(y1_c, int(min(polygon[1::2])))
|
||||
y2_c = max(y2_c, int(max(polygon[1::2])))
|
||||
|
||||
mask = np.asarray(image)[..., 0].astype(np.float32) / 255
|
||||
|
||||
mask = torch.from_numpy(mask.astype(np.float32)).unsqueeze(0)
|
||||
# label = remove_angle_bracket_content(label)
|
||||
return (mask, label)
|
||||
|
||||
|
||||
class LS_LoadFlorence2Model:
|
||||
def __init__(self):
|
||||
self.model = None
|
||||
self.processor = None
|
||||
self.version = None
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
model_list = list(model_repos.keys())
|
||||
return {
|
||||
"required": {
|
||||
"version": (model_list,{"default": model_list[0]}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("FLORENCE2",)
|
||||
RETURN_NAMES = ("florence2_model",)
|
||||
FUNCTION = "load"
|
||||
CATEGORY = '😺dzNodes/LayerMask'
|
||||
|
||||
def load(self, version):
|
||||
if self.version != version:
|
||||
self.model, self.processor = load_model(version)
|
||||
self.version = version
|
||||
|
||||
return ({'model': self.model, 'processor': self.processor, 'version': self.version, 'device': device},)
|
||||
|
||||
|
||||
class Florence2Ultra:
|
||||
def __init__(self):
|
||||
self.NODE_NAME = 'Florence2Ultra'
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
segment_task_list = [
|
||||
"referring expression segmentation",
|
||||
"region to segmentation",
|
||||
"open vocabulary detection",
|
||||
]
|
||||
method_list = ['VITMatte', 'VITMatte(local)', 'PyMatting', 'GuidedFilter', ]
|
||||
device_list = ['cuda','cpu']
|
||||
return {
|
||||
"required": {
|
||||
"florence2_model": ("FLORENCE2",),
|
||||
"image": ("IMAGE",),
|
||||
"task": (segment_task_list,{"default": segment_task_list[0]}),
|
||||
"text_input": ("STRING", {"default": "subject"}),
|
||||
"detail_method": (method_list,),
|
||||
"detail_erode": ("INT", {"default": 6, "min": 1, "max": 255, "step": 1}),
|
||||
"detail_dilate": ("INT", {"default": 6, "min": 1, "max": 255, "step": 1}),
|
||||
"black_point": ("FLOAT", {"default": 0.01, "min": 0.01, "max": 0.98, "step": 0.01, "display": "slider"}),
|
||||
"white_point": ("FLOAT", {"default": 0.99, "min": 0.02, "max": 0.99, "step": 0.01, "display": "slider"}),
|
||||
"process_detail": ("BOOLEAN", {"default": True}),
|
||||
"device": (device_list,),
|
||||
"max_megapixels": ("FLOAT", {"default": 2.0, "min": 1, "max": 999, "step": 0.1}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE", "MASK",)
|
||||
RETURN_NAMES = ("image", "mask",)
|
||||
FUNCTION = "florence2_ultra"
|
||||
CATEGORY = '😺dzNodes/LayerMask'
|
||||
|
||||
def florence2_ultra(self, florence2_model, image, task, text_input,
|
||||
detail_method, detail_erode, detail_dilate,
|
||||
black_point, white_point, process_detail, device, max_megapixels):
|
||||
max_new_tokens = 512
|
||||
num_beams = 3
|
||||
do_sample = False
|
||||
fill_mask = False
|
||||
|
||||
ret_images = []
|
||||
ret_masks = []
|
||||
|
||||
if detail_method == 'VITMatte(local)':
|
||||
local_files_only = True
|
||||
else:
|
||||
local_files_only = False
|
||||
|
||||
model = florence2_model['model']
|
||||
processor = florence2_model['processor']
|
||||
|
||||
for i in image:
|
||||
img = tensor2pil(i).convert("RGB")
|
||||
|
||||
results, _ = process_image(model, processor, img, task,
|
||||
max_new_tokens, num_beams, do_sample,
|
||||
fill_mask, text_input)
|
||||
|
||||
if isinstance(results, dict):
|
||||
results["width"] = img.width
|
||||
results["height"] = img.height
|
||||
|
||||
_mask, _ = decode_f_bboxes(results)
|
||||
|
||||
if process_detail:
|
||||
detail_range = detail_erode + detail_dilate
|
||||
if detail_method == 'GuidedFilter':
|
||||
_mask = guided_filter_alpha(i, _mask, detail_range // 6 + 1)
|
||||
_mask = tensor2pil(histogram_remap(_mask, black_point, white_point))
|
||||
elif detail_method == 'PyMatting':
|
||||
_mask = tensor2pil(mask_edge_detail(i, _mask, detail_range // 8 + 1, black_point, white_point))
|
||||
else:
|
||||
_trimap = generate_VITMatte_trimap(_mask, detail_erode, detail_dilate)
|
||||
_mask = generate_VITMatte(img, _trimap, local_files_only=local_files_only, device=device, max_megapixels=max_megapixels)
|
||||
_mask = tensor2pil(histogram_remap(pil2tensor(_mask), black_point, white_point))
|
||||
else:
|
||||
_mask = tensor2pil(_mask)
|
||||
|
||||
ret_image = RGB2RGBA(img, _mask.convert('L'))
|
||||
ret_images.append(pil2tensor(ret_image))
|
||||
ret_masks.append(image2mask(_mask))
|
||||
|
||||
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
|
||||
|
||||
|
||||
class Florence2Image2Prompt:
|
||||
|
||||
def __init__(self):
|
||||
self.NODE_NAME = 'Florence2Image2Prompt'
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
caption_task_list = [
|
||||
"caption",
|
||||
"detailed caption",
|
||||
"more detailed caption",
|
||||
"object detection",
|
||||
"dense region caption",
|
||||
"region proposal",
|
||||
"caption to phrase grounding",
|
||||
"open vocabulary detection",
|
||||
"region to category",
|
||||
"region to description",
|
||||
"OCR",
|
||||
"OCR with region"
|
||||
]
|
||||
return {
|
||||
"required": {
|
||||
"florence2_model": ("FLORENCE2",),
|
||||
"image": ("IMAGE",),
|
||||
"task": (caption_task_list,{"default": caption_task_list[2]}),
|
||||
"text_input": ("STRING", {"default": ""}),
|
||||
"max_new_tokens": ("INT", {"default": 1024, "step": 1}),
|
||||
"num_beams": ("INT", {"default": 3, "min": 1, "step": 1}),
|
||||
"do_sample": ('BOOLEAN', {"default": False}),
|
||||
"fill_mask": ('BOOLEAN', {"default": False}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING", "IMAGE",)
|
||||
RETURN_NAMES = ("text", "preview_image",)
|
||||
FUNCTION = "florence2_image2prompt"
|
||||
CATEGORY = '😺dzNodes/LayerUtility/Prompt'
|
||||
|
||||
def florence2_image2prompt(self, florence2_model, image, task, text_input,
|
||||
max_new_tokens, num_beams, do_sample, fill_mask):
|
||||
|
||||
model = florence2_model['model']
|
||||
processor = florence2_model['processor']
|
||||
|
||||
img = tensor2pil(image[0])
|
||||
caption = ""
|
||||
results, output_image = process_image(model, processor, img, task, max_new_tokens, num_beams,
|
||||
do_sample, fill_mask,
|
||||
text_input)
|
||||
|
||||
if isinstance(results, dict):
|
||||
results["width"] = img.width
|
||||
results["height"] = img.height
|
||||
|
||||
if output_image == None:
|
||||
output_image = image[0].detach().clone().unsqueeze(0)
|
||||
else:
|
||||
output_image = np.asarray(output_image).astype(np.float32) / 255
|
||||
output_image = torch.from_numpy(output_image).unsqueeze(0)
|
||||
|
||||
_, caption = decode_f_bboxes(results)
|
||||
|
||||
return (remove_angle_bracket_content(caption), output_image,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"LayerMask: Florence2Ultra": Florence2Ultra,
|
||||
"LayerMask: LoadFlorence2Model": LS_LoadFlorence2Model,
|
||||
"LayerUtility: Florence2Image2Prompt": Florence2Image2Prompt
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"LayerMask: Florence2Ultra": "LayerMask: Florence2 Ultra",
|
||||
"LayerMask: LoadFlorence2Model": "LayerMask: Load Florence2 Model",
|
||||
"LayerUtility: Florence2Image2Prompt": "LayerUtility: Florence2 Image2Prompt"
|
||||
}
|
||||
+1
-1
@@ -30,7 +30,7 @@ from PIL import Image, ImageFilter, ImageChops, ImageDraw, ImageOps, ImageEnhanc
|
||||
from skimage import img_as_float, img_as_ubyte
|
||||
import torchvision.transforms.functional as TF
|
||||
import torch.nn.functional as F
|
||||
from transformers import AutoModel, AutoProcessor, StoppingCriteria, StoppingCriteriaList
|
||||
from transformers import AutoModel, AutoProcessor, StoppingCriteria, StoppingCriteriaList, AutoModelForCausalLM
|
||||
import colorsys
|
||||
from typing import Union
|
||||
import folder_paths
|
||||
|
||||
+2
-2
@@ -1,9 +1,9 @@
|
||||
[project]
|
||||
name = "comfyui_layerstyle"
|
||||
description = "A set of nodes for ComfyUI it generate image like Adobe Photoshop's Layer Style. the Drop Shadow is first completed node, and follow-up work is in progress."
|
||||
version = "1.0.19"
|
||||
version = "1.0.20"
|
||||
license = "MIT"
|
||||
dependencies = ["numpy", "pillow", "torch", "matplotlib", "Scipy", "scikit_image", "opencv-contrib-python", "pymatting", "segment_anything", "timm", "addict", "yapf", "colour-science", "wget", "mediapipe", "loguru", "typer_config", "fastapi", "rich", "google-generativeai", "diffusers", "omegaconf", "tqdm", "transformers", "kornia", "image-reward", "ultralytics", "blend_modes", "blind-watermark", "qrcode", "pyzbar", "psd-tools"]
|
||||
dependencies = ["numpy", "pillow", "torch", "matplotlib", "Scipy", "scikit_image", "opencv-contrib-python", "pymatting", "segment_anything", "timm", "addict", "yapf", "colour-science", "wget", "mediapipe", "loguru", "typer_config", "fastapi", "rich", "google-generativeai", "diffusers", "omegaconf", "tqdm", "transformers", "kornia", "image-reward", "ultralytics", "blend_modes", "blind-watermark", "qrcode", "pyzbar", "transparent-background", "huggingface_hub", "psd-tools"]
|
||||
|
||||
[project.urls]
|
||||
Repository = "https://github.com/chflame163/ComfyUI_LayerStyle"
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
huggingface_hub==0.23.3
|
||||
transformers>=4.38.1
|
||||
transformers>=4.38.2
|
||||
protobuf>=4.25.3
|
||||
opencv-contrib-python>=4.9.0.80
|
||||
@@ -30,4 +30,5 @@ blind-watermark
|
||||
qrcode
|
||||
pyzbar
|
||||
transparent-background
|
||||
huggingface_hub
|
||||
psd-tools
|
||||
|
||||
@@ -0,0 +1,212 @@
|
||||
{
|
||||
"last_node_id": 17,
|
||||
"last_link_id": 13,
|
||||
"nodes": [
|
||||
{
|
||||
"id": 3,
|
||||
"type": "LoadImage",
|
||||
"pos": [
|
||||
492,
|
||||
822
|
||||
],
|
||||
"size": {
|
||||
"0": 315,
|
||||
"1": 314
|
||||
},
|
||||
"flags": {},
|
||||
"order": 0,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
6
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
},
|
||||
{
|
||||
"name": "MASK",
|
||||
"type": "MASK",
|
||||
"links": null,
|
||||
"shape": 3,
|
||||
"slot_index": 1
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LoadImage"
|
||||
},
|
||||
"widgets_values": [
|
||||
"fox_512x512.png",
|
||||
"image"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "LayerMask: LoadFlorence2Model",
|
||||
"pos": [
|
||||
493,
|
||||
675
|
||||
],
|
||||
"size": {
|
||||
"0": 315,
|
||||
"1": 58
|
||||
},
|
||||
"flags": {},
|
||||
"order": 1,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "florence2_model",
|
||||
"type": "FLORENCE2",
|
||||
"links": [
|
||||
5
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LayerMask: LoadFlorence2Model"
|
||||
},
|
||||
"widgets_values": [
|
||||
"base"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 10,
|
||||
"type": "LayerUtility: Florence2Image2Prompt",
|
||||
"pos": [
|
||||
918,
|
||||
813
|
||||
],
|
||||
"size": {
|
||||
"0": 367.79998779296875,
|
||||
"1": 198
|
||||
},
|
||||
"flags": {},
|
||||
"order": 2,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "florence2_model",
|
||||
"type": "FLORENCE2",
|
||||
"link": 5
|
||||
},
|
||||
{
|
||||
"name": "image",
|
||||
"type": "IMAGE",
|
||||
"link": 6
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "text",
|
||||
"type": "STRING",
|
||||
"links": [
|
||||
7
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
},
|
||||
{
|
||||
"name": "preview_image",
|
||||
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After Width: | Height: | Size: 354 KiB |
Reference in New Issue
Block a user