132 lines
5.5 KiB
Markdown
132 lines
5.5 KiB
Markdown
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# Documentation
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- Class name: GLIGENTextBoxApplyBatch
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- Category: KJNodes/experimental
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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GLIGENTextBoxApplyBatch节点是用于批量处理文本框应用的节点，可以高效地在多个图像或帧中应用文本叠加或修改。这个节点简化了向图像添加文本的过程，特别适合需要在一系列图像中应用一致文本元素的场景，如视频帧或相关图像集合。
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# Input types
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## Required
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- conditioning_to
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- 指定将应用文本框修改的目标条件数据，作为文本叠加过程的基础。
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- Comfy dtype: CONDITIONING
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- Python dtype: list
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- latents
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- 提供批次中图像的潜在表示，用作应用文本框修改的基础。
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- Comfy dtype: LATENT
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- Python dtype: list
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- clip
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- 表示用于编码文本输入的CLIP模型，有助于生成应用于图像的基于文本的特征。
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- Comfy dtype: CLIP
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- Python dtype: object
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- gligen_textbox_model
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- 指定用于生成文本框叠加的模型，实现图像文本的自定义和应用。
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- Comfy dtype: GLIGEN
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- Python dtype: object
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- text
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- 允许指定要在整个图像批次中应用的文本内容，作为文本叠加或修改的主要内容。
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- Comfy dtype: STRING
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- Python dtype: str
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- width
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- 定义文本框叠加的宽度，允许根据图像自定义叠加大小。
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- Comfy dtype: INT
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- Python dtype: int
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- height
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- 指定文本框叠加的高度，可以精确控制叠加尺寸。
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- Comfy dtype: INT
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- Python dtype: int
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- coordinates
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- 提供文本框叠加的定位信息，决定文本将应用在图像的哪个位置。
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- Comfy dtype: STRING
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- Python dtype: tuple
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- interpolation
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- 确定文本框应用的插值方法，影响文本叠加在图像上的平滑度和混合效果。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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# Output types
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- conditioning
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- 反映应用文本框叠加后修改的条件数据，将文本修改整合到条件框架中。
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- Comfy dtype: CONDITIONING
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- Python dtype: list
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- image
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- 展示应用了文本框叠加的最终图像，展示对图像批次所做的视觉修改。
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- Comfy dtype: IMAGE
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- Python dtype: list
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## Usage tips
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- Infra type: `CPU`
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- Common nodes: unknown
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## Source code
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```python
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class GLIGENTextBoxApplyBatch:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"conditioning_to": ("CONDITIONING", ),
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"latents": ("LATENT", ),
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"clip": ("CLIP", ),
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"gligen_textbox_model": ("GLIGEN", ),
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"text": ("STRING", {"multiline": True}),
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"width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
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"height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
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"coordinates": ("STRING", {"multiline": True}),
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"interpolation": (
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[
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'straight',
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'CubicSpline',
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],
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{
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"default": 'CubicSpline'
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}),
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}}
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RETURN_TYPES = ("CONDITIONING", "IMAGE",)
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FUNCTION = "append"
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CATEGORY = "KJNodes/experimental"
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DESCRIPTION = """
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Experimental, does not function yet as ComfyUI base changes are needed
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"""
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def append(self, latents, conditioning_to, clip, gligen_textbox_model, text, width, height, coordinates, interpolation):
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coordinates_dict = parse_coordinates(coordinates)
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batch_size = sum(tensor.size(0) for tensor in latents.values())
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c = []
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cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True)
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# Interpolate coordinates for the entire batch
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if interpolation == 'CubicSpline':
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interpolated_coords = interpolate_coordinates_with_curves(coordinates_dict, batch_size)
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if interpolation == 'straight':
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interpolated_coords = interpolate_coordinates(coordinates_dict, batch_size)
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plot_image_tensor = plot_to_tensor(coordinates_dict, interpolated_coords, 512, 512, height)
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for t in conditioning_to:
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n = [t[0], t[1].copy()]
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position_params_batch = [[] for _ in range(batch_size)] # Initialize a list of empty lists for each batch item
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for i in range(batch_size):
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x_position, y_position = interpolated_coords[i]
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position_param = (cond_pooled, height // 8, width // 8, y_position // 8, x_position // 8)
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position_params_batch[i].append(position_param) # Append position_param to the correct sublist
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print("x ",x_position, "y ", y_position)
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prev = []
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if "gligen" in n[1]:
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prev = n[1]['gligen'][2]
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else:
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prev = [[] for _ in range(batch_size)]
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# Concatenate prev and position_params_batch, ensuring both are lists of lists
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# and each sublist corresponds to a batch item
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combined_position_params = [prev_item + batch_item for prev_item, batch_item in zip(prev, position_params_batch)]
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n[1]['gligen'] = ("position", gligen_textbox_model, combined_position_params)
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c.append(n)
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return (c, plot_image_tensor,)
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```
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