4.9 KiB
4.9 KiB
Documentation
- Class name: samplerSimpleInpainting
- Category: EasyUse/Sampler
- Output node: True
- Repo Ref: https://github.com/yolain/ComfyUI-Easy-Use.git
该节点通过使用VAE模型来填充图像中缺失或指定的区域，从而促进图像修复过程。它与各种条件选项集成，根据用户规范细化修复效果。
Input types
Required
- pipe
- pipe对象是必需的，因为它包含了进行图像修复过程所需的模型和图像。它作为节点的主要输入，指导整个操作。
- Comfy dtype: PIPE_LINE
- Python dtype: Dict[str, Any]
- grow_mask_by
- 这个参数影响用于修复的遮罩的增长，决定了将受到影响的区域。它对于定义修复过程的范围至关重要。
- Comfy dtype: INT
- Python dtype: int
- image_output
- image_output参数决定了图像修复过程后结果图像的处理方式。它指定是预览、保存还是结合这些操作，影响输出展示。
- Comfy dtype: COMBO
- Python dtype: str
Optional
- additional
- 这个参数为修复过程提供了额外的条件选项，如微分扩散或模型条件。它增强了修复结果的灵活性和定制化。
- Comfy dtype: COMBO
- Python dtype: str
Output types
- pipe
- 更新后的pipe对象包含修复后的图像以及过程中所做的修改。它作为主要输出，封装了图像修复操作的结果。
- Comfy dtype: PIPE_LINE
- Python dtype: Dict[str, Any]
- image
- 图像输出代表修复后的结果，根据image_output参数，可能是预览、保存或两者兼有。它是图像修复过程的具体成果。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
Usage tips
- Infra type: GPU
Source code
class samplerSimpleInpainting:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {'required': {'pipe': ('PIPE_LINE',), 'grow_mask_by': ('INT', {'default': 6, 'min': 0, 'max': 64, 'step': 1}), 'image_output': (['Hide', 'Preview', 'Save', 'Hide/Save', 'Sender', 'Sender/Save'], {'default': 'Preview'}), 'link_id': ('INT', {'default': 0, 'min': 0, 'max': sys.maxsize, 'step': 1}), 'save_prefix': ('STRING', {'default': 'ComfyUI'}), 'additional': (['None', 'Differential Diffusion', 'Only InpaintModelConditioning'], {'default': 'None'})}, 'optional': {'model': ('MODEL',), 'mask': ('MASK',), 'patch': ('INPAINT_PATCH',)}, 'hidden': {'tile_size': 'INT', 'prompt': 'PROMPT', 'extra_pnginfo': 'EXTRA_PNGINFO', 'my_unique_id': 'UNIQUE_ID', 'embeddingsList': (folder_paths.get_filename_list('embeddings'),)}}
RETURN_TYPES = ('PIPE_LINE', 'IMAGE', 'VAE')
RETURN_NAMES = ('pipe', 'image', 'vae')
OUTPUT_NODE = True
FUNCTION = 'run'
CATEGORY = 'EasyUse/Sampler'
def run(self, pipe, grow_mask_by, image_output, link_id, save_prefix, additional, model=None, mask=None, patch=None, tile_size=None, prompt=None, extra_pnginfo=None, my_unique_id=None, force_full_denoise=False, disable_noise=False):
fooocus_model = None
model = model if model is not None else pipe['model']
latent = pipe['samples'] if 'samples' in pipe else None
positive = pipe['positive']
negative = pipe['negative']
pixels = pipe['images'] if pipe and 'images' in pipe else None
vae = pipe['vae'] if pipe and 'vae' in pipe else None
if 'noise_mask' in latent and mask is None:
mask = latent['noise_mask']
else:
if pixels is None:
raise Exception('No Images found')
if vae is None:
raise Exception('No VAE found')
(latent,) = VAEEncodeForInpaint().encode(vae, pixels, mask, grow_mask_by)
mask = latent['noise_mask']
if mask is not None:
if additional != 'None':
(positive, negative, latent) = InpaintModelConditioning().encode(positive, negative, pixels, vae, mask)
if additional == 'Differential Diffusion':
cls = ALL_NODE_CLASS_MAPPINGS['DifferentialDiffusion']
if cls is not None:
(model,) = cls().apply(model)
else:
raise Exception('Differential Diffusion not found,please update comfyui')
if patch is not None:
worker = InpaintWorker(node_name='easy kSamplerInpainting')
(fooocus_model,) = worker.patch(model, latent, patch)
new_pipe = {**pipe, 'model': fooocus_model if fooocus_model is not None else model, 'positive': positive, 'negative': negative, 'vae': vae, 'samples': latent, 'loader_settings': pipe['loader_settings']}
else:
new_pipe = pipe
del pipe
return samplerFull().run(new_pipe, None, None, None, None, None, image_output, link_id, save_prefix, None, None, None, None, None, None, None, None, tile_size, prompt, extra_pnginfo, my_unique_id, force_full_denoise, disable_noise)