412 lines
14 KiB
Python
412 lines
14 KiB
Python
from .utils import PipelineFactory, Text2ImgPipelineCreator, InpaintPipelineCreator
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from .utils import convert_images_to_tensors, filter_lora, scale_lora, invert_mask
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from diffusers.utils import load_image
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import folder_paths
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import torch
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import numpy as np
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import os
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import cv2
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from PIL import Image
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'''To do
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- Investigate the model caching
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- Deduce the model architecture, SDXL or SD (see B-Lora Comfy code)
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- change pipeline instanciation
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'''
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# Registering the needed pipelines
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PipelineFactory.register_creator('text2img', Text2ImgPipelineCreator)
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PipelineFactory.register_creator('inpainting', InpaintPipelineCreator)
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class Text2ImgStableDiffusionPipeline:
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'''
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This node generates the Stable Diffusion Pipeline for Text2Img.
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Required inputs :
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- is_sdxl : whether the loaded model has an SDXL base
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- low_vram : whether to activate low VRAM options
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- model : the model that has to be instanciated
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Optional outputs:
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- vae : which VAE to use - copy the name of the VAE from Hugging Face
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- controlnet_model : which ControlNet model to use - copy the name of the ControlNet from Hugging Face
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Ouputs:
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Pipeline
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'''
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def __init__(self) -> None:
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {
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"is_sdxl": ("BOOLEAN", {"default": True}),
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"low_vram": ("BOOLEAN", {"default": True}),
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"model": (folder_paths.get_filename_list("checkpoints"),),
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},
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"optional":
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{
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"vae": ("STRING", {"multiline": False}),
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"controlnet_model" : ("STRING", {"multiline": False}),
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}
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}
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RETURN_TYPES = ("PIPELINE",)
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FUNCTION = "create_text2img_pipeline"
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CATEGORY = "Diffusers-in-Comfy"
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def create_text2img_pipeline(self, is_sdxl, low_vram, model, vae, controlnet_model):
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factory = PipelineFactory()
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pipeline = factory.get_pipeline(low_vram, 'text2img', model, vae, controlnet_model, is_sdxl, torch_dtype=torch.float16)
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return (pipeline,)
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class InpaintingStableDiffusionPipeline:
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'''
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This node generates the Stable Diffusion Pipeline for inpainting.
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Required inputs :
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- is_sdxl : whether the loaded model has an SDXL base
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- low_vram : whether to activate low VRAM options
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- model : the model that has to be instanciated
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Optional outputs:
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- vae : which VAE to use - copy the name of the VAE from Hugging Face
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- controlnet_model : which ControlNet model to use - copy the name of the ControlNet from Hugging Face
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Ouputs:
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Pipeline
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'''
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def __init__(self) -> None:
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {
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"is_sdxl": ("BOOLEAN", {"default": True}),
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"low_vram": ("BOOLEAN", {"default": True}),
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"model": (folder_paths.get_filename_list("checkpoints"),),
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},
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"optional":
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{
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"vae": ("STRING", {"multiline": False}),
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"controlnet_model" : ("STRING", {"multiline": False}),
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}
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}
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RETURN_TYPES = ("PIPELINE",)
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FUNCTION = "create_inpaint_pipeline"
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CATEGORY = "Diffusers-in-Comfy"
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def create_inpaint_pipeline(self, is_sdxl, low_vram, model, vae, controlnet_model):
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factory = PipelineFactory()
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pipeline = factory.get_pipeline(low_vram, 'inpainting', model, vae, controlnet_model, is_sdxl, torch_dtype=torch.float16)
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return (pipeline,)
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class MakeCanny:
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'''
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This node allows you to transform your image into a canny mask.
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Required inputs:
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- image_path : the string path to your image on your computer, or an URL
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- high_threshold : high threshold for the edges to be calculated
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- low_threshold : low_threshold for the edges to be calculated
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Output:
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- an image "Canny" that can then be processed further down the Comfy Workflow
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- a tensor 'Canny Preview" that is an image converted to tensor to be displayed by Comfy's preview image node
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'''
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def __init__(self) -> None:
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {
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"image_path" : ("STRING", {"multiline": True}),
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"high_threshold": ("INT", {"default": 200, "min":0, "max":255}),
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"low_threshold": ("INT", {"default": 100, "min":0, "max":255}),
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}}
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RETURN_TYPES = ("IMAGE", "IMAGE")
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RETURN_NAMES = ("Canny", "Canny Preview")
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FUNCTION = "create_canny"
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CATEGORY = "Diffusers-in-Comfy"
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def create_canny(self, image_path, low_threshold, high_threshold):
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original_image = load_image(image_path)
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image = np.array(original_image)
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image = cv2.Canny(image, low_threshold, high_threshold)
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image = image[:, :, None]
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image = np.concatenate([image, image, image], axis=2)
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canny = Image.fromarray(image)
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return (canny, convert_images_to_tensors([canny]),)
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class ImageInference:
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"""
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This class proceeds to the inference of the image based on the pipeline and its components.
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Required inputs:
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- pipeline : the Stable Diffusion Pipeline
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- seed : seed for the generation of the image
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- positive : the positive prompt
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- negative : the negative prompt
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- steps : the number of inference steps
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- width : width of the generated image
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- height : height of the generated image
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- cfg : guidance scale
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Optional inputs:
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- controlnet_image : the image coming out of the Canny Node
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- controlnet_scale : weight of the canny to control the inferred image
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- input_image : the image that has to be inpainted
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- mask_image : the mask of the area where the image has to be inpainted
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Output:
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- an image
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"""
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def __init__(self) -> None:
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {
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"pipeline": ("PIPELINE",),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"positive": ("STRING", {"multiline": True}),
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"negative": ("STRING", {"multiline": True}),
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"steps": ("INT", {"default": 50, "min": 1, "max": 10000}),
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"width": ("INT", {"default": 512, "min": 1, "max": 8192, "step": 1}),
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"height": ("INT", {"default": 512, "min": 1, "max": 8192, "step": 1}),
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"cfg": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
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},
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"optional": {
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"controlnet_image" : ("IMAGE",),
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"controlnet_scale": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step":0.1, "round": 0.01}),
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"input_image" : ("STRING", {"multiline": True}),
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"mask_image" : ("STRING", {"multiline": True}),
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"mask_invert" : ("BOOLEAN",{"default":True}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("Result",)
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FUNCTION = "infer_image"
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CATEGORY = "Diffusers-in-Comfy"
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def infer_image(self,
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pipeline,
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seed,
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steps,
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cfg,
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positive,
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negative,
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width,
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height,
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controlnet_image=None,
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controlnet_scale=None,
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input_image=None,
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mask_image=None,
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mask_invert=True):
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# If we make the generation run on CPU here, it will give a different image than GPU
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# IF there are still VRAM issues, try changing this to 'cpu'
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generator = torch.Generator(device='cuda').manual_seed(seed)
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args = {
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"prompt": positive,
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"generator": generator,
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"num_inference_steps" : steps,
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"guidance_scale" : cfg,
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"width" : width,
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"height" : height,
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}
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if negative != '':
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args['negative'] = negative
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if controlnet_image:
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args['image'] = controlnet_image
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args['controlnet_conditioning_scale'] = float(controlnet_scale)
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if input_image != '':
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input_image = load_image(input_image)
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args['image']= load_image(input_image)
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if mask_image != '':
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mask_image = load_image(mask_image)
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if mask_invert:
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mask_image = invert_mask(mask_image)
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args['mask_image'] = mask_image
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images = pipeline(**args).images
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# Clear GPU memory after inference
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if pipeline.device == 'cuda':
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torch.cuda.empty_cache()
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return (convert_images_to_tensors(images),)
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class LoRALoader:
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"""
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This node allows to load a LoRA to the pipeline.
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Required inputs:
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- pipeline : the Stable diffusion pipeline to connect the LoRA to
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- lora_name : name of your lora in the Lora folder. Only write the namen with the extension, not the total path
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- lora_scale : weight of the lora on the generated output
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Output:
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- pipeline with the LoRA loaded to the Unet
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"""
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def __init__(self) -> None:
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {"required":{
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"pipeline" : ("PIPELINE",),
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"lora_name" : (folder_paths.get_filename_list("loras"),),
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"lora_scale" : ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.1, "round": 0.01}),
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}}
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RETURN_TYPES = ("PIPELINE",)
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FUNCTION = "load_lora"
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CATEGORY = "Diffusers-in-Comfy"
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def load_lora(self, pipeline, lora_name, lora_scale):
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lora_path = folder_paths.get_full_path("loras", lora_name)
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lora_name = os.path.splitext(lora_name)[0]
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pipeline.load_lora_weights(lora_path, adapter_name=lora_name)
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pipeline.set_adapters([lora_name], adapter_weights=[lora_scale])
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return (pipeline,)
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class BLoRALoader:
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"""
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This node allows to load a B-Lora to the Unet.
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Required:
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- pipeline : the Stable Diffusion pipeline to which the B-Lora must be applied
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- style_lora_name : name of the lora you want to apply as style lora (only write the name + extension inside your lora folder, not total path)
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- style_lora_scale : weight of the style lora on the generated output
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- content_lora_name : name of the lora you want to apply as content lora (only write the name + extension inside your lora folder, not total path)
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- content_lora_scale : weight of the content lora on the generated output
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Output:
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- pipeline with the B-LoRA loaded into its Unet.
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"""
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BLOCKS = {
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'content': ['unet.up_blocks.0.attentions.0'],
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'style': ['unet.up_blocks.0.attentions.1'],
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}
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def __init__(self) -> None:
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {"required":{
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"pipeline" : ("PIPELINE",),
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"style_lora_name" : ("STRING", {"multiline": False}),
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"style_lora_scale" : ("FLOAT", {"default": 1.1, "min": 0.0, "max": 1.1, "step":0.1, "round": 0.01}),
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"content_lora_name" : ("STRING", {"multiline": False}),
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"content_lora_scale" : ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.1, "round": 0.01}),
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}}
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RETURN_TYPES = ("PIPELINE",)
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FUNCTION = "load_b_lora_to_unet"
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CATEGORY = "Diffusers-in-Comfy"
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def load_b_lora_to_unet(self,
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pipeline,
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style_lora_name,
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content_lora_name,
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style_lora_scale=1.,
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content_lora_scale=1.):
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if style_lora_name != '' :
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style_lora_path = folder_paths.get_full_path("loras", style_lora_name)
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style_lora_state_dict, _ = pipeline.lora_state_dict(style_lora_path)
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style_lora = filter_lora(style_lora_state_dict, self.BLOCKS['style'])
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style_lora = scale_lora(style_lora, style_lora_scale)
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else:
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style_lora = {}
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if content_lora_name != '':
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content_lora_path = folder_paths.get_full_path("loras", content_lora_name)
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content_lora_state_dict, _ = pipeline.lora_state_dict(content_lora_path)
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content_lora = filter_lora(content_lora_state_dict, self.BLOCKS['content'])
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content_lora = scale_lora(content_lora, content_lora_scale)
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else:
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content_lora = {}
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merged_lora = {**style_lora, **content_lora}
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pipeline.load_lora_into_unet(merged_lora, None, pipeline.unet)
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return (pipeline,)
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NODE_CLASS_MAPPINGS = {
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"CreateText2ImgPipeline": Text2ImgStableDiffusionPipeline,
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"CreateInpaintPipeline": InpaintingStableDiffusionPipeline,
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"ImageInference": ImageInference,
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"LoRALoader" : LoRALoader,
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"BLoRALoader" : BLoRALoader,
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"MakeCanny": MakeCanny,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"CreateText2ImagePipeline" : "Text2ImgStableDiffusionPipeline",
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"CreateInpaintPipeline": "InpaintingStableDiffusionPipeline",
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"ImageInference" : "ImageInference",
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"LoRALoader" : "LoRALoader",
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"BLoRALoader" : "BLoRALoader",
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"MakeCanny": "MakeCanny",
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} |