279 lines
9.8 KiB
Python
279 lines
9.8 KiB
Python
import torch
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import os
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import sys
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import json
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import hashlib
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from PIL import Image
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from PIL.PngImagePlugin import PngInfo
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import numpy as np
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sys.path.append(os.path.join(sys.path[0], "comfy"))
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import comfy.samplers
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import comfy.sd
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supported_ckpt_extensions = ['.ckpt']
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try:
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import safetensors.torch
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supported_ckpt_extensions += ['.safetensors']
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except:
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print("Could not import safetensors, safetensors support disabled.")
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def filter_files_extensions(files, extensions):
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return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions, files)))
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class CLIPTextEncode:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"text": ("STRING", {"multiline": True}), "clip": ("CLIP", )}}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "encode"
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def encode(self, clip, text):
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return (clip.encode(text), )
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class VAEDecode:
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def __init__(self, device="cpu"):
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self.device = device
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "decode"
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def decode(self, vae, samples):
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return (vae.decode(samples), )
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class VAEEncode:
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def __init__(self, device="cpu"):
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self.device = device
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "encode"
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def encode(self, vae, pixels):
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x = (pixels.shape[1] // 64) * 64
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y = (pixels.shape[2] // 64) * 64
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if pixels.shape[1] != x or pixels.shape[2] != y:
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pixels = pixels[:,:x,:y,:]
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return (vae.encode(pixels), )
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class CheckpointLoader:
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models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
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config_dir = os.path.join(models_dir, "configs")
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ckpt_dir = os.path.join(models_dir, "checkpoints")
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "config_name": (filter_files_extensions(os.listdir(s.config_dir), '.yaml'), ),
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"ckpt_name": (filter_files_extensions(os.listdir(s.ckpt_dir), supported_ckpt_extensions), )}}
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RETURN_TYPES = ("MODEL", "CLIP", "VAE")
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FUNCTION = "load_checkpoint"
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def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True):
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config_path = os.path.join(self.config_dir, config_name)
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ckpt_path = os.path.join(self.ckpt_dir, ckpt_name)
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return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True)
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class VAELoader:
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models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
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vae_dir = os.path.join(models_dir, "vae")
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "vae_name": (filter_files_extensions(os.listdir(s.vae_dir), supported_ckpt_extensions), )}}
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RETURN_TYPES = ("VAE",)
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FUNCTION = "load_vae"
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#TODO: scale factor?
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def load_vae(self, vae_name):
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vae_path = os.path.join(self.vae_dir, vae_name)
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vae = comfy.sd.VAE(ckpt_path=vae_path)
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return (vae,)
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class EmptyLatentImage:
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def __init__(self, device="cpu"):
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self.device = device
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
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"height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 64})}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "generate"
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def generate(self, width, height, batch_size=1):
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latent = torch.zeros([batch_size, 4, height // 8, width // 8])
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return (latent, )
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class LatentUpscale:
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upscale_methods = ["nearest-exact", "bilinear", "area"]
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crop_methods = ["disabled", "center"]
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,),
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"width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
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"height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
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"crop": (s.crop_methods,)}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "upscale"
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def upscale(self, samples, upscale_method, width, height, crop):
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if crop == "center":
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old_width = samples.shape[3]
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old_height = samples.shape[2]
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old_aspect = old_width / old_height
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new_aspect = width / height
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x = 0
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y = 0
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if old_aspect > new_aspect:
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x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
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elif old_aspect < new_aspect:
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y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
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s = samples[:,:,y:old_height-y,x:old_width-x]
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else:
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s = samples
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s = torch.nn.functional.interpolate(s, size=(height // 8, width // 8), mode=upscale_method)
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return (s,)
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class KSampler:
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def __init__(self, device="cuda"):
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self.device = device
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"model": ("MODEL",),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
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"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"latent_image": ("LATENT", ),
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"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "sample"
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def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):
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noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=torch.manual_seed(seed), device="cpu")
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model = model.to(self.device)
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noise = noise.to(self.device)
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latent_image = latent_image.to(self.device)
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if positive.shape[0] < noise.shape[0]:
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positive = torch.cat([positive] * noise.shape[0])
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if negative.shape[0] < noise.shape[0]:
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negative = torch.cat([negative] * noise.shape[0])
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positive = positive.to(self.device)
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negative = negative.to(self.device)
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if sampler_name in comfy.samplers.KSampler.SAMPLERS:
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sampler = comfy.samplers.KSampler(model, steps=steps, device=self.device, sampler=sampler_name, scheduler=scheduler, denoise=denoise)
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else:
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#other samplers
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pass
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samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image)
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samples = samples.cpu()
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model = model.cpu()
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return (samples, )
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class SaveImage:
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def __init__(self):
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self.output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output")
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"images": ("IMAGE", ),
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"filename_prefix": ("STRING", {"default": "ComfyUI"})},
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"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
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}
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RETURN_TYPES = ()
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FUNCTION = "save_images"
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OUTPUT_NODE = True
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def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
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def map_filename(filename):
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prefix_len = len(filename_prefix)
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prefix = filename[:prefix_len + 1]
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try:
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digits = int(filename[prefix_len + 1:].split('_')[0])
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except:
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digits = 0
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return (digits, prefix)
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try:
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counter = max(filter(lambda a: a[1][:-1] == filename_prefix and a[1][-1] == "_", map(map_filename, os.listdir(self.output_dir))))[0] + 1
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except ValueError:
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counter = 1
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for image in images:
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i = 255. * image.cpu().numpy()
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img = Image.fromarray(i.astype(np.uint8))
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metadata = PngInfo()
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if prompt is not None:
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metadata.add_text("prompt", json.dumps(prompt))
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if extra_pnginfo is not None:
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for x in extra_pnginfo:
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metadata.add_text(x, json.dumps(extra_pnginfo[x]))
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img.save(f"output/{filename_prefix}_{counter:05}_.png", pnginfo=metadata, optimize=True)
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counter += 1
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class LoadImage:
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input_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"image": (os.listdir(s.input_dir), )},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "load_image"
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def load_image(self, image):
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image_path = os.path.join(self.input_dir, image)
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image = Image.open(image_path).convert("RGB")
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image[None])[None,]
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return image
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@classmethod
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def IS_CHANGED(s, image):
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image_path = os.path.join(s.input_dir, image)
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m = hashlib.sha256()
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with open(image_path, 'rb') as f:
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m.update(f.read())
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return m.digest().hex()
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NODE_CLASS_MAPPINGS = {
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"KSampler": KSampler,
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"CheckpointLoader": CheckpointLoader,
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"CLIPTextEncode": CLIPTextEncode,
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"VAEDecode": VAEDecode,
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"VAEEncode": VAEEncode,
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"VAELoader": VAELoader,
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"EmptyLatentImage": EmptyLatentImage,
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"LatentUpscale": LatentUpscale,
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"SaveImage": SaveImage,
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"LoadImage": LoadImage
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}
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