The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
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import os
from comfy_extras.chainner_models import model_loading
from comfy.sd import load_torch_file
import comfy.model_management
from nodes import filter_files_extensions, recursive_search, supported_ckpt_extensions
import torch
import comfy.utils
class UpscaleModelLoader:
models_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.realpath(__file__))), "models")
upscale_model_dir = os.path.join(models_dir, "upscale_models")
@classmethod
def INPUT_TYPES(s):
return {"required": { "model_name": (filter_files_extensions(recursive_search(s.upscale_model_dir), supported_ckpt_extensions), ),
}}
RETURN_TYPES = ("UPSCALE_MODEL",)
FUNCTION = "load_model"
CATEGORY = "loaders"
def load_model(self, model_name):
model_path = os.path.join(self.upscale_model_dir, model_name)
sd = load_torch_file(model_path)
out = model_loading.load_state_dict(sd).eval()
return (out, )
class ImageUpscaleWithModel:
@classmethod
def INPUT_TYPES(s):
return {"required": { "upscale_model": ("UPSCALE_MODEL",),
"image": ("IMAGE",),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "upscale"
CATEGORY = "image"
def upscale(self, upscale_model, image):
device = comfy.model_management.get_torch_device()
upscale_model.to(device)
in_img = image.movedim(-1,-3).to(device)
s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=128 + 64, tile_y=128 + 64, overlap = 8, upscale_amount=upscale_model.scale)
upscale_model.cpu()
s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0)
return (s,)
NODE_CLASS_MAPPINGS = {
"UpscaleModelLoader": UpscaleModelLoader,
"ImageUpscaleWithModel": ImageUpscaleWithModel
}