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 import model_management
import torch
import comfy.utils
import folder_paths
class UpscaleModelLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model_name": (folder_paths.get_filename_list("upscale_models"), ),
}}
RETURN_TYPES = ("UPSCALE_MODEL",)
FUNCTION = "load_model"
CATEGORY = "loaders"
def load_model(self, model_name):
model_path = folder_paths.get_full_path("upscale_models", model_name)
sd = comfy.utils.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/upscaling"
def upscale(self, upscale_model, image):
device = 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
}