HiresFix¶
Documentation¶
- Class name:
easy hiresFix - Category:
EasyUse/Fix - Output node:
True
The easy hiresFix node is designed to enhance the resolution and detail of images, typically used as a post-processing step to refine and improve the visual quality of generated images. It focuses on fixing high-resolution aspects, making it suitable for applications requiring detailed and clear visual outputs.
Input types¶
Required¶
model_name- Specifies the neural network model used for the upscale process, impacting the quality and method of enhancement.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
rescale_after_model- Determines whether the image is rescaled after being processed by the model, affecting the final image size and detail.
- Comfy dtype:
COMBO[BOOLEAN] - Python dtype:
bool
rescale_method- Defines the method used for rescaling the image, influencing the quality and appearance of the final output.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
rescale- Specifies how the image is rescaled, by percentage or to specific dimensions, directly impacting the output size.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
percent- The percentage to rescale the image by when 'rescale' is set to 'by percentage', affecting the output image size.
- Comfy dtype:
INT - Python dtype:
int
width- The target width of the image when 'rescale' is set to 'to Width/Height', determining the output dimensions.
- Comfy dtype:
INT - Python dtype:
int
height- The target height of the image when 'rescale' is set to 'to Width/Height', determining the output dimensions.
- Comfy dtype:
INT - Python dtype:
int
longer_side- Specifies the longer side dimension for rescaling while maintaining aspect ratio, used when 'rescale' is set to 'to longer side - maintain aspect'.
- Comfy dtype:
INT - Python dtype:
int
crop- Indicates whether and how the image should be cropped, affecting the composition of the final output.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
image_output- Determines the format of the output image, influencing how the result is saved or displayed.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
link_id- A unique identifier for linking the processed image with other elements or processes, useful for tracking and organization.
- Comfy dtype:
INT - Python dtype:
str
save_prefix- A prefix added to the file name of the saved image, useful for organization and identification.
- Comfy dtype:
STRING - Python dtype:
str
Optional¶
pipe- An optional pipeline of processes the image may go through before or after the upscale process, affecting the overall workflow.
- Comfy dtype:
PIPE_LINE - Python dtype:
dict
image- The input image to be upscaled, serving as the direct subject of the enhancement process.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
vae- An optional VAE model that can be used in conjunction with the upscale process for additional image processing.
- Comfy dtype:
VAE - Python dtype:
torch.nn.Module
Output types¶
pipe- Comfy dtype:
PIPE_LINE - The output pipeline, potentially including the upscaled image among other processed elements.
- Python dtype:
dict
- Comfy dtype:
image- Comfy dtype:
IMAGE - The upscaled image, showcasing improved resolution and detail.
- Python dtype:
torch.Tensor
- Comfy dtype:
latent- Comfy dtype:
LATENT - A latent representation of the image, useful for further processing or analysis.
- Python dtype:
torch.Tensor
- Comfy dtype:
Usage tips¶
- Infra type:
GPU - Common nodes: unknown
Source code¶
class hiresFix:
upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos", "bislerp"]
crop_methods = ["disabled", "center"]
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model_name": (folder_paths.get_filename_list("upscale_models"),),
"rescale_after_model": ([False, True], {"default": True}),
"rescale_method": (s.upscale_methods,),
"rescale": (["by percentage", "to Width/Height", 'to longer side - maintain aspect'],),
"percent": ("INT", {"default": 50, "min": 0, "max": 1000, "step": 1}),
"width": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"longer_side": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"crop": (s.crop_methods,),
"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"}),
},
"optional": {
"pipe": ("PIPE_LINE",),
"image": ("IMAGE",),
"vae": ("VAE",),
},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID",
},
}
RETURN_TYPES = ("PIPE_LINE", "IMAGE", "LATENT", )
RETURN_NAMES = ('pipe', 'image', "latent", )
FUNCTION = "upscale"
CATEGORY = "EasyUse/Fix"
OUTPUT_NODE = True
def vae_encode_crop_pixels(self, pixels):
x = (pixels.shape[1] // 8) * 8
y = (pixels.shape[2] // 8) * 8
if pixels.shape[1] != x or pixels.shape[2] != y:
x_offset = (pixels.shape[1] % 8) // 2
y_offset = (pixels.shape[2] % 8) // 2
pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
return pixels
def upscale(self, model_name, rescale_after_model, rescale_method, rescale, percent, width, height,
longer_side, crop, image_output, link_id, save_prefix, pipe=None, image=None, vae=None, prompt=None,
extra_pnginfo=None, my_unique_id=None):
new_pipe = {}
if pipe is not None:
image = image if image is not None else pipe["images"]
vae = vae if vae is not None else pipe.get("vae")
elif image is None or vae is None:
raise ValueError("pipe or image or vae missing.")
# Load Model
model_path = folder_paths.get_full_path("upscale_models", model_name)
sd = comfy.utils.load_torch_file(model_path, safe_load=True)
upscale_model = model_loading.load_state_dict(sd).eval()
# Model upscale
device = comfy.model_management.get_torch_device()
upscale_model.to(device)
in_img = image.movedim(-1, -3).to(device)
tile = 128 + 64
overlap = 8
steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile,
tile_y=tile, overlap=overlap)
pbar = comfy.utils.ProgressBar(steps)
s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap,
upscale_amount=upscale_model.scale, pbar=pbar)
upscale_model.cpu()
s = torch.clamp(s.movedim(-3, -1), min=0, max=1.0)
# Post Model Rescale
if rescale_after_model == True:
samples = s.movedim(-1, 1)
orig_height = samples.shape[2]
orig_width = samples.shape[3]
if rescale == "by percentage" and percent != 0:
height = percent / 100 * orig_height
width = percent / 100 * orig_width
if (width > MAX_RESOLUTION):
width = MAX_RESOLUTION
if (height > MAX_RESOLUTION):
height = MAX_RESOLUTION
width = easySampler.enforce_mul_of_64(width)
height = easySampler.enforce_mul_of_64(height)
elif rescale == "to longer side - maintain aspect":
longer_side = easySampler.enforce_mul_of_64(longer_side)
if orig_width > orig_height:
width, height = longer_side, easySampler.enforce_mul_of_64(longer_side * orig_height / orig_width)
else:
width, height = easySampler.enforce_mul_of_64(longer_side * orig_width / orig_height), longer_side
s = comfy.utils.common_upscale(samples, width, height, rescale_method, crop)
s = s.movedim(1, -1)
# vae encode
pixels = self.vae_encode_crop_pixels(s)
t = vae.encode(pixels[:, :, :, :3])
if pipe is not None:
new_pipe = {
"model": pipe['model'],
"positive": pipe['positive'],
"negative": pipe['negative'],
"vae": vae,
"clip": pipe['clip'],
"samples": {"samples": t},
"images": s,
"seed": pipe['seed'],
"loader_settings": {
**pipe["loader_settings"],
}
}
del pipe
else:
new_pipe = {}
results = easySave(s, save_prefix, image_output, prompt, extra_pnginfo)
if image_output in ("Sender", "Sender&Save"):
PromptServer.instance.send_sync("img-send", {"link_id": link_id, "images": results})
if image_output in ("Hide", "Hide&Save"):
return (new_pipe, s, {"samples": t},)
return {"ui": {"images": results},
"result": (new_pipe, s, {"samples": t},)}