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Merge branch 'comfyanonymous:master' into feature/preview-latent

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Dr.Lt.Data 2023-06-12 20:57:36 +09:00 committed by GitHub
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2 changed files with 51 additions and 15 deletions
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44
comfy/sd.py
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@ -544,6 +544,19 @@ class VAE:
/ 3.0) / 2.0, min=0.0, max=1.0)
return output
def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
steps = pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
pbar = utils.ProgressBar(steps)
encode_fn = lambda a: self.first_stage_model.encode(2. * a.to(self.device) - 1.).sample() * self.scale_factor
samples = utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
samples /= 3.0
return samples
def decode(self, samples_in):
model_management.unload_model()
self.first_stage_model = self.first_stage_model.to(self.device)
@ -574,28 +587,29 @@ class VAE:
def encode(self, pixel_samples):
model_management.unload_model()
self.first_stage_model = self.first_stage_model.to(self.device)
pixel_samples = pixel_samples.movedim(-1,1).to(self.device)
samples = self.first_stage_model.encode(2. * pixel_samples - 1.).sample() * self.scale_factor
pixel_samples = pixel_samples.movedim(-1,1)
try:
free_memory = model_management.get_free_memory(self.device)
batch_number = int((free_memory * 0.7) / (2078 * pixel_samples.shape[2] * pixel_samples.shape[3])) #NOTE: this constant along with the one in the decode above are estimated from the mem usage for the VAE and could change.
batch_number = max(1, batch_number)
samples = torch.empty((pixel_samples.shape[0], 4, round(pixel_samples.shape[2] // 8), round(pixel_samples.shape[3] // 8)), device="cpu")
for x in range(0, pixel_samples.shape[0], batch_number):
pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.device)
samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).sample().cpu() * self.scale_factor
except model_management.OOM_EXCEPTION as e:
print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
samples = self.encode_tiled_(pixel_samples)
self.first_stage_model = self.first_stage_model.cpu()
samples = samples.cpu()
return samples
def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
model_management.unload_model()
self.first_stage_model = self.first_stage_model.to(self.device)
pixel_samples = pixel_samples.movedim(-1,1).to(self.device)
steps = pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
pbar = utils.ProgressBar(steps)
samples = utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
samples += utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
samples += utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
samples /= 3.0
pixel_samples = pixel_samples.movedim(-1,1)
samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap)
self.first_stage_model = self.first_stage_model.cpu()
samples = samples.cpu()
return samples
def broadcast_image_to(tensor, target_batch_size, batched_number):

22
nodes.py
View File

@ -1315,6 +1315,26 @@ class ImageScale:
s = s.movedim(1,-1)
return (s,)
class ImageScaleBy:
upscale_methods = ["nearest-exact", "bilinear", "area"]
@classmethod
def INPUT_TYPES(s):
return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,),
"scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 8.0, "step": 0.01}),}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "upscale"
CATEGORY = "image/upscaling"
def upscale(self, image, upscale_method, scale_by):
samples = image.movedim(-1,1)
width = round(samples.shape[3] * scale_by)
height = round(samples.shape[2] * scale_by)
s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
s = s.movedim(1,-1)
return (s,)
class ImageInvert:
@classmethod
@ -1413,6 +1433,7 @@ NODE_CLASS_MAPPINGS = {
"LoadImage": LoadImage,
"LoadImageMask": LoadImageMask,
"ImageScale": ImageScale,
"ImageScaleBy": ImageScaleBy,
"ImageInvert": ImageInvert,
"ImagePadForOutpaint": ImagePadForOutpaint,
"ConditioningAverage ": ConditioningAverage ,
@ -1495,6 +1516,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"LoadImage": "Load Image",
"LoadImageMask": "Load Image (as Mask)",
"ImageScale": "Upscale Image",
"ImageScaleBy": "Upscale Image By",
"ImageUpscaleWithModel": "Upscale Image (using Model)",
"ImageInvert": "Invert Image",
"ImagePadForOutpaint": "Pad Image for Outpainting",