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Change latent resolution step to 8.

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comfyanonymous 2 years ago
parent 66c8aa5c3e
commit ba8a4c3667
2 changed files with 33 additions and 40 deletions
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1
comfy/ldm/modules/diffusionmodules/openaimodel.py
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@ -108,7 +108,6 @@ class Upsample(nn.Module):
self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
def forward(self, x, output_shape=None):
print("upsample", output_shape)
assert x.shape[1] == self.channels
if self.dims == 3:
shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2]

72
nodes.py
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@ -94,10 +94,10 @@ class ConditioningSetArea:
@classmethod
def INPUT_TYPES(s):
return {"required": {"conditioning": ("CONDITIONING", ),
"width": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"height": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"width": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("CONDITIONING",)
@ -188,16 +188,21 @@ class VAEEncode:
CATEGORY = "latent"
def encode(self, vae, pixels):
x = (pixels.shape[1] // 64) * 64
y = (pixels.shape[2] // 64) * 64
@staticmethod
def vae_encode_crop_pixels(pixels):
x = (pixels.shape[1] // 8) * 8
y = (pixels.shape[2] // 8) * 8
if pixels.shape[1] != x or pixels.shape[2] != y:
pixels = pixels[:,:x,:y,:]
t = vae.encode(pixels[:,:,:,:3])
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 encode(self, vae, pixels):
pixels = self.vae_encode_crop_pixels(pixels)
t = vae.encode(pixels[:,:,:,:3])
return ({"samples":t}, )
class VAEEncodeTiled:
def __init__(self, device="cpu"):
self.device = device
@ -211,13 +216,10 @@ class VAEEncodeTiled:
CATEGORY = "_for_testing"
def encode(self, vae, pixels):
x = (pixels.shape[1] // 64) * 64
y = (pixels.shape[2] // 64) * 64
if pixels.shape[1] != x or pixels.shape[2] != y:
pixels = pixels[:,:x,:y,:]
pixels = VAEEncode.vae_encode_crop_pixels(pixels)
t = vae.encode_tiled(pixels[:,:,:,:3])
return ({"samples":t}, )
class VAEEncodeForInpaint:
def __init__(self, device="cpu"):
self.device = device
@ -231,14 +233,16 @@ class VAEEncodeForInpaint:
CATEGORY = "latent/inpaint"
def encode(self, vae, pixels, mask, grow_mask_by=6):
x = (pixels.shape[1] // 64) * 64
y = (pixels.shape[2] // 64) * 64
x = (pixels.shape[1] // 8) * 8
y = (pixels.shape[2] // 8) * 8
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
pixels = pixels.clone()
if pixels.shape[1] != x or pixels.shape[2] != y:
pixels = pixels[:,:x,:y,:]
mask = mask[:,:,:x,: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,:]
mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]
#grow mask by a few pixels to keep things seamless in latent space
if grow_mask_by == 0:
@ -610,8 +614,8 @@ class EmptyLatentImage:
@classmethod
def INPUT_TYPES(s):
return {"required": { "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
return {"required": { "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64})}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "generate"
@ -649,8 +653,8 @@ class LatentUpscale:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,),
"width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"crop": (s.crop_methods,)}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "upscale"
@ -752,8 +756,8 @@ class LatentCrop:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples": ("LATENT",),
"width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
}}
@ -778,16 +782,6 @@ class LatentCrop:
new_width = width // 8
to_x = new_width + x
to_y = new_height + y
def enforce_image_dim(d, to_d, max_d):
if to_d > max_d:
leftover = (to_d - max_d) % 8
to_d = max_d
d -= leftover
return (d, to_d)
#make sure size is always multiple of 64
x, to_x = enforce_image_dim(x, to_x, samples.shape[3])
y, to_y = enforce_image_dim(y, to_y, samples.shape[2])
s['samples'] = samples[:,:,y:to_y, x:to_x]
return (s,)
@ -1105,10 +1099,10 @@ class ImagePadForOutpaint:
return {
"required": {
"image": ("IMAGE",),
"left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
}
}

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