KenSchae
/
ComfyUI
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Browse Source

Change latent resolution step to 8.

pull/613/head
comfyanonymous 2 years ago
parent
66c8aa5c3e
commit
ba8a4c3667
2 changed files with 33 additions and 40 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 1
      comfy/ldm/modules/diffusionmodules/openaimodel.py
  2. 72
      nodes.py

1
comfy/ldm/modules/diffusionmodules/openaimodel.py
Unescape View File

@ -108,7 +108,6 @@ class Upsample(nn.Module):
self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding) self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
def forward(self, x, output_shape=None): def forward(self, x, output_shape=None):
print("upsample", output_shape)
assert x.shape[1] == self.channels assert x.shape[1] == self.channels
if self.dims == 3: if self.dims == 3:
shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2] shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2]

72
nodes.py
Unescape View File

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

Write Preview
Loading…
Cancel
Save