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Allow controlling downscale and upscale methods in PatchModelAddDownscale.

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comfyanonymous 2023-11-22 03:23:16 -05:00
parent 72741105a6
commit c3ae99a749
2 changed files with 11 additions and 5 deletions
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6
comfy/utils.py
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@ -318,7 +318,9 @@ def bislerp(samples, width, height):
coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear") coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear")
coords_2 = coords_2.to(torch.int64) coords_2 = coords_2.to(torch.int64)
return ratios, coords_1, coords_2 return ratios, coords_1, coords_2
orig_dtype = samples.dtype
samples = samples.float()
n,c,h,w = samples.shape n,c,h,w = samples.shape
h_new, w_new = (height, width) h_new, w_new = (height, width)
@ -347,7 +349,7 @@ def bislerp(samples, width, height):
result = slerp(pass_1, pass_2, ratios) result = slerp(pass_1, pass_2, ratios)
result = result.reshape(n, h_new, w_new, c).movedim(-1, 1) result = result.reshape(n, h_new, w_new, c).movedim(-1, 1)
return result return result.to(orig_dtype)
def lanczos(samples, width, height): def lanczos(samples, width, height):
images = [Image.fromarray(np.clip(255. * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples] images = [Image.fromarray(np.clip(255. * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples]

10
comfy_extras/nodes_model_downscale.py
View File

@ -1,6 +1,8 @@
import torch import torch
import comfy.utils
class PatchModelAddDownscale: class PatchModelAddDownscale:
upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"]
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",), return {"required": { "model": ("MODEL",),
@ -9,13 +11,15 @@ class PatchModelAddDownscale:
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}), "end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}),
"downscale_after_skip": ("BOOLEAN", {"default": True}), "downscale_after_skip": ("BOOLEAN", {"default": True}),
"downscale_method": (s.upscale_methods,),
"upscale_method": (s.upscale_methods,),
}} }}
RETURN_TYPES = ("MODEL",) RETURN_TYPES = ("MODEL",)
FUNCTION = "patch" FUNCTION = "patch"
CATEGORY = "_for_testing" CATEGORY = "_for_testing"
def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip): def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method):
sigma_start = model.model.model_sampling.percent_to_sigma(start_percent) sigma_start = model.model.model_sampling.percent_to_sigma(start_percent)
sigma_end = model.model.model_sampling.percent_to_sigma(end_percent) sigma_end = model.model.model_sampling.percent_to_sigma(end_percent)
@ -23,12 +27,12 @@ class PatchModelAddDownscale:
if transformer_options["block"][1] == block_number: if transformer_options["block"][1] == block_number:
sigma = transformer_options["sigmas"][0].item() sigma = transformer_options["sigmas"][0].item()
if sigma <= sigma_start and sigma >= sigma_end: if sigma <= sigma_start and sigma >= sigma_end:
h = torch.nn.functional.interpolate(h, scale_factor=(1.0 / downscale_factor), mode="bicubic", align_corners=False) h = comfy.utils.common_upscale(h, round(h.shape[-1] * (1.0 / downscale_factor)), round(h.shape[-2] * (1.0 / downscale_factor)), downscale_method, "disabled")
return h return h
def output_block_patch(h, hsp, transformer_options): def output_block_patch(h, hsp, transformer_options):
if h.shape[2] != hsp.shape[2]: if h.shape[2] != hsp.shape[2]:
h = torch.nn.functional.interpolate(h, size=(hsp.shape[2], hsp.shape[3]), mode="bicubic", align_corners=False) h = comfy.utils.common_upscale(h, hsp.shape[-1], hsp.shape[-2], upscale_method, "disabled")
return h, hsp return h, hsp
m = model.clone() m = model.clone()