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ComfyUI
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minor changes for tiled sampler

This commit is contained in:
BlenderNeko 2023-05-12 23:49:09 +02:00
parent 8ea165dd1e
commit d9e088ddfd
2 changed files with 10 additions and 7 deletions
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2
comfy/ldm/modules/tomesd.py
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@ -36,7 +36,7 @@ def bipartite_soft_matching_random2d(metric: torch.Tensor,
""" """
B, N, _ = metric.shape B, N, _ = metric.shape
if r <= 0: if r <= 0 or w == 1 or h == 1:
return do_nothing, do_nothing return do_nothing, do_nothing
gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather

15
comfy/sd.py
View File

@ -581,10 +581,7 @@ class VAE:
samples = samples.cpu() samples = samples.cpu()
return samples return samples
def resize_image_to(tensor, target_latent_tensor, batched_number): def broadcast_image_to(tensor, target_batch_size, batched_number):
tensor = utils.common_upscale(tensor, target_latent_tensor.shape[3] * 8, target_latent_tensor.shape[2] * 8, 'nearest-exact', "center")
target_batch_size = target_latent_tensor.shape[0]
current_batch_size = tensor.shape[0] current_batch_size = tensor.shape[0]
print(current_batch_size, target_batch_size) print(current_batch_size, target_batch_size)
if current_batch_size == 1: if current_batch_size == 1:
@ -623,7 +620,9 @@ class ControlNet:
if self.cond_hint is not None: if self.cond_hint is not None:
del self.cond_hint del self.cond_hint
self.cond_hint = None self.cond_hint = None
self.cond_hint = resize_image_to(self.cond_hint_original, x_noisy, batched_number).to(self.control_model.dtype).to(self.device) self.cond_hint = utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device)
if x_noisy.shape[0] != self.cond_hint.shape[0]:
self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
if self.control_model.dtype == torch.float16: if self.control_model.dtype == torch.float16:
precision_scope = torch.autocast precision_scope = torch.autocast
@ -794,10 +793,14 @@ class T2IAdapter:
if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
if self.cond_hint is not None: if self.cond_hint is not None:
del self.cond_hint del self.cond_hint
self.control_input = None
self.cond_hint = None self.cond_hint = None
self.cond_hint = resize_image_to(self.cond_hint_original, x_noisy, batched_number).float().to(self.device) self.cond_hint = utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").float().to(self.device)
if self.channels_in == 1 and self.cond_hint.shape[1] > 1: if self.channels_in == 1 and self.cond_hint.shape[1] > 1:
self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True) self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)
if x_noisy.shape[0] != self.cond_hint.shape[0]:
self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
if self.control_input is None:
self.t2i_model.to(self.device) self.t2i_model.to(self.device)
self.control_input = self.t2i_model(self.cond_hint) self.control_input = self.t2i_model(self.cond_hint)
self.t2i_model.cpu() self.t2i_model.cpu()