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Update ldm dir with latest upstream stable diffusion changes.

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comfyanonymous 2023-02-09 13:47:36 -05:00
parent 642516a3a6
commit 1f6a467e92
5 changed files with 21 additions and 10 deletions
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7
comfy/ldm/models/diffusion/ddim.py
View File

@ -8,16 +8,17 @@ from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, mak
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
def __init__(self, model, schedule="linear", device=torch.device("cuda"), **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
self.device = device
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != torch.device("cuda"):
attr = attr.to(torch.device("cuda"))
if attr.device != self.device:
attr = attr.to(self.device)
setattr(self, name, attr)
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):

8
comfy/ldm/models/diffusion/ddpm.py
View File

@ -1331,7 +1331,13 @@ class DiffusionWrapper(torch.nn.Module):
cc = torch.cat(c_crossattn, 1)
else:
cc = c_crossattn
out = self.diffusion_model(x, t, context=cc)
if hasattr(self, "scripted_diffusion_model"):
# TorchScript changes names of the arguments
# with argument cc defined as context=cc scripted model will produce
# an error: RuntimeError: forward() is missing value for argument 'argument_3'.
out = self.scripted_diffusion_model(x, t, cc)
else:
out = self.diffusion_model(x, t, context=cc)
elif self.conditioning_key == 'hybrid':
xc = torch.cat([x] + c_concat, dim=1)
cc = torch.cat(c_crossattn, 1)

7
comfy/ldm/models/diffusion/dpm_solver/sampler.py
View File

@ -11,16 +11,17 @@ MODEL_TYPES = {
class DPMSolverSampler(object):
def __init__(self, model, **kwargs):
def __init__(self, model, device=torch.device("cuda"), **kwargs):
super().__init__()
self.model = model
self.device = device
to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != torch.device("cuda"):
attr = attr.to(torch.device("cuda"))
if attr.device != self.device:
attr = attr.to(self.device)
setattr(self, name, attr)
@torch.no_grad()

7
comfy/ldm/models/diffusion/plms.py
View File

@ -10,16 +10,17 @@ from ldm.models.diffusion.sampling_util import norm_thresholding
class PLMSSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
def __init__(self, model, schedule="linear", device=torch.device("cuda"), **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
self.device = device
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != torch.device("cuda"):
attr = attr.to(torch.device("cuda"))
if attr.device != self.device:
attr = attr.to(self.device)
setattr(self, name, attr)
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):

2
comfy/ldm/modules/diffusionmodules/openaimodel.py
View File

@ -454,6 +454,7 @@ class UNetModel(nn.Module):
num_classes=None,
use_checkpoint=False,
use_fp16=False,
use_bf16=False,
num_heads=-1,
num_head_channels=-1,
num_heads_upsample=-1,
@ -518,6 +519,7 @@ class UNetModel(nn.Module):
self.num_classes = num_classes
self.use_checkpoint = use_checkpoint
self.dtype = th.float16 if use_fp16 else th.float32
self.dtype = th.bfloat16 if use_bf16 else self.dtype
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample