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Add support for unCLIP SD2.x models.

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See _for_testing/unclip in the UI for the new nodes.

unCLIPCheckpointLoader is used to load them.

unCLIPConditioning is used to add the image cond and takes as input a
CLIPVisionEncode output which has been moved to the conditioning section.
This commit is contained in:
comfyanonymous
2023-04-01 23:19:15 -04:00
parent 0d972b85e6
commit 809bcc8ceb
17 changed files with 593 additions and 113 deletions
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11
comfy/ldm/models/diffusion/dpm_solver/dpm_solver.py
View File

@@ -307,7 +307,16 @@ def model_wrapper(
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t_continuous] * 2)
c_in = torch.cat([unconditional_condition, condition])
if isinstance(condition, dict):
assert isinstance(unconditional_condition, dict)
c_in = dict()
for k in condition:
if isinstance(condition[k], list):
c_in[k] = [torch.cat([unconditional_condition[k][i], condition[k][i]]) for i in range(len(condition[k]))]
else:
c_in[k] = torch.cat([unconditional_condition[k], condition[k]])
else:
c_in = torch.cat([unconditional_condition, condition])
noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
return noise_uncond + guidance_scale * (noise - noise_uncond)

24
comfy/ldm/models/diffusion/dpm_solver/sampler.py
View File

@@ -3,7 +3,6 @@ import torch
from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
MODEL_TYPES = {
"eps": "noise",
"v": "v"
@@ -51,12 +50,20 @@ class DPMSolverSampler(object):
):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
ctmp = conditioning[list(conditioning.keys())[0]]
while isinstance(ctmp, list): ctmp = ctmp[0]
if isinstance(ctmp, torch.Tensor):
cbs = ctmp.shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
elif isinstance(conditioning, list):
for ctmp in conditioning:
if ctmp.shape[0] != batch_size:
print(f"Warning: Got {ctmp.shape[0]} conditionings but batch-size is {batch_size}")
else:
if conditioning.shape[0] != batch_size:
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
if isinstance(conditioning, torch.Tensor):
if conditioning.shape[0] != batch_size:
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
# sampling
C, H, W = shape
@@ -83,6 +90,7 @@ class DPMSolverSampler(object):
)
dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True)
x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2,
lower_order_final=True)
return x.to(device), None
return x.to(device), None