diff options
Diffstat (limited to 'modules/sd_samplers_kdiffusion.py')
| -rw-r--r-- | modules/sd_samplers_kdiffusion.py | 45 |
1 files changed, 35 insertions, 10 deletions
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 71581b76..5552a8dc 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -53,6 +53,28 @@ k_diffusion_scheduler = { }
+def catenate_conds(conds):
+ if not isinstance(conds[0], dict):
+ return torch.cat(conds)
+
+ return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
+
+
+def subscript_cond(cond, a, b):
+ if not isinstance(cond, dict):
+ return cond[a:b]
+
+ return {key: vec[a:b] for key, vec in cond.items()}
+
+
+def pad_cond(tensor, repeats, empty):
+ if not isinstance(tensor, dict):
+ return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
+
+ tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
+ return tensor
+
+
class CFGDenoiser(torch.nn.Module):
"""
Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
@@ -105,10 +127,13 @@ class CFGDenoiser(torch.nn.Module): if shared.sd_model.model.conditioning_key == "crossattn-adm":
image_uncond = torch.zeros_like(image_cond)
- make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm}
+ make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm}
else:
image_uncond = image_cond
- make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
+ if isinstance(uncond, dict):
+ make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
+ else:
+ make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]}
if not is_edit_model:
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
@@ -140,28 +165,28 @@ class CFGDenoiser(torch.nn.Module): num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
if num_repeats < 0:
- tensor = torch.cat([tensor, empty.repeat((tensor.shape[0], -num_repeats, 1))], axis=1)
+ tensor = pad_cond(tensor, -num_repeats, empty)
self.padded_cond_uncond = True
elif num_repeats > 0:
- uncond = torch.cat([uncond, empty.repeat((uncond.shape[0], num_repeats, 1))], axis=1)
+ uncond = pad_cond(uncond, num_repeats, empty)
self.padded_cond_uncond = True
if tensor.shape[1] == uncond.shape[1] or skip_uncond:
if is_edit_model:
- cond_in = torch.cat([tensor, uncond, uncond])
+ cond_in = catenate_conds([tensor, uncond, uncond])
elif skip_uncond:
cond_in = tensor
else:
- cond_in = torch.cat([tensor, uncond])
+ cond_in = catenate_conds([tensor, uncond])
if shared.batch_cond_uncond:
- x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
+ x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
else:
x_out = torch.zeros_like(x_in)
for batch_offset in range(0, x_out.shape[0], batch_size):
a = batch_offset
b = a + batch_size
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b]))
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
else:
x_out = torch.zeros_like(x_in)
batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
@@ -170,14 +195,14 @@ class CFGDenoiser(torch.nn.Module): b = min(a + batch_size, tensor.shape[0])
if not is_edit_model:
- c_crossattn = [tensor[a:b]]
+ c_crossattn = subscript_cond(tensor, a, b)
else:
c_crossattn = torch.cat([tensor[a:b]], uncond)
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
if not skip_uncond:
- x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
+ x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
denoised_image_indexes = [x[0][0] for x in conds_list]
if skip_uncond:
|