Merge branch 'master' into support_any_resolution

1 files changed, 8 insertions, 6 deletions

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 4fe67854..4edd8c60 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -127,7 +127,8 @@ class InterruptedException(BaseException):
 class VanillaStableDiffusionSampler:

     def __init__(self, constructor, sd_model):

         self.sampler = constructor(sd_model)

-        self.orig_p_sample_ddim = self.sampler.p_sample_ddim if hasattr(self.sampler, 'p_sample_ddim') else self.sampler.p_sample_plms

+        self.is_plms = hasattr(self.sampler, 'p_sample_plms')

+        self.orig_p_sample_ddim = self.sampler.p_sample_plms if self.is_plms else self.sampler.p_sample_ddim

         self.mask = None

         self.nmask = None

         self.init_latent = None

@@ -218,7 +219,6 @@ class VanillaStableDiffusionSampler:
         self.mask = p.mask if hasattr(p, 'mask') else None

         self.nmask = p.nmask if hasattr(p, 'nmask') else None

 

-

     def adjust_steps_if_invalid(self, p, num_steps):

         if  (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):

             valid_step = 999 / (1000 // num_steps)

@@ -227,7 +227,6 @@ class VanillaStableDiffusionSampler:
         

         return num_steps

 

-

     def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):

         steps, t_enc = setup_img2img_steps(p, steps)

         steps = self.adjust_steps_if_invalid(p, steps)

@@ -260,9 +259,10 @@ class VanillaStableDiffusionSampler:
         steps = self.adjust_steps_if_invalid(p, steps or p.steps)

 

         # Wrap the conditioning models with additional image conditioning for inpainting model

+        # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape

         if image_conditioning is not None:

-            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}

-            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}

+            conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}

+            unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}

 

         samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])

 

@@ -350,7 +350,9 @@ class TorchHijack:
 

 class KDiffusionSampler:

     def __init__(self, funcname, sd_model):

-        self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)

+        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser

+

+        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)

         self.funcname = funcname

         self.func = getattr(k_diffusion.sampling, self.funcname)

         self.extra_params = sampler_extra_params.get(funcname, [])