initial refiner support

1 files changed, 24 insertions, 6 deletions

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index db71a549..be1bd35e 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -2,7 +2,7 @@ from collections import deque
 import torch

 import inspect

 import k_diffusion.sampling

-from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_extra

+from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_extra, sd_models

 

 from modules.processing import StableDiffusionProcessing

 from modules.shared import opts, state

@@ -87,15 +87,25 @@ class CFGDenoiser(torch.nn.Module):
     negative prompt.

     """

 

-    def __init__(self, model):

+    def __init__(self):

         super().__init__()

-        self.inner_model = model

+        self.model_wrap = None

         self.mask = None

         self.nmask = None

         self.init_latent = None

+        self.steps = None

         self.step = 0

         self.image_cfg_scale = None

         self.padded_cond_uncond = False

+        self.p = None

+

+    @property

+    def inner_model(self):

+        if self.model_wrap is None:

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

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

+

+        return self.model_wrap

 

     def combine_denoised(self, x_out, conds_list, uncond, cond_scale):

         denoised_uncond = x_out[-uncond.shape[0]:]

@@ -113,10 +123,15 @@ class CFGDenoiser(torch.nn.Module):
 

         return denoised

 

+    def update_inner_model(self):

+        self.model_wrap = None

+

     def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):

         if state.interrupted or state.skipped:

             raise sd_samplers_common.InterruptedException

 

+        sd_samplers_common.apply_refiner(self)

+

         # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,

         # so is_edit_model is set to False to support AND composition.

         is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0

@@ -267,13 +282,13 @@ class TorchHijack:
 

 class KDiffusionSampler:

     def __init__(self, funcname, sd_model):

-        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.p = None

         self.funcname = funcname

         self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)

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

-        self.model_wrap_cfg = CFGDenoiser(self.model_wrap)

+        self.model_wrap_cfg = CFGDenoiser()

+        self.model_wrap = self.model_wrap_cfg.inner_model

         self.sampler_noises = None

         self.stop_at = None

         self.eta = None

@@ -305,6 +320,7 @@ class KDiffusionSampler:
         shared.total_tqdm.update()

 

     def launch_sampling(self, steps, func):

+        self.model_wrap_cfg.steps = steps

         state.sampling_steps = steps

         state.sampling_step = 0

 

@@ -324,6 +340,8 @@ class KDiffusionSampler:
         return p.steps

 

     def initialize(self, p: StableDiffusionProcessing):

+        self.p = p

+        self.model_wrap_cfg.p = p

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

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

         self.model_wrap_cfg.step = 0