added highres fix feature

1 files changed, 32 insertions, 23 deletions

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index af6811a1..1fc9d18c 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -38,9 +38,9 @@ samplers = [
 samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']

 

 

-def setup_img2img_steps(p):

-    if opts.img2img_fix_steps:

-        steps = int(p.steps / min(p.denoising_strength, 0.999))

+def setup_img2img_steps(p, steps=None):

+    if opts.img2img_fix_steps or steps is not None:

+        steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0

         t_enc = p.steps - 1

     else:

         steps = p.steps

@@ -115,8 +115,8 @@ class VanillaStableDiffusionSampler:
         self.step += 1

         return res

 

-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):

-        steps, t_enc = setup_img2img_steps(p)

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

+        steps, t_enc = setup_img2img_steps(p, steps)

 

         # existing code fails with cetain step counts, like 9

         try:

@@ -127,16 +127,16 @@ class VanillaStableDiffusionSampler:
         x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)

 

         self.sampler.p_sample_ddim = self.p_sample_ddim_hook

-        self.mask = p.mask

-        self.nmask = p.nmask

-        self.init_latent = p.init_latent

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

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

+        self.init_latent = x

         self.step = 0

 

         samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)

 

         return samples

 

-    def sample(self, p, x, conditioning, unconditional_conditioning):

+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):

         for fieldname in ['p_sample_ddim', 'p_sample_plms']:

             if hasattr(self.sampler, fieldname):

                 setattr(self.sampler, fieldname, self.p_sample_ddim_hook)

@@ -145,11 +145,13 @@ class VanillaStableDiffusionSampler:
         self.init_latent = None

         self.step = 0

 

+        steps = steps or p.steps

+

         # existing code fails with cetin step counts, like 9

         try:

-            samples_ddim, _ = self.sampler.sample(S=p.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)

+            samples_ddim, _ = 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)

         except Exception:

-            samples_ddim, _ = self.sampler.sample(S=p.steps+1, 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)

+            samples_ddim, _ = self.sampler.sample(S=steps+1, 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)

 

         return samples_ddim

 

@@ -186,7 +188,7 @@ class CFGDenoiser(torch.nn.Module):
         return denoised

 

 

-def extended_trange(count, *args, **kwargs):

+def extended_trange(sampler, count, *args, **kwargs):

     state.sampling_steps = count

     state.sampling_step = 0

 

@@ -194,6 +196,9 @@ def extended_trange(count, *args, **kwargs):
         if state.interrupted:

             break

 

+        if sampler.stop_at is not None and x > sampler.stop_at:

+            break

+

         yield x

 

         state.sampling_step += 1

@@ -222,6 +227,7 @@ class KDiffusionSampler:
         self.model_wrap_cfg = CFGDenoiser(self.model_wrap)

         self.sampler_noises = None

         self.sampler_noise_index = 0

+        self.stop_at = None

 

     def callback_state(self, d):

         store_latent(d["denoised"])

@@ -240,8 +246,8 @@ class KDiffusionSampler:
         self.sampler_noise_index += 1

         return res

 

-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):

-        steps, t_enc = setup_img2img_steps(p)

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

+        steps, t_enc = setup_img2img_steps(p, steps)

 

         sigmas = self.model_wrap.get_sigmas(steps)

 

@@ -251,33 +257,36 @@ class KDiffusionSampler:
 

         sigma_sched = sigmas[steps - t_enc - 1:]

 

-        self.model_wrap_cfg.mask = p.mask

-        self.model_wrap_cfg.nmask = p.nmask

-        self.model_wrap_cfg.init_latent = p.init_latent

+        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.init_latent = x

         self.model_wrap.step = 0

         self.sampler_noise_index = 0

 

         if hasattr(k_diffusion.sampling, 'trange'):

-            k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)

+            k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)

 

         if self.sampler_noises is not None:

             k_diffusion.sampling.torch = TorchHijack(self)

 

         return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)

 

-    def sample(self, p, x, conditioning, unconditional_conditioning):

-        sigmas = self.model_wrap.get_sigmas(p.steps)

+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):

+        steps = steps or p.steps

+

+        sigmas = self.model_wrap.get_sigmas(steps)

         x = x * sigmas[0]

 

         self.model_wrap_cfg.step = 0

         self.sampler_noise_index = 0

 

         if hasattr(k_diffusion.sampling, 'trange'):

-            k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)

+            k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)

 

         if self.sampler_noises is not None:

             k_diffusion.sampling.torch = TorchHijack(self)

 

-        samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)

-        return samples_ddim

+        samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)

+

+        return samples