added support for automatically installing latest k-diffusion

added eta parameter to parameters output for generated images
split eta settings into ancestral and ddim (because they have different default values)

3 files changed, 52 insertions, 45 deletions

diff --git a/modules/processing.py b/modules/processing.py
index e6b84684..358a1b11 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -49,7 +49,7 @@ def apply_color_correction(correction, image):
 

 

 class StableDiffusionProcessing:

-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):

+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):

         self.sd_model = sd_model

         self.outpath_samples: str = outpath_samples

         self.outpath_grids: str = outpath_grids

@@ -75,15 +75,15 @@ class StableDiffusionProcessing:
         self.do_not_save_grid: bool = do_not_save_grid

         self.extra_generation_params: dict = extra_generation_params or {}

         self.overlay_images = overlay_images

+        self.eta = eta

         self.paste_to = None

         self.color_corrections = None

         self.denoising_strength: float = 0

-        

-        self.eta = opts.eta

+

         self.ddim_discretize = opts.ddim_discretize

         self.s_churn = opts.s_churn

         self.s_tmin = opts.s_tmin

-        self.s_tmax = float('inf') # not representable as a standard ui option

+        self.s_tmax = float('inf')  # not representable as a standard ui option

         self.s_noise = opts.s_noise

         

         if not seed_enable_extras:

@@ -271,6 +271,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),

         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),

         "Denoising strength": getattr(p, 'denoising_strength', None),

+        "Eta": (None if p.sampler.eta == p.sampler.default_eta else p.sampler.eta),

     }

 

     generation_params.update(p.extra_generation_params)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index a1183997..3588aae6 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -40,10 +40,8 @@ samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
 

 sampler_extra_params = {

     'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],

-    'sample_euler_ancestral': ['eta'],

     'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],

     'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],

-    'sample_dpm_2_ancestral': ['eta'],

 }

 

 def setup_img2img_steps(p, steps=None):

@@ -101,6 +99,8 @@ class VanillaStableDiffusionSampler:
         self.init_latent = None

         self.sampler_noises = None

         self.step = 0

+        self.eta = None

+        self.default_eta = 0.0

 

     def number_of_needed_noises(self, p):

         return 0

@@ -123,20 +123,29 @@ class VanillaStableDiffusionSampler:
         self.step += 1

         return res

 

+    def initialize(self, p):

+        self.eta = p.eta or opts.eta_ddim

+

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

+            if hasattr(self.sampler, fieldname):

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

+

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

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

+

     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:

-            self.sampler.make_schedule(ddim_num_steps=steps,  ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)

+            self.sampler.make_schedule(ddim_num_steps=steps,  ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)

         except Exception:

-            self.sampler.make_schedule(ddim_num_steps=steps+1,ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)

+            self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)

 

         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 if hasattr(p, 'mask') else None

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

+        self.initialize(p)

+

         self.init_latent = x

         self.step = 0

 

@@ -145,11 +154,8 @@ class VanillaStableDiffusionSampler:
         return samples

 

     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)

-        self.mask = None

-        self.nmask = None

+        self.initialize(p)

+

         self.init_latent = None

         self.step = 0

 

@@ -157,9 +163,9 @@ class VanillaStableDiffusionSampler:
 

         # existing code fails with cetin step counts, like 9

         try:

-            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, eta=p.eta)

+            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, eta=self.eta)

         except Exception:

-            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, eta=p.eta)

+            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, eta=self.eta)

 

         return samples_ddim

 

@@ -237,6 +243,8 @@ class KDiffusionSampler:
         self.sampler_noises = None

         self.sampler_noise_index = 0

         self.stop_at = None

+        self.eta = None

+        self.default_eta = 1.0

 

     def callback_state(self, d):

         store_latent(d["denoised"])

@@ -255,22 +263,12 @@ class KDiffusionSampler:
         self.sampler_noise_index += 1

         return res

 

-    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)

-

-        noise = noise * sigmas[steps - t_enc - 1]

-

-        xi = x + noise

-

-        sigma_sched = sigmas[steps - t_enc - 1:]

-

+    def initialize(self, 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.init_latent = x

         self.model_wrap.step = 0

         self.sampler_noise_index = 0

+        self.eta = p.eta or opts.eta_ancestral

 

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

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

@@ -283,6 +281,25 @@ class KDiffusionSampler:
             if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:

                 extra_params_kwargs[param_name] = getattr(p, param_name)

 

+        if 'eta' in inspect.signature(self.func).parameters:

+            extra_params_kwargs['eta'] = self.eta

+

+        return extra_params_kwargs

+

+    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)

+

+        noise = noise * sigmas[steps - t_enc - 1]

+        xi = x + noise

+

+        extra_params_kwargs = self.initialize(p)

+

+        sigma_sched = sigmas[steps - t_enc - 1:]

+

+        self.model_wrap_cfg.init_latent = x

+

         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, **extra_params_kwargs)

 

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

@@ -291,19 +308,7 @@ class KDiffusionSampler:
         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(self, *args, **kwargs)

-

-        if self.sampler_noises is not None:

-            k_diffusion.sampling.torch = TorchHijack(self)

-

-        extra_params_kwargs = {}

-        for param_name in self.extra_params:

-            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:

-                extra_params_kwargs[param_name] = getattr(p, param_name)

+        extra_params_kwargs = self.initialize(p)

 

         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, **extra_params_kwargs)

 

diff --git a/modules/shared.py b/modules/shared.py
index ae459e14..39cf89bc 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -221,8 +221,9 @@ options_templates.update(options_section(('ui', "User interface"), {
 }))

 

 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {

-  "eta": OptionInfo(0.0, "DDIM and K Ancestral eta", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

-  "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform','quad']}),

+  "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

+  "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

+  "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),

   's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

   's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

   's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),