Merge branch 'master' into master

1 files changed, 51 insertions, 8 deletions

diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py
index d03131cd..083da18c 100644
--- a/modules/sd_samplers_compvis.py
+++ b/modules/sd_samplers_compvis.py
@@ -7,19 +7,27 @@ import torch
 

 from modules.shared import state

 from modules import sd_samplers_common, prompt_parser, shared

+import modules.models.diffusion.uni_pc

 

 

 samplers_data_compvis = [

     sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),

     sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),

+    sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {}),

 ]

 

 

 class VanillaStableDiffusionSampler:

     def __init__(self, constructor, sd_model):

         self.sampler = constructor(sd_model)

+        self.is_ddim = hasattr(self.sampler, 'p_sample_ddim')

         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.is_unipc = isinstance(self.sampler, modules.models.diffusion.uni_pc.UniPCSampler)

+        self.orig_p_sample_ddim = None

+        if self.is_plms:

+            self.orig_p_sample_ddim = self.sampler.p_sample_plms

+        elif self.is_ddim:

+            self.orig_p_sample_ddim = self.sampler.p_sample_ddim

         self.mask = None

         self.nmask = None

         self.init_latent = None

@@ -45,6 +53,15 @@ class VanillaStableDiffusionSampler:
             return self.last_latent

 

     def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):

+        x_dec, ts, cond, unconditional_conditioning = self.before_sample(x_dec, ts, cond, unconditional_conditioning)

+

+        res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)

+

+        x_dec, ts, cond, unconditional_conditioning, res = self.after_sample(x_dec, ts, cond, unconditional_conditioning, res)

+

+        return res

+

+    def before_sample(self, x, ts, cond, unconditional_conditioning):

         if state.interrupted or state.skipped:

             raise sd_samplers_common.InterruptedException

 

@@ -76,7 +93,7 @@ class VanillaStableDiffusionSampler:
 

         if self.mask is not None:

             img_orig = self.sampler.model.q_sample(self.init_latent, ts)

-            x_dec = img_orig * self.mask + self.nmask * x_dec

+            x = img_orig * self.mask + self.nmask * x

 

         # Wrap the image conditioning back up since the DDIM code can accept the dict directly.

         # Note that they need to be lists because it just concatenates them later.

@@ -84,12 +101,13 @@ class VanillaStableDiffusionSampler:
             cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}

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

 

-        res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)

+        return x, ts, cond, unconditional_conditioning

 

+    def update_step(self, last_latent):

         if self.mask is not None:

-            self.last_latent = self.init_latent * self.mask + self.nmask * res[1]

+            self.last_latent = self.init_latent * self.mask + self.nmask * last_latent

         else:

-            self.last_latent = res[1]

+            self.last_latent = last_latent

 

         sd_samplers_common.store_latent(self.last_latent)

 

@@ -97,26 +115,51 @@ class VanillaStableDiffusionSampler:
         state.sampling_step = self.step

         shared.total_tqdm.update()

 

-        return res

+    def after_sample(self, x, ts, cond, uncond, res):

+        if not self.is_unipc:

+            self.update_step(res[1])

+

+        return x, ts, cond, uncond, res

+

+    def unipc_after_update(self, x, model_x):

+        self.update_step(x)

 

     def initialize(self, p):

         self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim

         if self.eta != 0.0:

             p.extra_generation_params["Eta DDIM"] = self.eta

 

+        if self.is_unipc:

+            keys = [

+                ('UniPC variant', 'uni_pc_variant'),

+                ('UniPC skip type', 'uni_pc_skip_type'),

+                ('UniPC order', 'uni_pc_order'),

+                ('UniPC lower order final', 'uni_pc_lower_order_final'),

+            ]

+

+            for name, key in keys:

+                v = getattr(shared.opts, key)

+                if v != shared.opts.get_default(key):

+                    p.extra_generation_params[name] = v

+

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

             if hasattr(self.sampler, fieldname):

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

+        if self.is_unipc:

+            self.sampler.set_hooks(lambda x, t, c, u: self.before_sample(x, t, c, u), lambda x, t, c, u, r: self.after_sample(x, t, c, u, r), lambda x, mx: self.unipc_after_update(x, mx))

 

         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'):

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

+            if self.config.name == 'UniPC' and num_steps < shared.opts.uni_pc_order:

+                num_steps = shared.opts.uni_pc_order

             valid_step = 999 / (1000 // num_steps)

             if valid_step == math.floor(valid_step):

                 return int(valid_step) + 1

-        

+

         return num_steps

 

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