Merge pull request #12371 from AUTOMATIC1111/refiner

initial refiner support

8 files changed, 131 insertions, 28 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 44d47e8c..efa6eafa 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -377,6 +377,9 @@ class StableDiffusionProcessing:
         self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)

         self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)

 

+    def get_conds(self):

+        return self.c, self.uc

+

     def parse_extra_network_prompts(self):

         self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)

 

@@ -611,6 +614,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}

 

     try:

+        # after running refiner, the refiner model is not unloaded - webui swaps back to main model here

+        if shared.sd_model.sd_checkpoint_info.title != opts.sd_model_checkpoint:

+            sd_models.reload_model_weights()

+

         # if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint

         if sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_checkpoint')) is None:

             p.override_settings.pop('sd_model_checkpoint', None)

@@ -710,6 +717,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if state.interrupted:

                 break

 

+            sd_models.reload_model_weights()  # model can be changed for example by refiner

+

             p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]

             p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]

             p.seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]

@@ -1201,6 +1210,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 with devices.autocast():

                     extra_networks.activate(self, self.extra_network_data)

 

+    def get_conds(self):

+        if self.is_hr_pass:

+            return self.hr_c, self.hr_uc

+

+        return super().get_conds()

+

+

     def parse_extra_network_prompts(self):

         res = super().parse_extra_network_prompts()

 

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 7a866a07..a178adca 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -295,11 +295,27 @@ def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
     return res

 

 

+class SkipWritingToConfig:

+    """This context manager prevents load_model_weights from writing checkpoint name to the config when it loads weight."""

+

+    skip = False

+    previous = None

+

+    def __enter__(self):

+        self.previous = SkipWritingToConfig.skip

+        SkipWritingToConfig.skip = True

+        return self

+

+    def __exit__(self, exc_type, exc_value, exc_traceback):

+        SkipWritingToConfig.skip = self.previous

+

+

 def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):

     sd_model_hash = checkpoint_info.calculate_shorthash()

     timer.record("calculate hash")

 

-    shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title

+    if not SkipWritingToConfig.skip:

+        shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title

 

     if state_dict is None:

         state_dict = get_checkpoint_state_dict(checkpoint_info, timer)

@@ -624,8 +640,11 @@ def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
         timer.record("send model to device")

 

         model_data.set_sd_model(already_loaded)

-        shared.opts.data["sd_model_checkpoint"] = already_loaded.sd_checkpoint_info.title

-        shared.opts.data["sd_checkpoint_hash"] = already_loaded.sd_checkpoint_info.sha256

+

+        if not SkipWritingToConfig.skip:

+            shared.opts.data["sd_model_checkpoint"] = already_loaded.sd_checkpoint_info.title

+            shared.opts.data["sd_checkpoint_hash"] = already_loaded.sd_checkpoint_info.sha256

+

         print(f"Using already loaded model {already_loaded.sd_checkpoint_info.title}: done in {timer.summary()}")

         return model_data.sd_model

     elif shared.opts.sd_checkpoints_limit > 1 and len(model_data.loaded_sd_models) < shared.opts.sd_checkpoints_limit:

diff --git a/modules/sd_samplers_cfg_denoiser.py b/modules/sd_samplers_cfg_denoiser.py
index d826222c..a532e013 100644
--- a/modules/sd_samplers_cfg_denoiser.py
+++ b/modules/sd_samplers_cfg_denoiser.py
@@ -38,16 +38,24 @@ class CFGDenoiser(torch.nn.Module):
     negative prompt.

     """

 

-    def __init__(self, model, sampler):

+    def __init__(self, sampler):

         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.sampler = sampler

+        self.model_wrap = None

+        self.p = None

+

+    @property

+    def inner_model(self):

+        raise NotImplementedError()

+

 

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

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

@@ -68,10 +76,21 @@ class CFGDenoiser(torch.nn.Module):
     def get_pred_x0(self, x_in, x_out, sigma):

         return x_out

 

+    def update_inner_model(self):

+        self.model_wrap = None

+

+        c, uc = self.p.get_conds()

+        self.sampler.sampler_extra_args['cond'] = c

+        self.sampler.sampler_extra_args['uncond'] = uc

+

     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

 

+        if sd_samplers_common.apply_refiner(self):

+            cond = self.sampler.sampler_extra_args['cond']

+            uncond = self.sampler.sampler_extra_args['uncond']

+

         # 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

diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 97bc0804..35c4d657 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -3,7 +3,7 @@ from collections import namedtuple
 import numpy as np

 import torch

 from PIL import Image

-from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared

+from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models

 from modules.shared import opts, state

 import k_diffusion.sampling

 

@@ -131,6 +131,35 @@ def replace_torchsde_browinan():
 replace_torchsde_browinan()

 

 

+def apply_refiner(sampler):

+    completed_ratio = sampler.step / sampler.steps

+

+    if completed_ratio <= shared.opts.sd_refiner_switch_at:

+        return False

+

+    if shared.opts.sd_refiner_checkpoint == "None":

+        return False

+

+    if shared.sd_model.sd_checkpoint_info.title == shared.opts.sd_refiner_checkpoint:

+        return False

+

+    refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(shared.opts.sd_refiner_checkpoint)

+    if refiner_checkpoint_info is None:

+        raise Exception(f'Could not find checkpoint with name {shared.opts.sd_refiner_checkpoint}')

+

+    sampler.p.extra_generation_params['Refiner'] = refiner_checkpoint_info.short_title

+    sampler.p.extra_generation_params['Refiner switch at'] = shared.opts.sd_refiner_switch_at

+

+    with sd_models.SkipWritingToConfig():

+        sd_models.reload_model_weights(info=refiner_checkpoint_info)

+

+    devices.torch_gc()

+    sampler.p.setup_conds()

+    sampler.update_inner_model()

+

+    return True

+

+

 class TorchHijack:

     """This is here to replace torch.randn_like of k-diffusion.

 

@@ -176,8 +205,9 @@ class Sampler:
 

         self.conditioning_key = shared.sd_model.model.conditioning_key

 

-        self.model_wrap = None

+        self.p = None

         self.model_wrap_cfg = None

+        self.sampler_extra_args = None

 

     def callback_state(self, d):

         step = d['i']

@@ -189,6 +219,7 @@ class Sampler:
         shared.total_tqdm.update()

 

     def launch_sampling(self, steps, func):

+        self.model_wrap_cfg.steps = steps

         state.sampling_steps = steps

         state.sampling_step = 0

 

@@ -208,6 +239,8 @@ class Sampler:
         return p.steps

 

     def initialize(self, p) -> dict:

+        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

diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/modules/sd_samplers_compvis.py
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 5613b8c1..95a43cef 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -1,8 +1,7 @@
 import torch

 import inspect

 import k_diffusion.sampling

-from modules import sd_samplers_common, sd_samplers_extra

-from modules.sd_samplers_cfg_denoiser import CFGDenoiser

+from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser

 

 from modules.shared import opts

 import modules.shared as shared

@@ -53,17 +52,24 @@ k_diffusion_scheduler = {
 }

 

 

+class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):

+    @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

+

+

 class KDiffusionSampler(sd_samplers_common.Sampler):

     def __init__(self, funcname, sd_model):

-

         super().__init__(funcname)

 

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

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

 

-        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.model_wrap_cfg = CFGDenoiser(self.model_wrap, self)

+        self.model_wrap_cfg = CFGDenoiserKDiffusion(self)

+        self.model_wrap = self.model_wrap_cfg.inner_model

 

     def get_sigmas(self, p, steps):

         discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)

@@ -144,7 +150,7 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
 

         self.model_wrap_cfg.init_latent = x

         self.last_latent = x

-        extra_args = {

+        self.sampler_extra_args = {

             'cond': conditioning,

             'image_cond': image_conditioning,

             'uncond': unconditional_conditioning,

@@ -152,7 +158,7 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
             's_min_uncond': self.s_min_uncond

         }

 

-        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))

+        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))

 

         if self.model_wrap_cfg.padded_cond_uncond:

             p.extra_generation_params["Pad conds"] = True

@@ -184,13 +190,14 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
             extra_params_kwargs['noise_sampler'] = noise_sampler

 

         self.last_latent = x

-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={

+        self.sampler_extra_args = {

             'cond': conditioning,

             'image_cond': image_conditioning,

             'uncond': unconditional_conditioning,

             'cond_scale': p.cfg_scale,

             's_min_uncond': self.s_min_uncond

-        }, disable=False, callback=self.callback_state, **extra_params_kwargs))

+        }

+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))

 

         if self.model_wrap_cfg.padded_cond_uncond:

             p.extra_generation_params["Pad conds"] = True

diff --git a/modules/sd_samplers_timesteps.py b/modules/sd_samplers_timesteps.py
index f61799a8..16572c7e 100644
--- a/modules/sd_samplers_timesteps.py
+++ b/modules/sd_samplers_timesteps.py
@@ -45,10 +45,10 @@ class CompVisTimestepsVDenoiser(torch.nn.Module):
 

 class CFGDenoiserTimesteps(CFGDenoiser):

 

-    def __init__(self, model, sampler):

-        super().__init__(model, sampler)

+    def __init__(self, sampler):

+        super().__init__(sampler)

 

-        self.alphas = model.inner_model.alphas_cumprod

+        self.alphas = shared.sd_model.alphas_cumprod

 

     def get_pred_x0(self, x_in, x_out, sigma):

         ts = int(sigma.item())

@@ -61,6 +61,14 @@ class CFGDenoiserTimesteps(CFGDenoiser):
 

         return pred_x0

 

+    @property

+    def inner_model(self):

+        if self.model_wrap is None:

+            denoiser = CompVisTimestepsVDenoiser if shared.sd_model.parameterization == "v" else CompVisTimestepsDenoiser

+            self.model_wrap = denoiser(shared.sd_model)

+

+        return self.model_wrap

+

 

 class CompVisSampler(sd_samplers_common.Sampler):

     def __init__(self, funcname, sd_model):

@@ -69,9 +77,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
         self.eta_option_field = 'eta_ddim'

         self.eta_infotext_field = 'Eta DDIM'

 

-        denoiser = CompVisTimestepsVDenoiser if sd_model.parameterization == "v" else CompVisTimestepsDenoiser

-        self.model_wrap = denoiser(sd_model)

-        self.model_wrap_cfg = CFGDenoiserTimesteps(self.model_wrap, self)

+        self.model_wrap_cfg = CFGDenoiserTimesteps(self)

 

     def get_timesteps(self, p, steps):

         discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)

@@ -107,7 +113,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
 

         self.model_wrap_cfg.init_latent = x

         self.last_latent = x

-        extra_args = {

+        self.sampler_extra_args = {

             'cond': conditioning,

             'image_cond': image_conditioning,

             'uncond': unconditional_conditioning,

@@ -115,7 +121,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
             's_min_uncond': self.s_min_uncond

         }

 

-        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))

+        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))

 

         if self.model_wrap_cfg.padded_cond_uncond:

             p.extra_generation_params["Pad conds"] = True

@@ -133,13 +139,14 @@ class CompVisSampler(sd_samplers_common.Sampler):
             extra_params_kwargs['timesteps'] = timesteps

 

         self.last_latent = x

-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={

+        self.sampler_extra_args = {

             'cond': conditioning,

             'image_cond': image_conditioning,

             'uncond': unconditional_conditioning,

             'cond_scale': p.cfg_scale,

             's_min_uncond': self.s_min_uncond

-        }, disable=False, callback=self.callback_state, **extra_params_kwargs))

+        }

+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))

 

         if self.model_wrap_cfg.padded_cond_uncond:

             p.extra_generation_params["Pad conds"] = True

diff --git a/modules/shared_options.py b/modules/shared_options.py
index 9ae51f18..1e5b64ea 100644
--- a/modules/shared_options.py
+++ b/modules/shared_options.py
@@ -140,6 +140,8 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),

     "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU", "NV"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors; use NV to produce same picture as on NVidia videocards"),

     "tiling": OptionInfo(False, "Tiling", infotext='Tiling').info("produce a tileable picture"),

+    "sd_refiner_checkpoint": OptionInfo("None", "Refiner checkpoint", gr.Dropdown, lambda: {"choices": ["None"] + shared_items.list_checkpoint_tiles()}, refresh=shared_items.refresh_checkpoints, infotext="Refiner").info("switch to another model in the middle of generation"),

+    "sd_refiner_switch_at": OptionInfo(1.0, "Refiner switch at", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}, infotext='Refiner switch at').info("fraction of sampling steps when the swtch to refiner model should happen; 1=never, 0.5=switch in the middle of generation"),

 }))

 

 options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {