initial refiner support

modules/processing.py

@@ -666,6 +666,10 @@ 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)

modules/sd_models.py

@@ -289,11 +289,27 @@ 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)

modules/sd_samplers_common.py

@@ -2,7 +2,7 @@ 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
 
 SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
@@ -127,3 +127,20 @@ 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 and shared.sd_model.sd_checkpoint_info.title != shared.opts.sd_refiner_checkpoint:
+        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}')
+
+        with sd_models.SkipWritingToConfig():
+            sd_models.reload_model_weights(info=refiner_checkpoint_info)
+
+        devices.torch_gc()
+
+        sampler.update_inner_model()
+
+        sampler.p.setup_conds()

modules/sd_samplers_compvis.py

@@ -19,7 +19,8 @@ samplers_data_compvis = [
 
 class VanillaStableDiffusionSampler:
     def __init__(self, constructor, sd_model):
-        self.sampler = constructor(sd_model)
+        self.p = None
+        self.sampler = constructor(shared.sd_model)
         self.is_ddim = hasattr(self.sampler, 'p_sample_ddim')
         self.is_plms = hasattr(self.sampler, 'p_sample_plms')
         self.is_unipc = isinstance(self.sampler, modules.models.diffusion.uni_pc.UniPCSampler)
@@ -32,6 +33,7 @@ class VanillaStableDiffusionSampler:
         self.nmask = None
         self.init_latent = None
         self.sampler_noises = None
+        self.steps = None
         self.step = 0
         self.stop_at = None
         self.eta = None
@@ -44,6 +46,7 @@ class VanillaStableDiffusionSampler:
         return 0
 
     def launch_sampling(self, steps, func):
+        self.steps = steps
         state.sampling_steps = steps
         state.sampling_step = 0
 
@@ -61,10 +64,15 @@ class VanillaStableDiffusionSampler:
 
         return res
 
+    def update_inner_model(self):
+        self.sampler.model = shared.sd_model
+
     def before_sample(self, x, ts, cond, unconditional_conditioning):
         if state.interrupted or state.skipped:
             raise sd_samplers_common.InterruptedException
 
+        sd_samplers_common.apply_refiner(self)
+
         if self.stop_at is not None and self.step > self.stop_at:
             raise sd_samplers_common.InterruptedException
 
@@ -134,6 +142,8 @@ class VanillaStableDiffusionSampler:
         self.update_step(x)
 
     def initialize(self, p):
+        self.p = p
+
         if self.is_ddim:
             self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim
         else:

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

modules/shared.py

@@ -461,6 +461,8 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
     "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"),
+    "sd_refiner_checkpoint": OptionInfo(None, "Refiner checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints).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}).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"), {