Merge branch 'dev' into patch-1

1 files changed, 0 insertions, 224 deletions

diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py
index 4a8396f9..e69de29b 100644
--- a/modules/sd_samplers_compvis.py
+++ b/modules/sd_samplers_compvis.py
@@ -1,224 +0,0 @@
-import math

-import ldm.models.diffusion.ddim

-import ldm.models.diffusion.plms

-

-import numpy as np

-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), [], {"default_eta_is_0": True, "uses_ensd": True, "no_sdxl": True}),

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

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

-]

-

-

-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.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

-        self.sampler_noises = None

-        self.step = 0

-        self.stop_at = None

-        self.eta = None

-        self.config = None

-        self.last_latent = None

-

-        self.conditioning_key = sd_model.model.conditioning_key

-

-    def number_of_needed_noises(self, p):

-        return 0

-

-    def launch_sampling(self, steps, func):

-        state.sampling_steps = steps

-        state.sampling_step = 0

-

-        try:

-            return func()

-        except sd_samplers_common.InterruptedException:

-            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, *args, unconditional_conditioning=unconditional_conditioning, **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

-

-        if self.stop_at is not None and self.step > self.stop_at:

-            raise sd_samplers_common.InterruptedException

-

-        # Have to unwrap the inpainting conditioning here to perform pre-processing

-        image_conditioning = None

-        uc_image_conditioning = None

-        if isinstance(cond, dict):

-            if self.conditioning_key == "crossattn-adm":

-                image_conditioning = cond["c_adm"]

-                uc_image_conditioning = unconditional_conditioning["c_adm"]

-            else:

-                image_conditioning = cond["c_concat"][0]

-            cond = cond["c_crossattn"][0]

-            unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]

-

-        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)

-        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)

-

-        assert all(len(conds) == 1 for conds in conds_list), 'composition via AND is not supported for DDIM/PLMS samplers'

-        cond = tensor

-

-        # for DDIM, shapes must match, we can't just process cond and uncond independently;

-        # filling unconditional_conditioning with repeats of the last vector to match length is

-        # not 100% correct but should work well enough

-        if unconditional_conditioning.shape[1] < cond.shape[1]:

-            last_vector = unconditional_conditioning[:, -1:]

-            last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])

-            unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])

-        elif unconditional_conditioning.shape[1] > cond.shape[1]:

-            unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]

-

-        if self.mask is not None:

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

-            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.

-        if image_conditioning is not None:

-            if self.conditioning_key == "crossattn-adm":

-                cond = {"c_adm": image_conditioning, "c_crossattn": [cond]}

-                unconditional_conditioning = {"c_adm": uc_image_conditioning, "c_crossattn": [unconditional_conditioning]}

-            else:

-                cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}

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

-

-        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 * last_latent

-        else:

-            self.last_latent = last_latent

-

-        sd_samplers_common.store_latent(self.last_latent)

-

-        self.step += 1

-        state.sampling_step = self.step

-        shared.total_tqdm.update()

-

-    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):

-        if self.is_ddim:

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

-        else:

-            self.eta = 0.0

-

-        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') 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):

-        steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)

-        steps = self.adjust_steps_if_invalid(p, steps)

-        self.initialize(p)

-

-        self.sampler.make_schedule(ddim_num_steps=steps, 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.init_latent = x

-        self.last_latent = x

-        self.step = 0

-

-        # Wrap the conditioning models with additional image conditioning for inpainting model

-        if image_conditioning is not None:

-            if self.conditioning_key == "crossattn-adm":

-                conditioning = {"c_adm": image_conditioning, "c_crossattn": [conditioning]}

-                unconditional_conditioning = {"c_adm": torch.zeros_like(image_conditioning), "c_crossattn": [unconditional_conditioning]}

-            else:

-                conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}

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

-

-        samples = self.launch_sampling(t_enc + 1, lambda: 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, steps=None, image_conditioning=None):

-        self.initialize(p)

-

-        self.init_latent = None

-        self.last_latent = x

-        self.step = 0

-

-        steps = self.adjust_steps_if_invalid(p, steps or p.steps)

-

-        # Wrap the conditioning models with additional image conditioning for inpainting model

-        # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape

-        if image_conditioning is not None:

-            if self.conditioning_key == "crossattn-adm":

-                conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_adm": image_conditioning}

-                unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_adm": torch.zeros_like(image_conditioning)}

-            else:

-                conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}

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

-

-        samples_ddim = self.launch_sampling(steps, lambda: 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)[0])

-

-        return samples_ddim