Merge branch 'master' into improved-hr-conflict-test

1 files changed, 160 insertions, 0 deletions

diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py
new file mode 100644
index 00000000..d03131cd
--- /dev/null
+++ b/modules/sd_samplers_compvis.py
@@ -0,0 +1,160 @@
+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

+

+

+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), [], {}),

+]

+

+

+class VanillaStableDiffusionSampler:

+    def __init__(self, constructor, sd_model):

+        self.sampler = constructor(sd_model)

+        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.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):

+        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

+        if isinstance(cond, dict):

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

+

+        # 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:

+            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)

+

+        if self.mask is not None:

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

+        else:

+            self.last_latent = res[1]

+

+        sd_samplers_common.store_latent(self.last_latent)

+

+        self.step += 1

+        state.sampling_step = self.step

+        shared.total_tqdm.update()

+

+        return res

+

+    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

+

+        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 adjust_steps_if_invalid(self, p, num_steps):

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

+            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:

+            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:

+            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