Split history sd_samplers.py to sd_samplers_kdiffusion.py

modules/sd_samplers_kdiffusion.py

@@ -0,0 +1,339 @@
+from collections import deque
+import torch
+import inspect
+import k_diffusion.sampling
+import ldm.models.diffusion.ddim
+import ldm.models.diffusion.plms
+from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_compvis
+
+from modules.shared import opts, state
+import modules.shared as shared
+from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
+
+# imports for functions that previously were here and are used by other modules
+from modules.sd_samplers_common import samples_to_image_grid, sample_to_image
+
+
+samplers_k_diffusion = [
+    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
+    ('Euler', 'sample_euler', ['k_euler'], {}),
+    ('LMS', 'sample_lms', ['k_lms'], {}),
+    ('Heun', 'sample_heun', ['k_heun'], {}),
+    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),
+    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
+    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
+    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
+    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
+    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
+    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
+    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
+    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
+]
+
+samplers_data_k_diffusion = [
+    sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
+    for label, funcname, aliases, options in samplers_k_diffusion
+    if hasattr(k_diffusion.sampling, funcname)
+]
+
+all_samplers = [
+    *samplers_data_k_diffusion,
+    sd_samplers_common.SamplerData('DDIM', lambda model: sd_samplers_compvis.VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
+    sd_samplers_common.SamplerData('PLMS', lambda model: sd_samplers_compvis.VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
+]
+all_samplers_map = {x.name: x for x in all_samplers}
+
+samplers = []
+samplers_for_img2img = []
+samplers_map = {}
+
+
+def create_sampler(name, model):
+    if name is not None:
+        config = all_samplers_map.get(name, None)
+    else:
+        config = all_samplers[0]
+
+    assert config is not None, f'bad sampler name: {name}'
+
+    sampler = config.constructor(model)
+    sampler.config = config
+
+    return sampler
+
+
+def set_samplers():
+    global samplers, samplers_for_img2img
+
+    hidden = set(opts.hide_samplers)
+    hidden_img2img = set(opts.hide_samplers + ['PLMS'])
+
+    samplers = [x for x in all_samplers if x.name not in hidden]
+    samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
+
+    samplers_map.clear()
+    for sampler in all_samplers:
+        samplers_map[sampler.name.lower()] = sampler.name
+        for alias in sampler.aliases:
+            samplers_map[alias.lower()] = sampler.name
+
+
+set_samplers()
+
+sampler_extra_params = {
+    'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+    'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+    'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+}
+
+
+class CFGDenoiser(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.inner_model = model
+        self.mask = None
+        self.nmask = None
+        self.init_latent = None
+        self.step = 0
+
+    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+        denoised_uncond = x_out[-uncond.shape[0]:]
+        denoised = torch.clone(denoised_uncond)
+
+        for i, conds in enumerate(conds_list):
+            for cond_index, weight in conds:
+                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+
+        return denoised
+
+    def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
+        if state.interrupted or state.skipped:
+            raise sd_samplers_common.InterruptedException
+
+        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
+        uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
+
+        batch_size = len(conds_list)
+        repeats = [len(conds_list[i]) for i in range(batch_size)]
+
+        x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+        image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
+        sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
+
+        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
+        cfg_denoiser_callback(denoiser_params)
+        x_in = denoiser_params.x
+        image_cond_in = denoiser_params.image_cond
+        sigma_in = denoiser_params.sigma
+
+        if tensor.shape[1] == uncond.shape[1]:
+            cond_in = torch.cat([tensor, uncond])
+
+            if shared.batch_cond_uncond:
+                x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
+            else:
+                x_out = torch.zeros_like(x_in)
+                for batch_offset in range(0, x_out.shape[0], batch_size):
+                    a = batch_offset
+                    b = a + batch_size
+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
+        else:
+            x_out = torch.zeros_like(x_in)
+            batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
+            for batch_offset in range(0, tensor.shape[0], batch_size):
+                a = batch_offset
+                b = min(a + batch_size, tensor.shape[0])
+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]})
+
+            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
+
+        devices.test_for_nans(x_out, "unet")
+
+        if opts.live_preview_content == "Prompt":
+            sd_samplers_common.store_latent(x_out[0:uncond.shape[0]])
+        elif opts.live_preview_content == "Negative prompt":
+            sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
+
+        denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
+
+        if self.mask is not None:
+            denoised = self.init_latent * self.mask + self.nmask * denoised
+
+        self.step += 1
+
+        return denoised
+
+
+class TorchHijack:
+    def __init__(self, sampler_noises):
+        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
+        # implementation.
+        self.sampler_noises = deque(sampler_noises)
+
+    def __getattr__(self, item):
+        if item == 'randn_like':
+            return self.randn_like
+
+        if hasattr(torch, item):
+            return getattr(torch, item)
+
+        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+
+    def randn_like(self, x):
+        if self.sampler_noises:
+            noise = self.sampler_noises.popleft()
+            if noise.shape == x.shape:
+                return noise
+
+        if x.device.type == 'mps':
+            return torch.randn_like(x, device=devices.cpu).to(x.device)
+        else:
+            return torch.randn_like(x)
+
+
+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.funcname = funcname
+        self.func = getattr(k_diffusion.sampling, self.funcname)
+        self.extra_params = sampler_extra_params.get(funcname, [])
+        self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
+        self.sampler_noises = None
+        self.stop_at = None
+        self.eta = None
+        self.default_eta = 1.0
+        self.config = None
+        self.last_latent = None
+
+        self.conditioning_key = sd_model.model.conditioning_key
+
+    def callback_state(self, d):
+        step = d['i']
+        latent = d["denoised"]
+        if opts.live_preview_content == "Combined":
+            sd_samplers_common.store_latent(latent)
+        self.last_latent = latent
+
+        if self.stop_at is not None and step > self.stop_at:
+            raise sd_samplers_common.InterruptedException
+
+        state.sampling_step = step
+        shared.total_tqdm.update()
+
+    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 number_of_needed_noises(self, p):
+        return p.steps
+
+    def initialize(self, 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
+        self.eta = p.eta or opts.eta_ancestral
+
+        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
+
+        extra_params_kwargs = {}
+        for param_name in self.extra_params:
+            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
+                extra_params_kwargs[param_name] = getattr(p, param_name)
+
+        if 'eta' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['eta'] = self.eta
+
+        return extra_params_kwargs
+
+    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)
+        if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
+            discard_next_to_last_sigma = True
+            p.extra_generation_params["Discard penultimate sigma"] = True
+
+        steps += 1 if discard_next_to_last_sigma else 0
+
+        if p.sampler_noise_scheduler_override:
+            sigmas = p.sampler_noise_scheduler_override(steps)
+        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
+            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+
+            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
+        else:
+            sigmas = self.model_wrap.get_sigmas(steps)
+
+        if discard_next_to_last_sigma:
+            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+
+        return sigmas
+
+    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)
+
+        sigmas = self.get_sigmas(p, steps)
+
+        sigma_sched = sigmas[steps - t_enc - 1:]
+        xi = x + noise * sigma_sched[0]
+        
+        extra_params_kwargs = self.initialize(p)
+        if 'sigma_min' in inspect.signature(self.func).parameters:
+            ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
+            extra_params_kwargs['sigma_min'] = sigma_sched[-2]
+        if 'sigma_max' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['sigma_max'] = sigma_sched[0]
+        if 'n' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['n'] = len(sigma_sched) - 1
+        if 'sigma_sched' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['sigma_sched'] = sigma_sched
+        if 'sigmas' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['sigmas'] = sigma_sched
+
+        self.model_wrap_cfg.init_latent = x
+        self.last_latent = x
+
+        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
+            'cond': conditioning, 
+            'image_cond': image_conditioning, 
+            'uncond': unconditional_conditioning, 
+            'cond_scale': p.cfg_scale
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+        return samples
+
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
+        steps = steps or p.steps
+
+        sigmas = self.get_sigmas(p, steps)
+
+        x = x * sigmas[0]
+
+        extra_params_kwargs = self.initialize(p)
+        if 'sigma_min' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
+            extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
+            if 'n' in inspect.signature(self.func).parameters:
+                extra_params_kwargs['n'] = steps
+        else:
+            extra_params_kwargs['sigmas'] = sigmas
+
+        self.last_latent = x
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
+            'cond': conditioning, 
+            'image_cond': image_conditioning, 
+            'uncond': unconditional_conditioning, 
+            'cond_scale': p.cfg_scale
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+        return samples
+