add CFG denoiser implementation for DDIM, PLMS and UniPC (this is the commit when you can run both old and new implementations to compare them)

modules/sd_samplers_timesteps.py

@@ -0,0 +1,147 @@
+import torch
+import inspect
+from modules import devices, sd_samplers_common, sd_samplers_timesteps_impl
+from modules.sd_samplers_cfg_denoiser import CFGDenoiser
+
+from modules.shared import opts
+import modules.shared as shared
+
+samplers_timesteps = [
+    ('k_DDIM', sd_samplers_timesteps_impl.ddim, ['k_ddim'], {}),
+    ('k_PLMS', sd_samplers_timesteps_impl.plms, ['k_plms'], {}),
+    ('k_UniPC', sd_samplers_timesteps_impl.unipc, ['k_unipc'], {}),
+]
+
+
+samplers_data_timesteps = [
+    sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: CompVisSampler(funcname, model), aliases, options)
+    for label, funcname, aliases, options in samplers_timesteps
+]
+
+
+class CompVisTimestepsDenoiser(torch.nn.Module):
+    def __init__(self, model, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.inner_model = model
+
+    def forward(self, input, timesteps, **kwargs):
+        return self.inner_model.apply_model(input, timesteps, **kwargs)
+
+
+class CompVisTimestepsVDenoiser(torch.nn.Module):
+    def __init__(self, model, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.inner_model = model
+
+    def predict_eps_from_z_and_v(self, x_t, t, v):
+        return self.inner_model.sqrt_alphas_cumprod[t.to(torch.int), None, None, None] * v + self.inner_model.sqrt_one_minus_alphas_cumprod[t.to(torch.int), None, None, None] * x_t
+
+    def forward(self, input, timesteps, **kwargs):
+        model_output = self.inner_model.apply_model(input, timesteps, **kwargs)
+        e_t = self.predict_eps_from_z_and_v(input, timesteps, model_output)
+        return e_t
+
+
+class CFGDenoiserTimesteps(CFGDenoiser):
+
+    def __init__(self, model, sampler):
+        super().__init__(model, sampler)
+
+        self.alphas = model.inner_model.alphas_cumprod
+
+    def get_pred_x0(self, x_in, x_out, sigma):
+        ts = int(sigma.item())
+
+        s_in = x_in.new_ones([x_in.shape[0]])
+        a_t = self.alphas[ts].item() * s_in
+        sqrt_one_minus_at = (1 - a_t).sqrt()
+
+        pred_x0 = (x_in - sqrt_one_minus_at * x_out) / a_t.sqrt()
+
+        return pred_x0
+
+
+class CompVisSampler(sd_samplers_common.Sampler):
+    def __init__(self, funcname, sd_model):
+        super().__init__(funcname)
+
+        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)
+
+    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)
+        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
+
+        timesteps = torch.clip(torch.asarray(list(range(0, 1000, 1000 // steps)), device=devices.device) + 1, 0, 999)
+
+        return timesteps
+
+    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)
+
+        timesteps = self.get_timesteps(p, steps)
+        timesteps_sched = timesteps[:t_enc]
+
+        alphas_cumprod = shared.sd_model.alphas_cumprod
+        sqrt_alpha_cumprod = torch.sqrt(alphas_cumprod[timesteps[t_enc]])
+        sqrt_one_minus_alpha_cumprod = torch.sqrt(1 - alphas_cumprod[timesteps[t_enc]])
+
+        xi = x * sqrt_alpha_cumprod + noise * sqrt_one_minus_alpha_cumprod
+
+        extra_params_kwargs = self.initialize(p)
+        parameters = inspect.signature(self.func).parameters
+
+        if 'timesteps' in parameters:
+            extra_params_kwargs['timesteps'] = timesteps_sched
+        if 'is_img2img' in parameters:
+            extra_params_kwargs['is_img2img'] = True
+
+        self.model_wrap_cfg.init_latent = x
+        self.last_latent = x
+        extra_args = {
+            'cond': conditioning,
+            'image_cond': image_conditioning,
+            'uncond': unconditional_conditioning,
+            'cond_scale': p.cfg_scale,
+            '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))
+
+        if self.model_wrap_cfg.padded_cond_uncond:
+            p.extra_generation_params["Pad conds"] = True
+
+        return samples
+
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        steps = steps or p.steps
+        timesteps = self.get_timesteps(p, steps)
+
+        extra_params_kwargs = self.initialize(p)
+        parameters = inspect.signature(self.func).parameters
+
+        if 'timesteps' in parameters:
+            extra_params_kwargs['timesteps'] = timesteps
+
+        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,
+            's_min_uncond': self.s_min_uncond
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+        if self.model_wrap_cfg.padded_cond_uncond:
+            p.extra_generation_params["Pad conds"] = True
+
+        return samples
+