DiffSynth-Studio 2.0 major update

diffsynth/diffusion/flow_match.py

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+import torch, math
+from typing_extensions import Literal
+
+
+class FlowMatchScheduler():
+
+    def __init__(self, template: Literal["FLUX.1", "Wan", "Qwen-Image", "FLUX.2", "Z-Image"] = "FLUX.1"):
+        self.set_timesteps_fn = {
+            "FLUX.1": FlowMatchScheduler.set_timesteps_flux,
+            "Wan": FlowMatchScheduler.set_timesteps_wan,
+            "Qwen-Image": FlowMatchScheduler.set_timesteps_qwen_image,
+            "FLUX.2": FlowMatchScheduler.set_timesteps_flux2,
+            "Z-Image": FlowMatchScheduler.set_timesteps_z_image,
+        }.get(template, FlowMatchScheduler.set_timesteps_flux)
+        self.num_train_timesteps = 1000
+
+    @staticmethod
+    def set_timesteps_flux(num_inference_steps=100, denoising_strength=1.0, shift=None):
+        sigma_min = 0.003/1.002
+        sigma_max = 1.0
+        shift = 3 if shift is None else shift
+        num_train_timesteps = 1000
+        sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
+        sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps)
+        sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
+        timesteps = sigmas * num_train_timesteps
+        return sigmas, timesteps
+    
+    @staticmethod
+    def set_timesteps_wan(num_inference_steps=100, denoising_strength=1.0, shift=None):
+        sigma_min = 0.0
+        sigma_max = 1.0
+        shift = 5 if shift is None else shift
+        num_train_timesteps = 1000
+        sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
+        sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
+        sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
+        timesteps = sigmas * num_train_timesteps
+        return sigmas, timesteps
+    
+    @staticmethod
+    def _calculate_shift_qwen_image(image_seq_len, base_seq_len=256, max_seq_len=8192, base_shift=0.5, max_shift=0.9):
+        m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+        b = base_shift - m * base_seq_len
+        mu = image_seq_len * m + b
+        return mu
+    
+    @staticmethod
+    def set_timesteps_qwen_image(num_inference_steps=100, denoising_strength=1.0, exponential_shift_mu=None, dynamic_shift_len=None):
+        sigma_min = 0.0
+        sigma_max = 1.0
+        num_train_timesteps = 1000
+        shift_terminal = 0.02
+        # Sigmas
+        sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
+        sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
+        # Mu
+        if exponential_shift_mu is not None:
+            mu = exponential_shift_mu
+        elif dynamic_shift_len is not None:
+            mu = FlowMatchScheduler._calculate_shift_qwen_image(dynamic_shift_len)
+        else:
+            mu = 0.8
+        sigmas = math.exp(mu) / (math.exp(mu) + (1 / sigmas - 1))
+        # Shift terminal
+        one_minus_z = 1 - sigmas
+        scale_factor = one_minus_z[-1] / (1 - shift_terminal)
+        sigmas = 1 - (one_minus_z / scale_factor)
+        # Timesteps
+        timesteps = sigmas * num_train_timesteps
+        return sigmas, timesteps
+    
+    @staticmethod
+    def compute_empirical_mu(image_seq_len, num_steps):
+        a1, b1 = 8.73809524e-05, 1.89833333
+        a2, b2 = 0.00016927, 0.45666666
+
+        if image_seq_len > 4300:
+            mu = a2 * image_seq_len + b2
+            return float(mu)
+
+        m_200 = a2 * image_seq_len + b2
+        m_10 = a1 * image_seq_len + b1
+
+        a = (m_200 - m_10) / 190.0
+        b = m_200 - 200.0 * a
+        mu = a * num_steps + b
+
+        return float(mu)
+    
+    @staticmethod
+    def set_timesteps_flux2(num_inference_steps=100, denoising_strength=1.0, dynamic_shift_len=1024//16*1024//16):
+        sigma_min = 1 / num_inference_steps
+        sigma_max = 1.0
+        num_train_timesteps = 1000
+        sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
+        sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps)
+        mu = FlowMatchScheduler.compute_empirical_mu(dynamic_shift_len, num_inference_steps)
+        sigmas = math.exp(mu) / (math.exp(mu) + (1 / sigmas - 1))
+        timesteps = sigmas * num_train_timesteps
+        return sigmas, timesteps
+
+    @staticmethod
+    def set_timesteps_z_image(num_inference_steps=100, denoising_strength=1.0, shift=None, target_timesteps=None):
+        sigma_min = 0.0
+        sigma_max = 1.0
+        shift = 3 if shift is None else shift
+        num_train_timesteps = 1000
+        sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
+        sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
+        sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
+        timesteps = sigmas * num_train_timesteps
+        if target_timesteps is not None:
+            target_timesteps = target_timesteps.to(dtype=timesteps.dtype, device=timesteps.device)
+            for timestep in target_timesteps:
+                timestep_id = torch.argmin((timesteps - timestep).abs())
+                timesteps[timestep_id] = timestep
+        return sigmas, timesteps
+    
+    def set_training_weight(self):
+        steps = 1000
+        x = self.timesteps
+        y = torch.exp(-2 * ((x - steps / 2) / steps) ** 2)
+        y_shifted = y - y.min()
+        bsmntw_weighing = y_shifted * (steps / y_shifted.sum())
+        if len(self.timesteps) != 1000:
+            # This is an empirical formula.
+            bsmntw_weighing = bsmntw_weighing * (len(self.timesteps) / steps)
+            bsmntw_weighing = bsmntw_weighing + bsmntw_weighing[1]
+        self.linear_timesteps_weights = bsmntw_weighing
+        
+    def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, training=False, **kwargs):
+        self.sigmas, self.timesteps = self.set_timesteps_fn(
+            num_inference_steps=num_inference_steps,
+            denoising_strength=denoising_strength,
+            **kwargs,
+        )
+        if training:
+            self.set_training_weight()
+            self.training = True
+        else:
+            self.training = False
+
+    def step(self, model_output, timestep, sample, to_final=False, **kwargs):
+        if isinstance(timestep, torch.Tensor):
+            timestep = timestep.cpu()
+        timestep_id = torch.argmin((self.timesteps - timestep).abs())
+        sigma = self.sigmas[timestep_id]
+        if to_final or timestep_id + 1 >= len(self.timesteps):
+            sigma_ = 0
+        else:
+            sigma_ = self.sigmas[timestep_id + 1]
+        prev_sample = sample + model_output * (sigma_ - sigma)
+        return prev_sample
+    
+    def return_to_timestep(self, timestep, sample, sample_stablized):
+        if isinstance(timestep, torch.Tensor):
+            timestep = timestep.cpu()
+        timestep_id = torch.argmin((self.timesteps - timestep).abs())
+        sigma = self.sigmas[timestep_id]
+        model_output = (sample - sample_stablized) / sigma
+        return model_output
+    
+    def add_noise(self, original_samples, noise, timestep):
+        if isinstance(timestep, torch.Tensor):
+            timestep = timestep.cpu()
+        timestep_id = torch.argmin((self.timesteps - timestep).abs())
+        sigma = self.sigmas[timestep_id]
+        sample = (1 - sigma) * original_samples + sigma * noise
+        return sample
+    
+    def training_target(self, sample, noise, timestep):
+        target = noise - sample
+        return target
+    
+    def training_weight(self, timestep):
+        timestep_id = torch.argmin((self.timesteps - timestep.to(self.timesteps.device)).abs())
+        weights = self.linear_timesteps_weights[timestep_id]
+        return weights