diff --git a/extensions_built_in/diffusion_models/hidream/hidream_o1_model.py b/extensions_built_in/diffusion_models/hidream/hidream_o1_model.py index 1011808..4f64aba 100644 --- a/extensions_built_in/diffusion_models/hidream/hidream_o1_model.py +++ b/extensions_built_in/diffusion_models/hidream/hidream_o1_model.py @@ -95,6 +95,7 @@ class FakeTextEncoder(torch.nn.Module): self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.config = FakeConfig() self.config.scaling_factor = scaling_factor + self.x0_pred = True # the model predicts x0, not noise @property def dtype(self): @@ -463,11 +464,13 @@ class HidreamO1Model(BaseModel): # Model emits an x0-prediction; convert to flow-matching velocity # (x_1 - x_0) so it matches the loss target from get_loss_target. - sigma = (timestep.float() / 1000.0).clamp_min(T_EPS).to(device) - while sigma.dim() < latent_model_input.dim(): - sigma = sigma.unsqueeze(-1) - pred = (latent_model_input.float().to(device) - x0_pred.float()) / sigma - return pred.to(in_dtype) + # sigma = (timestep.float() / 1000.0).clamp_min(T_EPS).to(device) + # while sigma.dim() < latent_model_input.dim(): + # sigma = sigma.unsqueeze(-1) + # pred = (latent_model_input.float().to(device) - x0_pred.float()) / sigma + + # return pred.to(in_dtype) + return x0_pred def get_prompt_embeds(self, prompt: list) -> AdvancedPromptEmbeds: if not isinstance(prompt, list): @@ -487,15 +490,15 @@ class HidreamO1Model(BaseModel): def save_model(self, output_path, meta, save_dtype): from toolkit.util.quantize import dequantize_if_quantized transformer: Qwen3VLForConditionalGeneration = unwrap_model(self.model) + # dequantize any quantized (e.g. torchao) weights so we save plain full precision tensors; + # strip torch.compile's "._orig_mod." prefixes so the checkpoint loads into a fresh module + save_dict = { + k.replace("._orig_mod.", "."): dequantize_if_quantized(v).clone().to("cpu", dtype=save_dtype) + for k, v in transformer.state_dict().items() + } if self.is_comfy_weight: - sd = transformer.state_dict() - save_dict = {} - for key, value in sd.items(): - if "lm_head.weight" in key: - continue # comfy checkpoint doesnt have the lm head, so skip it - # dequantize any quantized (e.g. torchao) weights so we save plain full precision tensors - save_dict[key] = dequantize_if_quantized(value).clone().to("cpu", dtype=save_dtype) - + # comfy checkpoint doesnt have the lm head, so skip it + save_dict = {k: v for k, v in save_dict.items() if "lm_head.weight" not in k} if not output_path.endswith(".safetensors"): output_path += ".safetensors" meta = get_meta_for_safetensors(meta, name=self.arch) @@ -503,6 +506,7 @@ class HidreamO1Model(BaseModel): else: transformer.save_pretrained( save_directory=output_path, + state_dict=save_dict, safe_serialization=True, ) @@ -517,7 +521,7 @@ class HidreamO1Model(BaseModel): noise = kwargs.get("noise") batch = kwargs.get("batch") noise_scale = self.noise_scale - return (noise * noise_scale - batch.latents).detach() + return (batch.latents).detach() def get_base_model_version(self): return self.arch diff --git a/extensions_built_in/sd_trainer/SDTrainer.py b/extensions_built_in/sd_trainer/SDTrainer.py index 90d18a0..27f990b 100644 --- a/extensions_built_in/sd_trainer/SDTrainer.py +++ b/extensions_built_in/sd_trainer/SDTrainer.py @@ -962,7 +962,8 @@ class SDTrainer(BaseSDTrainProcess): timestep_weight = self.sd.noise_scheduler.get_weights_for_timesteps( timesteps, v2=self.train_config.linear_timesteps2, - timestep_type=self.train_config.timestep_type + timestep_type=self.train_config.timestep_type, + x0_pred=self.sd.x0_pred, ).to(loss.device, dtype=loss.dtype) if len(loss.shape) == 4: timestep_weight = timestep_weight.view(-1, 1, 1, 1).detach() @@ -1079,6 +1080,8 @@ class SDTrainer(BaseSDTrainProcess): if additional_model_loss is not None: loss = loss + additional_model_loss self.additional_logs["additional_model_loss"] = additional_model_loss.item() + # per-term breakdown, if the model keeps one + self.additional_logs.update(getattr(self.sd, "additional_loss_logs", None) or {}) if self.train_config.max_loss_debug and self.train_config.max_loss is not None: if loss.item() > self.train_config.max_loss: diff --git a/toolkit/samplers/custom_flowmatch_sampler.py b/toolkit/samplers/custom_flowmatch_sampler.py index fe2e6fa..6ded680 100644 --- a/toolkit/samplers/custom_flowmatch_sampler.py +++ b/toolkit/samplers/custom_flowmatch_sampler.py @@ -4,7 +4,7 @@ from torch.distributions import LogNormal from diffusers import FlowMatchEulerDiscreteScheduler import torch import numpy as np -from toolkit.timestep_weighing.default_weighing_scheme import default_weighing_scheme +from toolkit.timestep_weighing.default_weighing_scheme import default_weighing_scheme, x0_weighing_scheme def calculate_shift( @@ -56,15 +56,16 @@ class CustomFlowMatchEulerDiscreteScheduler(FlowMatchEulerDiscreteScheduler): self.linear_timesteps_weights2 = hbsmntw_weighing pass - def get_weights_for_timesteps(self, timesteps: torch.Tensor, v2=False, timestep_type="linear") -> torch.Tensor: + def get_weights_for_timesteps(self, timesteps: torch.Tensor, v2=False, timestep_type="linear", x0_pred=False) -> torch.Tensor: # Get the indices of the timesteps step_indices = [(self.timesteps == t).nonzero().item() for t in timesteps] # Get the weights for the timesteps if timestep_type == "weighted": + weighing_scheme = x0_weighing_scheme if x0_pred else default_weighing_scheme weights = torch.tensor( - [default_weighing_scheme[i] for i in step_indices], + [weighing_scheme[i] for i in step_indices], device=timesteps.device, dtype=timesteps.dtype ) diff --git a/toolkit/timestep_weighing/default_weighing_scheme.py b/toolkit/timestep_weighing/default_weighing_scheme.py index 7269974..c5b9924 100644 --- a/toolkit/timestep_weighing/default_weighing_scheme.py +++ b/toolkit/timestep_weighing/default_weighing_scheme.py @@ -1001,4 +1001,1011 @@ default_weighing_scheme = [ 0.4447800815105438, 0.4453802704811096, 0.4472601115703583 -] \ No newline at end of file +] + +# x0 (x-prediction) weights, extracted from the learned LossScaler table (scale_loss.ema) of +# /mnt/Data/train/out/ui/ostris_o1_te/ostris_o1_te_000007250.safetensors. weight(t) = ema(t=1) / ema(t), +# so full noise is 1.0 and low-noise timesteps are lifted to the same expected loss. 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'sigmoid'], + 'config.process[0].train.timestep_type': ['weighted', 'sigmoid'], 'config.process[0].network.conv': [undefined, 16], 'config.process[0].network.conv_alpha': [undefined, 16], 'config.process[0].train.max_loss': [1.0, undefined],