Upload extensions_built_in/diffusion_models/wan22/wan22_14b_model.py with huggingface_hub

extensions_built_in/diffusion_models/wan22/wan22_14b_model.py

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+from functools import partial
+import os
+from typing import Any, Dict, Optional, Union, List
+from typing_extensions import Self
+import torch
+import yaml
+from toolkit.accelerator import unwrap_model
+from toolkit.basic import flush
+from toolkit.models.wan21.wan_utils import add_first_frame_conditioning
+from toolkit.prompt_utils import PromptEmbeds
+from PIL import Image
+from diffusers import UniPCMultistepScheduler
+import torch
+from toolkit.config_modules import GenerateImageConfig, ModelConfig
+from toolkit.samplers.custom_flowmatch_sampler import (
+    CustomFlowMatchEulerDiscreteScheduler,
+)
+from toolkit.util.quantize import quantize_model
+from .wan22_pipeline import Wan22Pipeline
+from diffusers import WanTransformer3DModel
+
+from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO
+from torchvision.transforms import functional as TF
+
+from toolkit.models.wan21.wan21 import Wan21
+from .wan22_5b_model import (
+    scheduler_config,
+    time_text_monkeypatch,
+)
+from safetensors.torch import load_file, save_file
+
+
+boundary_ratio_t2v = 0.875
+boundary_ratio_i2v = 0.9
+
+scheduler_configUniPC = {
+    "_class_name": "UniPCMultistepScheduler",
+    "_diffusers_version": "0.35.0.dev0",
+    "beta_end": 0.02,
+    "beta_schedule": "linear",
+    "beta_start": 0.0001,
+    "disable_corrector": [],
+    "dynamic_thresholding_ratio": 0.995,
+    "final_sigmas_type": "zero",
+    "flow_shift": 3.0,
+    "lower_order_final": True,
+    "num_train_timesteps": 1000,
+    "predict_x0": True,
+    "prediction_type": "flow_prediction",
+    "rescale_betas_zero_snr": False,
+    "sample_max_value": 1.0,
+    "solver_order": 2,
+    "solver_p": None,
+    "solver_type": "bh2",
+    "steps_offset": 0,
+    "thresholding": False,
+    "time_shift_type": "exponential",
+    "timestep_spacing": "linspace",
+    "trained_betas": None,
+    "use_beta_sigmas": False,
+    "use_dynamic_shifting": False,
+    "use_exponential_sigmas": False,
+    "use_flow_sigmas": True,
+    "use_karras_sigmas": False,
+}
+
+
+class DualWanTransformer3DModel(torch.nn.Module):
+    def __init__(
+        self,
+        transformer_1: WanTransformer3DModel,
+        transformer_2: WanTransformer3DModel,
+        torch_dtype: Optional[Union[str, torch.dtype]] = None,
+        device: Optional[Union[str, torch.device]] = None,
+        boundary_ratio: float = boundary_ratio_t2v,
+        low_vram: bool = False,
+    ) -> None:
+        super().__init__()
+        self.transformer_1: WanTransformer3DModel = transformer_1
+        self.transformer_2: WanTransformer3DModel = transformer_2
+        self.torch_dtype: torch.dtype = torch_dtype
+        self.device_torch: torch.device = device
+        self.boundary_ratio: float = boundary_ratio
+        self.boundary: float = self.boundary_ratio * 1000
+        self.low_vram: bool = low_vram
+        self._active_transformer_name = "transformer_1"  # default to transformer_1
+
+    @property
+    def device(self) -> torch.device:
+        return self.device_torch
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.torch_dtype
+
+    @property
+    def config(self):
+        return self.transformer_1.config
+
+    @property
+    def transformer(self) -> WanTransformer3DModel:
+        return getattr(self, self._active_transformer_name)
+
+    def enable_gradient_checkpointing(self):
+        """
+        Enable gradient checkpointing for both transformers.
+        """
+        self.transformer_1.enable_gradient_checkpointing()
+        self.transformer_2.enable_gradient_checkpointing()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        timestep: torch.LongTensor,
+        encoder_hidden_states: torch.Tensor,
+        encoder_hidden_states_image: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        **kwargs
+    ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
+        # determine if doing high noise or low noise by meaning the timestep.
+        # timesteps are in the range of 0 to 1000, so we can use a threshold
+        with torch.no_grad():
+            if timestep.float().mean().item() > self.boundary:
+                t_name = "transformer_1"
+            else:
+                t_name = "transformer_2"
+
+            # check if we are changing the active transformer, if so, we need to swap the one in
+            # vram if low_vram is enabled
+            # todo swap the loras as well
+            if t_name != self._active_transformer_name:
+                if self.low_vram:
+                    getattr(self, self._active_transformer_name).to("cpu")
+                    getattr(self, t_name).to(self.device_torch)
+                    torch.cuda.empty_cache()
+                self._active_transformer_name = t_name
+
+        if self.transformer.device != hidden_states.device:
+            if self.low_vram:
+                # move other transformer to cpu
+                other_tname = (
+                    "transformer_1" if t_name == "transformer_2" else "transformer_2"
+                )
+                getattr(self, other_tname).to("cpu")
+
+            self.transformer.to(hidden_states.device)
+
+        return self.transformer(
+            hidden_states=hidden_states,
+            timestep=timestep,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_hidden_states_image=encoder_hidden_states_image,
+            return_dict=return_dict,
+            attention_kwargs=attention_kwargs,
+        )
+
+    def to(self, *args, **kwargs) -> Self:
+        # do not do to, this will be handled separately
+        return self
+
+
+class Wan2214bModel(Wan21):
+    arch = "wan22_14b"
+    _wan_generation_scheduler_config = scheduler_configUniPC
+    _wan_expand_timesteps = False
+    _wan_vae_path = "ai-toolkit/wan2.1-vae"
+
+    def __init__(
+        self,
+        device,
+        model_config: ModelConfig,
+        dtype="bf16",
+        custom_pipeline=None,
+        noise_scheduler=None,
+        **kwargs,
+    ):
+        super().__init__(
+            device=device,
+            model_config=model_config,
+            dtype=dtype,
+            custom_pipeline=custom_pipeline,
+            noise_scheduler=noise_scheduler,
+            **kwargs,
+        )
+        # target it so we can target both transformers
+        self.target_lora_modules = ["DualWanTransformer3DModel"]
+        self._wan_cache = None
+
+        self.is_multistage = True
+        # multistage boundaries split the models up when sampling timesteps
+        # for wan 2.2 14b. the timesteps are 1000-875 for transformer 1 and 875-0 for transformer 2
+        self.multistage_boundaries: List[float] = [0.875, 0.0]
+
+        self.train_high_noise = model_config.model_kwargs.get("train_high_noise", True)
+        self.train_low_noise = model_config.model_kwargs.get("train_low_noise", True)
+
+        self.trainable_multistage_boundaries: List[int] = []
+        if self.train_high_noise:
+            self.trainable_multistage_boundaries.append(0)
+        if self.train_low_noise:
+            self.trainable_multistage_boundaries.append(1)
+
+        if len(self.trainable_multistage_boundaries) == 0:
+            raise ValueError(
+                "At least one of train_high_noise or train_low_noise must be True in model.model_kwargs"
+            )
+        
+        # if we are only training one or the other, the target LoRA modules will be the wan transformer class
+        if not self.train_high_noise or not self.train_low_noise:
+            self.target_lora_modules = ["WanTransformer3DModel"]
+
+    @property
+    def max_step_saves_to_keep_multiplier(self):
+        # the cleanup mechanism checks this to see how many saves to keep
+        # if we are training a LoRA, we need to set this to 2 so we keep both the high noise and low noise LoRAs at saves to keep
+        if (
+            self.network is not None
+            and self.network.network_config.split_multistage_loras
+        ):
+            return 2
+        return 1
+
+    def load_model(self):
+        # load model from patent parent. Wan21 not immediate parent
+        # super().load_model()
+        super().load_model()
+
+        # we have to split up the model on the pipeline
+        self.pipeline.transformer = self.model.transformer_1
+        self.pipeline.transformer_2 = self.model.transformer_2
+
+        # patch the condition embedder
+        self.model.transformer_1.condition_embedder.forward = partial(
+            time_text_monkeypatch, self.model.transformer_1.condition_embedder
+        )
+        self.model.transformer_2.condition_embedder.forward = partial(
+            time_text_monkeypatch, self.model.transformer_2.condition_embedder
+        )
+
+    def get_bucket_divisibility(self):
+        # 8x compression  and 2x2 patch size
+        return 16
+
+    def load_wan_transformer(self, transformer_path, subfolder=None):
+        if self.model_config.split_model_over_gpus:
+            raise ValueError(
+                "Splitting model over gpus is not supported for Wan2.2 models"
+            )
+
+        if (
+            self.model_config.assistant_lora_path is not None
+            or self.model_config.inference_lora_path is not None
+        ):
+            raise ValueError(
+                "Assistant LoRA is not supported for Wan2.2 models currently"
+            )
+
+        if self.model_config.lora_path is not None:
+            raise ValueError(
+                "Loading LoRA is not supported for Wan2.2 models currently"
+            )
+
+        # transformer path can be a directory that ends with /transformer or a hf path.
+
+        transformer_path_1 = transformer_path
+        subfolder_1 = subfolder
+
+        transformer_path_2 = transformer_path
+        subfolder_2 = subfolder
+
+        if subfolder_2 is None:
+            # we have a local path, replace it with transformer_2 folder
+            transformer_path_2 = os.path.join(
+                os.path.dirname(transformer_path_1), "transformer_2"
+            )
+        else:
+            # we have a hf path, replace it with transformer_2 subfolder
+            subfolder_2 = "transformer_2"
+
+        self.print_and_status_update("Loading transformer 1")
+        dtype = self.torch_dtype
+        transformer_1 = WanTransformer3DModel.from_pretrained(
+            transformer_path_1,
+            subfolder=subfolder_1,
+            torch_dtype=dtype,
+        ).to(dtype=dtype)
+
+        flush()
+
+        if not self.model_config.low_vram:
+            # quantize on the device
+            transformer_1.to(self.quantize_device, dtype=dtype)
+            flush()
+
+        if self.model_config.quantize and self.model_config.accuracy_recovery_adapter is None:
+            # todo handle two ARAs
+            self.print_and_status_update("Quantizing Transformer 1")
+            quantize_model(self, transformer_1)
+            flush()
+
+        if self.model_config.low_vram:
+            self.print_and_status_update("Moving transformer 1 to CPU")
+            transformer_1.to("cpu")
+
+        self.print_and_status_update("Loading transformer 2")
+        dtype = self.torch_dtype
+        transformer_2 = WanTransformer3DModel.from_pretrained(
+            transformer_path_2,
+            subfolder=subfolder_2,
+            torch_dtype=dtype,
+        ).to(dtype=dtype)
+
+        flush()
+
+        if not self.model_config.low_vram:
+            # quantize on the device
+            transformer_2.to(self.quantize_device, dtype=dtype)
+            flush()
+
+        if self.model_config.quantize and self.model_config.accuracy_recovery_adapter is None:
+            # todo handle two ARAs
+            self.print_and_status_update("Quantizing Transformer 2")
+            quantize_model(self, transformer_2)
+            flush()
+
+        if self.model_config.low_vram:
+            self.print_and_status_update("Moving transformer 2 to CPU")
+            transformer_2.to("cpu")
+
+        # make the combined model
+        self.print_and_status_update("Creating DualWanTransformer3DModel")
+        transformer = DualWanTransformer3DModel(
+            transformer_1=transformer_1,
+            transformer_2=transformer_2,
+            torch_dtype=self.torch_dtype,
+            device=self.device_torch,
+            boundary_ratio=boundary_ratio_t2v,
+            low_vram=self.model_config.low_vram,
+        )
+        
+        if self.model_config.quantize and self.model_config.accuracy_recovery_adapter is not None:
+            # apply the accuracy recovery adapter to both transformers
+            self.print_and_status_update("Applying Accuracy Recovery Adapter to Transformers")
+            quantize_model(self, transformer)
+            flush()
+            
+        return transformer
+
+    def get_generation_pipeline(self):
+        scheduler = UniPCMultistepScheduler(**self._wan_generation_scheduler_config)
+        pipeline = Wan22Pipeline(
+            vae=self.vae,
+            transformer=self.model.transformer_1,
+            transformer_2=self.model.transformer_2,
+            text_encoder=self.text_encoder,
+            tokenizer=self.tokenizer,
+            scheduler=scheduler,
+            expand_timesteps=self._wan_expand_timesteps,
+            device=self.device_torch,
+            aggressive_offload=self.model_config.low_vram,
+            # todo detect if it is i2v or t2v
+            boundary_ratio=boundary_ratio_t2v,
+        )
+
+        # pipeline = pipeline.to(self.device_torch)
+
+        return pipeline
+
+    # static method to get the scheduler
+    @staticmethod
+    def get_train_scheduler():
+        scheduler = CustomFlowMatchEulerDiscreteScheduler(**scheduler_config)
+        return scheduler
+
+    def get_base_model_version(self):
+        return "wan_2.2_14b"
+
+    def generate_single_image(
+        self,
+        pipeline: Wan22Pipeline,
+        gen_config: GenerateImageConfig,
+        conditional_embeds: PromptEmbeds,
+        unconditional_embeds: PromptEmbeds,
+        generator: torch.Generator,
+        extra: dict,
+    ):
+        return super().generate_single_image(
+            pipeline=pipeline,
+            gen_config=gen_config,
+            conditional_embeds=conditional_embeds,
+            unconditional_embeds=unconditional_embeds,
+            generator=generator,
+            extra=extra,
+        )
+
+    def get_noise_prediction(
+        self,
+        latent_model_input: torch.Tensor,
+        timestep: torch.Tensor,  # 0 to 1000 scale
+        text_embeddings: PromptEmbeds,
+        batch: DataLoaderBatchDTO,
+        **kwargs,
+    ):
+        # todo do we need to override this? Adjust timesteps?
+        return super().get_noise_prediction(
+            latent_model_input=latent_model_input,
+            timestep=timestep,
+            text_embeddings=text_embeddings,
+            batch=batch,
+            **kwargs,
+        )
+
+    def get_model_has_grad(self):
+        return False
+
+    def get_te_has_grad(self):
+        return False
+
+    def save_model(self, output_path, meta, save_dtype):
+        transformer_combo: DualWanTransformer3DModel = unwrap_model(self.model)
+        transformer_combo.transformer_1.save_pretrained(
+            save_directory=os.path.join(output_path, "transformer"),
+            safe_serialization=True,
+        )
+        transformer_combo.transformer_2.save_pretrained(
+            save_directory=os.path.join(output_path, "transformer_2"),
+            safe_serialization=True,
+        )
+
+        meta_path = os.path.join(output_path, "aitk_meta.yaml")
+        with open(meta_path, "w") as f:
+            yaml.dump(meta, f)
+
+    def save_lora(
+        self,
+        state_dict: Dict[str, torch.Tensor],
+        output_path: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ):
+        if not self.network.network_config.split_multistage_loras:
+            # just save as a combo lora
+            save_file(state_dict, output_path, metadata=metadata)
+            return
+
+        # we need to build out both dictionaries for high and low noise LoRAs
+        high_noise_lora = {}
+        low_noise_lora = {}
+        
+        only_train_high_noise = self.train_high_noise and not self.train_low_noise
+        only_train_low_noise = self.train_low_noise and not self.train_high_noise
+
+        for key in state_dict:
+            if ".transformer_1." in key or only_train_high_noise:
+                # this is a high noise LoRA
+                new_key = key.replace(".transformer_1.", ".")
+                high_noise_lora[new_key] = state_dict[key]
+            elif ".transformer_2." in key or only_train_low_noise:
+                # this is a low noise LoRA
+                new_key = key.replace(".transformer_2.", ".")
+                low_noise_lora[new_key] = state_dict[key]
+
+        # loras have either LORA_MODEL_NAME_000005000.safetensors or LORA_MODEL_NAME.safetensors
+        if len(high_noise_lora.keys()) > 0:
+            # save the high noise LoRA
+            high_noise_lora_path = output_path.replace(
+                ".safetensors", "_high_noise.safetensors"
+            )
+            save_file(high_noise_lora, high_noise_lora_path, metadata=metadata)
+
+        if len(low_noise_lora.keys()) > 0:
+            # save the low noise LoRA
+            low_noise_lora_path = output_path.replace(
+                ".safetensors", "_low_noise.safetensors"
+            )
+            save_file(low_noise_lora, low_noise_lora_path, metadata=metadata)
+
+    def load_lora(self, file: str):
+        # if it doesnt have high_noise or low_noise, it is a combo LoRA
+        if (
+            "_high_noise.safetensors" not in file
+            and "_low_noise.safetensors" not in file
+        ):
+            # this is a combined LoRA, we dont need to split it up
+            sd = load_file(file)
+            return sd
+
+        # we may have been passed the high_noise or the low_noise LoRA path, but we need to load both
+        high_noise_lora_path = file.replace(
+            "_low_noise.safetensors", "_high_noise.safetensors"
+        )
+        low_noise_lora_path = file.replace(
+            "_high_noise.safetensors", "_low_noise.safetensors"
+        )
+
+        combined_dict = {}
+
+        if os.path.exists(high_noise_lora_path) and self.train_high_noise:
+            # load the high noise LoRA
+            high_noise_lora = load_file(high_noise_lora_path)
+            for key in high_noise_lora:
+                new_key = key.replace(
+                    "diffusion_model.", "diffusion_model.transformer_1."
+                )
+                combined_dict[new_key] = high_noise_lora[key]
+        if os.path.exists(low_noise_lora_path) and self.train_low_noise:
+            # load the low noise LoRA
+            low_noise_lora = load_file(low_noise_lora_path)
+            for key in low_noise_lora:
+                new_key = key.replace(
+                    "diffusion_model.", "diffusion_model.transformer_2."
+                )
+                combined_dict[new_key] = low_noise_lora[key]
+        
+        # if we are not training both stages, we wont have transformer designations in the keys
+        if not self.train_high_noise or not self.train_low_noise:
+            new_dict = {}
+            for key in combined_dict:
+                if ".transformer_1." in key:
+                    new_key = key.replace(".transformer_1.", ".")
+                elif ".transformer_2." in key:
+                    new_key = key.replace(".transformer_2.", ".")
+                else:
+                    new_key = key
+                new_dict[new_key] = combined_dict[key]
+            combined_dict = new_dict
+
+        return combined_dict
+    
+    def generate_single_image(
+        self,
+        pipeline,
+        gen_config: GenerateImageConfig,
+        conditional_embeds: PromptEmbeds,
+        unconditional_embeds: PromptEmbeds,
+        generator: torch.Generator,
+        extra: dict,
+    ):
+        # reactivate progress bar since this is slooooow
+        pipeline.set_progress_bar_config(disable=False)
+        # todo, figure out how to do video
+        output = pipeline(
+            prompt_embeds=conditional_embeds.text_embeds.to(
+                self.device_torch, dtype=self.torch_dtype),
+            negative_prompt_embeds=unconditional_embeds.text_embeds.to(
+                self.device_torch, dtype=self.torch_dtype),
+            height=gen_config.height,
+            width=gen_config.width,
+            num_inference_steps=gen_config.num_inference_steps,
+            guidance_scale=gen_config.guidance_scale,
+            latents=gen_config.latents,
+            num_frames=gen_config.num_frames,
+            generator=generator,
+            return_dict=False,
+            output_type="pil",
+            **extra
+        )[0]
+
+        # shape = [1, frames, channels, height, width]
+        batch_item = output[0]  # list of pil images
+        if gen_config.num_frames > 1:
+            return batch_item  # return the frames.
+        else:
+            # get just the first image
+            img = batch_item[0]
+        return img
+
+    def get_model_to_train(self):
+        # todo, loras wont load right unless they have the transformer_1 or transformer_2 in the key.
+        # called when setting up the LoRA. We only need to get the model for the stages we want to train.
+        if self.train_high_noise and self.train_low_noise:
+            # we are training both stages, return the unified model
+            return self.model
+        elif self.train_high_noise:
+            # we are only training the high noise stage, return transformer_1
+            return self.model.transformer_1
+        elif self.train_low_noise:
+            # we are only training the low noise stage, return transformer_2
+            return self.model.transformer_2
+        else:
+            raise ValueError(
+                "At least one of train_high_noise or train_low_noise must be True in model.model_kwargs"
+            )