Merge pull request #264 from ostris/cogview4

Added basics for CogView4. Broken as hell though. Dont use.

.gitmodules

@@ -1,12 +1,16 @@
 [submodule "repositories/sd-scripts"]
 	path = repositories/sd-scripts
 	url = https://github.com/kohya-ss/sd-scripts.git
+	commit = b78c0e2a69e52ce6c79abc6c8c82d1a9cabcf05c
 [submodule "repositories/leco"]
 	path = repositories/leco
 	url = https://github.com/p1atdev/LECO
+	commit = 9294adf40218e917df4516737afb13f069a6789d
 [submodule "repositories/batch_annotator"]
 	path = repositories/batch_annotator
 	url = https://github.com/ostris/batch-annotator
+	commit = 420e142f6ad3cc14b3ea0500affc2c6c7e7544bf
 [submodule "repositories/ipadapter"]
 	path = repositories/ipadapter
 	url = https://github.com/tencent-ailab/IP-Adapter.git
+	commit = 5a18b1f3660acaf8bee8250692d6fb3548a19b14

extensions_built_in/sd_trainer/SDTrainer.py

@@ -380,9 +380,19 @@ class SDTrainer(BaseSDTrainProcess):
         elif self.sd.prediction_type == 'v_prediction':
             # v-parameterization training
             target = self.sd.noise_scheduler.get_velocity(batch.tensor, noise, timesteps)
-
+        
+        elif hasattr(self.sd, 'get_loss_target'):
+            target = self.sd.get_loss_target(
+                noise=noise, 
+                batch=batch, 
+                timesteps=timesteps,
+            ).detach()
+            
         elif self.sd.is_flow_matching:
+            # forward ODE
             target = (noise - batch.latents).detach()
+            # reverse ODE
+            # target = (batch.latents - noise).detach()
         else:
             target = noise
             

jobs/process/BaseSDTrainProcess.py

@@ -68,6 +68,8 @@ import transformers
 import diffusers
 import hashlib
 
+from toolkit.util.get_model import get_model_class
+
 def flush():
     torch.cuda.empty_cache()
     gc.collect()
@@ -666,7 +668,6 @@ class BaseSDTrainProcess(BaseTrainProcess):
         # # prepare all the models stuff for accelerator (hopefully we dont miss any)
         self.sd.vae = self.accelerator.prepare(self.sd.vae)
         if self.sd.unet is not None:
-            self.sd.unet_unwrapped = self.sd.unet
             self.sd.unet = self.accelerator.prepare(self.sd.unet)
             # todo always tdo it?
             self.modules_being_trained.append(self.sd.unet)
@@ -1103,11 +1104,19 @@ class BaseSDTrainProcess(BaseTrainProcess):
                     if timestep_type is None:
                         timestep_type = self.train_config.timestep_type
                     
+                    patch_size = 1
+                    if self.sd.is_flux:
+                        # flux is a patch size of 1, but latents are divided by 2, so we need to double it
+                        patch_size = 2
+                    elif hasattr(self.sd.unet.config, 'patch_size'):
+                        patch_size = self.sd.unet.config.patch_size
+                    
                     self.sd.noise_scheduler.set_train_timesteps(
                         num_train_timesteps,
                         device=self.device_torch,
                         timestep_type=timestep_type,
-                        latents=latents
+                        latents=latents,
+                        patch_size=patch_size,
                     )
                 else:
                     self.sd.noise_scheduler.set_timesteps(
@@ -1401,21 +1410,26 @@ class BaseSDTrainProcess(BaseTrainProcess):
                 model_config_to_load.name_or_path = latest_save_path
                 self.load_training_state_from_metadata(latest_save_path)
 
-        # get the noise scheduler
-        arch = 'sd'
-        if self.model_config.is_pixart:
-            arch = 'pixart'
-        if self.model_config.is_flux:
-            arch = 'flux'
-        if self.model_config.is_lumina2:
-            arch = 'lumina2'
-        sampler = get_sampler(
-            self.train_config.noise_scheduler,
-            {
-                "prediction_type": "v_prediction" if self.model_config.is_v_pred else "epsilon",
-            },
-            arch=arch,
-        )
+        ModelClass = get_model_class(self.model_config)
+        # if the model class has get_train_scheduler static method
+        if hasattr(ModelClass, 'get_train_scheduler'):
+            sampler = ModelClass.get_train_scheduler()
+        else:
+            # get the noise scheduler
+            arch = 'sd'
+            if self.model_config.is_pixart:
+                arch = 'pixart'
+            if self.model_config.is_flux:
+                arch = 'flux'
+            if self.model_config.is_lumina2:
+                arch = 'lumina2'
+            sampler = get_sampler(
+                self.train_config.noise_scheduler,
+                {
+                    "prediction_type": "v_prediction" if self.model_config.is_v_pred else "epsilon",
+                },
+                arch=arch,
+            )
 
         if self.train_config.train_refiner and self.model_config.refiner_name_or_path is not None and self.network_config is None:
             previous_refiner_save = self.get_latest_save_path(self.job.name + '_refiner')
@@ -1423,7 +1437,7 @@ class BaseSDTrainProcess(BaseTrainProcess):
                 model_config_to_load.refiner_name_or_path = previous_refiner_save
                 self.load_training_state_from_metadata(previous_refiner_save)
 
-        self.sd = StableDiffusion(
+        self.sd = ModelClass(
             device=self.device,
             model_config=model_config_to_load,
             dtype=self.train_config.dtype,
@@ -1559,6 +1573,9 @@ class BaseSDTrainProcess(BaseTrainProcess):
                 # if is_lycoris:
                 #     preset = PRESET['full']
                 # NetworkClass.apply_preset(preset)
+                
+                if hasattr(self.sd, 'target_lora_modules'):
+                    network_kwargs['target_lin_modules'] = self.sd.target_lora_modules
 
                 self.network = NetworkClass(
                     text_encoder=text_encoder,
@@ -1587,6 +1604,7 @@ class BaseSDTrainProcess(BaseTrainProcess):
                     network_config=self.network_config,
                     network_type=self.network_config.type,
                     transformer_only=self.network_config.transformer_only,
+                    is_transformer=self.sd.is_transformer,
                     **network_kwargs
                 )
 

requirements.txt

@@ -1,8 +1,8 @@
 torch==2.5.1
 torchvision==0.20.1
 safetensors
-git+https://github.com/huggingface/diffusers@28f48f4051e80082cbe97f2d62b365dbb01040ec
-transformers
+git+https://github.com/huggingface/diffusers@24c062aaa19f5626d03d058daf8afffa2dfd49f7
+transformers==4.49.0
 lycoris-lora==1.8.3
 flatten_json
 pyyaml

testing/test_vae.py

@@ -29,7 +29,7 @@ def paramiter_count(model):
     return int(paramiter_count)
 
 
-def calculate_metrics(vae, images, max_imgs=-1):
+def calculate_metrics(vae, images, max_imgs=-1, save_output=False):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     vae = vae.to(device)
     lpips_model = lpips.LPIPS(net='alex').to(device)
@@ -44,6 +44,9 @@ def calculate_metrics(vae, images, max_imgs=-1):
     # ])
     # needs values between -1 and 1
     to_tensor = ToTensor()
+    
+    # remove _reconstructed.png files
+    images = [img for img in images if not img.endswith("_reconstructed.png")]
 
     if max_imgs > 0 and len(images) > max_imgs:
         images = images[:max_imgs]
@@ -82,6 +85,15 @@ def calculate_metrics(vae, images, max_imgs=-1):
     avg_rfid = 0
     avg_psnr = sum(psnr_scores) / len(psnr_scores)
     avg_lpips = sum(lpips_scores) / len(lpips_scores)
+    
+    if save_output:
+        filename_no_ext = os.path.splitext(os.path.basename(img_path))[0]
+        folder = os.path.dirname(img_path)
+        save_path = os.path.join(folder, filename_no_ext + "_reconstructed.png")
+        reconstructed = (reconstructed + 1) / 2
+        reconstructed = reconstructed.clamp(0, 1)
+        reconstructed = transforms.ToPILImage()(reconstructed[0].cpu())
+        reconstructed.save(save_path)
 
     return avg_rfid, avg_psnr, avg_lpips
 
@@ -91,18 +103,23 @@ def main():
     parser.add_argument("--vae_path", type=str, required=True, help="Path to the VAE model")
     parser.add_argument("--image_folder", type=str, required=True, help="Path to the folder containing images")
     parser.add_argument("--max_imgs", type=int, default=-1, help="Max num of images. Default is -1 for all images.")
+    # boolean store true
+    parser.add_argument("--save_output", action="store_true", help="Save the output images")
     args = parser.parse_args()
 
     if  os.path.isfile(args.vae_path):
         vae = AutoencoderKL.from_single_file(args.vae_path)
     else:
-        vae = AutoencoderKL.from_pretrained(args.vae_path)
+        try:
+            vae = AutoencoderKL.from_pretrained(args.vae_path)
+        except:
+            vae = AutoencoderKL.from_pretrained(args.vae_path, subfolder="vae")
     vae.eval()
     vae = vae.to(device)
     print(f"Model has {paramiter_count(vae)} parameters")
     images = load_images(args.image_folder)
 
-    avg_rfid, avg_psnr, avg_lpips = calculate_metrics(vae, images, args.max_imgs)
+    avg_rfid, avg_psnr, avg_lpips = calculate_metrics(vae, images, args.max_imgs, args.save_output)
 
     # print(f"Average rFID: {avg_rfid}")
     print(f"Average PSNR: {avg_psnr}")

toolkit/config_modules.py

@@ -432,6 +432,9 @@ class TrainConfig:
         self.force_consistent_noise = kwargs.get('force_consistent_noise', False)
 
 
+ModelArch = Literal['sd1', 'sd2', 'sd3', 'sdxl', 'pixart', 'pixart_sigma', 'auraflow', 'flux', 'flex2', 'lumina2', 'vega', 'ssd', 'wan21']
+
+
 class ModelConfig:
     def __init__(self, **kwargs):
         self.name_or_path: str = kwargs.get('name_or_path', None)
@@ -509,6 +512,36 @@ class ModelConfig:
         self.split_model_other_module_param_count_scale = kwargs.get("split_model_other_module_param_count_scale", 0.3)
         
         self.te_name_or_path = kwargs.get("te_name_or_path", None)
+        
+        self.arch: ModelArch = kwargs.get("arch", None)
+        
+        # handle migrating to new model arch
+        if self.arch is None:
+            if kwargs.get('is_v2', False):
+                self.arch = 'sd2'
+            elif kwargs.get('is_v3', False):
+                self.arch = 'sd3'
+            elif kwargs.get('is_xl', False):
+                self.arch = 'sdxl'
+            elif kwargs.get('is_pixart', False):
+                self.arch = 'pixart'
+            elif kwargs.get('is_pixart_sigma', False):
+                self.arch = 'pixart_sigma'
+            elif kwargs.get('is_auraflow', False):
+                self.arch = 'auraflow'
+            elif kwargs.get('is_flux', False):
+                self.arch = 'flux'
+            elif kwargs.get('is_flex2', False):
+                self.arch = 'flex2'
+            elif kwargs.get('is_lumina2', False):
+                self.arch = 'lumina2'
+            elif kwargs.get('is_vega', False):
+                self.arch = 'vega'
+            elif kwargs.get('is_ssd', False):
+                self.arch = 'ssd'
+            else:
+                self.arch = 'sd1'
+        
 
 
 class EMAConfig:

toolkit/lora_special.py

@@ -178,6 +178,7 @@ class LoRASpecialNetwork(ToolkitNetworkMixin, LoRANetwork):
             transformer_only: bool = False,
             peft_format: bool = False,
             is_assistant_adapter: bool = False,
+            is_transformer: bool = False,
             **kwargs
     ) -> None:
         """
@@ -237,9 +238,11 @@ class LoRASpecialNetwork(ToolkitNetworkMixin, LoRANetwork):
         self.network_config: NetworkConfig = kwargs.get("network_config", None)
 
         self.peft_format = peft_format
+        self.is_transformer = is_transformer
+        
 
         # always do peft for flux only for now
-        if self.is_flux or self.is_v3 or self.is_lumina2:
+        if self.is_flux or self.is_v3 or self.is_lumina2 or is_transformer:
             # don't do peft format for lokr
             if self.network_type.lower() != "lokr":
                 self.peft_format = True
@@ -282,7 +285,7 @@ class LoRASpecialNetwork(ToolkitNetworkMixin, LoRANetwork):
             unet_prefix = self.LORA_PREFIX_UNET
             if self.peft_format:
                 unet_prefix = self.PEFT_PREFIX_UNET
-            if is_pixart or is_v3 or is_auraflow or is_flux or is_lumina2:
+            if is_pixart or is_v3 or is_auraflow or is_flux or is_lumina2 or self.is_transformer:
                 unet_prefix = f"lora_transformer"
                 if self.peft_format:
                     unet_prefix = "transformer"
@@ -341,6 +344,11 @@ class LoRASpecialNetwork(ToolkitNetworkMixin, LoRANetwork):
                         if self.transformer_only and self.is_v3 and is_unet:
                             if "transformer_blocks" not in lora_name:
                                 skip = True
+                        
+                        # handle custom models
+                        if self.transformer_only and is_unet and hasattr(root_module, 'transformer_blocks'):
+                            if "transformer_blocks" not in lora_name:
+                                skip = True
 
                         if (is_linear or is_conv2d) and not skip:
 

toolkit/models/cogview4.py

@@ -0,0 +1,466 @@
+# DONT USE THIS!. IT DOES NOT WORK YET!
+# Will revisit this when they release more info on how it was trained. 
+
+import weakref
+from diffusers import CogView4Pipeline
+import torch
+import yaml
+
+from toolkit.basic import flush
+from toolkit.config_modules import GenerateImageConfig, ModelConfig
+from toolkit.dequantize import patch_dequantization_on_save
+from toolkit.models.base_model import BaseModel
+from toolkit.prompt_utils import PromptEmbeds
+
+import os
+import copy
+from toolkit.config_modules import ModelConfig, GenerateImageConfig, ModelArch
+import torch
+import diffusers
+from diffusers import AutoencoderKL, CogView4Transformer2DModel, CogView4Pipeline
+from optimum.quanto import freeze, qfloat8, QTensor, qint4
+from toolkit.util.quantize import quantize
+from transformers import GlmModel, AutoTokenizer
+from diffusers import FlowMatchEulerDiscreteScheduler
+from typing import TYPE_CHECKING
+from toolkit.accelerator import unwrap_model
+from toolkit.samplers.custom_flowmatch_sampler import CustomFlowMatchEulerDiscreteScheduler
+
+if TYPE_CHECKING:
+    from toolkit.lora_special import LoRASpecialNetwork
+
+# remove this after a bug is fixed in diffusers code. This is a workaround.
+
+
+class FakeModel:
+    def __init__(self, model):
+        self.model_ref = weakref.ref(model)
+        pass
+
+    @property
+    def device(self):
+        return self.model_ref().device
+
+
+scheduler_config = {
+    "base_image_seq_len": 256,
+    "base_shift": 0.25,
+    "invert_sigmas": False,
+    "max_image_seq_len": 4096,
+    "max_shift": 0.75,
+    "num_train_timesteps": 1000,
+    "shift": 1.0,
+    "shift_terminal": None,
+    "time_shift_type": "linear",
+    "use_beta_sigmas": False,
+    "use_dynamic_shifting": True,
+    "use_exponential_sigmas": False,
+    "use_karras_sigmas": False
+}
+
+
+class CogView4(BaseModel):
+    def __init__(
+            self,
+            device,
+            model_config: ModelConfig,
+            dtype='bf16',
+            custom_pipeline=None,
+            noise_scheduler=None,
+            **kwargs
+    ):
+        super().__init__(device, model_config, dtype,
+                         custom_pipeline, noise_scheduler, **kwargs)
+        self.is_flow_matching = True
+        self.is_transformer = True
+        self.target_lora_modules = ['CogView4Transformer2DModel']
+
+        # cache for holding noise
+        self.effective_noise = None
+
+    # static method to get the scheduler
+    @staticmethod
+    def get_train_scheduler():
+        scheduler = CustomFlowMatchEulerDiscreteScheduler(**scheduler_config)
+        return scheduler
+
+    def load_model(self):
+        dtype = self.torch_dtype
+        base_model_path = "THUDM/CogView4-6B"
+        model_path = self.model_config.name_or_path
+
+        self.print_and_status_update("Loading CogView4 model")
+        # base_model_path = "black-forest-labs/FLUX.1-schnell"
+        base_model_path = self.model_config.name_or_path_original
+        subfolder = 'transformer'
+        transformer_path = model_path
+        if os.path.exists(transformer_path):
+            subfolder = None
+            transformer_path = os.path.join(transformer_path, 'transformer')
+            # check if the path is a full checkpoint.
+            te_folder_path = os.path.join(model_path, 'text_encoder')
+            # if we have the te, this folder is a full checkpoint, use it as the base
+            if os.path.exists(te_folder_path):
+                base_model_path = model_path
+
+        self.print_and_status_update("Loading GlmModel")
+        tokenizer = AutoTokenizer.from_pretrained(
+            base_model_path, subfolder="tokenizer", torch_dtype=dtype)
+        text_encoder = GlmModel.from_pretrained(
+            base_model_path, subfolder="text_encoder", torch_dtype=dtype)
+
+        text_encoder.to(self.device_torch, dtype=dtype)
+        flush()
+
+        if self.model_config.quantize_te:
+            self.print_and_status_update("Quantizing GlmModel")
+            quantize(text_encoder, weights=qfloat8)
+            freeze(text_encoder)
+            flush()
+
+        # hack to fix diffusers bug workaround
+        text_encoder.model = FakeModel(text_encoder)
+
+        self.print_and_status_update("Loading transformer")
+        transformer = CogView4Transformer2DModel.from_pretrained(
+            transformer_path,
+            subfolder=subfolder,
+            torch_dtype=dtype,
+        )
+
+        if self.model_config.split_model_over_gpus:
+            raise ValueError(
+                "Splitting model over gpus is not supported for CogViewModels models")
+
+        transformer.to(self.quantize_device, dtype=dtype)
+        flush()
+
+        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 CogViewModels models currently")
+
+        if self.model_config.lora_path is not None:
+            raise ValueError(
+                "Loading LoRA is not supported for CogViewModels models currently")
+
+        flush()
+
+        if self.model_config.quantize:
+            quantization_args = self.model_config.quantize_kwargs
+            if 'exclude' not in quantization_args:
+                quantization_args['exclude'] = []
+            if 'include' not in quantization_args:
+                quantization_args['include'] = []
+
+            # Be more specific with the include pattern to exactly match transformer blocks
+            quantization_args['include'] += ["transformer_blocks.*"]
+
+            # Exclude all LayerNorm layers within transformer blocks
+            quantization_args['exclude'] += [
+                "transformer_blocks.*.norm1",
+                "transformer_blocks.*.norm2",
+                "transformer_blocks.*.norm2_context",
+                "transformer_blocks.*.attn1.norm_q",
+                "transformer_blocks.*.attn1.norm_k"
+            ]
+
+            # patch the state dict method
+            patch_dequantization_on_save(transformer)
+            quantization_type = qfloat8
+            self.print_and_status_update("Quantizing transformer")
+            quantize(transformer, weights=quantization_type, **quantization_args)
+            freeze(transformer)
+            transformer.to(self.device_torch)
+        else:
+            transformer.to(self.device_torch, dtype=dtype)
+
+        flush()
+
+        scheduler = CogView4.get_train_scheduler()
+        self.print_and_status_update("Loading VAE")
+        vae = AutoencoderKL.from_pretrained(
+            base_model_path, subfolder="vae", torch_dtype=dtype)
+        flush()
+
+        self.print_and_status_update("Making pipe")
+        pipe: CogView4Pipeline = CogView4Pipeline(
+            scheduler=scheduler,
+            text_encoder=None,
+            tokenizer=tokenizer,
+            vae=vae,
+            transformer=None,
+        )
+        pipe.text_encoder = text_encoder
+        pipe.transformer = transformer
+
+        self.print_and_status_update("Preparing Model")
+
+        text_encoder = pipe.text_encoder
+        tokenizer = pipe.tokenizer
+
+        pipe.transformer = pipe.transformer.to(self.device_torch)
+
+        flush()
+        text_encoder.to(self.device_torch)
+        text_encoder.requires_grad_(False)
+        text_encoder.eval()
+        pipe.transformer = pipe.transformer.to(self.device_torch)
+        flush()
+        self.pipeline = pipe
+        self.model = transformer
+        self.vae = vae
+        self.text_encoder = text_encoder
+        self.tokenizer = tokenizer
+
+    def get_generation_pipeline(self):
+        scheduler = CogView4.get_train_scheduler()
+        pipeline = CogView4Pipeline(
+            vae=self.vae,
+            transformer=self.unet,
+            text_encoder=self.text_encoder,
+            tokenizer=self.tokenizer,
+            scheduler=scheduler,
+        )
+        return pipeline
+
+    def generate_single_image(
+        self,
+        pipeline: CogView4Pipeline,
+        gen_config: GenerateImageConfig,
+        conditional_embeds: PromptEmbeds,
+        unconditional_embeds: PromptEmbeds,
+        generator: torch.Generator,
+        extra: dict,
+    ):
+        img = 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,
+            generator=generator,
+            **extra
+        ).images[0]
+        return img
+
+    def get_noise_prediction(
+        self,
+        latent_model_input: torch.Tensor,
+        timestep: torch.Tensor,  # 0 to 1000 scale
+        text_embeddings: PromptEmbeds,
+        **kwargs
+    ):
+        # target_size = (height, width)
+        target_size = latent_model_input.shape[-2:]
+        # multiply by 8
+        target_size = (target_size[0] * 8, target_size[1] * 8)
+        crops_coords_top_left = torch.tensor(
+            [(0, 0)], dtype=self.torch_dtype, device=self.device_torch)
+
+        original_size = torch.tensor(
+            [target_size], dtype=self.torch_dtype, device=self.device_torch)
+        target_size = original_size.clone()
+        noise_pred_cond = self.model(
+            hidden_states=latent_model_input,
+            encoder_hidden_states=text_embeddings.text_embeds,
+            timestep=timestep,
+            original_size=original_size,
+            target_size=target_size,
+            crop_coords=crops_coords_top_left,
+            return_dict=False,
+        )[0]
+        return noise_pred_cond
+
+    def get_prompt_embeds(self, prompt: str) -> PromptEmbeds:
+        prompt_embeds, _ = self.pipeline.encode_prompt(
+            prompt,
+            do_classifier_free_guidance=False,
+            device=self.device_torch,
+            dtype=self.torch_dtype,
+        )
+        return PromptEmbeds(prompt_embeds)
+
+    def get_model_has_grad(self):
+        return self.model.proj_out.weight.requires_grad
+
+    def get_te_has_grad(self):
+        return self.text_encoder.layers[0].mlp.down_proj.weight.requires_grad
+
+    def save_model(self, output_path, meta, save_dtype):
+        # only save the unet
+        transformer: CogView4Transformer2DModel = unwrap_model(self.model)
+        transformer.save_pretrained(
+            save_directory=os.path.join(output_path, 'transformer'),
+            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 get_loss_target(self, *args, **kwargs):
+        noise = kwargs.get('noise')
+        effective_noise = self.effective_noise
+        batch = kwargs.get('batch')
+        if batch is None:
+            raise ValueError("Batch is not provided")
+        if noise is None:
+            raise ValueError("Noise is not provided")
+        # return batch.latents
+        # return (batch.latents - noise).detach()
+        return (noise - batch.latents).detach()
+        # return (batch.latents).detach()
+        # return (effective_noise - batch.latents).detach()
+
+    def _get_low_res_latents(self, latents):
+        # todo prevent needing to do this and grab the tensor another way.
+        with torch.no_grad():
+            # Decode latents to image space
+            images = self.decode_latents(
+                latents, device=latents.device, dtype=latents.dtype)
+
+            # Downsample by a factor of 2 using bilinear interpolation
+            B, C, H, W = images.shape
+            low_res_images = torch.nn.functional.interpolate(
+                images,
+                size=(H // 2, W // 2),
+                mode="bilinear",
+                align_corners=False
+            )
+
+            # Upsample back to original resolution to match expected VAE input dimensions
+            upsampled_low_res_images = torch.nn.functional.interpolate(
+                low_res_images,
+                size=(H, W),
+                mode="bilinear",
+                align_corners=False
+            )
+
+            # Encode the low-resolution images back to latent space
+            low_res_latents = self.encode_images(
+                upsampled_low_res_images, device=latents.device, dtype=latents.dtype)
+            return low_res_latents
+
+    # def add_noise(
+    #         self,
+    #         original_samples: torch.FloatTensor,
+    #         noise: torch.FloatTensor,
+    #         timesteps: torch.IntTensor,
+    #         **kwargs,
+    # ) -> torch.FloatTensor:
+    #     relay_start_point = 500
+
+    #     # Store original samples for loss calculation
+    #     self.original_samples = original_samples
+
+    #     # Prepare chunks for batch processing
+    #     original_samples_chunks = torch.chunk(
+    #         original_samples, original_samples.shape[0], dim=0)
+    #     noise_chunks = torch.chunk(noise, noise.shape[0], dim=0)
+    #     timesteps_chunks = torch.chunk(timesteps, timesteps.shape[0], dim=0)
+
+    #     # Get the low res latents only if needed
+    #     low_res_latents_chunks = None
+
+    #     # Handle case where timesteps is a single value for all samples
+    #     if len(timesteps_chunks) == 1 and len(timesteps_chunks) != len(original_samples_chunks):
+    #         timesteps_chunks = [timesteps_chunks[0]] * len(original_samples_chunks)
+
+    #     noisy_latents_chunks = []
+    #     effective_noise_chunks = []  # Store the effective noise for each sample
+
+    #     for idx in range(original_samples.shape[0]):
+    #         t = timesteps_chunks[idx]
+    #         t_01 = (t / 1000).to(original_samples_chunks[idx].device)
+
+    #         # Flowmatching interpolation between original and noise
+    #         if t > relay_start_point:
+    #             # Standard flowmatching - direct linear interpolation
+    #             noisy_latents = (1 - t_01) * original_samples_chunks[idx] + t_01 * noise_chunks[idx]
+    #             effective_noise_chunks.append(noise_chunks[idx])  # Effective noise is just the noise
+    #         else:
+    #             # Relay flowmatching case - only compute low_res_latents if needed
+    #             if low_res_latents_chunks is None:
+    #                 low_res_latents = self._get_low_res_latents(original_samples)
+    #                 low_res_latents_chunks = torch.chunk(low_res_latents, low_res_latents.shape[0], dim=0)
+
+    #             # Calculate the relay ratio (0 to 1)
+    #             t_ratio = t.float() / relay_start_point
+    #             t_ratio = torch.clamp(t_ratio, 0.0, 1.0)
+
+    #             # First blend between original and low-res based on t_ratio
+    #             z0_t = (1 - t_ratio) * original_samples_chunks[idx] + t_ratio * low_res_latents_chunks[idx]
+
+    #             added_lor_res_noise =  z0_t - original_samples_chunks[idx]
+
+    #             # Then apply flowmatching interpolation between this blended state and noise
+    #             noisy_latents = (1 - t_01) * z0_t + t_01 * noise_chunks[idx]
+
+    #             # For prediction target, we need to store the effective "source"
+    #             effective_noise_chunks.append(noise_chunks[idx] + added_lor_res_noise)
+
+    #         noisy_latents_chunks.append(noisy_latents)
+
+    #     noisy_latents = torch.cat(noisy_latents_chunks, dim=0)
+    #     self.effective_noise = torch.cat(effective_noise_chunks, dim=0)  # Store for loss calculation
+
+    #     return noisy_latents
+
+    # def add_noise(
+    #         self,
+    #         original_samples: torch.FloatTensor,
+    #         noise: torch.FloatTensor,
+    #         timesteps: torch.IntTensor,
+    #         **kwargs,
+    # ) -> torch.FloatTensor:
+    #     relay_start_point = 500
+
+    #     # Store original samples for loss calculation
+    #     self.original_samples = original_samples
+
+    #     # Prepare chunks for batch processing
+    #     original_samples_chunks = torch.chunk(
+    #         original_samples, original_samples.shape[0], dim=0)
+    #     noise_chunks = torch.chunk(noise, noise.shape[0], dim=0)
+    #     timesteps_chunks = torch.chunk(timesteps, timesteps.shape[0], dim=0)
+
+    #     # Get the low res latents only if needed
+    #     low_res_latents = self._get_low_res_latents(original_samples)
+    #     low_res_latents_chunks = torch.chunk(low_res_latents, low_res_latents.shape[0], dim=0)
+
+    #     # Handle case where timesteps is a single value for all samples
+    #     if len(timesteps_chunks) == 1 and len(timesteps_chunks) != len(original_samples_chunks):
+    #         timesteps_chunks = [timesteps_chunks[0]] * len(original_samples_chunks)
+
+    #     noisy_latents_chunks = []
+    #     effective_noise_chunks = []  # Store the effective noise for each sample
+
+    #     for idx in range(original_samples.shape[0]):
+    #         t = timesteps_chunks[idx]
+    #         t_01 = (t / 1000).to(original_samples_chunks[idx].device)
+
+    #         lrln = low_res_latents_chunks[idx] - original_samples_chunks[idx]
+    #         # lrln = lrln * (1 - t_01)
+
+    #         # make the noise an interpolation between noise and low_res_latents with
+    #         # being noise at t_01=1 and low_res_latents at t_01=0
+    #         new_noise = t_01 * noise_chunks[idx] + (1 - t_01) * lrln
+    #         # new_noise = noise_chunks[idx] + lrln
+    #         # new_noise = noise_chunks[idx] + lrln
+
+    #         # Then apply flowmatching interpolation between this blended state and noise
+    #         noisy_latents = (1 - t_01) * original_samples + t_01 * new_noise
+
+    #         # For prediction target, we need to store the effective "source"
+    #         effective_noise_chunks.append(new_noise)
+
+    #     noisy_latents_chunks.append(noisy_latents)
+
+    #     noisy_latents = torch.cat(noisy_latents_chunks, dim=0)
+    #     self.effective_noise = torch.cat(effective_noise_chunks, dim=0)  # Store for loss calculation
+
+    #     return noisy_latents

toolkit/models/wan21.py

@@ -0,0 +1,82 @@
+# WIP, coming soon ish
+import torch
+from toolkit.config_modules import GenerateImageConfig, ModelConfig
+from toolkit.models.base_model import BaseModel
+from toolkit.prompt_utils import PromptEmbeds
+from toolkit.paths import REPOS_ROOT
+import sys
+import os
+
+import gc
+import logging
+import math
+import os
+import random
+import sys
+import types
+from contextlib import contextmanager
+from functools import partial
+
+import torch
+import torch.cuda.amp as amp
+import torch.distributed as dist
+from tqdm import tqdm
+
+
+class Wan21(BaseModel):
+    def __init__(
+            self,
+            device,
+            model_config: ModelConfig,
+            dtype='bf16',
+            custom_pipeline=None,
+            noise_scheduler=None,
+            **kwargs
+    ):
+        super().__init__(device, model_config, dtype,
+                         custom_pipeline, noise_scheduler, **kwargs)
+        self.is_flow_matching = True
+        raise NotImplementedError("Wan21 is not implemented yet")
+    # these must be implemented in child classes
+
+    def load_model(self):
+        pass
+
+    def get_generation_pipeline(self):
+        # override this in child classes
+        raise NotImplementedError(
+            "get_generation_pipeline must be implemented in child classes")
+
+    def generate_single_image(
+        self,
+        pipeline,
+        gen_config: GenerateImageConfig,
+        conditional_embeds: PromptEmbeds,
+        unconditional_embeds: PromptEmbeds,
+        generator: torch.Generator,
+        extra: dict,
+    ):
+        # override this in child classes
+        raise NotImplementedError(
+            "generate_single_image must be implemented in child classes")
+
+    def get_noise_prediction(
+        latent_model_input: torch.Tensor,
+        timestep: torch.Tensor,  # 0 to 1000 scale
+        text_embeddings: PromptEmbeds,
+        **kwargs
+    ):
+        raise NotImplementedError(
+            "get_noise_prediction must be implemented in child classes")
+
+    def get_prompt_embeds(self, prompt: str) -> PromptEmbeds:
+        raise NotImplementedError(
+            "get_prompt_embeds must be implemented in child classes")
+    
+    def get_model_has_grad(self):
+        raise NotImplementedError(
+            "get_model_has_grad must be implemented in child classes")
+        
+    def get_te_has_grad(self):
+        raise NotImplementedError(
+            "get_te_has_grad must be implemented in child classes")

toolkit/samplers/custom_flowmatch_sampler.py

@@ -44,7 +44,8 @@ class CustomFlowMatchEulerDiscreteScheduler(FlowMatchEulerDiscreteScheduler):
             hbsmntw_weighing = y_shifted * (num_timesteps / y_shifted.sum())
 
             # flatten second half to max
-            hbsmntw_weighing[num_timesteps // 2:] = hbsmntw_weighing[num_timesteps // 2:].max()
+            hbsmntw_weighing[num_timesteps //
+                             2:] = hbsmntw_weighing[num_timesteps // 2:].max()
 
             # Create linear timesteps from 1000 to 0
             timesteps = torch.linspace(1000, 0, num_timesteps, device='cpu')
@@ -56,7 +57,8 @@ class CustomFlowMatchEulerDiscreteScheduler(FlowMatchEulerDiscreteScheduler):
 
     def get_weights_for_timesteps(self, timesteps: torch.Tensor, v2=False) -> torch.Tensor:
         # Get the indices of the timesteps
-        step_indices = [(self.timesteps == t).nonzero().item() for t in timesteps]
+        step_indices = [(self.timesteps == t).nonzero().item()
+                        for t in timesteps]
 
         # Get the weights for the timesteps
         if v2:
@@ -70,7 +72,8 @@ class CustomFlowMatchEulerDiscreteScheduler(FlowMatchEulerDiscreteScheduler):
         sigmas = self.sigmas.to(device=device, dtype=dtype)
         schedule_timesteps = self.timesteps.to(device)
         timesteps = timesteps.to(device)
-        step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
+        step_indices = [(schedule_timesteps == t).nonzero().item()
+                        for t in timesteps]
 
         sigma = sigmas[step_indices].flatten()
         while len(sigma.shape) < n_dim:
@@ -84,27 +87,24 @@ class CustomFlowMatchEulerDiscreteScheduler(FlowMatchEulerDiscreteScheduler):
             noise: torch.Tensor,
             timesteps: torch.Tensor,
     ) -> torch.Tensor:
-        ## ref https://github.com/huggingface/diffusers/blob/fbe29c62984c33c6cf9cf7ad120a992fe6d20854/examples/dreambooth/train_dreambooth_sd3.py#L1578
-        ## Add noise according to flow matching.
-        ## zt = (1 - texp) * x + texp * z1
-
-        # sigmas = get_sigmas(timesteps, n_dim=model_input.ndim, dtype=model_input.dtype)
-        # noisy_model_input = (1.0 - sigmas) * model_input + sigmas * noise
-
-        # timestep needs to be in [0, 1], we store them in [0, 1000]
-        # noisy_sample = (1 - timestep) * latent + timestep * noise
         t_01 = (timesteps / 1000).to(original_samples.device)
-        noisy_model_input = (1 - t_01) * original_samples + t_01 * noise
-
-        # n_dim = original_samples.ndim
-        # sigmas = self.get_sigmas(timesteps, n_dim, original_samples.dtype, original_samples.device)
-        # noisy_model_input = (1.0 - sigmas) * original_samples + sigmas * noise
+        # forward ODE
+        noisy_model_input = (1.0 - t_01) * original_samples + t_01 * noise
+        # reverse ODE
+        # noisy_model_input = (1 - t_01) * noise + t_01 * original_samples
         return noisy_model_input
 
     def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:
         return sample
 
-    def set_train_timesteps(self, num_timesteps, device, timestep_type='linear', latents=None):
+    def set_train_timesteps(
+        self,
+        num_timesteps,
+        device,
+        timestep_type='linear',
+        latents=None,
+        patch_size=1
+    ):
         self.timestep_type = timestep_type
         if timestep_type == 'linear':
             timesteps = torch.linspace(1000, 0, num_timesteps, device=device)
@@ -124,42 +124,67 @@ class CustomFlowMatchEulerDiscreteScheduler(FlowMatchEulerDiscreteScheduler):
             self.timesteps = timesteps.to(device=device)
 
             return timesteps
-        elif timestep_type == 'flux_shift' or timestep_type == 'lumina2_shift':
+        elif timestep_type in ['flux_shift', 'lumina2_shift', 'shift']:
             # matches inference dynamic shifting
             timesteps = np.linspace(
-                self._sigma_to_t(self.sigma_max), self._sigma_to_t(self.sigma_min), num_timesteps
+                self._sigma_to_t(self.sigma_max), self._sigma_to_t(
+                    self.sigma_min), num_timesteps
             )
 
             sigmas = timesteps / self.config.num_train_timesteps
-            
-            if latents is None:
-                raise ValueError('latents is None')
-            
-            h = latents.shape[2] // 2  # Divide by ph
-            w = latents.shape[3] // 2  # Divide by pw
-            image_seq_len = h * w
 
-            # todo need to know the mu for the shift
-            mu = calculate_shift(
-                image_seq_len,
-                self.config.get("base_image_seq_len", 256),
-                self.config.get("max_image_seq_len", 4096),
-                self.config.get("base_shift", 0.5),
-                self.config.get("max_shift", 1.16),
-            )
-            sigmas = self.time_shift(mu, 1.0, sigmas)
+            if self.config.use_dynamic_shifting:
+                if latents is None:
+                    raise ValueError('latents is None')
 
-            sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
+                # for flux we double up the patch size before sending her to simulate the latent reduction
+                h = latents.shape[2]
+                w = latents.shape[3]
+                image_seq_len = h * w // (patch_size**2)
+
+                mu = calculate_shift(
+                    image_seq_len,
+                    self.config.get("base_image_seq_len", 256),
+                    self.config.get("max_image_seq_len", 4096),
+                    self.config.get("base_shift", 0.5),
+                    self.config.get("max_shift", 1.16),
+                )
+                sigmas = self.time_shift(mu, 1.0, sigmas)
+            else:
+                sigmas = self.shift * sigmas / (1 + (self.shift - 1) * sigmas)
+
+            if self.config.shift_terminal:
+                sigmas = self.stretch_shift_to_terminal(sigmas)
+
+            if self.config.use_karras_sigmas:
+                sigmas = self._convert_to_karras(
+                    in_sigmas=sigmas, num_inference_steps=self.config.num_train_timesteps)
+            elif self.config.use_exponential_sigmas:
+                sigmas = self._convert_to_exponential(
+                    in_sigmas=sigmas, num_inference_steps=self.config.num_train_timesteps)
+            elif self.config.use_beta_sigmas:
+                sigmas = self._convert_to_beta(
+                    in_sigmas=sigmas, num_inference_steps=self.config.num_train_timesteps)
+
+            sigmas = torch.from_numpy(sigmas).to(
+                dtype=torch.float32, device=device)
             timesteps = sigmas * self.config.num_train_timesteps
 
-            sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
+            if self.config.invert_sigmas:
+                sigmas = 1.0 - sigmas
+                timesteps = sigmas * self.config.num_train_timesteps
+                sigmas = torch.cat(
+                    [sigmas, torch.ones(1, device=sigmas.device)])
+            else:
+                sigmas = torch.cat(
+                    [sigmas, torch.zeros(1, device=sigmas.device)])
 
             self.timesteps = timesteps.to(device=device)
             self.sigmas = sigmas
-            
+
             self.timesteps = timesteps.to(device=device)
             return timesteps
-            
+
         elif timestep_type == 'lognorm_blend':
             # disgtribute timestepd to the center/early and blend in linear
             alpha = 0.75
@@ -173,7 +198,8 @@ class CustomFlowMatchEulerDiscreteScheduler(FlowMatchEulerDiscreteScheduler):
             t1 = ((1 - t1/t1.max()) * 1000)
 
             # add half of linear
-            t2 = torch.linspace(1000, 0, int(num_timesteps * (1 - alpha)), device=device)
+            t2 = torch.linspace(1000, 0, int(
+                num_timesteps * (1 - alpha)), device=device)
             timesteps = torch.cat((t1, t2))
 
             # Sort the timesteps in descending order

toolkit/stable_diffusion_model.py

@@ -29,7 +29,7 @@ from toolkit.ip_adapter import IPAdapter
 from library.model_util import convert_unet_state_dict_to_sd, convert_text_encoder_state_dict_to_sd_v2, \
     convert_vae_state_dict, load_vae
 from toolkit import train_tools
-from toolkit.config_modules import ModelConfig, GenerateImageConfig
+from toolkit.config_modules import ModelConfig, GenerateImageConfig, ModelArch
 from toolkit.metadata import get_meta_for_safetensors
 from toolkit.models.decorator import Decorator
 from toolkit.paths import REPOS_ROOT, KEYMAPS_ROOT
@@ -64,7 +64,8 @@ from toolkit.paths import ORIG_CONFIGS_ROOT, DIFFUSERS_CONFIGS_ROOT
 from huggingface_hub import hf_hub_download
 from toolkit.models.flux import add_model_gpu_splitter_to_flux, bypass_flux_guidance, restore_flux_guidance
 
-from optimum.quanto import freeze, qfloat8, quantize, QTensor, qint4
+from optimum.quanto import freeze, qfloat8, QTensor, qint4
+from toolkit.util.quantize import quantize
 from toolkit.accelerator import get_accelerator, unwrap_model
 from typing import TYPE_CHECKING
 from toolkit.print import print_acc
@@ -160,7 +161,6 @@ class StableDiffusion:
         self.pipeline: Union[None, 'StableDiffusionPipeline', 'CustomStableDiffusionXLPipeline', 'PixArtAlphaPipeline']
         self.vae: Union[None, 'AutoencoderKL']
         self.unet: Union[None, 'UNet2DConditionModel']
-        self.unet_unwrapped: Union[None, 'UNet2DConditionModel']
         self.text_encoder: Union[None, 'CLIPTextModel', List[Union['CLIPTextModel', 'CLIPTextModelWithProjection']]]
         self.tokenizer: Union[None, 'CLIPTokenizer', List['CLIPTokenizer']]
         self.noise_scheduler: Union[None, 'DDPMScheduler'] = noise_scheduler
@@ -177,16 +177,17 @@ class StableDiffusion:
         self.network = None
         self.adapter: Union['ControlNetModel', 'T2IAdapter', 'IPAdapter', 'ReferenceAdapter', None] = None
         self.decorator: Union[Decorator, None] = None
-        self.is_xl = model_config.is_xl
-        self.is_v2 = model_config.is_v2
-        self.is_ssd = model_config.is_ssd
-        self.is_v3 = model_config.is_v3
-        self.is_vega = model_config.is_vega
-        self.is_pixart = model_config.is_pixart
-        self.is_auraflow = model_config.is_auraflow
-        self.is_flux = model_config.is_flux
-        self.is_flex2 = model_config.is_flex2
-        self.is_lumina2 = model_config.is_lumina2
+        self.arch: ModelArch = model_config.arch
+        # self.is_xl = model_config.is_xl
+        # self.is_v2 = model_config.is_v2
+        # self.is_ssd = model_config.is_ssd
+        # self.is_v3 = model_config.is_v3
+        # self.is_vega = model_config.is_vega
+        # self.is_pixart = model_config.is_pixart
+        # self.is_auraflow = model_config.is_auraflow
+        # self.is_flux = model_config.is_flux
+        # self.is_flex2 = model_config.is_flex2
+        # self.is_lumina2 = model_config.is_lumina2
 
         self.use_text_encoder_1 = model_config.use_text_encoder_1
         self.use_text_encoder_2 = model_config.use_text_encoder_2
@@ -204,6 +205,53 @@ class StableDiffusion:
         self.invert_assistant_lora = False
         self._after_sample_img_hooks = []
         self._status_update_hooks = []
+        # todo update this based on the model
+        self.is_transformer = False
+        
+    # properties for old arch for backwards compatibility
+    @property
+    def is_xl(self):
+        return self.arch == 'sdxl'
+    
+    @property
+    def is_v2(self):
+        return self.arch == 'sd2'
+    
+    @property
+    def is_ssd(self):
+        return self.arch == 'ssd'
+    
+    @property
+    def is_v3(self):
+        return self.arch == 'sd3'
+    
+    @property
+    def is_vega(self):
+        return self.arch == 'vega'
+    
+    @property
+    def is_pixart(self):
+        return self.arch == 'pixart'
+    
+    @property
+    def is_auraflow(self):
+        return self.arch == 'auraflow'
+    
+    @property
+    def is_flux(self):
+        return self.arch == 'flux'
+    
+    @property
+    def is_flex2(self):
+        return self.arch == 'flex2'
+    
+    @property
+    def is_lumina2(self):
+        return self.arch == 'lumina2'
+    
+    @property
+    def unet_unwrapped(self):
+        return unwrap_model(self.unet)
 
     def load_model(self):
         if self.is_loaded:
@@ -935,7 +983,6 @@ class StableDiffusion:
         if self.is_pixart or self.is_v3 or self.is_auraflow or self.is_flux or self.is_lumina2:
             # pixart and sd3 dont use a unet
             self.unet = pipe.transformer
-            self.unet_unwrapped = pipe.transformer
         else:
             self.unet: 'UNet2DConditionModel' = pipe.unet
         self.vae: 'AutoencoderKL' = pipe.vae.to(self.vae_device_torch, dtype=self.vae_torch_dtype)
@@ -1734,7 +1781,8 @@ class StableDiffusion:
             self,
             original_samples: torch.FloatTensor,
             noise: torch.FloatTensor,
-            timesteps: torch.IntTensor
+            timesteps: torch.IntTensor,
+            **kwargs,
     ) -> torch.FloatTensor:
         original_samples_chunks = torch.chunk(original_samples, original_samples.shape[0], dim=0)
         noise_chunks = torch.chunk(noise, noise.shape[0], dim=0)

toolkit/util/get_model.py

@@ -0,0 +1,12 @@
+from toolkit.stable_diffusion_model import StableDiffusion
+from toolkit.config_modules import ModelConfig
+
+def get_model_class(config: ModelConfig):
+    if config.arch == "wan21":
+        from toolkit.models.wan21 import Wan21
+        return Wan21
+    elif config.arch == "cogview4":
+        from toolkit.models.cogview4 import CogView4
+        return CogView4
+    else:
+        return StableDiffusion

toolkit/util/quantize.py

@@ -0,0 +1,55 @@
+from fnmatch import fnmatch
+from typing import Any, Dict, List, Optional, Union
+import torch
+
+from optimum.quanto.quantize import _quantize_submodule
+from optimum.quanto.tensor import Optimizer, qtype
+
+# the quantize function in quanto had a bug where it was using exclude instead of include
+
+
+def quantize(
+    model: torch.nn.Module,
+    weights: Optional[Union[str, qtype]] = None,
+    activations: Optional[Union[str, qtype]] = None,
+    optimizer: Optional[Optimizer] = None,
+    include: Optional[Union[str, List[str]]] = None,
+    exclude: Optional[Union[str, List[str]]] = None,
+):
+    """Quantize the specified model submodules
+
+    Recursively quantize the submodules of the specified parent model.
+
+    Only modules that have quantized counterparts will be quantized.
+
+    If include patterns are specified, the submodule name must match one of them.
+
+    If exclude patterns are specified, the submodule must not match one of them.
+
+    Include or exclude patterns are Unix shell-style wildcards which are NOT regular expressions. See
+    https://docs.python.org/3/library/fnmatch.html for more details.
+
+    Note: quantization happens in-place and modifies the original model and its descendants.
+
+    Args:
+        model (`torch.nn.Module`): the model whose submodules will be quantized.
+        weights (`Optional[Union[str, qtype]]`): the qtype for weights quantization.
+        activations (`Optional[Union[str, qtype]]`): the qtype for activations quantization.
+        include (`Optional[Union[str, List[str]]]`):
+            Patterns constituting the allowlist. If provided, module names must match at
+            least one pattern from the allowlist.
+        exclude (`Optional[Union[str, List[str]]]`):
+            Patterns constituting the denylist. If provided, module names must not match
+            any patterns from the denylist.
+    """
+    if include is not None:
+        include = [include] if isinstance(include, str) else include
+    if exclude is not None:
+        exclude = [exclude] if isinstance(exclude, str) else exclude
+    for name, m in model.named_modules():
+        if include is not None and not any(fnmatch(name, pattern) for pattern in include):
+            continue
+        if exclude is not None and any(fnmatch(name, pattern) for pattern in exclude):
+            continue
+        _quantize_submodule(model, name, m, weights=weights,
+                            activations=activations, optimizer=optimizer)