SDXL should be working, but I broke something where it is not converging.

jobs/BaseJob.py

@@ -17,6 +17,7 @@ class BaseJob:
             raise ValueError('config is required')
 
         self.config = config['config']
+        self.raw_config = config
         self.job = config['job']
         self.name = self.get_conf('name', required=True)
         if 'meta' in config:

jobs/TrainJob.py

@@ -1,3 +1,4 @@
+import json
 import os
 
 from jobs import BaseJob
@@ -6,7 +7,7 @@ from collections import OrderedDict
 from typing import List
 from jobs.process import BaseExtractProcess, TrainFineTuneProcess
 from datetime import datetime
-
+import yaml
 from toolkit.paths import REPOS_ROOT
 
 import sys
@@ -16,6 +17,7 @@ sys.path.append(REPOS_ROOT)
 process_dict = {
     'vae': 'TrainVAEProcess',
     'slider': 'TrainSliderProcess',
+    'lora_hack': 'TrainLoRAHack',
 }
 
 
@@ -37,6 +39,13 @@ class TrainJob(BaseJob):
         # loads the processes from the config
         self.load_processes(process_dict)
 
+    def save_training_config(self):
+        timestamp = datetime.now().strftime('%Y%m%d-%H%M%S')
+        os.makedirs(self.training_folder, exist_ok=True)
+        save_dif = os.path.join(self.training_folder, f'run_config_{timestamp}.yaml')
+        with open(save_dif, 'w') as f:
+            yaml.dump(self.raw_config, f)
+
     def run(self):
         super().run()
         print("")

jobs/process/BaseSDTrainProcess.py

@@ -2,7 +2,6 @@ import time
 from collections import OrderedDict
 import os
 
-from leco.train_util import predict_noise
 from toolkit.kohya_model_util import load_vae
 from toolkit.lora_special import LoRASpecialNetwork
 from toolkit.optimizer import get_optimizer
@@ -12,7 +11,7 @@ import sys
 sys.path.append(REPOS_ROOT)
 sys.path.append(os.path.join(REPOS_ROOT, 'leco'))
 
-from diffusers import StableDiffusionPipeline
+from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline
 
 from jobs.process import BaseTrainProcess
 from toolkit.metadata import get_meta_for_safetensors
@@ -24,6 +23,7 @@ from tqdm import tqdm
 
 from leco import train_util, model_util
 from toolkit.config_modules import SaveConfig, LogingConfig, SampleConfig, NetworkConfig, TrainConfig, ModelConfig
+from toolkit.stable_diffusion_model import StableDiffusion, PromptEmbeds
 
 
 def flush():
@@ -35,15 +35,6 @@ UNET_IN_CHANNELS = 4  # Stable Diffusion の in_channels は 4 で固定。XLも
 VAE_SCALE_FACTOR = 8  # 2 ** (len(vae.config.block_out_channels) - 1) = 8
 
 
-class StableDiffusion:
-    def __init__(self, vae, tokenizer, text_encoder, unet, noise_scheduler):
-        self.vae = vae
-        self.tokenizer = tokenizer
-        self.text_encoder = text_encoder
-        self.unet = unet
-        self.noise_scheduler = noise_scheduler
-
-
 class BaseSDTrainProcess(BaseTrainProcess):
     def __init__(self, process_id: int, job, config: OrderedDict):
         super().__init__(process_id, job, config)
@@ -80,26 +71,44 @@ class BaseSDTrainProcess(BaseTrainProcess):
         original_device_dict = {
             'vae': self.sd.vae.device,
             'unet': self.sd.unet.device,
-            'text_encoder': self.sd.text_encoder.device,
             # 'tokenizer': self.sd.tokenizer.device,
         }
 
+        # handle sdxl text encoder
+        if isinstance(self.sd.text_encoder, list):
+            for encoder, i in zip(self.sd.text_encoder, range(len(self.sd.text_encoder))):
+                original_device_dict[f'text_encoder_{i}'] = encoder.device
+                encoder.to(self.device_torch)
+        else:
+            original_device_dict['text_encoder'] = self.sd.text_encoder.device
+            self.sd.text_encoder.to(self.device_torch)
+
         self.sd.vae.to(self.device_torch)
         self.sd.unet.to(self.device_torch)
-        self.sd.text_encoder.to(self.device_torch)
+        # self.sd.text_encoder.to(self.device_torch)
         # self.sd.tokenizer.to(self.device_torch)
         # TODO add clip skip
-
-        pipeline = StableDiffusionPipeline(
-            vae=self.sd.vae,
-            unet=self.sd.unet,
-            text_encoder=self.sd.text_encoder,
-            tokenizer=self.sd.tokenizer,
-            scheduler=self.sd.noise_scheduler,
-            safety_checker=None,
-            feature_extractor=None,
-            requires_safety_checker=False,
-        )
+        if self.sd.is_xl:
+            pipeline = StableDiffusionXLPipeline(
+                vae=self.sd.vae,
+                unet=self.sd.unet,
+                text_encoder=self.sd.text_encoder[0],
+                text_encoder_2=self.sd.text_encoder[1],
+                tokenizer=self.sd.tokenizer[0],
+                tokenizer_2=self.sd.tokenizer[1],
+                scheduler=self.sd.noise_scheduler,
+            )
+        else:
+            pipeline = StableDiffusionPipeline(
+                vae=self.sd.vae,
+                unet=self.sd.unet,
+                text_encoder=self.sd.text_encoder,
+                tokenizer=self.sd.tokenizer,
+                scheduler=self.sd.noise_scheduler,
+                safety_checker=None,
+                feature_extractor=None,
+                requires_safety_checker=False,
+            )
         # disable progress bar
         pipeline.set_progress_bar_config(disable=True)
 
@@ -118,7 +127,8 @@ class BaseSDTrainProcess(BaseTrainProcess):
                             'multiplier': self.network.multiplier,
                         })
 
-                for i in tqdm(range(len(self.sample_config.prompts)), desc=f"Generating Samples - step: {step}", leave=False):
+                for i in tqdm(range(len(self.sample_config.prompts)), desc=f"Generating Samples - step: {step}",
+                              leave=False):
                     raw_prompt = self.sample_config.prompts[i]
 
                     neg = self.sample_config.neg
@@ -180,7 +190,11 @@ class BaseSDTrainProcess(BaseTrainProcess):
 
         self.sd.vae.to(original_device_dict['vae'])
         self.sd.unet.to(original_device_dict['unet'])
-        self.sd.text_encoder.to(original_device_dict['text_encoder'])
+        if isinstance(self.sd.text_encoder, list):
+            for encoder, i in zip(self.sd.text_encoder, range(len(self.sd.text_encoder))):
+                encoder.to(original_device_dict[f'text_encoder_{i}'])
+        else:
+            self.sd.text_encoder.to(original_device_dict['text_encoder'])
         if self.network is not None:
             self.network.train()
             self.network.multiplier = start_multiplier
@@ -267,23 +281,90 @@ class BaseSDTrainProcess(BaseTrainProcess):
         # return loss
         return 0.0
 
-    # ref: https://github.com/huggingface/diffusers/blob/0bab447670f47c28df60fbd2f6a0f833f75a16f5/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L746
-    def diffuse_some_steps(
+    def get_time_ids_from_latents(self, latents):
+        bs, ch, h, w = list(latents.shape)
+
+        height = h * VAE_SCALE_FACTOR
+        width = w * VAE_SCALE_FACTOR
+
+        dtype = get_torch_dtype(self.train_config.dtype)
+
+        if self.sd.is_xl:
+            prompt_ids = train_util.get_add_time_ids(
+                height,
+                width,
+                dynamic_crops=False,  # look into this
+                dtype=dtype,
+            ).to(self.device_torch, dtype=dtype)
+            return train_util.concat_embeddings(
+                prompt_ids, prompt_ids, bs
+            )
+        else:
+            return None
+
+    def predict_noise(
             self,
             latents: torch.FloatTensor,
-            text_embeddings: torch.FloatTensor,
-            total_timesteps: int = 1000,
-            start_timesteps=0,
+            text_embeddings: PromptEmbeds,
+            timestep: int,
+            guidance_scale=7.5,
+            guidance_rescale=0.7,
+            add_time_ids=None,
             **kwargs,
     ):
-
-        for timestep in tqdm(self.sd.noise_scheduler.timesteps[start_timesteps:total_timesteps], leave=False):
-            noise_pred = train_util.predict_noise(
-                self.sd.unet, self.sd.noise_scheduler, timestep, latents, text_embeddings, **kwargs
+        if self.sd.is_xl:
+            if add_time_ids is None:
+                add_time_ids = self.get_time_ids_from_latents(latents)
+            # todo LECOs code looks like it is omitting noise_pred
+            noise_pred = train_util.predict_noise_xl(
+                self.sd.unet,
+                self.sd.noise_scheduler,
+                timestep,
+                latents,
+                text_embeddings.text_embeds,
+                text_embeddings.pooled_embeds,
+                add_time_ids,
+                guidance_scale=guidance_scale,
+                guidance_rescale=guidance_rescale
             )
 
             # compute the previous noisy sample x_t -> x_t-1
             latents = self.sd.noise_scheduler.step(noise_pred, timestep, latents).prev_sample
+        else:
+            noise_pred = train_util.predict_noise(
+                self.sd.unet,
+                self.sd.noise_scheduler,
+                timestep,
+                latents,
+                text_embeddings.text_embeds,
+                guidance_scale=guidance_scale
+            )
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.sd.noise_scheduler.step(noise_pred, timestep, latents).prev_sample
+        return latents
+
+    # ref: https://github.com/huggingface/diffusers/blob/0bab447670f47c28df60fbd2f6a0f833f75a16f5/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L746
+    def diffuse_some_steps(
+            self,
+            latents: torch.FloatTensor,
+            text_embeddings: PromptEmbeds,
+            total_timesteps: int = 1000,
+            start_timesteps=0,
+            guidance_scale=1,
+            add_time_ids=None,
+            **kwargs,
+    ):
+
+        for timestep in tqdm(self.sd.noise_scheduler.timesteps[start_timesteps:total_timesteps], leave=False):
+            latents = self.predict_noise(
+                latents,
+                text_embeddings,
+                timestep,
+                guidance_scale=guidance_scale,
+                add_time_ids=add_time_ids,
+                **kwargs,
+            )
 
         # return latents_steps
         return latents
@@ -296,20 +377,35 @@ class BaseSDTrainProcess(BaseTrainProcess):
 
         dtype = get_torch_dtype(self.train_config.dtype)
 
-        tokenizer, text_encoder, unet, noise_scheduler = model_util.load_models(
-            self.model_config.name_or_path,
-            scheduler_name=self.train_config.noise_scheduler,
-            v2=self.model_config.is_v2,
-            v_pred=self.model_config.is_v_pred,
-        )
+        if self.model_config.is_xl:
+            tokenizer, text_encoders, unet, noise_scheduler = model_util.load_models_xl(
+                self.model_config.name_or_path,
+                scheduler_name=self.train_config.noise_scheduler,
+                weight_dtype=dtype,
+            )
+
+            for text_encoder in text_encoders:
+                text_encoder.to(self.device_torch, dtype=dtype)
+                text_encoder.requires_grad_(False)
+                text_encoder.eval()
+
+            text_encoder = text_encoders
+        else:
+            tokenizer, text_encoder, unet, noise_scheduler = model_util.load_models(
+                self.model_config.name_or_path,
+                scheduler_name=self.train_config.noise_scheduler,
+                v2=self.model_config.is_v2,
+                v_pred=self.model_config.is_v_pred,
+            )
+
+            text_encoder.to(self.device_torch, dtype=dtype)
+            text_encoder.eval()
+
         # just for now or of we want to load a custom one
         # put on cpu for now, we only need it when sampling
         vae = load_vae(self.model_config.name_or_path, dtype=dtype).to('cpu', dtype=dtype)
         vae.eval()
-        self.sd = StableDiffusion(vae, tokenizer, text_encoder, unet, noise_scheduler)
-
-        text_encoder.to(self.device_torch, dtype=dtype)
-        text_encoder.eval()
+        self.sd = StableDiffusion(vae, tokenizer, text_encoder, unet, noise_scheduler, is_xl=self.model_config.is_xl)
 
         unet.to(self.device_torch, dtype=dtype)
         if self.train_config.xformers:
@@ -323,7 +419,9 @@ class BaseSDTrainProcess(BaseTrainProcess):
                 unet=unet,
                 lora_dim=self.network_config.rank,
                 multiplier=1.0,
-                alpha=self.network_config.alpha
+                alpha=self.network_config.alpha,
+                train_unet=self.train_config.train_unet,
+                train_text_encoder=self.train_config.train_text_encoder,
             )
 
             self.network.force_to(self.device_torch, dtype=dtype)
@@ -376,8 +474,11 @@ class BaseSDTrainProcess(BaseTrainProcess):
         self.hook_before_train_loop()
 
         # sample first
-        self.print("Generating baseline samples before training")
-        self.sample(0)
+        if self.train_config.skip_first_sample:
+            self.print("Skipping first sample due to config setting")
+        else:
+            self.print("Generating baseline samples before training")
+            self.sample(0)
 
         self.progress_bar = tqdm(
             total=self.train_config.steps,

jobs/process/TrainLoRAHack.py

@@ -0,0 +1,76 @@
+# ref:
+# - https://github.com/p1atdev/LECO/blob/main/train_lora.py
+import time
+from collections import OrderedDict
+import os
+
+from toolkit.config_modules import SliderConfig
+from toolkit.paths import REPOS_ROOT
+import sys
+
+sys.path.append(REPOS_ROOT)
+sys.path.append(os.path.join(REPOS_ROOT, 'leco'))
+from toolkit.train_tools import get_torch_dtype, apply_noise_offset
+import gc
+
+import torch
+from leco import train_util, model_util
+from leco.prompt_util import PromptEmbedsCache
+from .BaseSDTrainProcess import BaseSDTrainProcess, StableDiffusion
+
+
+def flush():
+    torch.cuda.empty_cache()
+    gc.collect()
+
+
+class LoRAHack:
+    def __init__(self, **kwargs):
+        self.type = kwargs.get('type', 'suppression')
+
+
+class TrainLoRAHack(BaseSDTrainProcess):
+    def __init__(self, process_id: int, job, config: OrderedDict):
+        super().__init__(process_id, job, config)
+        self.hack_config = LoRAHack(**self.get_conf('hack', {}))
+
+    def hook_before_train_loop(self):
+        # we don't need text encoder so move it to cpu
+        self.sd.text_encoder.to("cpu")
+        flush()
+        # end hook_before_train_loop
+
+        if self.hack_config.type == 'suppression':
+            # set all params to self.current_suppression
+            params = self.network.parameters()
+            for param in params:
+                # get random noise for each param
+                noise = torch.randn_like(param) - 0.5
+                # apply noise to param
+                param.data = noise * 0.001
+
+
+    def supress_loop(self):
+        dtype = get_torch_dtype(self.train_config.dtype)
+
+
+        loss_dict = OrderedDict(
+            {'sup': 0.0}
+        )
+        # increase noise
+        for param in self.network.parameters():
+            # get random noise for each param
+            noise = torch.randn_like(param) - 0.5
+            # apply noise to param
+            param.data = param.data + noise * 0.001
+
+
+
+        return loss_dict
+
+    def hook_train_loop(self):
+        if self.hack_config.type == 'suppression':
+            return self.supress_loop()
+        else:
+            raise NotImplementedError(f'unknown hack type: {self.hack_config.type}')
+        # end hook_train_loop

jobs/process/TrainSliderProcess.py

@@ -3,19 +3,22 @@
 import time
 from collections import OrderedDict
 import os
+from typing import Optional
 
 from toolkit.config_modules import SliderConfig
 from toolkit.paths import REPOS_ROOT
 import sys
 
+from toolkit.stable_diffusion_model import PromptEmbeds
+
 sys.path.append(REPOS_ROOT)
 sys.path.append(os.path.join(REPOS_ROOT, 'leco'))
 from toolkit.train_tools import get_torch_dtype, apply_noise_offset
 import gc
+from toolkit import train_tools
 
 import torch
 from leco import train_util, model_util
-from leco.prompt_util import PromptEmbedsCache
 from .BaseSDTrainProcess import BaseSDTrainProcess, StableDiffusion
 
 
@@ -29,7 +32,6 @@ def flush():
     gc.collect()
 
 
-
 class EncodedPromptPair:
     def __init__(
             self,
@@ -54,6 +56,19 @@ class EncodedPromptPair:
         self.weight = weight
 
 
+class PromptEmbedsCache:  # 使いまわしたいので
+    prompts: dict[str, PromptEmbeds] = {}
+
+    def __setitem__(self, __name: str, __value: PromptEmbeds) -> None:
+        self.prompts[__name] = __value
+
+    def __getitem__(self, __name: str) -> Optional[PromptEmbeds]:
+        if __name in self.prompts:
+            return self.prompts[__name]
+        else:
+            return None
+
+
 class EncodedAnchor:
     def __init__(
             self,
@@ -89,19 +104,17 @@ class TrainSliderProcess(BaseSDTrainProcess):
         with torch.no_grad():
             neutral = ""
             for target in self.slider_config.targets:
+                # build the cache
+                for prompt in [
+                    target.target_class,
+                    target.positive,
+                    target.negative,
+                    neutral  # empty neutral
+                ]:
+                    if cache[prompt] is None:
+                        cache[prompt] = self.sd.encode_prompt(prompt)
                 for resolution in self.slider_config.resolutions:
                     width, height = resolution
-                    # build the cache
-                    for prompt in [
-                        target.target_class,
-                        target.positive,
-                        target.negative,
-                        neutral  # empty neutral
-                    ]:
-                        if cache[prompt] == None:
-                            cache[prompt] = train_util.encode_prompts(
-                                self.sd.tokenizer, self.sd.text_encoder, [prompt]
-                            )
                     only_erase = len(target.positive.strip()) == 0
                     only_enhance = len(target.negative.strip()) == 0
 
@@ -184,9 +197,7 @@ class TrainSliderProcess(BaseSDTrainProcess):
                     anchor.neg_prompt  # empty neutral
                 ]:
                     if cache[prompt] == None:
-                        cache[prompt] = train_util.encode_prompts(
-                            self.sd.tokenizer, self.sd.text_encoder, [prompt]
-                        )
+                        cache[prompt] = self.sd.encode_prompt(prompt)
 
                 anchor_pairs += [
                     EncodedAnchor(
@@ -198,7 +209,12 @@ class TrainSliderProcess(BaseSDTrainProcess):
 
         # move to cpu to save vram
         # We don't need text encoder anymore, but keep it on cpu for sampling
-        self.sd.text_encoder.to("cpu")
+        # if text encoder is list
+        if isinstance(self.sd.text_encoder, list):
+            for encoder in self.sd.text_encoder:
+                encoder.to("cpu")
+        else:
+            self.sd.text_encoder.to("cpu")
         self.prompt_cache = cache
         self.prompt_pairs = prompt_pairs
         self.anchor_pairs = anchor_pairs
@@ -220,6 +236,7 @@ class TrainSliderProcess(BaseSDTrainProcess):
         negative = prompt_pair.negative
         positive = prompt_pair.positive
         weight = prompt_pair.weight
+        multiplier = prompt_pair.multiplier
 
         unet = self.sd.unet
         noise_scheduler = self.sd.noise_scheduler
@@ -227,8 +244,20 @@ class TrainSliderProcess(BaseSDTrainProcess):
         lr_scheduler = self.lr_scheduler
         loss_function = torch.nn.MSELoss()
 
+        def get_noise_pred(p, n):
+            return self.predict_noise(
+                latents=denoised_latents,
+                text_embeddings=train_tools.concat_prompt_embeddings(
+                    p,  # unconditional
+                    n,  # positive
+                    self.train_config.batch_size,
+                ),
+                timestep=current_timestep,
+                guidance_scale=1,
+            )
+
         # set network multiplier
-        self.network.multiplier = prompt_pair.multiplier
+        self.network.multiplier = multiplier
 
         with torch.no_grad():
             self.sd.noise_scheduler.set_timesteps(
@@ -254,9 +283,10 @@ class TrainSliderProcess(BaseSDTrainProcess):
 
             with self.network:
                 assert self.network.is_active
+                self.network.multiplier = multiplier
                 denoised_latents = self.diffuse_some_steps(
                     latents,  # pass simple noise latents
-                    train_util.concat_embeddings(
+                    train_tools.concat_prompt_embeddings(
                         positive,  # unconditional
                         target_class,  # target
                         self.train_config.batch_size,
@@ -272,43 +302,11 @@ class TrainSliderProcess(BaseSDTrainProcess):
                 int(timesteps_to * 1000 / self.train_config.max_denoising_steps)
             ]
 
-            # with network: 0 weight LoRA is enabled outside "with network:"
-            positive_latents = train_util.predict_noise(  # positive_latents
-                unet,
-                noise_scheduler,
-                current_timestep,
-                denoised_latents,
-                train_util.concat_embeddings(
-                    positive,  # unconditional
-                    negative,  # positive
-                    self.train_config.batch_size,
-                ),
-                guidance_scale=1,
-            ).to("cpu", dtype=torch.float32)
-            neutral_latents = train_util.predict_noise(
-                unet,
-                noise_scheduler,
-                current_timestep,
-                denoised_latents,
-                train_util.concat_embeddings(
-                    positive,  # unconditional
-                    neutral,  # neutral
-                    self.train_config.batch_size,
-                ),
-                guidance_scale=1,
-            ).to("cpu", dtype=torch.float32)
-            unconditional_latents = train_util.predict_noise(
-                unet,
-                noise_scheduler,
-                current_timestep,
-                denoised_latents,
-                train_util.concat_embeddings(
-                    positive,  # unconditional
-                    positive,  # unconditional
-                    self.train_config.batch_size,
-                ),
-                guidance_scale=1,
-            ).to("cpu", dtype=torch.float32)
+            positive_latents = get_noise_pred(positive, negative)
+
+            neutral_latents = get_noise_pred(positive, neutral)
+
+            unconditional_latents = get_noise_pred(positive, positive)
 
         anchor_loss = None
         if len(self.anchor_pairs) > 0:
@@ -317,51 +315,19 @@ class TrainSliderProcess(BaseSDTrainProcess):
                 torch.randint(0, len(self.anchor_pairs), (1,)).item()
             ]
             with torch.no_grad():
-                anchor_target_noise = train_util.predict_noise(
-                    unet,
-                    noise_scheduler,
-                    current_timestep,
-                    denoised_latents,
-                    train_util.concat_embeddings(
-                        anchor.prompt,
-                        anchor.neg_prompt,
-                        self.train_config.batch_size,
-                    ),
-                    guidance_scale=1,
-                ).to("cpu", dtype=torch.float32)
+                anchor_target_noise = get_noise_pred(anchor.prompt, anchor.neg_prompt)
             with self.network:
                 # anchor whatever weight  prompt pair is using
                 pos_nem_mult = 1.0 if prompt_pair.multiplier > 0 else -1.0
                 self.network.multiplier = anchor.multiplier * pos_nem_mult
-                anchor_pred_noise = train_util.predict_noise(
-                    unet,
-                    noise_scheduler,
-                    current_timestep,
-                    denoised_latents,
-                    train_util.concat_embeddings(
-                        anchor.prompt,
-                        anchor.neg_prompt,
-                        self.train_config.batch_size,
-                    ),
-                    guidance_scale=1,
-                ).to("cpu", dtype=torch.float32)
+
+                anchor_pred_noise = get_noise_pred(anchor.prompt, anchor.neg_prompt)
 
                 self.network.multiplier = prompt_pair.multiplier
 
         with self.network:
             self.network.multiplier = prompt_pair.multiplier
-            target_latents = train_util.predict_noise(
-                unet,
-                noise_scheduler,
-                current_timestep,
-                denoised_latents,
-                train_util.concat_embeddings(
-                    positive,  # unconditional
-                    target_class,  # target
-                    self.train_config.batch_size,
-                ),
-                guidance_scale=1,
-            ).to("cpu", dtype=torch.float32)
+            target_latents = get_noise_pred(positive, target_class)
 
             # if self.logging_config.verbose:
             #     self.print("target_latents:", target_latents[0, 0, :5, :5])

jobs/process/__init__.py

@@ -6,3 +6,4 @@ from .BaseTrainProcess import BaseTrainProcess
 from .TrainVAEProcess import TrainVAEProcess
 from .BaseMergeProcess import BaseMergeProcess
 from .TrainSliderProcess import TrainSliderProcess
+from .TrainLoRAHack import TrainLoRAHack

requirements.txt

@@ -9,4 +9,5 @@ accelerator
 pyyaml
 oyaml
 tensorboard
-kornia
+kornia
+invisible-watermark

toolkit/config_modules.py

@@ -50,12 +50,14 @@ class TrainConfig:
         self.train_text_encoder = kwargs.get('train_text_encoder', True)
         self.noise_offset = kwargs.get('noise_offset', 0.0)
         self.optimizer_params = kwargs.get('optimizer_params', {})
+        self.skip_first_sample = kwargs.get('skip_first_sample', False)
 
 
 class ModelConfig:
     def __init__(self, **kwargs):
         self.name_or_path: str = kwargs.get('name_or_path', None)
         self.is_v2: bool = kwargs.get('is_v2', False)
+        self.is_xl: bool = kwargs.get('is_xl', False)
         self.is_v_pred: bool = kwargs.get('is_v_pred', False)
 
         if self.name_or_path is None:

toolkit/lora_special.py

@@ -1,8 +1,10 @@
 import os
 import sys
-from typing import List
+from typing import List, Optional, Dict, Type, Union
 
 import torch
+from transformers import CLIPTextModel
+
 from .paths import SD_SCRIPTS_ROOT
 
 sys.path.append(SD_SCRIPTS_ROOT)
@@ -14,26 +16,40 @@ class LoRASpecialNetwork(LoRANetwork):
     _multiplier: float = 1.0
     is_active: bool = False
 
+    NUM_OF_BLOCKS = 12  # フルモデル相当でのup,downの層の数
+
+    UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"]
+    UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
+    TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
+    LORA_PREFIX_UNET = "lora_unet"
+    LORA_PREFIX_TEXT_ENCODER = "lora_te"
+
+    # SDXL: must starts with LORA_PREFIX_TEXT_ENCODER
+    LORA_PREFIX_TEXT_ENCODER1 = "lora_te1"
+    LORA_PREFIX_TEXT_ENCODER2 = "lora_te2"
+
     def __init__(
             self,
-            text_encoder,
+            text_encoder: Union[List[CLIPTextModel], CLIPTextModel],
             unet,
-            multiplier=1.0,
-            lora_dim=4,
-            alpha=1,
-            dropout=None,
-            rank_dropout=None,
-            module_dropout=None,
-            conv_lora_dim=None,
-            conv_alpha=None,
-            block_dims=None,
-            block_alphas=None,
-            conv_block_dims=None,
-            conv_block_alphas=None,
-            modules_dim=None,
-            modules_alpha=None,
-            module_class=LoRAModule,
-            varbose=False,
+            multiplier: float = 1.0,
+            lora_dim: int = 4,
+            alpha: float = 1,
+            dropout: Optional[float] = None,
+            rank_dropout: Optional[float] = None,
+            module_dropout: Optional[float] = None,
+            conv_lora_dim: Optional[int] = None,
+            conv_alpha: Optional[float] = None,
+            block_dims: Optional[List[int]] = None,
+            block_alphas: Optional[List[float]] = None,
+            conv_block_dims: Optional[List[int]] = None,
+            conv_block_alphas: Optional[List[float]] = None,
+            modules_dim: Optional[Dict[str, int]] = None,
+            modules_alpha: Optional[Dict[str, int]] = None,
+            module_class: Type[object] = LoRAModule,
+            varbose: Optional[bool] = False,
+            train_text_encoder: Optional[bool] = True,
+            train_unet: Optional[bool] = True,
     ) -> None:
         """
         LoRA network: すごく引数が多いが、パターンは以下の通り
@@ -75,8 +91,21 @@ class LoRASpecialNetwork(LoRANetwork):
                     f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}")
 
         # create module instances
-        def create_modules(is_unet, root_module: torch.nn.Module, target_replace_modules) -> List[LoRAModule]:
-            prefix = LoRANetwork.LORA_PREFIX_UNET if is_unet else LoRANetwork.LORA_PREFIX_TEXT_ENCODER
+        def create_modules(
+                is_unet: bool,
+                text_encoder_idx: Optional[int],  # None, 1, 2
+                root_module: torch.nn.Module,
+                target_replace_modules: List[torch.nn.Module],
+        ) -> List[LoRAModule]:
+            prefix = (
+                self.LORA_PREFIX_UNET
+                if is_unet
+                else (
+                    self.LORA_PREFIX_TEXT_ENCODER
+                    if text_encoder_idx is None
+                    else (self.LORA_PREFIX_TEXT_ENCODER1 if text_encoder_idx == 1 else self.LORA_PREFIX_TEXT_ENCODER2)
+                )
+            )
             loras = []
             skipped = []
             for name, module in root_module.named_modules():
@@ -92,11 +121,14 @@ class LoRASpecialNetwork(LoRANetwork):
 
                             dim = None
                             alpha = None
+
                             if modules_dim is not None:
+                                # モジュール指定あり
                                 if lora_name in modules_dim:
                                     dim = modules_dim[lora_name]
                                     alpha = modules_alpha[lora_name]
                             elif is_unet and block_dims is not None:
+                                # U-Netでblock_dims指定あり
                                 block_idx = get_block_index(lora_name)
                                 if is_linear or is_conv2d_1x1:
                                     dim = block_dims[block_idx]
@@ -105,6 +137,7 @@ class LoRASpecialNetwork(LoRANetwork):
                                     dim = conv_block_dims[block_idx]
                                     alpha = conv_block_alphas[block_idx]
                             else:
+                                # 通常、すべて対象とする
                                 if is_linear or is_conv2d_1x1:
                                     dim = self.lora_dim
                                     alpha = self.alpha
@@ -113,6 +146,7 @@ class LoRASpecialNetwork(LoRANetwork):
                                     alpha = self.conv_alpha
 
                             if dim is None or dim == 0:
+                                # skipした情報を出力
                                 if is_linear or is_conv2d_1x1 or (
                                         self.conv_lora_dim is not None or conv_block_dims is not None):
                                     skipped.append(lora_name)
@@ -131,8 +165,25 @@ class LoRASpecialNetwork(LoRANetwork):
                             loras.append(lora)
             return loras, skipped
 
-        self.text_encoder_loras, skipped_te = create_modules(False, text_encoder,
+        text_encoders = text_encoder if type(text_encoder) == list else [text_encoder]
+
+        # create LoRA for text encoder
+        # 毎回すべてのモジュールを作るのは無駄なので要検討
+        self.text_encoder_loras = []
+        skipped_te = []
+        if train_text_encoder:
+            for i, text_encoder in enumerate(text_encoders):
+                if len(text_encoders) > 1:
+                    index = i + 1
+                    print(f"create LoRA for Text Encoder {index}:")
+                else:
+                    index = None
+                    print(f"create LoRA for Text Encoder:")
+
+                text_encoder_loras, skipped = create_modules(False, index, text_encoder,
                                                              LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
+                self.text_encoder_loras.extend(text_encoder_loras)
+                skipped_te += skipped
         print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.")
 
         # extend U-Net target modules if conv2d 3x3 is enabled, or load from weights
@@ -140,7 +191,11 @@ class LoRASpecialNetwork(LoRANetwork):
         if modules_dim is not None or self.conv_lora_dim is not None or conv_block_dims is not None:
             target_modules += LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
 
-        self.unet_loras, skipped_un = create_modules(True, unet, target_modules)
+        if train_unet:
+            self.unet_loras, skipped_un = create_modules(True, None, unet, target_modules)
+        else:
+            self.unet_loras = []
+            skipped_un = []
         print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
 
         skipped = skipped_te + skipped_un
@@ -159,8 +214,7 @@ class LoRASpecialNetwork(LoRANetwork):
         # assertion
         names = set()
         for lora in self.text_encoder_loras + self.unet_loras:
-            # doesnt work on new diffusers. TODO make sure we are not missing something
-            # assert lora.lora_name not in names, f"duplicated lora name: {lora.lora_name}"
+            assert lora.lora_name not in names, f"duplicated lora name: {lora.lora_name}"
             names.add(lora.lora_name)
 
     def save_weights(self, file, dtype, metadata):

toolkit/stable_diffusion_model.py

@@ -0,0 +1,63 @@
+from typing import Union
+import sys
+import os
+from toolkit.paths import REPOS_ROOT
+sys.path.append(REPOS_ROOT)
+sys.path.append(os.path.join(REPOS_ROOT, 'leco'))
+from leco import train_util
+import torch
+
+
+class PromptEmbeds:
+    text_embeds: torch.FloatTensor
+    pooled_embeds: Union[torch.FloatTensor, None]
+
+    def __init__(self, args) -> None:
+        if isinstance(args, list) or isinstance(args, tuple):
+            # xl
+            self.text_embeds = args[0]
+            self.pooled_embeds = args[1]
+        else:
+            # sdv1.x, sdv2.x
+            self.text_embeds = args
+            self.pooled_embeds = None
+
+
+class StableDiffusion:
+    def __init__(
+            self,
+            vae,
+            tokenizer,
+            text_encoder,
+            unet,
+            noise_scheduler,
+            is_xl=False
+    ):
+        # text encoder has a list of 2 for xl
+        self.vae = vae
+        self.tokenizer = tokenizer
+        self.text_encoder = text_encoder
+        self.unet = unet
+        self.noise_scheduler = noise_scheduler
+        self.is_xl = is_xl
+
+    def encode_prompt(self, prompt, num_images_per_prompt=1) -> PromptEmbeds:
+        prompt = prompt
+        # if it is not a list, make it one
+        if not isinstance(prompt, list):
+            prompt = [prompt]
+        if self.is_xl:
+            return PromptEmbeds(
+                train_util.encode_prompts_xl(
+                    self.tokenizer,
+                    self.text_encoder,
+                    prompt,
+                    num_images_per_prompt=num_images_per_prompt,
+                )
+            )
+        else:
+            return PromptEmbeds(
+                train_util.encode_prompts(
+                    self.tokenizer, self.text_encoder, prompt
+                )
+            )

toolkit/train_tools.py

@@ -21,6 +21,8 @@ from library.lpw_stable_diffusion import StableDiffusionLongPromptWeightingPipel
 import torch
 import re
 
+from toolkit.stable_diffusion_model import PromptEmbeds
+
 SCHEDULER_LINEAR_START = 0.00085
 SCHEDULER_LINEAR_END = 0.0120
 SCHEDULER_TIMESTEPS = 1000
@@ -377,3 +379,19 @@ def apply_noise_offset(noise, noise_offset):
         return noise
     noise = noise + noise_offset * torch.randn((noise.shape[0], noise.shape[1], 1, 1), device=noise.device)
     return noise
+
+
+def concat_prompt_embeddings(
+        unconditional: PromptEmbeds,
+        conditional: PromptEmbeds,
+        n_imgs: int,
+):
+    text_embeds = torch.cat(
+        [unconditional.text_embeds, conditional.text_embeds]
+    ).repeat_interleave(n_imgs, dim=0)
+    pooled_embeds = None
+    if unconditional.pooled_embeds is not None and conditional.pooled_embeds is not None:
+        pooled_embeds = torch.cat(
+            [unconditional.pooled_embeds, conditional.pooled_embeds]
+        ).repeat_interleave(n_imgs, dim=0)
+    return PromptEmbeds([text_embeds, pooled_embeds])