Merge branch 'sd3' into lumina

.github/FUNDING.yml

@@ -0,0 +1,3 @@
+# These are supported funding model platforms
+
+github: kohya-ss

README.md

@@ -9,11 +9,26 @@ __Please update PyTorch to 2.4.0. We have tested with `torch==2.4.0` and `torchv
 The command to install PyTorch is as follows:
 `pip3 install torch==2.4.0 torchvision==0.19.0 --index-url https://download.pytorch.org/whl/cu124`
 
+If you are using DeepSpeed, please install DeepSpeed with `pip install deepspeed==0.16.7`.
+
 - [FLUX.1 training](#flux1-training)
 - [SD3 training](#sd3-training)
 
 ### Recent Updates
 
+May 1, 2025:
+- The error when training FLUX.1 with mixed precision in flux_train.py with DeepSpeed enabled has been resolved. Thanks to sharlynxy for PR [#2060](https://github.com/kohya-ss/sd-scripts/pull/2060). Please refer to the PR for details.
+  - If you enable DeepSpeed, please install DeepSpeed with `pip install deepspeed==0.16.7`.
+
+Apr 27, 2025:
+- FLUX.1 training now supports CFG scale in the sample generation during training. Please use `--g` option, to specify the CFG scale (note that `--l` is used as the embedded guidance scale.) PR [#2064](https://github.com/kohya-ss/sd-scripts/pull/2064).
+    - See [here](#sample-image-generation-during-training) for details.
+    - If you have any issues with this, please let us know.
+
+Apr 6, 2025:
+- IP noise gamma has been enabled in FLUX.1. Thanks to rockerBOO for PR [#1992](https://github.com/kohya-ss/sd-scripts/pull/1992). See the PR for details.
+    - `--ip_noise_gamma` and `--ip_noise_gamma_random_strength` are available.
+  
 Mar 30, 2025:
 - LoRA-GGPO is added for FLUX.1 LoRA training. Thank you to rockerBOO for PR [#1974](https://github.com/kohya-ss/sd-scripts/pull/1974). 
   - Specify `--network_args ggpo_sigma=0.03 ggpo_beta=0.01` in the command line or `network_args = ["ggpo_sigma=0.03", "ggpo_beta=0.01"]` in .toml file. See PR for details.
@@ -866,6 +881,14 @@ Note: Some user reports ``ValueError: fp16 mixed precision requires a GPU`` is o
 
 (Single GPU with id `0` will be used.)
 
+## DeepSpeed installation (experimental, Linux or WSL2 only)
+  
+To install DeepSpeed, run the following command in your activated virtual environment:
+
+```bash
+pip install deepspeed==0.16.7 
+```
+
 ## Upgrade
 
 When a new release comes out you can upgrade your repo with the following command:
@@ -1340,11 +1363,13 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
 
   Lines beginning with `#` are comments. You can specify options for the generated image with options like `--n` after the prompt. The following can be used.
 
-  * `--n` Negative prompt up to the next option.
+  * `--n` Negative prompt up to the next option. Ignored when CFG scale is `1.0`.
   * `--w` Specifies the width of the generated image.
   * `--h` Specifies the height of the generated image.
   * `--d` Specifies the seed of the generated image.
   * `--l` Specifies the CFG scale of the generated image.
+    * In guidance distillation models like FLUX.1, this value is used as the embedded guidance scale for backward compatibility.
+  * `--g` Specifies the CFG scale for the models with embedded guidance scale. The default is `1.0`, `1.0` means no CFG. In general, should not be changed unless you train the un-distilled FLUX.1 models.
   * `--s` Specifies the number of steps in the generation.
 
   The prompt weighting such as `( )` and `[ ]` are working.

finetune/tag_images_by_wd14_tagger.py

@@ -97,15 +97,19 @@ def main(args):
         else:
             for file in SUB_DIR_FILES:
                 hf_hub_download(
-                    args.repo_id,
-                    file,
+                    repo_id=args.repo_id,
+                    filename=file,
                     subfolder=SUB_DIR,
-                    cache_dir=os.path.join(model_location, SUB_DIR),
+                    local_dir=os.path.join(model_location, SUB_DIR),
                     force_download=True,
-                    force_filename=file,
                 )
         for file in files:
-            hf_hub_download(args.repo_id, file, cache_dir=model_location, force_download=True, force_filename=file)
+            hf_hub_download(
+                repo_id=args.repo_id,
+                filename=file,
+                local_dir=model_location,
+                force_download=True,
+            )
     else:
         logger.info("using existing wd14 tagger model")
 

library/deepspeed_utils.py

@@ -5,6 +5,8 @@ from accelerate import DeepSpeedPlugin, Accelerator
 
 from .utils import setup_logging
 
+from .device_utils import get_preferred_device
+
 setup_logging()
 import logging
 
@@ -94,6 +96,7 @@ def prepare_deepspeed_plugin(args: argparse.Namespace):
     deepspeed_plugin.deepspeed_config["train_batch_size"] = (
         args.train_batch_size * args.gradient_accumulation_steps * int(os.environ["WORLD_SIZE"])
     )
+    
     deepspeed_plugin.set_mixed_precision(args.mixed_precision)
     if args.mixed_precision.lower() == "fp16":
         deepspeed_plugin.deepspeed_config["fp16"]["initial_scale_power"] = 0  # preventing overflow.
@@ -122,18 +125,56 @@ def prepare_deepspeed_model(args: argparse.Namespace, **models):
     class DeepSpeedWrapper(torch.nn.Module):
         def __init__(self, **kw_models) -> None:
             super().__init__()
+            
             self.models = torch.nn.ModuleDict()
+            
+            wrap_model_forward_with_torch_autocast = args.mixed_precision is not "no"
 
             for key, model in kw_models.items():
                 if isinstance(model, list):
                     model = torch.nn.ModuleList(model)
+                                            
+                if wrap_model_forward_with_torch_autocast:
+                    model = self.__wrap_model_with_torch_autocast(model)  
+                
                 assert isinstance(
                     model, torch.nn.Module
                 ), f"model must be an instance of torch.nn.Module, but got {key} is {type(model)}"
+
                 self.models.update(torch.nn.ModuleDict({key: model}))
 
+        def __wrap_model_with_torch_autocast(self, model):
+            if isinstance(model, torch.nn.ModuleList):
+                model = torch.nn.ModuleList([self.__wrap_model_forward_with_torch_autocast(m) for m in model])
+            else:
+                model = self.__wrap_model_forward_with_torch_autocast(model)
+            return model
+
+        def __wrap_model_forward_with_torch_autocast(self, model):
+            
+            assert hasattr(model, "forward"), f"model must have a forward method."
+
+            forward_fn = model.forward
+
+            def forward(*args, **kwargs):
+                try:
+                    device_type = model.device.type
+                except AttributeError:
+                    logger.warning(
+                            "[DeepSpeed] model.device is not available. Using get_preferred_device() "
+                            "to determine the device_type for torch.autocast()."
+                    )                    
+                    device_type = get_preferred_device().type
+
+                with torch.autocast(device_type = device_type):
+                    return forward_fn(*args, **kwargs)
+
+            model.forward = forward
+            return model
+        
         def get_models(self):
             return self.models
+        
 
     ds_model = DeepSpeedWrapper(**models)
     return ds_model

library/flux_train_utils.py

@@ -40,7 +40,7 @@ def sample_images(
     text_encoders,
     sample_prompts_te_outputs,
     prompt_replacement=None,
-    controlnet=None
+    controlnet=None,
 ):
     if steps == 0:
         if not args.sample_at_first:
@@ -101,7 +101,7 @@ def sample_images(
                     steps,
                     sample_prompts_te_outputs,
                     prompt_replacement,
-                    controlnet
+                    controlnet,
                 )
     else:
         # Creating list with N elements, where each element is a list of prompt_dicts, and N is the number of processes available (number of devices available)
@@ -125,7 +125,7 @@ def sample_images(
                         steps,
                         sample_prompts_te_outputs,
                         prompt_replacement,
-                        controlnet
+                        controlnet,
                     )
 
     torch.set_rng_state(rng_state)
@@ -147,14 +147,16 @@ def sample_image_inference(
     steps,
     sample_prompts_te_outputs,
     prompt_replacement,
-    controlnet
+    controlnet,
 ):
     assert isinstance(prompt_dict, dict)
-    # negative_prompt = prompt_dict.get("negative_prompt")
+    negative_prompt = prompt_dict.get("negative_prompt")
     sample_steps = prompt_dict.get("sample_steps", 20)
     width = prompt_dict.get("width", 512)
     height = prompt_dict.get("height", 512)
-    scale = prompt_dict.get("scale", 3.5)
+    # TODO refactor variable names
+    cfg_scale = prompt_dict.get("guidance_scale", 1.0)
+    emb_guidance_scale = prompt_dict.get("scale", 3.5)
     seed = prompt_dict.get("seed")
     controlnet_image = prompt_dict.get("controlnet_image")
     prompt: str = prompt_dict.get("prompt", "")
@@ -162,8 +164,8 @@ def sample_image_inference(
 
     if prompt_replacement is not None:
         prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1])
-        # if negative_prompt is not None:
-        #     negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1])
+        if negative_prompt is not None:
+            negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1])
 
     if seed is not None:
         torch.manual_seed(seed)
@@ -173,16 +175,21 @@ def sample_image_inference(
         torch.seed()
         torch.cuda.seed()
 
-    # if negative_prompt is None:
-    #     negative_prompt = ""
+    if negative_prompt is None:
+        negative_prompt = ""
     height = max(64, height - height % 16)  # round to divisible by 16
     width = max(64, width - width % 16)  # round to divisible by 16
     logger.info(f"prompt: {prompt}")
-    # logger.info(f"negative_prompt: {negative_prompt}")
+    if cfg_scale != 1.0:
+        logger.info(f"negative_prompt: {negative_prompt}")
+    elif negative_prompt != "":
+        logger.info(f"negative prompt is ignored because scale is 1.0")
     logger.info(f"height: {height}")
     logger.info(f"width: {width}")
     logger.info(f"sample_steps: {sample_steps}")
-    logger.info(f"scale: {scale}")
+    logger.info(f"embedded guidance scale: {emb_guidance_scale}")
+    if cfg_scale != 1.0:
+        logger.info(f"CFG scale: {cfg_scale}")
     # logger.info(f"sample_sampler: {sampler_name}")
     if seed is not None:
         logger.info(f"seed: {seed}")
@@ -191,26 +198,37 @@ def sample_image_inference(
     tokenize_strategy = strategy_base.TokenizeStrategy.get_strategy()
     encoding_strategy = strategy_base.TextEncodingStrategy.get_strategy()
 
-    text_encoder_conds = []
-    if sample_prompts_te_outputs and prompt in sample_prompts_te_outputs:
-        text_encoder_conds = sample_prompts_te_outputs[prompt]
-        print(f"Using cached text encoder outputs for prompt: {prompt}")
-    if text_encoders is not None:
-        print(f"Encoding prompt: {prompt}")
-        tokens_and_masks = tokenize_strategy.tokenize(prompt)
-        # strategy has apply_t5_attn_mask option
-        encoded_text_encoder_conds = encoding_strategy.encode_tokens(tokenize_strategy, text_encoders, tokens_and_masks)
+    def encode_prompt(prpt):
+        text_encoder_conds = []
+        if sample_prompts_te_outputs and prpt in sample_prompts_te_outputs:
+            text_encoder_conds = sample_prompts_te_outputs[prpt]
+            print(f"Using cached text encoder outputs for prompt: {prpt}")
+        if text_encoders is not None:
+            print(f"Encoding prompt: {prpt}")
+            tokens_and_masks = tokenize_strategy.tokenize(prpt)
+            # strategy has apply_t5_attn_mask option
+            encoded_text_encoder_conds = encoding_strategy.encode_tokens(tokenize_strategy, text_encoders, tokens_and_masks)
 
-        # if text_encoder_conds is not cached, use encoded_text_encoder_conds
-        if len(text_encoder_conds) == 0:
-            text_encoder_conds = encoded_text_encoder_conds
-        else:
-            # if encoded_text_encoder_conds is not None, update cached text_encoder_conds
-            for i in range(len(encoded_text_encoder_conds)):
-                if encoded_text_encoder_conds[i] is not None:
-                    text_encoder_conds[i] = encoded_text_encoder_conds[i]
+            # if text_encoder_conds is not cached, use encoded_text_encoder_conds
+            if len(text_encoder_conds) == 0:
+                text_encoder_conds = encoded_text_encoder_conds
+            else:
+                # if encoded_text_encoder_conds is not None, update cached text_encoder_conds
+                for i in range(len(encoded_text_encoder_conds)):
+                    if encoded_text_encoder_conds[i] is not None:
+                        text_encoder_conds[i] = encoded_text_encoder_conds[i]
+        return text_encoder_conds
 
-    l_pooled, t5_out, txt_ids, t5_attn_mask = text_encoder_conds
+    l_pooled, t5_out, txt_ids, t5_attn_mask = encode_prompt(prompt)
+    # encode negative prompts
+    if cfg_scale != 1.0:
+        neg_l_pooled, neg_t5_out, _, neg_t5_attn_mask = encode_prompt(negative_prompt)
+        neg_t5_attn_mask = (
+            neg_t5_attn_mask.to(accelerator.device) if args.apply_t5_attn_mask and neg_t5_attn_mask is not None else None
+        )
+        neg_cond = (cfg_scale, neg_l_pooled, neg_t5_out, neg_t5_attn_mask)
+    else:
+        neg_cond = None
 
     # sample image
     weight_dtype = ae.dtype  # TOFO give dtype as argument
@@ -235,7 +253,20 @@ def sample_image_inference(
         controlnet_image = controlnet_image.permute(2, 0, 1).unsqueeze(0).to(weight_dtype).to(accelerator.device)
 
     with accelerator.autocast(), torch.no_grad():
-        x = denoise(flux, noise, img_ids, t5_out, txt_ids, l_pooled, timesteps=timesteps, guidance=scale, t5_attn_mask=t5_attn_mask, controlnet=controlnet, controlnet_img=controlnet_image)
+        x = denoise(
+            flux,
+            noise,
+            img_ids,
+            t5_out,
+            txt_ids,
+            l_pooled,
+            timesteps=timesteps,
+            guidance=emb_guidance_scale,
+            t5_attn_mask=t5_attn_mask,
+            controlnet=controlnet,
+            controlnet_img=controlnet_image,
+            neg_cond=neg_cond,
+        )
 
     x = flux_utils.unpack_latents(x, packed_latent_height, packed_latent_width)
 
@@ -305,21 +336,24 @@ def denoise(
     model: flux_models.Flux,
     img: torch.Tensor,
     img_ids: torch.Tensor,
-    txt: torch.Tensor,
+    txt: torch.Tensor,  # t5_out
     txt_ids: torch.Tensor,
-    vec: torch.Tensor,
+    vec: torch.Tensor,  # l_pooled
     timesteps: list[float],
     guidance: float = 4.0,
     t5_attn_mask: Optional[torch.Tensor] = None,
     controlnet: Optional[flux_models.ControlNetFlux] = None,
     controlnet_img: Optional[torch.Tensor] = None,
+    neg_cond: Optional[Tuple[float, torch.Tensor, torch.Tensor, torch.Tensor]] = None,
 ):
     # this is ignored for schnell
     guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
+    do_cfg = neg_cond is not None
 
     for t_curr, t_prev in zip(tqdm(timesteps[:-1]), timesteps[1:]):
         t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
         model.prepare_block_swap_before_forward()
+
         if controlnet is not None:
             block_samples, block_single_samples = controlnet(
                 img=img,
@@ -335,20 +369,48 @@ def denoise(
         else:
             block_samples = None
             block_single_samples = None
-        pred = model(
-            img=img,
-            img_ids=img_ids,
-            txt=txt,
-            txt_ids=txt_ids,
-            y=vec,
-            block_controlnet_hidden_states=block_samples,
-            block_controlnet_single_hidden_states=block_single_samples,
-            timesteps=t_vec,
-            guidance=guidance_vec,
-            txt_attention_mask=t5_attn_mask,
-        )
 
-        img = img + (t_prev - t_curr) * pred
+        if not do_cfg:
+            pred = model(
+                img=img,
+                img_ids=img_ids,
+                txt=txt,
+                txt_ids=txt_ids,
+                y=vec,
+                block_controlnet_hidden_states=block_samples,
+                block_controlnet_single_hidden_states=block_single_samples,
+                timesteps=t_vec,
+                guidance=guidance_vec,
+                txt_attention_mask=t5_attn_mask,
+            )
+
+            img = img + (t_prev - t_curr) * pred
+        else:
+            cfg_scale, neg_l_pooled, neg_t5_out, neg_t5_attn_mask = neg_cond
+            nc_c_t5_attn_mask = None if t5_attn_mask is None else torch.cat([neg_t5_attn_mask, t5_attn_mask], dim=0)
+
+            # TODO is it ok to use the same block samples for both cond and uncond?
+            block_samples = None if block_samples is None else torch.cat([block_samples, block_samples], dim=0)
+            block_single_samples = (
+                None if block_single_samples is None else torch.cat([block_single_samples, block_single_samples], dim=0)
+            )
+
+            nc_c_pred = model(
+                img=torch.cat([img, img], dim=0),
+                img_ids=torch.cat([img_ids, img_ids], dim=0),
+                txt=torch.cat([neg_t5_out, txt], dim=0),
+                txt_ids=torch.cat([txt_ids, txt_ids], dim=0),
+                y=torch.cat([neg_l_pooled, vec], dim=0),
+                block_controlnet_hidden_states=block_samples,
+                block_controlnet_single_hidden_states=block_single_samples,
+                timesteps=t_vec,
+                guidance=guidance_vec,
+                txt_attention_mask=nc_c_t5_attn_mask,
+            )
+            neg_pred, pred = torch.chunk(nc_c_pred, 2, dim=0)
+            pred = neg_pred + (pred - neg_pred) * cfg_scale
+
+            img = img + (t_prev - t_curr) * pred
 
     model.prepare_block_swap_before_forward()
     return img
@@ -365,8 +427,6 @@ def get_sigmas(noise_scheduler, timesteps, device, n_dim=4, dtype=torch.float32)
     step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
 
     sigma = sigmas[step_indices].flatten()
-    while len(sigma.shape) < n_dim:
-        sigma = sigma.unsqueeze(-1)
     return sigma
 
 
@@ -409,42 +469,34 @@ def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None):
 
 
 def get_noisy_model_input_and_timesteps(
-    args, noise_scheduler, latents, noise, device, dtype
+    args, noise_scheduler, latents: torch.Tensor, noise: torch.Tensor, device, dtype
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     bsz, _, h, w = latents.shape
-    sigmas = None
-
+    assert bsz > 0, "Batch size not large enough"
+    num_timesteps = noise_scheduler.config.num_train_timesteps
     if args.timestep_sampling == "uniform" or args.timestep_sampling == "sigmoid":
-        # Simple random t-based noise sampling
+        # Simple random sigma-based noise sampling
         if args.timestep_sampling == "sigmoid":
             # https://github.com/XLabs-AI/x-flux/tree/main
-            t = torch.sigmoid(args.sigmoid_scale * torch.randn((bsz,), device=device))
+            sigmas = torch.sigmoid(args.sigmoid_scale * torch.randn((bsz,), device=device))
         else:
-            t = torch.rand((bsz,), device=device)
+            sigmas = torch.rand((bsz,), device=device)
 
-        timesteps = t * 1000.0
-        t = t.view(-1, 1, 1, 1)
-        noisy_model_input = (1 - t) * latents + t * noise
+        timesteps = sigmas * num_timesteps
     elif args.timestep_sampling == "shift":
         shift = args.discrete_flow_shift
-        logits_norm = torch.randn(bsz, device=device)
-        logits_norm = logits_norm * args.sigmoid_scale  # larger scale for more uniform sampling
-        timesteps = logits_norm.sigmoid()
-        timesteps = (timesteps * shift) / (1 + (shift - 1) * timesteps)
-
-        t = timesteps.view(-1, 1, 1, 1)
-        timesteps = timesteps * 1000.0
-        noisy_model_input = (1 - t) * latents + t * noise
+        sigmas = torch.randn(bsz, device=device)
+        sigmas = sigmas * args.sigmoid_scale  # larger scale for more uniform sampling
+        sigmas = sigmas.sigmoid()
+        sigmas = (sigmas * shift) / (1 + (shift - 1) * sigmas)
+        timesteps = sigmas * num_timesteps
     elif args.timestep_sampling == "flux_shift":
-        logits_norm = torch.randn(bsz, device=device)
-        logits_norm = logits_norm * args.sigmoid_scale  # larger scale for more uniform sampling
-        timesteps = logits_norm.sigmoid()
-        mu = get_lin_function(y1=0.5, y2=1.15)((h // 2) * (w // 2))
-        timesteps = time_shift(mu, 1.0, timesteps)
-
-        t = timesteps.view(-1, 1, 1, 1)
-        timesteps = timesteps * 1000.0
-        noisy_model_input = (1 - t) * latents + t * noise
+        sigmas = torch.randn(bsz, device=device)
+        sigmas = sigmas * args.sigmoid_scale  # larger scale for more uniform sampling
+        sigmas = sigmas.sigmoid()
+        mu = get_lin_function(y1=0.5, y2=1.15)((h // 2) * (w // 2))  # we are pre-packed so must adjust for packed size
+        sigmas = time_shift(mu, 1.0, sigmas)
+        timesteps = sigmas * num_timesteps
     else:
         # Sample a random timestep for each image
         # for weighting schemes where we sample timesteps non-uniformly
@@ -455,12 +507,24 @@ def get_noisy_model_input_and_timesteps(
             logit_std=args.logit_std,
             mode_scale=args.mode_scale,
         )
-        indices = (u * noise_scheduler.config.num_train_timesteps).long()
+        indices = (u * num_timesteps).long()
         timesteps = noise_scheduler.timesteps[indices].to(device=device)
-
-        # Add noise according to flow matching.
         sigmas = get_sigmas(noise_scheduler, timesteps, device, n_dim=latents.ndim, dtype=dtype)
-        noisy_model_input = sigmas * noise + (1.0 - sigmas) * latents
+
+    # Broadcast sigmas to latent shape
+    sigmas = sigmas.view(-1, 1, 1, 1)
+
+    # Add noise to the latents according to the noise magnitude at each timestep
+    # (this is the forward diffusion process)
+    if args.ip_noise_gamma:
+        xi = torch.randn_like(latents, device=latents.device, dtype=dtype)
+        if args.ip_noise_gamma_random_strength:
+            ip_noise_gamma = torch.rand(1, device=latents.device, dtype=dtype) * args.ip_noise_gamma
+        else:
+            ip_noise_gamma = args.ip_noise_gamma
+        noisy_model_input = (1.0 - sigmas) * latents + sigmas * (noise + ip_noise_gamma * xi)
+    else:
+        noisy_model_input = (1.0 - sigmas) * latents + sigmas * noise
 
     return noisy_model_input.to(dtype), timesteps.to(dtype), sigmas
 
@@ -566,7 +630,7 @@ def add_flux_train_arguments(parser: argparse.ArgumentParser):
         "--controlnet_model_name_or_path",
         type=str,
         default=None,
-        help="path to controlnet (*.sft or *.safetensors) / controlnetのパス（*.sftまたは*.safetensors）"
+        help="path to controlnet (*.sft or *.safetensors) / controlnetのパス（*.sftまたは*.safetensors）",
     )
     parser.add_argument(
         "--t5xxl_max_token_length",

library/train_util.py

@@ -1070,8 +1070,11 @@ class BaseDataset(torch.utils.data.Dataset):
                     self.bucket_info["buckets"][i] = {"resolution": reso, "count": len(bucket)}
                     logger.info(f"bucket {i}: resolution {reso}, count: {len(bucket)}")
 
-            img_ar_errors = np.array(img_ar_errors)
-            mean_img_ar_error = np.mean(np.abs(img_ar_errors))
+            if len(img_ar_errors) == 0:
+                mean_img_ar_error = 0  # avoid NaN
+            else:
+                img_ar_errors = np.array(img_ar_errors)
+                mean_img_ar_error = np.mean(np.abs(img_ar_errors))
             self.bucket_info["mean_img_ar_error"] = mean_img_ar_error
             logger.info(f"mean ar error (without repeats): {mean_img_ar_error}")
 
@@ -5520,6 +5523,11 @@ def load_target_model(args, weight_dtype, accelerator, unet_use_linear_projectio
 
 
 def patch_accelerator_for_fp16_training(accelerator):
+    
+    from accelerate import DistributedType
+    if accelerator.distributed_type == DistributedType.DEEPSPEED:
+        return
+    
     org_unscale_grads = accelerator.scaler._unscale_grads_
 
     def _unscale_grads_replacer(optimizer, inv_scale, found_inf, allow_fp16):
@@ -6203,6 +6211,11 @@ def line_to_prompt_dict(line: str) -> dict:
                 prompt_dict["scale"] = float(m.group(1))
                 continue
 
+            m = re.match(r"g ([\d\.]+)", parg, re.IGNORECASE)
+            if m:  # guidance scale
+                prompt_dict["guidance_scale"] = float(m.group(1))
+                continue
+
             m = re.match(r"n (.+)", parg, re.IGNORECASE)
             if m:  # negative prompt
                 prompt_dict["negative_prompt"] = m.group(1)

networks/lora_flux.py

@@ -955,26 +955,26 @@ class LoRANetwork(torch.nn.Module):
         for lora in self.text_encoder_loras + self.unet_loras:
             lora.update_grad_norms()
 
-    def grad_norms(self) -> Tensor:
+    def grad_norms(self) -> Tensor | None:
         grad_norms = []
         for lora in self.text_encoder_loras + self.unet_loras:
             if hasattr(lora, "grad_norms") and lora.grad_norms is not None:
                 grad_norms.append(lora.grad_norms.mean(dim=0))
-        return torch.stack(grad_norms) if len(grad_norms) > 0 else torch.tensor([])
+        return torch.stack(grad_norms) if len(grad_norms) > 0 else None
 
-    def weight_norms(self) -> Tensor:
+    def weight_norms(self) -> Tensor | None:
         weight_norms = []
         for lora in self.text_encoder_loras + self.unet_loras:
             if hasattr(lora, "weight_norms") and lora.weight_norms is not None:
                 weight_norms.append(lora.weight_norms.mean(dim=0))
-        return torch.stack(weight_norms) if len(weight_norms) > 0 else torch.tensor([])
+        return torch.stack(weight_norms) if len(weight_norms) > 0 else None
 
-    def combined_weight_norms(self) -> Tensor:
+    def combined_weight_norms(self) -> Tensor | None:
         combined_weight_norms = []
         for lora in self.text_encoder_loras + self.unet_loras:
             if hasattr(lora, "combined_weight_norms") and lora.combined_weight_norms is not None:
                 combined_weight_norms.append(lora.combined_weight_norms.mean(dim=0))
-        return torch.stack(combined_weight_norms) if len(combined_weight_norms) > 0 else torch.tensor([])
+        return torch.stack(combined_weight_norms) if len(combined_weight_norms) > 0 else None
 
 
     def load_weights(self, file):

pytest.ini

@@ -6,3 +6,4 @@ filterwarnings =
     ignore::DeprecationWarning
     ignore::UserWarning
 		ignore::FutureWarning
+pythonpath = .

tests/library/test_flux_train_utils.py

@@ -0,0 +1,220 @@
+import pytest
+import torch
+from unittest.mock import MagicMock, patch
+from library.flux_train_utils import (
+    get_noisy_model_input_and_timesteps,
+)
+
+# Mock classes and functions
+class MockNoiseScheduler:
+    def __init__(self, num_train_timesteps=1000):
+        self.config = MagicMock()
+        self.config.num_train_timesteps = num_train_timesteps
+        self.timesteps = torch.arange(num_train_timesteps, dtype=torch.long)
+
+
+# Create fixtures for commonly used objects
+@pytest.fixture
+def args():
+    args = MagicMock()
+    args.timestep_sampling = "uniform"
+    args.weighting_scheme = "uniform"
+    args.logit_mean = 0.0
+    args.logit_std = 1.0
+    args.mode_scale = 1.0
+    args.sigmoid_scale = 1.0
+    args.discrete_flow_shift = 3.1582
+    args.ip_noise_gamma = None
+    args.ip_noise_gamma_random_strength = False
+    return args
+
+
+@pytest.fixture
+def noise_scheduler():
+    return MockNoiseScheduler(num_train_timesteps=1000)
+
+
+@pytest.fixture
+def latents():
+    return torch.randn(2, 4, 8, 8)
+
+
+@pytest.fixture
+def noise():
+    return torch.randn(2, 4, 8, 8)
+
+
+@pytest.fixture
+def device():
+    # return "cuda" if torch.cuda.is_available() else "cpu"
+    return "cpu"
+
+
+# Mock the required functions
+@pytest.fixture(autouse=True)
+def mock_functions():
+    with (
+        patch("torch.sigmoid", side_effect=torch.sigmoid),
+        patch("torch.rand", side_effect=torch.rand),
+        patch("torch.randn", side_effect=torch.randn),
+    ):
+        yield
+
+
+# Test different timestep sampling methods
+def test_uniform_sampling(args, noise_scheduler, latents, noise, device):
+    args.timestep_sampling = "uniform"
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (latents.shape[0],)
+    assert sigmas.shape == (latents.shape[0], 1, 1, 1)
+    assert noisy_input.dtype == dtype
+    assert timesteps.dtype == dtype
+
+
+def test_sigmoid_sampling(args, noise_scheduler, latents, noise, device):
+    args.timestep_sampling = "sigmoid"
+    args.sigmoid_scale = 1.0
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (latents.shape[0],)
+    assert sigmas.shape == (latents.shape[0], 1, 1, 1)
+
+
+def test_shift_sampling(args, noise_scheduler, latents, noise, device):
+    args.timestep_sampling = "shift"
+    args.sigmoid_scale = 1.0
+    args.discrete_flow_shift = 3.1582
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (latents.shape[0],)
+    assert sigmas.shape == (latents.shape[0], 1, 1, 1)
+
+
+def test_flux_shift_sampling(args, noise_scheduler, latents, noise, device):
+    args.timestep_sampling = "flux_shift"
+    args.sigmoid_scale = 1.0
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (latents.shape[0],)
+    assert sigmas.shape == (latents.shape[0], 1, 1, 1)
+
+
+def test_weighting_scheme(args, noise_scheduler, latents, noise, device):
+    # Mock the necessary functions for this specific test
+    with patch("library.flux_train_utils.compute_density_for_timestep_sampling", 
+               return_value=torch.tensor([0.3, 0.7], device=device)), \
+         patch("library.flux_train_utils.get_sigmas", 
+               return_value=torch.tensor([[0.3], [0.7]], device=device).view(-1, 1, 1, 1)):
+               
+        args.timestep_sampling = "other"  # Will trigger the weighting scheme path
+        args.weighting_scheme = "uniform"
+        args.logit_mean = 0.0
+        args.logit_std = 1.0
+        args.mode_scale = 1.0
+        dtype = torch.float32
+        
+        noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(
+            args, noise_scheduler, latents, noise, device, dtype
+        )
+        
+        assert noisy_input.shape == latents.shape
+        assert timesteps.shape == (latents.shape[0],)
+        assert sigmas.shape == (latents.shape[0], 1, 1, 1)
+
+
+# Test IP noise options
+def test_with_ip_noise(args, noise_scheduler, latents, noise, device):
+    args.ip_noise_gamma = 0.5
+    args.ip_noise_gamma_random_strength = False
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (latents.shape[0],)
+    assert sigmas.shape == (latents.shape[0], 1, 1, 1)
+
+
+def test_with_random_ip_noise(args, noise_scheduler, latents, noise, device):
+    args.ip_noise_gamma = 0.1
+    args.ip_noise_gamma_random_strength = True
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (latents.shape[0],)
+    assert sigmas.shape == (latents.shape[0], 1, 1, 1)
+
+
+# Test different data types
+def test_float16_dtype(args, noise_scheduler, latents, noise, device):
+    dtype = torch.float16
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.dtype == dtype
+    assert timesteps.dtype == dtype
+
+
+# Test different batch sizes
+def test_different_batch_size(args, noise_scheduler, device):
+    latents = torch.randn(5, 4, 8, 8)  # batch size of 5
+    noise = torch.randn(5, 4, 8, 8)
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (5,)
+    assert sigmas.shape == (5, 1, 1, 1)
+
+
+# Test different image sizes
+def test_different_image_size(args, noise_scheduler, device):
+    latents = torch.randn(2, 4, 16, 16)  # larger image size
+    noise = torch.randn(2, 4, 16, 16)
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (2,)
+    assert sigmas.shape == (2, 1, 1, 1)
+
+
+# Test edge cases
+def test_zero_batch_size(args, noise_scheduler, device):
+    with pytest.raises(AssertionError):  # expecting an error with zero batch size
+        latents = torch.randn(0, 4, 8, 8)
+        noise = torch.randn(0, 4, 8, 8)
+        dtype = torch.float32
+
+        get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+
+def test_different_timestep_count(args, device):
+    noise_scheduler = MockNoiseScheduler(num_train_timesteps=500)  # different timestep count
+    latents = torch.randn(2, 4, 8, 8)
+    noise = torch.randn(2, 4, 8, 8)
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (2,)
+    # Check that timesteps are within the proper range
+    assert torch.all(timesteps < 500)

train_network.py

@@ -389,7 +389,18 @@ class NetworkTrainer:
                 latents = typing.cast(torch.FloatTensor, batch["latents"].to(accelerator.device))
             else:
                 # latentに変換
-                latents = self.encode_images_to_latents(args, vae, batch["images"].to(accelerator.device, dtype=vae_dtype))
+                if args.vae_batch_size is None or len(batch["images"]) <= args.vae_batch_size:
+                    latents = self.encode_images_to_latents(args, vae, batch["images"].to(accelerator.device, dtype=vae_dtype))
+                else:
+                    chunks = [
+                        batch["images"][i : i + args.vae_batch_size] for i in range(0, len(batch["images"]), args.vae_batch_size)
+                    ]
+                    list_latents = []
+                    for chunk in chunks:
+                        with torch.no_grad():
+                            chunk = self.encode_images_to_latents(args, vae, chunk.to(accelerator.device, dtype=vae_dtype))
+                            list_latents.append(chunk)
+                    latents = torch.cat(list_latents, dim=0)
 
                 # NaNが含まれていれば警告を表示し0に置き換える
                 if torch.any(torch.isnan(latents)):
@@ -1433,11 +1444,13 @@ class NetworkTrainer:
                     max_mean_logs = {"Keys Scaled": keys_scaled, "Average key norm": mean_norm}
                 else:
                     if hasattr(network, "weight_norms"):
-                        mean_norm = network.weight_norms().mean().item()
-                        mean_grad_norm = network.grad_norms().mean().item()
-                        mean_combined_norm = network.combined_weight_norms().mean().item()
                         weight_norms = network.weight_norms()
-                        maximum_norm = weight_norms.max().item() if weight_norms.numel() > 0 else None
+                        mean_norm = weight_norms.mean().item() if weight_norms is not None else None
+                        grad_norms = network.grad_norms()
+                        mean_grad_norm = grad_norms.mean().item() if grad_norms is not None else None
+                        combined_weight_norms = network.combined_weight_norms()
+                        mean_combined_norm = combined_weight_norms.mean().item() if combined_weight_norms is not None else None
+                        maximum_norm = weight_norms.max().item() if weight_norms is not None else None
                         keys_scaled = None
                         max_mean_logs = {}
                     else: