diff --git a/library/flux_train_utils.py b/library/flux_train_utils.py index f7f06c5c..9f954f58 100644 --- a/library/flux_train_utils.py +++ b/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 +147,14 @@ 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) + scale = prompt_dict.get("scale", 1.0) # 1.0 means no guidance seed = prompt_dict.get("seed") controlnet_image = prompt_dict.get("controlnet_image") prompt: str = prompt_dict.get("prompt", "") @@ -162,8 +162,8 @@ 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 +173,18 @@ 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 scale != 1.0: + logger.info(f"negative_prompt: {negative_prompt}") logger.info(f"height: {height}") logger.info(f"width: {width}") logger.info(f"sample_steps: {sample_steps}") - logger.info(f"scale: {scale}") + if scale != 1.0: + logger.info(f"scale: {scale}") # logger.info(f"sample_sampler: {sampler_name}") if seed is not None: logger.info(f"seed: {seed}") @@ -191,26 +193,37 @@ 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 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 = (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 +248,20 @@ 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=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) @@ -305,22 +331,24 @@ 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, @@ -336,20 +364,48 @@ 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 @@ -567,7 +623,7 @@ 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",