diff --git a/docs/performance/flux.md b/docs/performance/flux.md index e93e8e5..b906b3a 100644 --- a/docs/performance/flux.md +++ b/docs/performance/flux.md @@ -17,8 +17,10 @@ Since Flux.1 does not utilize Classifier-Free Guidance (CFG), it is not compatib We conducted performance benchmarking using FLUX.1 [dev] with 28 diffusion steps. The following figure shows the scalability of Flux.1 on two 8xL40 Nodes, 16xL40 GPUs in total. -Consequently, the performance improvement dose not achieved with 16 GPUs, and for 1024px and 2048px tasks. +Althogh cfg parallel is not available, We can still achieve enhanced scalability by using PipeFusion as a method for parallel between nodes. +For the 1024px task, hybrid parallel on 16xL40 is 1.16x lower than on 8xL40, where the best configuration is ulysses=4 and pipefusion=4. For the 4096px task, hybrid parallel still benefits on 16 L40s, 1.9x lower than 8 GPUs, where the configuration is ulysses=2, ring=2, and pipefusion=4. +The performance improvement dose not achieved with 16 GPUs 2048px tasks.
+ scalability-flux_l40 +
+ +下图展示了Flux.1在8xA100 GPU上的可扩展性。 +对于1024px和2048px的图像生成任务,SP-Ulysses在单一并行方法中表现出最低的延迟。在这种情况下,最佳混合策略也是SP-Ulysses。 + +
+ scalability-flux_l40 +
+ +注意,上图所示的延迟尚未包括使用torch.compile,这将提供进一步的性能改进。 + +### Flux.1 Schnell的扩展性 +我们使用FLUX.1 [schnell]进行了性能基准测试,采用4个扩散步骤。 +由于扩散步骤非常少,我们不使用PipeFusion。 在8xA100 (80GB) NVLink互联的机器上，生成1024px图片，USP最佳策略是把所有并行度都给Ulysses，使用torch.compile之后的生成1024px图片仅需0.82秒！ @@ -54,7 +75,7 @@ xDiT还不支持Flux.1使用PipeFusion，因为schnell版本采样步数太少 alt="latency-flux_l40_2k"> -### VAE Parallel +### VAE并行 在A100上，单卡使用Flux.1超过2048px就会OOM。这是因为Activation内存需求增加，同时卷积算子引发memory spike，二者共同导致的。 @@ -68,3 +89,4 @@ prompt是"A hyperrealistic portrait of a weathered sailor in his 60s, with deep- latency-flux_l40 + diff --git a/setup.py b/setup.py index 452ce12..beb2c20 100644 --- a/setup.py +++ b/setup.py @@ -37,6 +37,8 @@ def get_cuda_version(): "pytest", "flask", "opencv-python", + "imageio", + "imageio-ffmpeg", ], extras_require={ "flash_attn": [ diff --git a/xfuser/model_executor/pipelines/pipeline_cogvideox.py b/xfuser/model_executor/pipelines/pipeline_cogvideox.py index 5d04352..d686b43 100644 --- a/xfuser/model_executor/pipelines/pipeline_cogvideox.py +++ b/xfuser/model_executor/pipelines/pipeline_cogvideox.py @@ -226,7 +226,9 @@ def __call__( max_sequence_length=max_sequence_length, device=device, ) - prompt_embeds = self._process_cfg_split_batch_latte(prompt_embeds, negative_prompt_embeds) + prompt_embeds = self._process_cfg_split_batch_latte( + prompt_embeds, negative_prompt_embeds + ) # 4. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps( @@ -253,7 +255,9 @@ def __call__( # 7. Create rotary embeds if required image_rotary_emb = ( - self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device) + self._prepare_rotary_positional_embeddings( + height, width, latents.size(1), device + ) if self.transformer.config.use_rotary_positional_embeddings else None ) @@ -263,7 +267,9 @@ def __call__( len(timesteps) - num_inference_steps * self.scheduler.order, 0 ) - latents, image_rotary_emb = self._init_sync_pipeline(latents, image_rotary_emb, latents.size(1)) + latents, image_rotary_emb = self._init_sync_pipeline( + latents, image_rotary_emb, latents.size(1) + ) with self.progress_bar(total=num_inference_steps) as progress_bar: # for DPM-solver++ old_pred_original_sample = None @@ -296,7 +302,18 @@ def __call__( # perform guidance if use_dynamic_cfg: self._guidance_scale = 1 + guidance_scale * ( - (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2 + ( + 1 + - math.cos( + math.pi + * ( + (num_inference_steps - t.item()) + / num_inference_steps + ) + ** 5.0 + ) + ) + / 2 ) if do_classifier_free_guidance: if get_classifier_free_guidance_world_size() == 1: @@ -339,7 +356,9 @@ def __call__( "negative_prompt_embeds", negative_prompt_embeds ) - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + if i == len(timesteps) - 1 or ( + (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0 + ): progress_bar.update() if get_sequence_parallel_world_size() > 1: @@ -377,14 +396,22 @@ def _init_sync_pipeline( image_rotary_emb = ( torch.cat( [ - image_rotary_emb[0].reshape(latents_frames, -1, d)[:, start_token_idx:end_token_idx].reshape(-1, d) + image_rotary_emb[0] + .reshape(latents_frames, -1, d)[ + :, start_token_idx:end_token_idx + ] + .reshape(-1, d) for start_token_idx, end_token_idx in get_runtime_state().pp_patches_token_start_end_idx_global ], dim=0, ), torch.cat( [ - image_rotary_emb[1].reshape(latents_frames, -1, d)[:, start_token_idx:end_token_idx].reshape(-1, d) + image_rotary_emb[1] + .reshape(latents_frames, -1, d)[ + :, start_token_idx:end_token_idx + ] + .reshape(-1, d) for start_token_idx, end_token_idx in get_runtime_state().pp_patches_token_start_end_idx_global ], dim=0,