351 lines
15 KiB
Markdown
351 lines
15 KiB
Markdown
# CFG-Parallel
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This section describes how to add CFG-Parallel (Classifier-Free Guidance Parallel) to a diffusion pipeline. We use the Qwen-Image pipeline as the reference implementation.
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---
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## Table of Contents
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- [Overview](#overview)
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- [Step-by-Step Implementation](#step-by-step-implementation)
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- [Customization](#customization)
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- [Testing](#testing)
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- [Troubleshooting](#troubleshooting)
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- [Reference Implementations](#reference-implementations)
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- [Summary](#summary)
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---
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## Overview
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### What is CFG-Parallel?
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In standard Classifier-Free Guidance, each diffusion step requires two forward passes through the transformer:
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1. **Positive/Conditional**: Guided by the text prompt
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2. **Negative/Unconditional**: Typically using empty or negative prompt
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Some models require 3 or more CFG branches (see [N-Branch CFG](#n-branch-cfg-3-branches)).
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CFG-Parallel eliminates this bottleneck by distributing the forward passes across different GPU ranks, allowing them to execute simultaneously rather than sequentially.
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### Architecture
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vLLM-omni provides `CFGParallelMixin` that encapsulates all CFG parallel logic. Pipelines inherit from this mixin and implement a `diffuse()` method that orchestrates the denoising loop.
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| Method | Purpose | Automatic Behavior |
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|--------|---------|-------------------|
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| [`predict_noise_maybe_with_cfg()`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/distributed/cfg_parallel/) | Predict noise with 2-branch CFG | Detects parallel mode, distributes computation, gathers results |
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| [`predict_noise_with_multi_branch_cfg()`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/distributed/cfg_parallel/) | Predict noise with N-branch CFG | Round-robin dispatches N branches across M GPUs |
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| [`scheduler_step_maybe_with_cfg()`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/distributed/cfg_parallel/) | Step scheduler | All ranks step locally (no broadcast needed) |
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| [`combine_cfg_noise()`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/distributed/cfg_parallel/) | Combine 2-branch predictions | Applies CFG formula with optional normalization |
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| [`combine_multi_branch_cfg_noise()`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/distributed/cfg_parallel/) | Combine N-branch predictions | Override for custom multi-branch combine logic |
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| [`predict_noise()`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/distributed/cfg_parallel/) | Forward pass wrapper | Override for custom transformer calls |
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| [`cfg_normalize_function()`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/distributed/cfg_parallel/) | Normalize CFG output | Override for custom normalization |
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### How It Works
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`predict_noise_maybe_with_cfg()` automatically detects and switches between two execution modes:
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- **CFG-Parallel mode** (when `cfg_world_size > 1`):
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- Rank 0 computes positive prompt prediction
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- Rank 1 computes negative prompt prediction
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- Results are gathered via `all_gather()`
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- All ranks compute CFG combine locally (deterministic, identical results)
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- **Sequential mode** (when `cfg_world_size == 1`):
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- Single rank computes both positive and negative predictions
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- Directly combines them with CFG formula
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`scheduler_step_maybe_with_cfg()` ensures consistent latent states across all ranks:
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- All ranks compute the scheduler step locally — no broadcast needed because `predict_noise_maybe_with_cfg` already ensures all ranks have identical noise predictions after `all_gather` + local combine.
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### N-Branch CFG (3+ branches)
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Some models require more than 2 CFG branches. For example, Bagel and OmniGen2 use 3 branches, DreamID Omni uses 4 branches.
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`predict_noise_with_multi_branch_cfg()` handles these by automatically dispatching N branches across M GPUs using round-robin (rule: branch `i` → rank `i % M`):
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| Branches (N) | GPUs (M) | Dispatch |
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|:---:|:---:|:---|
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| 3 | 2 | `[[0, 2], [1]]` |
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| 3 | 3 | `[[0], [1], [2]]` |
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| 4 | 2 | `[[0, 2], [1, 3]]` |
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| 4 | 3 | `[[0, 3], [1], [2]]` |
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| 4 | 4 | `[[0], [1], [2], [3]]` |
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When a rank handles multiple branches, it runs them sequentially. After `all_gather`, all ranks execute `combine_multi_branch_cfg_noise()` locally, producing identical results.
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---
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## Step-by-Step Implementation
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### Step 1: Inherit `CFGParallelMixin`
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Allow your pipeline to inherit from `CFGParallelMixin` and implements the `diffuse()` method for your specific model.
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**Example (Qwen-Image):**
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```python
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from vllm_omni.diffusion.distributed.cfg_parallel import CFGParallelMixin
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import torch.nn as nn
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class YourModelPipeline(nn.Module, CFGParallelMixin):
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def diffuse(self, ...) -> torch.Tensor:
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for i, t in enumerate(timesteps):
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# Prepare positive_kwargs (conditional) and negative_kwargs (unconditional)
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positive_kwargs = {...} # hidden_states, encoder_hidden_states, etc.
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negative_kwargs = {...} if do_true_cfg else None
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# Key method 1: Predict noise with automatic CFG parallel handling
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noise_pred = self.predict_noise_maybe_with_cfg(
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do_true_cfg=do_true_cfg,
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true_cfg_scale=true_cfg_scale,
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positive_kwargs=positive_kwargs,
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negative_kwargs=negative_kwargs,
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)
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# Key method 2: Step scheduler with automatic CFG synchronization
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latents = self.scheduler_step_maybe_with_cfg(
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noise_pred, t, latents, do_true_cfg
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)
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return latents
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```
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**Key Points:**
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- `positive_kwargs`: transformer arguments for conditional (text-guided) prediction
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- `negative_kwargs`: transformer arguments for unconditional prediction (set to `None` if CFG disabled)
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- For image editing pipelines, add `output_slice=image_seq_len` to extract the generative image portion
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- For models with 3+ CFG branches, see [Multi-Branch CFG](#multi-branch-cfg-3-branches) in the Customization section
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### Step 2: Call `diffuse`
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Call `self.diffuse` in your pipeline's forward function:
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```python
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import torch.nn as nn
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class YourModelPipeline(nn.Module, CFGParallelMixin):
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def forward(
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self,
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prompt: str,
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negative_prompt: str | None = None,
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guidance_scale: float = 3.5,
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num_inference_steps: int = 50,
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**kwargs,
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):
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# Encode prompts, Initialize latents, Get timesteps
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...
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# Run diffusion loop (calls the mixin's diffuse method)
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latents = self.diffuse(
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prompt_embeds=prompt_embeds,
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prompt_embeds_mask=prompt_embeds_mask,
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negative_prompt_embeds=negative_embeds,
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negative_prompt_embeds_mask=negative_mask,
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latents=latents,
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timesteps=timesteps,
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do_true_cfg=do_true_cfg,
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true_cfg_scale=guidance_scale,
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...
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)
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```
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---
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## Customization
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### Override `predict_noise()` for Custom Transformer Calls
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If your transformer requires custom prediction function, you can rewrite `predict_noise` function. Taking Wan2.2 as an example, which has two transformer models. The actual transformer to be called is determined by `self.transformer`.
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```python
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class Wan22Pipeline(nn.Module, CFGParallelMixin):
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def predict_noise(self, current_model: nn.Module | None = None, **kwargs: Any) -> torch.Tensor:
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if current_model is None:
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current_model = self.transformer
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return current_model(**kwargs)[0]
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```
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### Override `cfg_normalize_function()` for Custom Normalization
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Some models have their own normalization function. Taking LongCat Image model as an example:
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```python
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class LongCatImagePipeline(nn.Module, CFGParallelMixin):
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def cfg_normalize_function(self, noise_pred, comb_pred, cfg_renorm_min=0.0):
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"""
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Normalize the combined noise prediction.
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"""
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cond_norm = torch.norm(noise_pred, dim=-1, keepdim=True)
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noise_norm = torch.norm(comb_pred, dim=-1, keepdim=True)
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scale = (cond_norm / (noise_norm + 1e-8)).clamp(min=cfg_renorm_min, max=1.0)
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noise_pred = comb_pred * scale
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return noise_pred
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# The original cfg_normalize_function function in CFGParallelMixin
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# cond_norm = torch.norm(noise_pred, dim=-1, keepdim=True)
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# noise_norm = torch.norm(comb_pred, dim=-1, keepdim=True)
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# noise_pred = comb_pred * (cond_norm / noise_norm)
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# return noise_pred
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```
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### Multi-Branch CFG (3+ branches)
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For models with 3 or more CFG branches, use `predict_noise_with_multi_branch_cfg()` instead of `predict_noise_maybe_with_cfg()`, and override `combine_multi_branch_cfg_noise()` for custom combine logic. This interface also works for standard 2-branch CFG — just pass 2 branches in `branches_kwargs`.
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**Example (3-branch with dual guidance scale):**
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```python
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class YourMultiBranchPipeline(nn.Module, CFGParallelMixin):
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def combine_multi_branch_cfg_noise(self, predictions, true_cfg_scale, cfg_normalize=False):
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text_scale = true_cfg_scale["text"]
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image_scale = true_cfg_scale["image"]
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pos, ref, uncond = predictions
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return uncond + image_scale * (ref - uncond) + text_scale * (pos - ref)
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def diffuse(self, ...):
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for i, t in enumerate(timesteps):
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positive_kwargs = {...} # conditional prompt
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ref_neg_kwargs = {...} # negative prompt + reference
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uncond_kwargs = {...} # unconditional
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noise_pred = self.predict_noise_with_multi_branch_cfg(
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do_true_cfg=do_true_cfg,
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true_cfg_scale={"text": text_guidance_scale, "image": image_guidance_scale},
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branches_kwargs=[positive_kwargs, ref_neg_kwargs, uncond_kwargs],
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)
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latents = self.scheduler_step_maybe_with_cfg(noise_pred, t, latents, do_true_cfg)
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return latents
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```
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### Override Combine Functions
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There are two combine functions for different scenarios:
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- **`combine_cfg_noise()`** — Used by `predict_noise_maybe_with_cfg()`. Override when `predict_noise()` returns a tuple (e.g., video + audio) and you need per-element CFG logic.
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- **`combine_multi_branch_cfg_noise()`** — Used by `predict_noise_with_multi_branch_cfg()`. Override to implement custom multi-branch combine formulas (see [Multi-Branch CFG](#multi-branch-cfg-3-branches) above).
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### Implement a Composite Scheduler for Multi-Output Models
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When each output has its own denoising schedule, implement a composite scheduler that dispatches to per-output schedulers. Assign it to `self.scheduler` so the default `scheduler_step()` works without override.
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**Complete example (video + audio with separate schedulers and diffuse loop):**
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```python
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class VideoAudioScheduler:
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"""Composite scheduler dispatching to video and audio schedulers."""
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def __init__(self, video_scheduler, audio_scheduler):
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self.video_scheduler = video_scheduler
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self.audio_scheduler = audio_scheduler
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def step(self, noise_pred, t, latents, return_dict=False, generator=None):
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video_out = self.video_scheduler.step(noise_pred[0], t[0], latents[0], return_dict=False, generator=generator)[0]
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audio_out = self.audio_scheduler.step(noise_pred[1], t[1], latents[1], return_dict=False, generator=generator)[0]
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return ((video_out, audio_out),)
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class MyVideoAudioPipeline(nn.Module, CFGParallelMixin):
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def __init__(self, ...):
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self.scheduler = VideoAudioScheduler(video_sched, audio_sched)
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def predict_noise(self, **kwargs):
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video_pred, audio_pred = self.transformer(**kwargs)
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return (video_pred, audio_pred)
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def combine_cfg_noise(self, positive_noise_pred, negative_noise_pred, scale, normalize):
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# ... (as above)
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def diffuse(self, video_latents, audio_latents, timesteps_video, timesteps_audio, ...):
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for t_v, t_a in zip(timesteps_video, timesteps_audio):
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positive_kwargs = {...}
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negative_kwargs = {...} if do_true_cfg else None
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video_pred, audio_pred = self.predict_noise_maybe_with_cfg(
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do_true_cfg=do_true_cfg, true_cfg_scale=self.guidance_scale,
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positive_kwargs=positive_kwargs, negative_kwargs=negative_kwargs,
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)
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video_latents, audio_latents = self.scheduler_step_maybe_with_cfg(
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(video_pred, audio_pred), (t_v, t_a),
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(video_latents, audio_latents), do_true_cfg=do_true_cfg,
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generator=generator,
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)
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return video_latents, audio_latents
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```
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> **Note:** If you use a non-deterministic scheduler, e.g., DDPM, please set `self.scheduler_step_maybe_with_cfg(..., generator=torch.Generator(device).manual_seed(seed))` explicitly to control the randomness of scheduler step among ranks.
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---
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## Testing
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After adding CFG-Parallel support, test with:
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```bash
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cd examples/offline_inference/text_to_image
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python text_to_image.py \
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--model Your-org/your-model \
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--prompt "a cup of coffee on the table" \
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--negative-prompt "ugly, unclear" \
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--cfg-scale 4.0 \
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--num-inference-steps 50 \
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--output "cfg_enabled.png" \
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--cfg-parallel-size 2
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```
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**Verify:**
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1. Check logs for CFG parallel being activated
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2. Record the `e2e_time_ms` in the log and compare with CFG-Parallel disabled
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3. Compare the generated result quality with baseline
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4. Record comparison results in your PR
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---
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## Troubleshooting
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### Issue: CFG parallel not activating
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**Symptoms:** Generation still slow, logs don't show CFG parallel being used.
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**Causes & Solutions:**
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- **CFG is not enabled:**
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**Problem:** Guidance scale too low or negative prompt not provided.
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**Solution:** Ensure `guidance_scale > 1.0` and negative prompt is provided:
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```python
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images = pipeline(
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prompt="a cat",
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negative_prompt="", # Must provide (even if empty)
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guidance_scale=3.5, # Must be > 1.0
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)
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```
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---
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## Reference Implementations
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Complete examples in the codebase:
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| Model | Path | Pattern | Notes |
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|-------|------|---------|-------|
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| **Qwen-Image** | `vllm_omni/diffusion/models/qwen_image/cfg_parallel.py` | Mixin | Dual-stream transformer |
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| **Qwen-Image-Edit** | `vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image_edit.py` | Mixin | Image editing with `output_slice` |
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| **Wan2.2** | `vllm_omni/diffusion/models/wan2_2/pipeline_wan2_2.py` | Mixin | Dual-transformer architecture |
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| **CFGParallelMixin** | `vllm_omni/diffusion/distributed/cfg_parallel.py` | Base implementation | Core mixin class |
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---
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## Summary
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Adding CFG-Parallel support:
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1. ✅ **Create mixin** - Inherit from `CFGParallelMixin` and implement `diffuse()` method
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2. ✅ **(Optional) Customize** - Override `predict_noise()` or `cfg_normalize_function()` for custom behavior
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3. ✅ **(Optional) Multi-branch** - For 3+ branch models, use `predict_noise_with_multi_branch_cfg()` and override `combine_multi_branch_cfg_noise()`
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4. ✅ **Test** - Verify with `--cfg-parallel-size 2` (or 3/4 for multi-branch) and compare performance
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