ComfyUI support: SimpleTuner-io/ComfyUI-AnyFlow is the canonical AnyFlow ComfyUI integration and ships with a ready-made workflow for Anima few-step inference.

Experiment: AnyFlow on-policy DMD, v5 (stage1-v2 init + winning EMA config)

First stage-2 run from the second-generation forward adapter: initialized from stage1-v2's checkpoint-10000/ema (the lr-6e-5 arm that reached stage1-v1's 20k EMA bar in half the steps), using the EMA configuration that won the v1-v4 ablation grid — per-step updates with ema_decay: 0.99 — and caption dropout disabled. Same objective as the grid (on-policy DMD + co-trained forward, real score CFG 3.0, lr 2e-6, 1,500 steps, global batch 16, 65,536-sample e621 subset).

Outcome: the strongest few-step adapter of the campaign. The live weights turned fully sharp within 500 steps — clean line art, saturated color, correct prompt adherence at 4 steps — and held without adversarial drift through 1,500. The healthier stage-1 init translated directly into faster and cleaner DMD engagement than any grid cell. As throughout the campaign, the live (non-EMA) weights are the ship candidate.

bghira/anima-anyflow-e621-dmd-v5

This is a PEFT LoRA derived from circlestone-labs/Anima-Base-v1.0-Diffusers.

The main validation prompt used during training was:

masterpiece, best quality, score_7, safe, anime portrait of a young woman with blue hair wearing a white jacket, clean line art, detailed eyes, soft daylight

Validation settings

CFG: 1.0
CFG Rescale: 0.0
Steps: 4
Sampler: AnyFlowValidationScheduler (FlowMatchEulerDiscreteScheduler)
Seed: 42
Resolution: 1024x1024

Note: The validation settings are not necessarily the same as the training settings.

You can find some example images in the following gallery:

The text encoder was not trained. You may reuse the base model text encoder for inference.

Training settings

Training epochs: 0
Training steps: 1500
Learning rate: 2e-06
- Learning rate schedule: constant_with_warmup
- Warmup steps: 250
Max grad value: 1.0
Effective batch size: 16
- Micro-batch size: 4
- Gradient accumulation steps: 1
- Number of GPUs: 4
Gradient checkpointing: True
Prediction type: flow_matching[]
Optimizer: adamw_bf16
Trainable parameter precision: Pure BF16
Base model precision: no_change
Caption dropout probability: 0.0%
LoRA Rank: 128
LoRA Alpha: 128.0
LoRA Dropout: 0.0
LoRA initialisation style: default
LoRA mode: Standard

Datasets

anima-anyflow-e621-1024

Repeats: 0
Total number of images: 65536
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: square
Used for regularisation data: No

Inference

python

import torch
from diffusers import DiffusionPipeline

model_id = 'circlestone-labs/Anima-Base-v1.0-Diffusers'
adapter_id = 'bghira/anima-anyflow-e621-dmd-v5'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "masterpiece, best quality, score_7, safe, anime portrait of a young woman with blue hair wearing a white jacket, clean line art, detailed eyes, soft daylight"
negative_prompt = 'worst quality, low quality, score_1, score_2, score_3, blurry, cropped, artist name, signature'

## Optional: quantise the model to save on vram.
## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time.
#from optimum.quanto import quantize, freeze, qint8
#quantize(pipeline.transformer, weights=qint8)
#freeze(pipeline.transformer)
    
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
model_output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=4,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
    width=1024,
    height=1024,
    guidance_scale=1.0,
).images[0]

model_output.save("output.png", format="PNG")

Exponential Moving Average (EMA)

SimpleTuner generates a safetensors variant of the EMA weights and a pt file.

The safetensors file is intended to be used for inference, and the pt file is for continuing finetuning.

The EMA model may provide a more well-rounded result, but typically will feel undertrained compared to the full model as it is a running decayed average of the model weights.