simpletuner-example-hidream-peft-lora

This is a PEFT LoRA derived from HiDream-ai/HiDream-I1-Full.

The main validation prompt used during training was:

a kitty cat

Validation settings

• CFG: 3.0
• CFG Rescale: 0.0
• Steps: 30
• Sampler: FlowMatchEulerDiscreteScheduler
• Seed: 42
• Resolution: 768x768

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

You can find some example images in the following gallery:

Prompt

a kitty cat

Negative Prompt

None

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

Training settings

• Training epochs: 0

• Training steps: 10

• Learning rate: 5e-05

  • Learning rate schedule: cosine
  • Warmup steps: 400000

• Max grad value: 0.0

• Effective batch size: 1

  • Micro-batch size: 1
  • Gradient accumulation steps: 1
  • Number of GPUs: 1

• Gradient checkpointing: True

• Prediction type: flow_matching (extra parameters=['shift=3.0'])

• Optimizer: optimi-lion

• Trainable parameter precision: Pure BF16

• Base model precision: int8-quanto

• Caption dropout probability: 0.1%

• LoRA Rank: 128

• LoRA Alpha: 128.0

• LoRA Dropout: 0.1

• LoRA initialisation style: default

• LoRA mode: Standard

Datasets

dreambooth-512

• Repeats: 0
• Total number of images: 26
• Total number of aspect buckets: 3
• Resolution: 512 px
• Cropped: False
• Crop style: None
• Crop aspect: None
• Used for regularisation data: No

Inference

import torch
from diffusers import DiffusionPipeline

model_id = 'HiDream-ai/HiDream-I1-Full'
adapter_id = 'bghira/simpletuner-example-hidream-peft-lora'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "a kitty cat"
negative_prompt = 'None'

## Optional: quantise the model to save on vram.
## Note: The model was quantised during training, and so it is recommended to do the same 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=30,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
    width=768,
    height=768,
    guidance_scale=3.0,
).images[0]

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