hidream-reddit

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

The main validation prompt used during training was:

A photograph of a cat lounging on a sofa in a sunroom on a 1974 sunny day.

Validation settings

• CFG: 3.0
• CFG Rescale: 0.0
• Steps: 28
• Sampler: 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:

Prompt

unconditional (blank prompt)

Negative Prompt

blurry, cropped, ugly

Prompt

A naked woman is taking a selfie in the mirror.

Negative Prompt

blurry, cropped, ugly

Prompt

A group of naked people are playing Curling in the 1972 Olympic curling event

Negative Prompt

blurry, cropped, ugly

Prompt

A naked woman is lying in the grass, smiling.

Negative Prompt

blurry, cropped, ugly

Prompt

A naked man is lying in the grass.

Negative Prompt

blurry, cropped, ugly

Prompt

A naked man is taking a selfie in the mirror.

Negative Prompt

blurry, cropped, ugly

Prompt

A naked man is riding a giraffe at the zoo. Police are chasing behind him.

Negative Prompt

blurry, cropped, ugly

Prompt

A naked ghost man is going trick-or-treating on halloween. He is holding up a sign that reads 'Uncensored HiDream, now on Runware'

Negative Prompt

blurry, cropped, ugly

Prompt

A naked ghost woman is going trick-or-treating on halloween. She is holding up a sign that reads 'Uncensored HiDream, now on Runware'

Negative Prompt

blurry, cropped, ugly

Prompt

A psychedelic naked goddess with many hindu arms is blessing the animals of Noah's arc

Negative Prompt

blurry, cropped, ugly

Prompt

A woman with huge boobs is laughing, wearing Juggalos make-up at a camping festival in New Jersey, 2008

Negative Prompt

blurry, cropped, ugly

Prompt

A beautiful woman with large breasts is making out with her girlfriend. They have an aire of myseriousness, as they kiss deeply in the centre of the shopping mall food court.

Negative Prompt

blurry, cropped, ugly

Prompt

A photograph of a cat lounging on a sofa in a sunroom on a 1974 sunny day.

Negative Prompt

blurry, cropped, ugly

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

Training settings

• Training epochs: 7

• Training steps: 60500

• Learning rate: 1.0

  • Learning rate schedule: constant
  • Warmup steps: 500

• Max grad value: 0.1

• Effective batch size: 32

  • Micro-batch size: 4
  • Gradient accumulation steps: 1
  • Number of GPUs: 8

• Gradient checkpointing: True

• Prediction type: flow_matching (extra parameters=['flow_schedule_auto_shift', 'shift=0.0'])

• Optimizer: prodigy

• Trainable parameter precision: Pure BF16

• Base model precision: no_change

• Caption dropout probability: 10.0%

• LoRA Rank: 128

• LoRA Alpha: 128.0

• LoRA Dropout: 0.1

• LoRA initialisation style: default

Datasets

photo10k

• Repeats: 0
• Total number of images: ~10000
• Total number of aspect buckets: 1
• Resolution: 1.048576 megapixels
• Cropped: True
• Crop style: random
• Crop aspect: square
• Used for regularisation data: No

hidream-shutterstock

• Repeats: 0
• Total number of images: ~21056
• Total number of aspect buckets: 1
• Resolution: 1.048576 megapixels
• Cropped: True
• Crop style: random
• Crop aspect: square
• Used for regularisation data: No

hidream-normalnudes

• Repeats: 0
• Total number of images: ~1040
• Total number of aspect buckets: 1
• Resolution: 1.048576 megapixels
• Cropped: True
• Crop style: random
• Crop aspect: square
• Used for regularisation data: No

hidream-nsfw

• Repeats: 0
• Total number of images: ~10792
• Total number of aspect buckets: 1
• Resolution: 1.048576 megapixels
• Cropped: True
• Crop style: random
• Crop aspect: square
• Used for regularisation data: No

hidream-gay

• Repeats: 0
• Total number of images: ~1104
• Total number of aspect buckets: 1
• Resolution: 1.048576 megapixels
• Cropped: True
• Crop style: random
• Crop aspect: square
• Used for regularisation data: No

hidream-nijijourney

• Repeats: 0
• Total number of images: ~21504
• Total number of aspect buckets: 1
• Resolution: 1.048576 megapixels
• Cropped: True
• Crop style: random
• Crop aspect: square
• Used for regularisation data: No

hidream-bg20k-1024

• Repeats: 0
• Total number of images: ~89304
• Total number of aspect buckets: 1
• Resolution: 1.048576 megapixels
• Cropped: True
• Crop style: random
• Crop aspect: square
• Used for regularisation data: No

hidream-photo-aesthetics

• Repeats: 0
• Total number of images: ~33120
• Total number of aspect buckets: 1
• Resolution: 1.048576 megapixels
• Cropped: True
• Crop style: random
• Crop aspect: square
• Used for regularisation data: No

hidream-text-1mp

• Repeats: 5
• Total number of images: ~13192
• Total number of aspect buckets: 1
• Resolution: 1.048576 megapixels
• Cropped: True
• Crop style: random
• Crop aspect: square
• Used for regularisation data: No

Inference

import torch
from diffusers import DiffusionPipeline

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

prompt = "A photograph of a cat lounging on a sofa in a sunroom on a 1974 sunny day."
negative_prompt = 'blurry, cropped, ugly'

## 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=28,
    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=3.0,
).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.