--- license: creativeml-openrail-m base_model: "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS" tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - simpletuner - full inference: true widget: - text: 'unconditional (blank prompt)' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_0_0.png - text: 'Digital art of a topless anthro male wolf wearing a sun hat and blue banana-patterned swimming trunks' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png --- # pixart-sigma-test This is a full rank finetune derived from [PixArt-alpha/PixArt-Sigma-XL-2-1024-MS](https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS). The main validation prompt used during training was: ``` Digital art of a topless anthro male wolf wearing a sun hat and blue banana-patterned swimming trunks ``` ## Validation settings - CFG: `7.5` - CFG Rescale: `0.0` - Steps: `30` - Sampler: `None` - Seed: `42` - Resolution: `1024` Note: The validation settings are not necessarily the same as the [training settings](#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: 6 - Training steps: 200 - Learning rate: 0.0001 - Effective batch size: 160 - Micro-batch size: 2 - Gradient accumulation steps: 40 - Number of GPUs: 2 - Prediction type: epsilon - Rescaled betas zero SNR: False - Optimizer: AdamW, stochastic bf16 - Precision: Pure BF16 - Xformers: Enabled ## Datasets ### 4o-training-images-thinned - Repeats: 0 - Total number of images: ~4960 - Total number of aspect buckets: 1 - Resolution: 1.0 megapixels - Cropped: True - Crop style: center - Crop aspect: square ## Inference ```python import torch from diffusers import DiffusionPipeline model_id = 'pixart-sigma-test' pipeline = DiffusionPipeline.from_pretrained(model_id) prompt = "Digital art of a topless anthro male wolf wearing a sun hat and blue banana-patterned swimming trunks" negative_prompt = "blurry, cropped, ugly" pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') image = pipeline( prompt=prompt, negative_prompt='blurry, cropped, ugly', 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(1641421826), width=1152, height=768, guidance_scale=7.5, guidance_rescale=0.0, ).images[0] image.save("output.png", format="PNG") ```