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--- |
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library_name: diffusers |
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license: apache-2.0 |
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datasets: |
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- common-canvas/commoncatalog-cc-by |
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- alfredplpl/commoncatalog-cc-by-recap |
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language: |
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- en |
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--- |
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# CommonArt-PoC |
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CommonArt is a text-to-image generation model with authorized images only. |
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The architecture is based on DiT that is using by Stable Diffusion 3 and Sora. |
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## How to Get Started with the Model |
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You can use this model by diffusers library. |
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```python |
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import torch |
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from diffusers import Transformer2DModel, PixArtSigmaPipeline |
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device = "cpu" |
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weight_dtype = torch.float32 |
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transformer = Transformer2DModel.from_pretrained( |
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"alfredplpl/CommonArt-PoC", |
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torch_dtype=weight_dtype, |
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use_safetensors=True, |
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) |
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pipe = PixArtSigmaPipeline.from_pretrained( |
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"PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers", |
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transformer=transformer, |
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torch_dtype=weight_dtype, |
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use_safetensors=True, |
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) |
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pipe.to(device) |
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prompt = " A picturesque photograph of a serene coastline, capturing the tranquility of a sunrise over the ocean. The image shows a wide expanse of gently rolling sandy beach, with clear, turquoise water stretching into the horizon. Seashells and pebbles are scattered along the shore, and the sun's rays create a golden hue on the water's surface. The distant outline of a lighthouse can be seen, adding to the quaint charm of the scene. The sky is painted with soft pastel colors of dawn, gradually transitioning from pink to blue, creating a sense of peacefulness and beauty." |
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image = pipe(prompt,guidance_scale=4.5,max_squence_length=512).images[0] |
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image.save("beach.png") |
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``` |
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## Model Details |
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### Model Description |
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- **Developed by:** alfredplpl |
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- **Funded by [optional]:** alfredplpl |
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- **Shared by [optional]:** alfredplpl |
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- **Model type:** Diffusion transformer |
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- **Language(s) (NLP):** English |
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- **License:** Apache-2.0 |
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### Model Sources |
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- **Repository:** [Pixart-Sigma](https://github.com/PixArt-alpha/PixArt-sigma) |
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- **Paper:** [PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation](https://arxiv.org/abs/2403.04692) |
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## Uses |
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- Any purpose |
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### Direct Use |
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- To develop commercial text-to-image generation. |
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- To research non-commercial text-to-image generation. |
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### Out-of-Scope Use |
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- To generate misinformation. |
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## Bias, Risks, and Limitations |
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- limited represantation |
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## Training Details |
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### Training Data |
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I used these dataset to train the transformer. |
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- CommonCatalog CC BY |
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- CommonCatalog CC BY Extention |
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### Training Procedure |
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TBA |
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#### Training Hyperparameters |
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- **Training regime:** |
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```bash |
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_base_ = ['../PixArt_xl2_internal.py'] |
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data_root = "/mnt/my_raid/pixart" |
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image_list_json = ['data_info.json'] |
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data = dict( |
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type='InternalDataSigma', root='InternData', image_list_json=image_list_json, transform='default_train', |
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load_vae_feat=False, load_t5_feat=False, |
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) |
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image_size = 256 |
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# model setting |
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model = 'PixArt_XL_2' |
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mixed_precision = 'fp16' # ['fp16', 'fp32', 'bf16'] |
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fp32_attention = True |
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#load_from = "/mnt/my_raid/pixart/working/checkpoints/epoch_1_step_17500.pth" # https://huggingface.co/PixArt-alpha/PixArt-Sigma |
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#resume_from = dict(checkpoint="/mnt/my_raid/pixart/working/checkpoints/epoch_37_step_62039.pth", load_ema=False, resume_optimizer=True, resume_lr_scheduler=True) |
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vae_pretrained = "output/pretrained_models/pixart_sigma_sdxlvae_T5_diffusers/vae" # sdxl vae |
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multi_scale = False # if use multiscale dataset model training |
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pe_interpolation = 0.5 |
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# training setting |
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num_workers = 10 |
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train_batch_size = 64 # 64 as default |
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num_epochs = 200 # 3 |
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gradient_accumulation_steps = 1 |
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grad_checkpointing = True |
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gradient_clip = 0.2 |
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optimizer = dict(type='CAMEWrapper', lr=2e-5, weight_decay=0.0, betas=(0.9, 0.999, 0.9999), eps=(1e-30, 1e-16)) |
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lr_schedule_args = dict(num_warmup_steps=1000) |
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#visualize=True |
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#train_sampling_steps = 3 |
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#eval_sampling_steps = 3 |
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log_interval = 20 |
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save_model_epochs = 1 |
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#save_model_steps = 2500 |
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work_dir = 'output/debug' |
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# pixart-sigma |
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scale_factor = 0.13025 |
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real_prompt_ratio = 0.5 |
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model_max_length = 512 |
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class_dropout_prob = 0.1 |
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``` |
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## Environmental Impact |
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- **Hardware Type:** A6000x2 |
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- **Hours used:** 1000 |
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- **Compute Region:** Japan |
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- **Carbon Emitted:** Not so much |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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Diffusion Transformer |
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### Compute Infrastructure |
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Desktop PC |
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#### Hardware |
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A6000x2 |
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#### Software |
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[Pixart-Sigma repository](https://github.com/PixArt-alpha/PixArt-sigma) |
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## Model Card Contact |
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alfredplpl |
