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--- |
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license: creativeml-openrail-m |
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base_model: "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS" |
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tags: |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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- text-to-image |
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- diffusers |
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- full |
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inference: true |
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--- |
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# pixart-sigma |
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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). |
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The main validation prompt used during training was: |
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``` |
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a cute anime character named toast holding a sign that says SOON, sitting next to a red square on her left side, and a transparent sphere on her right side |
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``` |
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## Validation settings |
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- CFG: `6.5` |
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- CFG Rescale: `0.7` |
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- Steps: `30` |
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- Sampler: `unipc` |
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- Seed: `42` |
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- Resolutions: `1024x1024,1152x960,896x1152` |
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Note: The validation settings are not necessarily the same as the [training settings](#training-settings). |
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<Gallery /> |
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The text encoder **was not** trained. |
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You may reuse the base model text encoder for inference. |
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## Training settings |
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- Training epochs: 0 |
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- Training steps: 500 |
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- Learning rate: 1e-06 |
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- Effective batch size: 512 |
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- Micro-batch size: 32 |
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- Gradient accumulation steps: 2 |
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- Number of GPUs: 8 |
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- Prediction type: epsilon |
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- Rescaled betas zero SNR: False |
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- Optimizer: AdamW, stochastic bf16 |
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- Precision: Pure BF16 |
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- Xformers: Not used |
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## Datasets |
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### photo-concept-bucket |
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- Repeats: 0 |
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- Total number of images: ~559104 |
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- Total number of aspect buckets: 1 |
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- Resolution: 1.0 megapixels |
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- Cropped: True |
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- Crop style: center |
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- Crop aspect: square |
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## Inference |
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```python |
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import torch |
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from diffusers import DiffusionPipeline |
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model_id = "pixart-sigma" |
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prompt = "a cute anime character named toast holding a sign that says SOON, sitting next to a red square on her left side, and a transparent sphere on her right side" |
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negative_prompt = "malformed, disgusting, overexposed, washed-out" |
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pipeline = DiffusionPipeline.from_pretrained(model_id) |
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pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') |
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image = pipeline( |
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prompt=prompt, |
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negative_prompt='', |
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num_inference_steps=30, |
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generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), |
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width=1152, |
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height=768, |
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guidance_scale=6.5, |
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guidance_rescale=0.7, |
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).images[0] |
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image.save("output.png", format="PNG") |
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``` |
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