devxpy/glid-3-xl-stable 🖼️🔢📝❓ → 🖼️
About
Stable diffusion, but with more powerful in-painting & out-painting capabilities
Example Output
Prompt:
"Jon snow from the game of thrones"
Output
Performance Metrics
87.17s
Prediction Time
87.44s
Total Time
All Input Parameters
{
"mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png",
"prompt": "Jon snow from the game of thrones",
"edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png",
"num_outputs": 1,
"num_inference_steps": "500"
}
Input Parameters
- mask
- Black and white image to use as mask for inpainting over init_image. White pixels = keep, Black pixels = discard
- width
- Output image width (multiple of 8)
- height
- Output image height (multiple of 8)
- prompt (required)
- Input prompt
- outpaint
- edit_image
- The Image you want to edit. If this is provided, then mask is required.
- init_image
- init image to use
- num_outputs
- Number of images to output
- skip_timesteps
- How many diffusion steps to skip
- negative_prompt
- Negative text prompt
- num_inference_steps
- Number of denoising steps.
Output Schema
Output
Example Execution Logs
$ /root/.pyenv/versions/3.10.7/bin/python sample.py --model_path inpaint.pt --edit /tmp/tmpc_o91y0t205032562_345087143808969_4674423480430035886_n.png --mask /tmp/tmpvkqeu1cpface_mask.png --steps 500 --text Jon snow from the game of thrones --num_batches 1 Using device: cuda:0 making attention of type 'vanilla' with 512 in_channels Working with z of shape (1, 4, 32, 32) = 4096 dimensions. making attention of type 'vanilla' with 512 in_channels Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.19.self_attn.k_proj.bias', 'vision_model.encoder.layers.21.layer_norm1.weight', 'vision_model.encoder.layers.0.self_attn.out_proj.bias', 'vision_model.encoder.layers.14.layer_norm1.bias', 'vision_model.encoder.layers.21.mlp.fc2.weight', 'vision_model.encoder.layers.3.mlp.fc1.bias', 'vision_model.encoder.layers.11.mlp.fc1.weight', 'vision_model.encoder.layers.3.self_attn.v_proj.bias', 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'vision_model.encoder.layers.19.mlp.fc1.bias', 'vision_model.encoder.layers.22.self_attn.out_proj.weight', 'vision_model.encoder.layers.5.self_attn.k_proj.weight', 'vision_model.encoder.layers.12.self_attn.v_proj.weight', 'vision_model.encoder.layers.1.layer_norm1.weight', 'vision_model.encoder.layers.4.self_attn.q_proj.weight', 'vision_model.encoder.layers.12.self_attn.q_proj.weight', 'vision_model.encoder.layers.9.layer_norm1.weight', 'vision_model.encoder.layers.15.mlp.fc2.bias', 'vision_model.encoder.layers.16.mlp.fc2.bias', 'vision_model.encoder.layers.14.mlp.fc2.bias', 'vision_model.encoder.layers.19.self_attn.v_proj.bias', 'vision_model.encoder.layers.2.self_attn.q_proj.bias', 'vision_model.encoder.layers.5.self_attn.k_proj.bias', 'vision_model.encoder.layers.12.self_attn.k_proj.weight', 'vision_model.encoder.layers.19.self_attn.out_proj.weight', 'vision_model.encoder.layers.19.self_attn.out_proj.bias', 'vision_model.encoder.layers.4.layer_norm2.weight', 'vision_model.encoder.layers.14.mlp.fc2.weight', 'vision_model.encoder.layers.12.mlp.fc2.bias', 'vision_model.post_layernorm.weight', 'vision_model.encoder.layers.0.mlp.fc2.weight', 'vision_model.encoder.layers.14.self_attn.k_proj.bias', 'vision_model.encoder.layers.1.mlp.fc1.weight', 'vision_model.encoder.layers.22.layer_norm1.weight', 'vision_model.encoder.layers.17.mlp.fc1.weight', 'vision_model.encoder.layers.9.mlp.fc1.weight', 'logit_scale', 'vision_model.encoder.layers.12.layer_norm2.weight', 'vision_model.encoder.layers.9.self_attn.out_proj.weight', 'vision_model.encoder.layers.11.layer_norm1.bias', 'vision_model.encoder.layers.16.mlp.fc1.weight', 'vision_model.encoder.layers.18.self_attn.q_proj.bias', 'vision_model.encoder.layers.23.layer_norm2.bias', 'vision_model.encoder.layers.17.self_attn.k_proj.bias', 'vision_model.encoder.layers.18.mlp.fc1.weight'] - This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). /src/sample.py:354: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). 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Version Details
- Version ID
7d6a340e1815acf2b3b2ee0fcaf830fbbcd8697e9712ca63d81930c60484d2d7- Version Created
- October 27, 2022