Instructions to use PoolerSP/LogiLete with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use PoolerSP/LogiLete with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image # switch to "mps" for apple devices pipe = AutoPipelineForInpainting.from_pretrained("PoolerSP/LogiLete", dtype=torch.float16, device_map="cuda") img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" image = load_image(img_url).resize((1024, 1024)) mask_image = load_image(mask_url).resize((1024, 1024)) prompt = "a tiger sitting on a park bench" generator = torch.Generator(device="cuda").manual_seed(0) image = pipe( prompt=prompt, image=image, mask_image=mask_image, guidance_scale=8.0, num_inference_steps=20, # steps between 15 and 30 work well for us strength=0.99, # make sure to use `strength` below 1.0 generator=generator, ).images[0] - Notebooks
- Google Colab
- Kaggle
Create config.json
Browse files- unet/config.json +36 -0
unet/config.json
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{
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"_class_name": "UNet2DConditionModel",
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"_diffusers_version": "0.2.2",
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"act_fn": "silu",
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"attention_head_dim": 8,
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"block_out_channels": [
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320,
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640,
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1280,
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1280
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],
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"center_input_sample": false,
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"cross_attention_dim": 768,
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"down_block_types": [
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"DownBlock2D"
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],
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"downsample_padding": 1,
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"flip_sin_to_cos": true,
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"freq_shift": 0,
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"in_channels": 4,
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"layers_per_block": 2,
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"mid_block_scale_factor": 1,
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"norm_eps": 1e-05,
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"norm_num_groups": 32,
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"out_channels": 4,
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"sample_size": 64,
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"up_block_types": [
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"UpBlock2D",
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"CrossAttnUpBlock2D",
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"CrossAttnUpBlock2D",
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"CrossAttnUpBlock2D"
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]
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}
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