Instructions to use GiusCat/tiffusion-mars-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use GiusCat/tiffusion-mars-256 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("GiusCat/tiffusion-mars-256", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Delete checkpoint-4500
Browse files
checkpoint-4500/optimizer.bin
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checkpoint-4500/random_states_0.pkl
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checkpoint-4500/scaler.pt
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checkpoint-4500/scheduler.bin
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checkpoint-4500/unet/config.json
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{
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"_class_name": "UNet2DModel",
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"_diffusers_version": "0.24.0.dev0",
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"act_fn": "silu",
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"add_attention": true,
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"attention_head_dim": 8,
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"center_input_sample": false,
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"down_block_types": [
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"DownBlock2D",
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"DownBlock2D",
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"DownBlock2D",
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"AttnDownBlock2D",
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"DownBlock2D"
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],
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"downsample_padding": 1,
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"downsample_type": "conv",
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"dropout": 0.0,
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"flip_sin_to_cos": true,
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"in_channels": 1,
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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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"out_channels": 1,
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"time_embedding_type": "positional",
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"up_block_types": [
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"AttnUpBlock2D",
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"UpBlock2D",
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"UpBlock2D",
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"UpBlock2D",
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"UpBlock2D"
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"upsample_type": "conv"
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}
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checkpoint-4500/unet/diffusion_pytorch_model.safetensors
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