Instructions to use zelinzang/ddpm-ema-flowers-64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zelinzang/ddpm-ema-flowers-64 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zelinzang/ddpm-ema-flowers-64", 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
Epoch 380
Browse files- checkpoint-15500/optimizer.bin +3 -0
- checkpoint-15500/random_states_0.pkl +3 -0
- checkpoint-15500/scheduler.bin +3 -0
- checkpoint-15500/unet/config.json +46 -0
- checkpoint-15500/unet/diffusion_pytorch_model.bin +3 -0
- checkpoint-15500/unet_ema/config.json +53 -0
- checkpoint-15500/unet_ema/diffusion_pytorch_model.bin +3 -0
- logs/train_unconditional/events.out.tfevents.1678728297.pytorch-51b835aa-mhvxg.112.0 +2 -2
- unet/diffusion_pytorch_model.bin +1 -1
checkpoint-15500/optimizer.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:59fd0a3ac839579df947f7c0e84dd5e34ee953c04ec6c0a704253536a430888d
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size 909763235
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checkpoint-15500/random_states_0.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:aaa6ae075e590dfce2eae16e68353ef15e0ff939a041133b9ac91c3292755727
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size 15691
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checkpoint-15500/scheduler.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:7e8672617cfad18b3cb2b217d2bfedc3072a5b486d9fcf019ce03b42e6bdb8b0
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size 563
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checkpoint-15500/unet/config.json
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{
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"_class_name": "UNet2DModel",
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"_diffusers_version": "0.15.0.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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"block_out_channels": [
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128,
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128,
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256,
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256,
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512,
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512
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],
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"center_input_sample": false,
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"class_embed_type": null,
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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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"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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"flip_sin_to_cos": true,
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"freq_shift": 0,
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"in_channels": 3,
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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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"num_class_embeds": null,
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"out_channels": 3,
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"resnet_time_scale_shift": "default",
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"sample_size": 64,
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"time_embedding_type": "positional",
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"up_block_types": [
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"UpBlock2D",
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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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]
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}
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checkpoint-15500/unet/diffusion_pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:c73ec6be2a498ad87d366a1999f4c54c62a9be1e9705fa1bd5bcdf068b20a172
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size 454867325
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checkpoint-15500/unet_ema/config.json
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{
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"_class_name": "UNet2DModel",
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"_diffusers_version": "0.15.0.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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"block_out_channels": [
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128,
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128,
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256,
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256,
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512,
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512
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],
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"center_input_sample": false,
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"class_embed_type": null,
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"decay": 0.9999,
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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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"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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"flip_sin_to_cos": true,
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"freq_shift": 0,
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"in_channels": 3,
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"inv_gamma": 1.0,
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"layers_per_block": 2,
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"mid_block_scale_factor": 1,
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"min_decay": 0.0,
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"norm_eps": 1e-05,
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"norm_num_groups": 32,
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"num_class_embeds": null,
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"optimization_step": 15500,
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"out_channels": 3,
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"power": 0.75,
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"resnet_time_scale_shift": "default",
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"sample_size": 64,
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"time_embedding_type": "positional",
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"up_block_types": [
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"UpBlock2D",
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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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],
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"update_after_step": 0,
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"use_ema_warmup": true
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}
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checkpoint-15500/unet_ema/diffusion_pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:dbf9bc95b712d4d1ff5292941358049f881a859bb3c4325aab5955603147136c
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size 454860989
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logs/train_unconditional/events.out.tfevents.1678728297.pytorch-51b835aa-mhvxg.112.0
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size 7899472
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unet/diffusion_pytorch_model.bin
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size 454867325
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version https://git-lfs.github.com/spec/v1
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size 454867325
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