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Upload small random test pipeline for the consistency models EDM implementation (non-class conditional)

model_index.json ADDED
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+ {
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+ "_class_name": "KarrasEDMPipeline",
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+ "_diffusers_version": "0.22.0.dev0",
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+ "scheduler": [
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+ "diffusers",
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+ "KarrasEDMScheduler"
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+ ],
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+ "unet": [
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+ "diffusers",
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+ "UNet2DModel"
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+ ]
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+ }
scheduler/scheduler_config.json ADDED
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+ {
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+ "_class_name": "KarrasEDMScheduler",
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+ "_diffusers_version": "0.22.0.dev0",
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+ "clip_sample": false,
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+ "clip_sample_range": 1.0,
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+ "num_train_timesteps": 40,
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+ "precondition_type": "edm",
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+ "prediction_type": "edm",
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+ "rho": 7.0,
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+ "s_churn": 0.0,
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+ "s_noise": 1.0,
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+ "s_tmax": Infinity,
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+ "s_tmin": 0.0,
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+ "sigma_data": 0.5,
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+ "sigma_max": 80.0,
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+ "sigma_min": 0.002,
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+ "timestep_spacing": "linspace"
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+ }
unet/config.json ADDED
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+ {
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+ "_class_name": "UNet2DModel",
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+ "_diffusers_version": "0.22.0.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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+ "attn_norm_num_groups": 32,
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+ "block_out_channels": [
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+ 32,
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+ 64
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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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+ "ResnetDownsampleBlock2D",
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+ "AttnDownBlock2D"
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+ ],
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+ "downsample_padding": 1,
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+ "downsample_type": "resnet",
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+ "dropout": 0.0,
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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": "scale_shift",
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+ "sample_size": 32,
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+ "time_embedding_type": "positional",
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+ "up_block_types": [
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+ "AttnUpBlock2D",
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+ "ResnetUpsampleBlock2D"
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+ ],
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+ "upsample_type": "resnet"
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+ }
unet/diffusion_pytorch_model.safetensors ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:10fbd4bf346f20f687ec2a1654e6877d7fe8149e8bd621180353c06bea2d1f76
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+ size 4633412