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- Helios/checkpoints/Helios-Base/transformer/diffusion_pytorch_model.safetensors.index.json +1108 -0
- Helios/checkpoints/Helios-Base/transformer_init/config.json +32 -0
- Helios/checkpoints/Helios-Base/transformer_init/diffusion_pytorch_model.safetensors.index.json +1108 -0
- Helios/checkpoints/Helios-Base/vae/config.json +56 -0
- Helios/checkpoints/Helios-Distilled/guider/guider_config.json +10 -0
- Helios/checkpoints/Helios-Distilled/scheduler/scheduler_config.json +19 -0
- Helios/checkpoints/Helios-Distilled/text_encoder/config.json +34 -0
- Helios/checkpoints/Helios-Distilled/text_encoder/model.safetensors.index.json +249 -0
- Helios/checkpoints/Helios-Distilled/tokenizer/special_tokens_map.json +332 -0
- Helios/checkpoints/Helios-Distilled/tokenizer/tokenizer_config.json +2749 -0
- Helios/checkpoints/Helios-Distilled/transformer/config.json +32 -0
- Helios/checkpoints/Helios-Distilled/transformer/diffusion_pytorch_model.safetensors.index.json +1108 -0
- Helios/checkpoints/Helios-Distilled/transformer_ode/config.json +32 -0
- Helios/checkpoints/Helios-Distilled/transformer_ode/diffusion_pytorch_model.safetensors.index.json +1108 -0
- Helios/checkpoints/Helios-Distilled/vae/config.json +56 -0
- Helios/checkpoints/Helios-Mid/guider/guider_config.json +11 -0
- Helios/checkpoints/Helios-Mid/scheduler/scheduler_config.json +26 -0
- Helios/checkpoints/Helios-Mid/text_encoder/config.json +34 -0
- Helios/checkpoints/Helios-Mid/text_encoder/model.safetensors.index.json +249 -0
- Helios/checkpoints/Helios-Mid/tokenizer/special_tokens_map.json +332 -0
- Helios/checkpoints/Helios-Mid/tokenizer/tokenizer_config.json +2749 -0
- Helios/checkpoints/Helios-Mid/transformer/config.json +32 -0
- Helios/checkpoints/Helios-Mid/transformer/diffusion_pytorch_model.safetensors.index.json +1108 -0
- Helios/checkpoints/Helios-Mid/transformer_init/config.json +32 -0
- Helios/checkpoints/Helios-Mid/transformer_init/diffusion_pytorch_model.safetensors.index.json +1108 -0
- Helios/checkpoints/Helios-Mid/vae/config.json +56 -0
- Helios/eval/checkpoints/get_checkpoints.sh +1 -0
- Helios/eval/playground/helios_t2v_prompts.csv +0 -0
- Helios/eval/playground/results/all_models_merged.json +31 -0
- Helios/eval/playground/results/toy-video/aesthetic_results.json +17 -0
- Helios/eval/playground/results/toy-video/drifting_aesthetic_results.json +22 -0
- Helios/eval/playground/results/toy-video/drifting_motion_smoothness_results.json +22 -0
- Helios/eval/playground/results/toy-video/drifting_naturalness_results.json +27 -0
- Helios/eval/playground/results/toy-video/drifting_semantic_results.json +24 -0
- Helios/eval/playground/results/toy-video/merged_results.json +74 -0
- Helios/eval/playground/results/toy-video/motion_amplitude_results.json +17 -0
- Helios/eval/playground/results/toy-video/motion_smoothness_results.json +17 -0
- Helios/eval/playground/results/toy-video/naturalness_results.json +21 -0
- Helios/eval/playground/results/toy-video/semantic_results.json +19 -0
- Helios/eval/utils/convert_json_to_excel.py +136 -0
- Helios/eval/utils/extract_short_from_long.py +97 -0
- Helios/eval/utils/third_party/ViCLIP/__init__.py +0 -0
- Helios/eval/utils/third_party/ViCLIP/simple_tokenizer.py +147 -0
- Helios/eval/utils/third_party/ViCLIP/viclip.py +216 -0
- Helios/eval/utils/third_party/ViCLIP/viclip_text.py +260 -0
- Helios/eval/utils/third_party/ViCLIP/viclip_vision.py +364 -0
- Helios/eval/utils/third_party/amt/LICENSE +176 -0
- Helios/eval/utils/third_party/amt/README.md +167 -0
- Helios/eval/utils/third_party/amt/__init__.py +0 -0
- Helios/eval/utils/third_party/amt/benchmarks/__init__.py +0 -0
Helios/checkpoints/Helios-Base/transformer/diffusion_pytorch_model.safetensors.index.json
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|
| 1107 |
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|
| 1108 |
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|
Helios/checkpoints/Helios-Base/transformer_init/config.json
ADDED
|
@@ -0,0 +1,32 @@
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| 1 |
+
{
|
| 2 |
+
"_class_name": "HeliosTransformer3DModel",
|
| 3 |
+
"_diffusers_version": "0.37.0.dev0",
|
| 4 |
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
+
"in_channels": 16,
|
| 13 |
+
"is_amplify_history": false,
|
| 14 |
+
"num_attention_heads": 40,
|
| 15 |
+
"num_layers": 40,
|
| 16 |
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"out_channels": 16,
|
| 17 |
+
"patch_size": [
|
| 18 |
+
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|
| 19 |
+
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|
| 20 |
+
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|
| 21 |
+
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|
| 22 |
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"qk_norm": "rms_norm_across_heads",
|
| 23 |
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"rope_dim": [
|
| 24 |
+
44,
|
| 25 |
+
42,
|
| 26 |
+
42
|
| 27 |
+
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|
| 28 |
+
"rope_theta": 10000.0,
|
| 29 |
+
"guidance_cross_attn": true,
|
| 30 |
+
"text_dim": 4096,
|
| 31 |
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"zero_history_timestep": true
|
| 32 |
+
}
|
Helios/checkpoints/Helios-Base/transformer_init/diffusion_pytorch_model.safetensors.index.json
ADDED
|
@@ -0,0 +1,1108 @@
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| 1 |
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Helios/checkpoints/Helios-Base/vae/config.json
ADDED
|
@@ -0,0 +1,56 @@
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|
| 1 |
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|
| 2 |
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|
| 3 |
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|
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|
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|
| 11 |
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| 51 |
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|
| 53 |
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| 55 |
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|
| 56 |
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Helios/checkpoints/Helios-Distilled/guider/guider_config.json
ADDED
|
@@ -0,0 +1,10 @@
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{
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"_class_name": "ClassifierFreeGuidance",
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"_diffusers_version": "0.38.0.dev0",
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"enabled": true,
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"guidance_rescale": 0.0,
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"guidance_scale": 1.0,
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"stop": 1.0,
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"use_original_formulation": false
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Helios/checkpoints/Helios-Distilled/scheduler/scheduler_config.json
ADDED
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@@ -0,0 +1,19 @@
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{
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"_class_name": "HeliosDMDScheduler",
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| 3 |
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"_diffusers_version": "0.37.0.dev0",
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| 4 |
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"gamma": 0.3333333333333333,
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"num_train_timesteps": 1000,
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"prediction_type": "flow_prediction",
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| 7 |
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"scheduler_type": "dmd",
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| 8 |
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"shift": 1.0,
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"stage_range": [
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0,
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0.3333333333333333,
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0.6666666666666666,
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1
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],
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"stages": 3,
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"time_shift_type": "linear",
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| 17 |
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"use_dynamic_shifting": true,
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| 18 |
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"use_flow_sigmas": true
|
| 19 |
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}
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Helios/checkpoints/Helios-Distilled/text_encoder/config.json
ADDED
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@@ -0,0 +1,34 @@
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{
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| 2 |
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"_name_or_path": "google/umt5-xxl",
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| 3 |
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"architectures": [
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| 4 |
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"UMT5EncoderModel"
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| 5 |
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],
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| 6 |
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"classifier_dropout": 0.0,
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| 7 |
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"d_ff": 10240,
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"d_kv": 64,
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| 9 |
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"d_model": 4096,
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| 10 |
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"decoder_start_token_id": 0,
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"dense_act_fn": "gelu_new",
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| 12 |
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"dropout_rate": 0.1,
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| 13 |
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"eos_token_id": 1,
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| 14 |
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"feed_forward_proj": "gated-gelu",
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| 15 |
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"initializer_factor": 1.0,
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| 16 |
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"is_encoder_decoder": true,
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| 17 |
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"is_gated_act": true,
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| 18 |
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"layer_norm_epsilon": 1e-06,
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| 19 |
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"model_type": "umt5",
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| 20 |
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"num_decoder_layers": 24,
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| 21 |
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"num_heads": 64,
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| 22 |
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"num_layers": 24,
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| 23 |
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"output_past": true,
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| 24 |
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"pad_token_id": 0,
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| 25 |
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"relative_attention_max_distance": 128,
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| 26 |
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"relative_attention_num_buckets": 32,
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| 27 |
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"scalable_attention": true,
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| 28 |
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"tie_word_embeddings": true,
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| 29 |
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"tokenizer_class": "T5Tokenizer",
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| 30 |
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"torch_dtype": "float32",
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| 31 |
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"transformers_version": "4.48.0.dev0",
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| 32 |
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"use_cache": true,
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| 33 |
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"vocab_size": 256384
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| 34 |
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}
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Helios/checkpoints/Helios-Distilled/text_encoder/model.safetensors.index.json
ADDED
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@@ -0,0 +1,249 @@
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}
|
Helios/checkpoints/Helios-Distilled/tokenizer/special_tokens_map.json
ADDED
|
@@ -0,0 +1,332 @@
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|
|
|
|
| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<extra_id_0>",
|
| 4 |
+
"<extra_id_1>",
|
| 5 |
+
"<extra_id_2>",
|
| 6 |
+
"<extra_id_3>",
|
| 7 |
+
"<extra_id_4>",
|
| 8 |
+
"<extra_id_5>",
|
| 9 |
+
"<extra_id_6>",
|
| 10 |
+
"<extra_id_7>",
|
| 11 |
+
"<extra_id_8>",
|
| 12 |
+
"<extra_id_9>",
|
| 13 |
+
"<extra_id_10>",
|
| 14 |
+
"<extra_id_11>",
|
| 15 |
+
"<extra_id_12>",
|
| 16 |
+
"<extra_id_13>",
|
| 17 |
+
"<extra_id_14>",
|
| 18 |
+
"<extra_id_15>",
|
| 19 |
+
"<extra_id_16>",
|
| 20 |
+
"<extra_id_17>",
|
| 21 |
+
"<extra_id_18>",
|
| 22 |
+
"<extra_id_19>",
|
| 23 |
+
"<extra_id_20>",
|
| 24 |
+
"<extra_id_21>",
|
| 25 |
+
"<extra_id_22>",
|
| 26 |
+
"<extra_id_23>",
|
| 27 |
+
"<extra_id_24>",
|
| 28 |
+
"<extra_id_25>",
|
| 29 |
+
"<extra_id_26>",
|
| 30 |
+
"<extra_id_27>",
|
| 31 |
+
"<extra_id_28>",
|
| 32 |
+
"<extra_id_29>",
|
| 33 |
+
"<extra_id_30>",
|
| 34 |
+
"<extra_id_31>",
|
| 35 |
+
"<extra_id_32>",
|
| 36 |
+
"<extra_id_33>",
|
| 37 |
+
"<extra_id_34>",
|
| 38 |
+
"<extra_id_35>",
|
| 39 |
+
"<extra_id_36>",
|
| 40 |
+
"<extra_id_37>",
|
| 41 |
+
"<extra_id_38>",
|
| 42 |
+
"<extra_id_39>",
|
| 43 |
+
"<extra_id_40>",
|
| 44 |
+
"<extra_id_41>",
|
| 45 |
+
"<extra_id_42>",
|
| 46 |
+
"<extra_id_43>",
|
| 47 |
+
"<extra_id_44>",
|
| 48 |
+
"<extra_id_45>",
|
| 49 |
+
"<extra_id_46>",
|
| 50 |
+
"<extra_id_47>",
|
| 51 |
+
"<extra_id_48>",
|
| 52 |
+
"<extra_id_49>",
|
| 53 |
+
"<extra_id_50>",
|
| 54 |
+
"<extra_id_51>",
|
| 55 |
+
"<extra_id_52>",
|
| 56 |
+
"<extra_id_53>",
|
| 57 |
+
"<extra_id_54>",
|
| 58 |
+
"<extra_id_55>",
|
| 59 |
+
"<extra_id_56>",
|
| 60 |
+
"<extra_id_57>",
|
| 61 |
+
"<extra_id_58>",
|
| 62 |
+
"<extra_id_59>",
|
| 63 |
+
"<extra_id_60>",
|
| 64 |
+
"<extra_id_61>",
|
| 65 |
+
"<extra_id_62>",
|
| 66 |
+
"<extra_id_63>",
|
| 67 |
+
"<extra_id_64>",
|
| 68 |
+
"<extra_id_65>",
|
| 69 |
+
"<extra_id_66>",
|
| 70 |
+
"<extra_id_67>",
|
| 71 |
+
"<extra_id_68>",
|
| 72 |
+
"<extra_id_69>",
|
| 73 |
+
"<extra_id_70>",
|
| 74 |
+
"<extra_id_71>",
|
| 75 |
+
"<extra_id_72>",
|
| 76 |
+
"<extra_id_73>",
|
| 77 |
+
"<extra_id_74>",
|
| 78 |
+
"<extra_id_75>",
|
| 79 |
+
"<extra_id_76>",
|
| 80 |
+
"<extra_id_77>",
|
| 81 |
+
"<extra_id_78>",
|
| 82 |
+
"<extra_id_79>",
|
| 83 |
+
"<extra_id_80>",
|
| 84 |
+
"<extra_id_81>",
|
| 85 |
+
"<extra_id_82>",
|
| 86 |
+
"<extra_id_83>",
|
| 87 |
+
"<extra_id_84>",
|
| 88 |
+
"<extra_id_85>",
|
| 89 |
+
"<extra_id_86>",
|
| 90 |
+
"<extra_id_87>",
|
| 91 |
+
"<extra_id_88>",
|
| 92 |
+
"<extra_id_89>",
|
| 93 |
+
"<extra_id_90>",
|
| 94 |
+
"<extra_id_91>",
|
| 95 |
+
"<extra_id_92>",
|
| 96 |
+
"<extra_id_93>",
|
| 97 |
+
"<extra_id_94>",
|
| 98 |
+
"<extra_id_95>",
|
| 99 |
+
"<extra_id_96>",
|
| 100 |
+
"<extra_id_97>",
|
| 101 |
+
"<extra_id_98>",
|
| 102 |
+
"<extra_id_99>",
|
| 103 |
+
"<extra_id_100>",
|
| 104 |
+
"<extra_id_101>",
|
| 105 |
+
"<extra_id_102>",
|
| 106 |
+
"<extra_id_103>",
|
| 107 |
+
"<extra_id_104>",
|
| 108 |
+
"<extra_id_105>",
|
| 109 |
+
"<extra_id_106>",
|
| 110 |
+
"<extra_id_107>",
|
| 111 |
+
"<extra_id_108>",
|
| 112 |
+
"<extra_id_109>",
|
| 113 |
+
"<extra_id_110>",
|
| 114 |
+
"<extra_id_111>",
|
| 115 |
+
"<extra_id_112>",
|
| 116 |
+
"<extra_id_113>",
|
| 117 |
+
"<extra_id_114>",
|
| 118 |
+
"<extra_id_115>",
|
| 119 |
+
"<extra_id_116>",
|
| 120 |
+
"<extra_id_117>",
|
| 121 |
+
"<extra_id_118>",
|
| 122 |
+
"<extra_id_119>",
|
| 123 |
+
"<extra_id_120>",
|
| 124 |
+
"<extra_id_121>",
|
| 125 |
+
"<extra_id_122>",
|
| 126 |
+
"<extra_id_123>",
|
| 127 |
+
"<extra_id_124>",
|
| 128 |
+
"<extra_id_125>",
|
| 129 |
+
"<extra_id_126>",
|
| 130 |
+
"<extra_id_127>",
|
| 131 |
+
"<extra_id_128>",
|
| 132 |
+
"<extra_id_129>",
|
| 133 |
+
"<extra_id_130>",
|
| 134 |
+
"<extra_id_131>",
|
| 135 |
+
"<extra_id_132>",
|
| 136 |
+
"<extra_id_133>",
|
| 137 |
+
"<extra_id_134>",
|
| 138 |
+
"<extra_id_135>",
|
| 139 |
+
"<extra_id_136>",
|
| 140 |
+
"<extra_id_137>",
|
| 141 |
+
"<extra_id_138>",
|
| 142 |
+
"<extra_id_139>",
|
| 143 |
+
"<extra_id_140>",
|
| 144 |
+
"<extra_id_141>",
|
| 145 |
+
"<extra_id_142>",
|
| 146 |
+
"<extra_id_143>",
|
| 147 |
+
"<extra_id_144>",
|
| 148 |
+
"<extra_id_145>",
|
| 149 |
+
"<extra_id_146>",
|
| 150 |
+
"<extra_id_147>",
|
| 151 |
+
"<extra_id_148>",
|
| 152 |
+
"<extra_id_149>",
|
| 153 |
+
"<extra_id_150>",
|
| 154 |
+
"<extra_id_151>",
|
| 155 |
+
"<extra_id_152>",
|
| 156 |
+
"<extra_id_153>",
|
| 157 |
+
"<extra_id_154>",
|
| 158 |
+
"<extra_id_155>",
|
| 159 |
+
"<extra_id_156>",
|
| 160 |
+
"<extra_id_157>",
|
| 161 |
+
"<extra_id_158>",
|
| 162 |
+
"<extra_id_159>",
|
| 163 |
+
"<extra_id_160>",
|
| 164 |
+
"<extra_id_161>",
|
| 165 |
+
"<extra_id_162>",
|
| 166 |
+
"<extra_id_163>",
|
| 167 |
+
"<extra_id_164>",
|
| 168 |
+
"<extra_id_165>",
|
| 169 |
+
"<extra_id_166>",
|
| 170 |
+
"<extra_id_167>",
|
| 171 |
+
"<extra_id_168>",
|
| 172 |
+
"<extra_id_169>",
|
| 173 |
+
"<extra_id_170>",
|
| 174 |
+
"<extra_id_171>",
|
| 175 |
+
"<extra_id_172>",
|
| 176 |
+
"<extra_id_173>",
|
| 177 |
+
"<extra_id_174>",
|
| 178 |
+
"<extra_id_175>",
|
| 179 |
+
"<extra_id_176>",
|
| 180 |
+
"<extra_id_177>",
|
| 181 |
+
"<extra_id_178>",
|
| 182 |
+
"<extra_id_179>",
|
| 183 |
+
"<extra_id_180>",
|
| 184 |
+
"<extra_id_181>",
|
| 185 |
+
"<extra_id_182>",
|
| 186 |
+
"<extra_id_183>",
|
| 187 |
+
"<extra_id_184>",
|
| 188 |
+
"<extra_id_185>",
|
| 189 |
+
"<extra_id_186>",
|
| 190 |
+
"<extra_id_187>",
|
| 191 |
+
"<extra_id_188>",
|
| 192 |
+
"<extra_id_189>",
|
| 193 |
+
"<extra_id_190>",
|
| 194 |
+
"<extra_id_191>",
|
| 195 |
+
"<extra_id_192>",
|
| 196 |
+
"<extra_id_193>",
|
| 197 |
+
"<extra_id_194>",
|
| 198 |
+
"<extra_id_195>",
|
| 199 |
+
"<extra_id_196>",
|
| 200 |
+
"<extra_id_197>",
|
| 201 |
+
"<extra_id_198>",
|
| 202 |
+
"<extra_id_199>",
|
| 203 |
+
"<extra_id_200>",
|
| 204 |
+
"<extra_id_201>",
|
| 205 |
+
"<extra_id_202>",
|
| 206 |
+
"<extra_id_203>",
|
| 207 |
+
"<extra_id_204>",
|
| 208 |
+
"<extra_id_205>",
|
| 209 |
+
"<extra_id_206>",
|
| 210 |
+
"<extra_id_207>",
|
| 211 |
+
"<extra_id_208>",
|
| 212 |
+
"<extra_id_209>",
|
| 213 |
+
"<extra_id_210>",
|
| 214 |
+
"<extra_id_211>",
|
| 215 |
+
"<extra_id_212>",
|
| 216 |
+
"<extra_id_213>",
|
| 217 |
+
"<extra_id_214>",
|
| 218 |
+
"<extra_id_215>",
|
| 219 |
+
"<extra_id_216>",
|
| 220 |
+
"<extra_id_217>",
|
| 221 |
+
"<extra_id_218>",
|
| 222 |
+
"<extra_id_219>",
|
| 223 |
+
"<extra_id_220>",
|
| 224 |
+
"<extra_id_221>",
|
| 225 |
+
"<extra_id_222>",
|
| 226 |
+
"<extra_id_223>",
|
| 227 |
+
"<extra_id_224>",
|
| 228 |
+
"<extra_id_225>",
|
| 229 |
+
"<extra_id_226>",
|
| 230 |
+
"<extra_id_227>",
|
| 231 |
+
"<extra_id_228>",
|
| 232 |
+
"<extra_id_229>",
|
| 233 |
+
"<extra_id_230>",
|
| 234 |
+
"<extra_id_231>",
|
| 235 |
+
"<extra_id_232>",
|
| 236 |
+
"<extra_id_233>",
|
| 237 |
+
"<extra_id_234>",
|
| 238 |
+
"<extra_id_235>",
|
| 239 |
+
"<extra_id_236>",
|
| 240 |
+
"<extra_id_237>",
|
| 241 |
+
"<extra_id_238>",
|
| 242 |
+
"<extra_id_239>",
|
| 243 |
+
"<extra_id_240>",
|
| 244 |
+
"<extra_id_241>",
|
| 245 |
+
"<extra_id_242>",
|
| 246 |
+
"<extra_id_243>",
|
| 247 |
+
"<extra_id_244>",
|
| 248 |
+
"<extra_id_245>",
|
| 249 |
+
"<extra_id_246>",
|
| 250 |
+
"<extra_id_247>",
|
| 251 |
+
"<extra_id_248>",
|
| 252 |
+
"<extra_id_249>",
|
| 253 |
+
"<extra_id_250>",
|
| 254 |
+
"<extra_id_251>",
|
| 255 |
+
"<extra_id_252>",
|
| 256 |
+
"<extra_id_253>",
|
| 257 |
+
"<extra_id_254>",
|
| 258 |
+
"<extra_id_255>",
|
| 259 |
+
"<extra_id_256>",
|
| 260 |
+
"<extra_id_257>",
|
| 261 |
+
"<extra_id_258>",
|
| 262 |
+
"<extra_id_259>",
|
| 263 |
+
"<extra_id_260>",
|
| 264 |
+
"<extra_id_261>",
|
| 265 |
+
"<extra_id_262>",
|
| 266 |
+
"<extra_id_263>",
|
| 267 |
+
"<extra_id_264>",
|
| 268 |
+
"<extra_id_265>",
|
| 269 |
+
"<extra_id_266>",
|
| 270 |
+
"<extra_id_267>",
|
| 271 |
+
"<extra_id_268>",
|
| 272 |
+
"<extra_id_269>",
|
| 273 |
+
"<extra_id_270>",
|
| 274 |
+
"<extra_id_271>",
|
| 275 |
+
"<extra_id_272>",
|
| 276 |
+
"<extra_id_273>",
|
| 277 |
+
"<extra_id_274>",
|
| 278 |
+
"<extra_id_275>",
|
| 279 |
+
"<extra_id_276>",
|
| 280 |
+
"<extra_id_277>",
|
| 281 |
+
"<extra_id_278>",
|
| 282 |
+
"<extra_id_279>",
|
| 283 |
+
"<extra_id_280>",
|
| 284 |
+
"<extra_id_281>",
|
| 285 |
+
"<extra_id_282>",
|
| 286 |
+
"<extra_id_283>",
|
| 287 |
+
"<extra_id_284>",
|
| 288 |
+
"<extra_id_285>",
|
| 289 |
+
"<extra_id_286>",
|
| 290 |
+
"<extra_id_287>",
|
| 291 |
+
"<extra_id_288>",
|
| 292 |
+
"<extra_id_289>",
|
| 293 |
+
"<extra_id_290>",
|
| 294 |
+
"<extra_id_291>",
|
| 295 |
+
"<extra_id_292>",
|
| 296 |
+
"<extra_id_293>",
|
| 297 |
+
"<extra_id_294>",
|
| 298 |
+
"<extra_id_295>",
|
| 299 |
+
"<extra_id_296>",
|
| 300 |
+
"<extra_id_297>",
|
| 301 |
+
"<extra_id_298>",
|
| 302 |
+
"<extra_id_299>"
|
| 303 |
+
],
|
| 304 |
+
"bos_token": {
|
| 305 |
+
"content": "<s>",
|
| 306 |
+
"lstrip": false,
|
| 307 |
+
"normalized": false,
|
| 308 |
+
"rstrip": false,
|
| 309 |
+
"single_word": false
|
| 310 |
+
},
|
| 311 |
+
"eos_token": {
|
| 312 |
+
"content": "</s>",
|
| 313 |
+
"lstrip": false,
|
| 314 |
+
"normalized": false,
|
| 315 |
+
"rstrip": false,
|
| 316 |
+
"single_word": false
|
| 317 |
+
},
|
| 318 |
+
"pad_token": {
|
| 319 |
+
"content": "<pad>",
|
| 320 |
+
"lstrip": false,
|
| 321 |
+
"normalized": false,
|
| 322 |
+
"rstrip": false,
|
| 323 |
+
"single_word": false
|
| 324 |
+
},
|
| 325 |
+
"unk_token": {
|
| 326 |
+
"content": "<unk>",
|
| 327 |
+
"lstrip": false,
|
| 328 |
+
"normalized": false,
|
| 329 |
+
"rstrip": false,
|
| 330 |
+
"single_word": false
|
| 331 |
+
}
|
| 332 |
+
}
|
Helios/checkpoints/Helios-Distilled/tokenizer/tokenizer_config.json
ADDED
|
@@ -0,0 +1,2749 @@
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| 1 |
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{
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| 2 |
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| 3 |
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| 9 |
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| 10 |
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| 11 |
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| 16 |
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| 18 |
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| 25 |
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| 26 |
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| 28 |
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|
| 2021 |
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| 2022 |
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| 2026 |
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| 2027 |
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| 2028 |
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|
| 2029 |
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|
| 2030 |
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| 2031 |
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| 2033 |
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| 2034 |
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| 2035 |
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| 2036 |
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| 2037 |
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| 2038 |
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| 2039 |
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| 2041 |
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| 2042 |
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| 2044 |
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| 2045 |
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| 2046 |
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| 2047 |
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| 2049 |
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| 2050 |
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| 2051 |
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| 2052 |
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| 2053 |
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| 2054 |
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| 2060 |
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| 2066 |
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| 2068 |
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| 2069 |
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| 2070 |
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| 2072 |
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| 2073 |
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| 2074 |
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| 2075 |
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| 2076 |
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| 2077 |
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| 2080 |
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| 2081 |
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| 2084 |
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| 2090 |
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| 2092 |
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| 2093 |
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| 2100 |
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| 2556 |
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| 2568 |
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| 2592 |
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| 2598 |
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| 2600 |
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| 2602 |
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| 2603 |
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| 2605 |
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| 2610 |
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| 2614 |
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| 2616 |
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| 2617 |
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| 2618 |
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| 2619 |
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| 2620 |
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| 2621 |
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| 2622 |
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| 2623 |
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| 2624 |
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| 2625 |
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| 2626 |
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| 2627 |
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| 2628 |
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| 2629 |
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| 2630 |
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| 2634 |
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| 2636 |
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| 2637 |
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| 2638 |
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| 2639 |
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| 2640 |
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| 2641 |
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| 2642 |
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| 2643 |
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| 2646 |
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| 2647 |
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| 2648 |
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| 2649 |
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| 2650 |
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| 2651 |
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| 2652 |
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| 2653 |
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| 2654 |
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|
| 2655 |
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| 2656 |
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|
| 2657 |
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|
| 2658 |
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"<extra_id_221>",
|
| 2659 |
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"<extra_id_222>",
|
| 2660 |
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"<extra_id_223>",
|
| 2661 |
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|
| 2662 |
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|
| 2663 |
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"<extra_id_226>",
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| 2664 |
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"<extra_id_227>",
|
| 2665 |
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"<extra_id_228>",
|
| 2666 |
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| 2667 |
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| 2668 |
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|
| 2669 |
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|
| 2670 |
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|
| 2671 |
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| 2672 |
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| 2673 |
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| 2674 |
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|
| 2675 |
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|
| 2676 |
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|
| 2677 |
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"<extra_id_240>",
|
| 2678 |
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|
| 2679 |
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"<extra_id_242>",
|
| 2680 |
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|
| 2681 |
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"<extra_id_244>",
|
| 2682 |
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|
| 2683 |
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"<extra_id_246>",
|
| 2684 |
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|
| 2685 |
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"<extra_id_248>",
|
| 2686 |
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"<extra_id_249>",
|
| 2687 |
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"<extra_id_250>",
|
| 2688 |
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"<extra_id_251>",
|
| 2689 |
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"<extra_id_252>",
|
| 2690 |
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|
| 2691 |
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"<extra_id_254>",
|
| 2692 |
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|
| 2693 |
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|
| 2694 |
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|
| 2695 |
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|
| 2696 |
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"<extra_id_259>",
|
| 2697 |
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"<extra_id_260>",
|
| 2698 |
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|
| 2699 |
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|
| 2700 |
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"<extra_id_263>",
|
| 2701 |
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|
| 2702 |
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"<extra_id_265>",
|
| 2703 |
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"<extra_id_266>",
|
| 2704 |
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"<extra_id_267>",
|
| 2705 |
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"<extra_id_268>",
|
| 2706 |
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"<extra_id_269>",
|
| 2707 |
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"<extra_id_270>",
|
| 2708 |
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"<extra_id_271>",
|
| 2709 |
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"<extra_id_272>",
|
| 2710 |
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"<extra_id_273>",
|
| 2711 |
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"<extra_id_274>",
|
| 2712 |
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"<extra_id_275>",
|
| 2713 |
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"<extra_id_276>",
|
| 2714 |
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"<extra_id_277>",
|
| 2715 |
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"<extra_id_278>",
|
| 2716 |
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"<extra_id_279>",
|
| 2717 |
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"<extra_id_280>",
|
| 2718 |
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"<extra_id_281>",
|
| 2719 |
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"<extra_id_282>",
|
| 2720 |
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"<extra_id_283>",
|
| 2721 |
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"<extra_id_284>",
|
| 2722 |
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"<extra_id_285>",
|
| 2723 |
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"<extra_id_286>",
|
| 2724 |
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"<extra_id_287>",
|
| 2725 |
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"<extra_id_288>",
|
| 2726 |
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"<extra_id_289>",
|
| 2727 |
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"<extra_id_290>",
|
| 2728 |
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"<extra_id_291>",
|
| 2729 |
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"<extra_id_292>",
|
| 2730 |
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"<extra_id_293>",
|
| 2731 |
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"<extra_id_294>",
|
| 2732 |
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"<extra_id_295>",
|
| 2733 |
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"<extra_id_296>",
|
| 2734 |
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"<extra_id_297>",
|
| 2735 |
+
"<extra_id_298>",
|
| 2736 |
+
"<extra_id_299>"
|
| 2737 |
+
],
|
| 2738 |
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"bos_token": "<s>",
|
| 2739 |
+
"clean_up_tokenization_spaces": true,
|
| 2740 |
+
"eos_token": "</s>",
|
| 2741 |
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"extra_ids": 300,
|
| 2742 |
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"extra_special_tokens": {},
|
| 2743 |
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"model_max_length": 1000000000000000019884624838656,
|
| 2744 |
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"pad_token": "<pad>",
|
| 2745 |
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"sp_model_kwargs": {},
|
| 2746 |
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"spaces_between_special_tokens": false,
|
| 2747 |
+
"tokenizer_class": "T5Tokenizer",
|
| 2748 |
+
"unk_token": "<unk>"
|
| 2749 |
+
}
|
Helios/checkpoints/Helios-Distilled/transformer/config.json
ADDED
|
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_class_name": "HeliosTransformer3DModel",
|
| 3 |
+
"_diffusers_version": "0.37.0.dev0",
|
| 4 |
+
"added_kv_proj_dim": null,
|
| 5 |
+
"attention_head_dim": 128,
|
| 6 |
+
"cross_attn_norm": true,
|
| 7 |
+
"eps": 1e-06,
|
| 8 |
+
"ffn_dim": 13824,
|
| 9 |
+
"freq_dim": 256,
|
| 10 |
+
"has_multi_term_memory_patch": true,
|
| 11 |
+
"history_scale_mode": "per_head",
|
| 12 |
+
"in_channels": 16,
|
| 13 |
+
"is_amplify_history": false,
|
| 14 |
+
"num_attention_heads": 40,
|
| 15 |
+
"num_layers": 40,
|
| 16 |
+
"out_channels": 16,
|
| 17 |
+
"patch_size": [
|
| 18 |
+
1,
|
| 19 |
+
2,
|
| 20 |
+
2
|
| 21 |
+
],
|
| 22 |
+
"qk_norm": "rms_norm_across_heads",
|
| 23 |
+
"rope_dim": [
|
| 24 |
+
44,
|
| 25 |
+
42,
|
| 26 |
+
42
|
| 27 |
+
],
|
| 28 |
+
"rope_theta": 10000.0,
|
| 29 |
+
"guidance_cross_attn": true,
|
| 30 |
+
"text_dim": 4096,
|
| 31 |
+
"zero_history_timestep": true
|
| 32 |
+
}
|
Helios/checkpoints/Helios-Distilled/transformer/diffusion_pytorch_model.safetensors.index.json
ADDED
|
@@ -0,0 +1,1108 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1074 |
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"blocks.9.attn2.to_out.0.weight": "diffusion_pytorch_model-00002-of-00006.safetensors",
|
| 1075 |
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|
| 1076 |
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|
| 1077 |
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"blocks.9.attn2.to_v.bias": "diffusion_pytorch_model-00002-of-00006.safetensors",
|
| 1078 |
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"blocks.9.attn2.to_v.weight": "diffusion_pytorch_model-00002-of-00006.safetensors",
|
| 1079 |
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|
| 1080 |
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|
| 1081 |
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|
| 1082 |
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|
| 1083 |
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|
| 1084 |
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|
| 1085 |
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|
| 1086 |
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|
| 1087 |
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"condition_embedder.text_embedder.linear_1.weight": "diffusion_pytorch_model-00001-of-00006.safetensors",
|
| 1088 |
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"condition_embedder.text_embedder.linear_2.bias": "diffusion_pytorch_model-00001-of-00006.safetensors",
|
| 1089 |
+
"condition_embedder.text_embedder.linear_2.weight": "diffusion_pytorch_model-00001-of-00006.safetensors",
|
| 1090 |
+
"condition_embedder.time_embedder.linear_1.bias": "diffusion_pytorch_model-00001-of-00006.safetensors",
|
| 1091 |
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"condition_embedder.time_embedder.linear_1.weight": "diffusion_pytorch_model-00001-of-00006.safetensors",
|
| 1092 |
+
"condition_embedder.time_embedder.linear_2.bias": "diffusion_pytorch_model-00001-of-00006.safetensors",
|
| 1093 |
+
"condition_embedder.time_embedder.linear_2.weight": "diffusion_pytorch_model-00001-of-00006.safetensors",
|
| 1094 |
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"condition_embedder.time_proj.bias": "diffusion_pytorch_model-00001-of-00006.safetensors",
|
| 1095 |
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"condition_embedder.time_proj.weight": "diffusion_pytorch_model-00001-of-00006.safetensors",
|
| 1096 |
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"patch_embedding.bias": "diffusion_pytorch_model-00001-of-00006.safetensors",
|
| 1097 |
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"patch_embedding.weight": "diffusion_pytorch_model-00001-of-00006.safetensors",
|
| 1098 |
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"proj_out.bias": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1099 |
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"proj_out.weight": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1100 |
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"patch_short.bias": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1101 |
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"patch_short.weight": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1102 |
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"patch_mid.bias": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1103 |
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"patch_mid.weight": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1104 |
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"patch_long.bias": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1105 |
+
"patch_long.weight": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1106 |
+
"norm_out.scale_shift_table": "diffusion_pytorch_model-00001-of-00006.safetensors"
|
| 1107 |
+
}
|
| 1108 |
+
}
|
Helios/checkpoints/Helios-Distilled/transformer_ode/config.json
ADDED
|
@@ -0,0 +1,32 @@
|
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|
|
| 1 |
+
{
|
| 2 |
+
"_class_name": "HeliosTransformer3DModel",
|
| 3 |
+
"_diffusers_version": "0.37.0.dev0",
|
| 4 |
+
"added_kv_proj_dim": null,
|
| 5 |
+
"attention_head_dim": 128,
|
| 6 |
+
"cross_attn_norm": true,
|
| 7 |
+
"eps": 1e-06,
|
| 8 |
+
"ffn_dim": 13824,
|
| 9 |
+
"freq_dim": 256,
|
| 10 |
+
"has_multi_term_memory_patch": true,
|
| 11 |
+
"history_scale_mode": "per_head",
|
| 12 |
+
"in_channels": 16,
|
| 13 |
+
"is_amplify_history": false,
|
| 14 |
+
"num_attention_heads": 40,
|
| 15 |
+
"num_layers": 40,
|
| 16 |
+
"out_channels": 16,
|
| 17 |
+
"patch_size": [
|
| 18 |
+
1,
|
| 19 |
+
2,
|
| 20 |
+
2
|
| 21 |
+
],
|
| 22 |
+
"qk_norm": "rms_norm_across_heads",
|
| 23 |
+
"rope_dim": [
|
| 24 |
+
44,
|
| 25 |
+
42,
|
| 26 |
+
42
|
| 27 |
+
],
|
| 28 |
+
"rope_theta": 10000.0,
|
| 29 |
+
"guidance_cross_attn": true,
|
| 30 |
+
"text_dim": 4096,
|
| 31 |
+
"zero_history_timestep": true
|
| 32 |
+
}
|
Helios/checkpoints/Helios-Distilled/transformer_ode/diffusion_pytorch_model.safetensors.index.json
ADDED
|
@@ -0,0 +1,1108 @@
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|
| 1107 |
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|
| 1108 |
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|
Helios/checkpoints/Helios-Distilled/vae/config.json
ADDED
|
@@ -0,0 +1,56 @@
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|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
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|
| 3 |
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|
| 4 |
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
+
4,
|
| 10 |
+
4
|
| 11 |
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],
|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
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|
| 21 |
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|
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
+
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|
| 30 |
+
],
|
| 31 |
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|
| 32 |
+
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|
| 33 |
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|
| 34 |
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|
| 35 |
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2.6558,
|
| 36 |
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1.2196,
|
| 37 |
+
1.7708,
|
| 38 |
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2.6052,
|
| 39 |
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2.0743,
|
| 40 |
+
3.2687,
|
| 41 |
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2.1526,
|
| 42 |
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2.8652,
|
| 43 |
+
1.5579,
|
| 44 |
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1.6382,
|
| 45 |
+
1.1253,
|
| 46 |
+
2.8251,
|
| 47 |
+
1.916
|
| 48 |
+
],
|
| 49 |
+
"num_res_blocks": 2,
|
| 50 |
+
"temperal_downsample": [
|
| 51 |
+
false,
|
| 52 |
+
true,
|
| 53 |
+
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|
| 54 |
+
],
|
| 55 |
+
"z_dim": 16
|
| 56 |
+
}
|
Helios/checkpoints/Helios-Mid/guider/guider_config.json
ADDED
|
@@ -0,0 +1,11 @@
|
|
|
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|
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|
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|
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|
|
| 1 |
+
{
|
| 2 |
+
"_class_name": "ClassifierFreeZeroStarGuidance",
|
| 3 |
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"_diffusers_version": "0.38.0.dev0",
|
| 4 |
+
"enabled": true,
|
| 5 |
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"guidance_rescale": 0.0,
|
| 6 |
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"guidance_scale": 5.0,
|
| 7 |
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"start": 0.0,
|
| 8 |
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"stop": 1.0,
|
| 9 |
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"use_original_formulation": false,
|
| 10 |
+
"zero_init_steps": 2
|
| 11 |
+
}
|
Helios/checkpoints/Helios-Mid/scheduler/scheduler_config.json
ADDED
|
@@ -0,0 +1,26 @@
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_class_name": "HeliosScheduler",
|
| 3 |
+
"_diffusers_version": "0.37.0.dev0",
|
| 4 |
+
"disable_corrector": [],
|
| 5 |
+
"gamma": 0.3333333333333333,
|
| 6 |
+
"lower_order_final": true,
|
| 7 |
+
"num_train_timesteps": 1000,
|
| 8 |
+
"predict_x0": true,
|
| 9 |
+
"prediction_type": "flow_prediction",
|
| 10 |
+
"scheduler_type": "unipc",
|
| 11 |
+
"shift": 1.0,
|
| 12 |
+
"solver_order": 2,
|
| 13 |
+
"solver_p": null,
|
| 14 |
+
"solver_type": "bh2",
|
| 15 |
+
"stage_range": [
|
| 16 |
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0,
|
| 17 |
+
0.3333333333333333,
|
| 18 |
+
0.6666666666666666,
|
| 19 |
+
1
|
| 20 |
+
],
|
| 21 |
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"stages": 3,
|
| 22 |
+
"time_shift_type": "exponential",
|
| 23 |
+
"thresholding": false,
|
| 24 |
+
"use_dynamic_shifting": true,
|
| 25 |
+
"use_flow_sigmas": true
|
| 26 |
+
}
|
Helios/checkpoints/Helios-Mid/text_encoder/config.json
ADDED
|
@@ -0,0 +1,34 @@
|
|
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|
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|
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|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "google/umt5-xxl",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"UMT5EncoderModel"
|
| 5 |
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],
|
| 6 |
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"classifier_dropout": 0.0,
|
| 7 |
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"d_ff": 10240,
|
| 8 |
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"d_kv": 64,
|
| 9 |
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"d_model": 4096,
|
| 10 |
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"decoder_start_token_id": 0,
|
| 11 |
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"dense_act_fn": "gelu_new",
|
| 12 |
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"dropout_rate": 0.1,
|
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Helios/checkpoints/Helios-Mid/tokenizer/special_tokens_map.json
ADDED
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@@ -0,0 +1,332 @@
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|
| 2 |
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| 137 |
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| 146 |
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| 149 |
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|
| 303 |
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|
| 304 |
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| 305 |
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|
| 306 |
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| 307 |
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| 309 |
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|
| 310 |
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},
|
| 311 |
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|
| 312 |
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|
| 313 |
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| 314 |
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|
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|
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|
| 317 |
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|
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|
| 321 |
+
"normalized": false,
|
| 322 |
+
"rstrip": false,
|
| 323 |
+
"single_word": false
|
| 324 |
+
},
|
| 325 |
+
"unk_token": {
|
| 326 |
+
"content": "<unk>",
|
| 327 |
+
"lstrip": false,
|
| 328 |
+
"normalized": false,
|
| 329 |
+
"rstrip": false,
|
| 330 |
+
"single_word": false
|
| 331 |
+
}
|
| 332 |
+
}
|
Helios/checkpoints/Helios-Mid/tokenizer/tokenizer_config.json
ADDED
|
@@ -0,0 +1,2749 @@
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| 1 |
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{
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| 2 |
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| 3 |
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| 4 |
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| 5 |
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| 8 |
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| 9 |
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| 10 |
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| 11 |
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| 12 |
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| 13 |
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| 14 |
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| 15 |
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| 16 |
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| 17 |
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| 18 |
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| 19 |
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| 20 |
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| 21 |
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| 25 |
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| 26 |
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| 28 |
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| 32 |
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| 34 |
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| 36 |
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| 37 |
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| 76 |
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|
| 2508 |
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|
| 2509 |
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|
| 2510 |
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| 2512 |
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|
| 2513 |
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| 2514 |
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|
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| 2516 |
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| 2517 |
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| 2520 |
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| 2525 |
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| 2528 |
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| 2532 |
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| 2533 |
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| 2548 |
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|
| 2550 |
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|
| 2551 |
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|
| 2552 |
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|
| 2553 |
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|
| 2554 |
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|
| 2555 |
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| 2556 |
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| 2557 |
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| 2558 |
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| 2560 |
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| 2568 |
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| 2574 |
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| 2580 |
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| 2585 |
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| 2586 |
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| 2590 |
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| 2591 |
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| 2592 |
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| 2593 |
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| 2594 |
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| 2596 |
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| 2598 |
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| 2600 |
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| 2601 |
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| 2602 |
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| 2603 |
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| 2605 |
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| 2606 |
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| 2608 |
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| 2609 |
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| 2610 |
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| 2611 |
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| 2612 |
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| 2613 |
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| 2614 |
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| 2615 |
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| 2616 |
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| 2617 |
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| 2618 |
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|
| 2619 |
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| 2620 |
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| 2621 |
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| 2622 |
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| 2623 |
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| 2624 |
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| 2625 |
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| 2626 |
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| 2627 |
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| 2628 |
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| 2629 |
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| 2630 |
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| 2631 |
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| 2632 |
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| 2633 |
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| 2634 |
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| 2635 |
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| 2636 |
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| 2637 |
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| 2638 |
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| 2639 |
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| 2640 |
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| 2641 |
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| 2642 |
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| 2643 |
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| 2644 |
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| 2645 |
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| 2646 |
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| 2647 |
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| 2648 |
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| 2649 |
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| 2650 |
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| 2651 |
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| 2652 |
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| 2653 |
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|
| 2654 |
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|
| 2655 |
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|
| 2656 |
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|
| 2657 |
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"<extra_id_220>",
|
| 2658 |
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|
| 2659 |
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"<extra_id_222>",
|
| 2660 |
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"<extra_id_223>",
|
| 2661 |
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|
| 2662 |
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|
| 2663 |
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"<extra_id_226>",
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| 2664 |
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|
| 2665 |
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"<extra_id_228>",
|
| 2666 |
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|
| 2667 |
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| 2668 |
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|
| 2669 |
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"<extra_id_232>",
|
| 2670 |
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|
| 2671 |
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| 2672 |
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| 2673 |
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| 2674 |
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|
| 2675 |
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|
| 2676 |
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|
| 2677 |
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"<extra_id_240>",
|
| 2678 |
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|
| 2679 |
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"<extra_id_242>",
|
| 2680 |
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|
| 2681 |
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|
| 2682 |
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|
| 2683 |
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"<extra_id_246>",
|
| 2684 |
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|
| 2685 |
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"<extra_id_248>",
|
| 2686 |
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"<extra_id_249>",
|
| 2687 |
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"<extra_id_250>",
|
| 2688 |
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|
| 2689 |
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"<extra_id_252>",
|
| 2690 |
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|
| 2691 |
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|
| 2692 |
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|
| 2693 |
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|
| 2694 |
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|
| 2695 |
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|
| 2696 |
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|
| 2697 |
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"<extra_id_260>",
|
| 2698 |
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|
| 2699 |
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|
| 2700 |
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"<extra_id_263>",
|
| 2701 |
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"<extra_id_264>",
|
| 2702 |
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"<extra_id_265>",
|
| 2703 |
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"<extra_id_266>",
|
| 2704 |
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"<extra_id_267>",
|
| 2705 |
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"<extra_id_268>",
|
| 2706 |
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"<extra_id_269>",
|
| 2707 |
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"<extra_id_270>",
|
| 2708 |
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"<extra_id_271>",
|
| 2709 |
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"<extra_id_272>",
|
| 2710 |
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"<extra_id_273>",
|
| 2711 |
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"<extra_id_274>",
|
| 2712 |
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"<extra_id_275>",
|
| 2713 |
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"<extra_id_276>",
|
| 2714 |
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"<extra_id_277>",
|
| 2715 |
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"<extra_id_278>",
|
| 2716 |
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"<extra_id_279>",
|
| 2717 |
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"<extra_id_280>",
|
| 2718 |
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"<extra_id_281>",
|
| 2719 |
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"<extra_id_282>",
|
| 2720 |
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"<extra_id_283>",
|
| 2721 |
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"<extra_id_284>",
|
| 2722 |
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"<extra_id_285>",
|
| 2723 |
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"<extra_id_286>",
|
| 2724 |
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"<extra_id_287>",
|
| 2725 |
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"<extra_id_288>",
|
| 2726 |
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"<extra_id_289>",
|
| 2727 |
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"<extra_id_290>",
|
| 2728 |
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"<extra_id_291>",
|
| 2729 |
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"<extra_id_292>",
|
| 2730 |
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"<extra_id_293>",
|
| 2731 |
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"<extra_id_294>",
|
| 2732 |
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"<extra_id_295>",
|
| 2733 |
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"<extra_id_296>",
|
| 2734 |
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"<extra_id_297>",
|
| 2735 |
+
"<extra_id_298>",
|
| 2736 |
+
"<extra_id_299>"
|
| 2737 |
+
],
|
| 2738 |
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"bos_token": "<s>",
|
| 2739 |
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"clean_up_tokenization_spaces": true,
|
| 2740 |
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"eos_token": "</s>",
|
| 2741 |
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"extra_ids": 300,
|
| 2742 |
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"extra_special_tokens": {},
|
| 2743 |
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"model_max_length": 1000000000000000019884624838656,
|
| 2744 |
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"pad_token": "<pad>",
|
| 2745 |
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"sp_model_kwargs": {},
|
| 2746 |
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"spaces_between_special_tokens": false,
|
| 2747 |
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"tokenizer_class": "T5Tokenizer",
|
| 2748 |
+
"unk_token": "<unk>"
|
| 2749 |
+
}
|
Helios/checkpoints/Helios-Mid/transformer/config.json
ADDED
|
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_class_name": "HeliosTransformer3DModel",
|
| 3 |
+
"_diffusers_version": "0.37.0.dev0",
|
| 4 |
+
"added_kv_proj_dim": null,
|
| 5 |
+
"attention_head_dim": 128,
|
| 6 |
+
"cross_attn_norm": true,
|
| 7 |
+
"eps": 1e-06,
|
| 8 |
+
"ffn_dim": 13824,
|
| 9 |
+
"freq_dim": 256,
|
| 10 |
+
"has_multi_term_memory_patch": true,
|
| 11 |
+
"history_scale_mode": "per_head",
|
| 12 |
+
"in_channels": 16,
|
| 13 |
+
"is_amplify_history": false,
|
| 14 |
+
"num_attention_heads": 40,
|
| 15 |
+
"num_layers": 40,
|
| 16 |
+
"out_channels": 16,
|
| 17 |
+
"patch_size": [
|
| 18 |
+
1,
|
| 19 |
+
2,
|
| 20 |
+
2
|
| 21 |
+
],
|
| 22 |
+
"qk_norm": "rms_norm_across_heads",
|
| 23 |
+
"rope_dim": [
|
| 24 |
+
44,
|
| 25 |
+
42,
|
| 26 |
+
42
|
| 27 |
+
],
|
| 28 |
+
"rope_theta": 10000.0,
|
| 29 |
+
"guidance_cross_attn": true,
|
| 30 |
+
"text_dim": 4096,
|
| 31 |
+
"zero_history_timestep": true
|
| 32 |
+
}
|
Helios/checkpoints/Helios-Mid/transformer/diffusion_pytorch_model.safetensors.index.json
ADDED
|
@@ -0,0 +1,1108 @@
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
|
|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
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|
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|
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"patch_long.bias": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1105 |
+
"patch_long.weight": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1106 |
+
"norm_out.scale_shift_table": "diffusion_pytorch_model-00001-of-00006.safetensors"
|
| 1107 |
+
}
|
| 1108 |
+
}
|
Helios/checkpoints/Helios-Mid/transformer_init/config.json
ADDED
|
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_class_name": "HeliosTransformer3DModel",
|
| 3 |
+
"_diffusers_version": "0.37.0.dev0",
|
| 4 |
+
"added_kv_proj_dim": null,
|
| 5 |
+
"attention_head_dim": 128,
|
| 6 |
+
"cross_attn_norm": true,
|
| 7 |
+
"eps": 1e-06,
|
| 8 |
+
"ffn_dim": 13824,
|
| 9 |
+
"freq_dim": 256,
|
| 10 |
+
"has_multi_term_memory_patch": true,
|
| 11 |
+
"history_scale_mode": "per_head",
|
| 12 |
+
"in_channels": 16,
|
| 13 |
+
"is_amplify_history": false,
|
| 14 |
+
"num_attention_heads": 40,
|
| 15 |
+
"num_layers": 40,
|
| 16 |
+
"out_channels": 16,
|
| 17 |
+
"patch_size": [
|
| 18 |
+
1,
|
| 19 |
+
2,
|
| 20 |
+
2
|
| 21 |
+
],
|
| 22 |
+
"qk_norm": "rms_norm_across_heads",
|
| 23 |
+
"rope_dim": [
|
| 24 |
+
44,
|
| 25 |
+
42,
|
| 26 |
+
42
|
| 27 |
+
],
|
| 28 |
+
"rope_theta": 10000.0,
|
| 29 |
+
"guidance_cross_attn": true,
|
| 30 |
+
"text_dim": 4096,
|
| 31 |
+
"zero_history_timestep": true
|
| 32 |
+
}
|
Helios/checkpoints/Helios-Mid/transformer_init/diffusion_pytorch_model.safetensors.index.json
ADDED
|
@@ -0,0 +1,1108 @@
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|
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|
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|
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|
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|
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|
| 1089 |
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|
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|
| 1091 |
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|
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|
| 1093 |
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|
| 1094 |
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|
| 1095 |
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|
| 1096 |
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"patch_embedding.bias": "diffusion_pytorch_model-00001-of-00006.safetensors",
|
| 1097 |
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"patch_embedding.weight": "diffusion_pytorch_model-00001-of-00006.safetensors",
|
| 1098 |
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"proj_out.bias": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1099 |
+
"proj_out.weight": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1100 |
+
"patch_short.bias": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1101 |
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"patch_short.weight": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1102 |
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"patch_mid.bias": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1103 |
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"patch_mid.weight": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1104 |
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"patch_long.bias": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1105 |
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"patch_long.weight": "diffusion_pytorch_model-00006-of-00006.safetensors",
|
| 1106 |
+
"norm_out.scale_shift_table": "diffusion_pytorch_model-00001-of-00006.safetensors"
|
| 1107 |
+
}
|
| 1108 |
+
}
|
Helios/checkpoints/Helios-Mid/vae/config.json
ADDED
|
@@ -0,0 +1,56 @@
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|
| 1 |
+
{
|
| 2 |
+
"_class_name": "AutoencoderKLWan",
|
| 3 |
+
"_diffusers_version": "0.37.0.dev0",
|
| 4 |
+
"attn_scales": [],
|
| 5 |
+
"base_dim": 96,
|
| 6 |
+
"dim_mult": [
|
| 7 |
+
1,
|
| 8 |
+
2,
|
| 9 |
+
4,
|
| 10 |
+
4
|
| 11 |
+
],
|
| 12 |
+
"dropout": 0.0,
|
| 13 |
+
"latents_mean": [
|
| 14 |
+
-0.7571,
|
| 15 |
+
-0.7089,
|
| 16 |
+
-0.9113,
|
| 17 |
+
0.1075,
|
| 18 |
+
-0.1745,
|
| 19 |
+
0.9653,
|
| 20 |
+
-0.1517,
|
| 21 |
+
1.5508,
|
| 22 |
+
0.4134,
|
| 23 |
+
-0.0715,
|
| 24 |
+
0.5517,
|
| 25 |
+
-0.3632,
|
| 26 |
+
-0.1922,
|
| 27 |
+
-0.9497,
|
| 28 |
+
0.2503,
|
| 29 |
+
-0.2921
|
| 30 |
+
],
|
| 31 |
+
"latents_std": [
|
| 32 |
+
2.8184,
|
| 33 |
+
1.4541,
|
| 34 |
+
2.3275,
|
| 35 |
+
2.6558,
|
| 36 |
+
1.2196,
|
| 37 |
+
1.7708,
|
| 38 |
+
2.6052,
|
| 39 |
+
2.0743,
|
| 40 |
+
3.2687,
|
| 41 |
+
2.1526,
|
| 42 |
+
2.8652,
|
| 43 |
+
1.5579,
|
| 44 |
+
1.6382,
|
| 45 |
+
1.1253,
|
| 46 |
+
2.8251,
|
| 47 |
+
1.916
|
| 48 |
+
],
|
| 49 |
+
"num_res_blocks": 2,
|
| 50 |
+
"temperal_downsample": [
|
| 51 |
+
false,
|
| 52 |
+
true,
|
| 53 |
+
true
|
| 54 |
+
],
|
| 55 |
+
"z_dim": 16
|
| 56 |
+
}
|
Helios/eval/checkpoints/get_checkpoints.sh
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
hf download BestWishYsh/HeliosBench-Weights --local-dir ./
|
Helios/eval/playground/helios_t2v_prompts.csv
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
Helios/eval/playground/results/all_models_merged.json
ADDED
|
@@ -0,0 +1,31 @@
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|
| 1 |
+
{
|
| 2 |
+
"num_models": 1,
|
| 3 |
+
"score_type": "rating",
|
| 4 |
+
"metrics": [
|
| 5 |
+
"aesthetic",
|
| 6 |
+
"drifting_aesthetic",
|
| 7 |
+
"drifting_motion_smoothness",
|
| 8 |
+
"drifting_naturalness",
|
| 9 |
+
"drifting_semantic",
|
| 10 |
+
"motion_amplitude",
|
| 11 |
+
"motion_smoothness",
|
| 12 |
+
"naturalness",
|
| 13 |
+
"semantic",
|
| 14 |
+
"total_weighted_rating"
|
| 15 |
+
],
|
| 16 |
+
"models": {
|
| 17 |
+
"toy-video": {
|
| 18 |
+
"aesthetic": 9,
|
| 19 |
+
"motion_amplitude": 3,
|
| 20 |
+
"motion_smoothness": 10,
|
| 21 |
+
"naturalness": 7,
|
| 22 |
+
"semantic": 8,
|
| 23 |
+
"drifting_aesthetic": 8,
|
| 24 |
+
"drifting_motion_smoothness": 10,
|
| 25 |
+
"drifting_naturalness": 10,
|
| 26 |
+
"drifting_semantic": 10,
|
| 27 |
+
"total_weighted_rating": 8.247
|
| 28 |
+
}
|
| 29 |
+
},
|
| 30 |
+
"rating_scale": 10
|
| 31 |
+
}
|
Helios/eval/playground/results/toy-video/aesthetic_results.json
ADDED
|
@@ -0,0 +1,17 @@
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|
| 1 |
+
{
|
| 2 |
+
"metric": "aesthetic",
|
| 3 |
+
"average_score": 0.6564263701438904,
|
| 4 |
+
"num_videos": 2,
|
| 5 |
+
"per_video_results": [
|
| 6 |
+
{
|
| 7 |
+
"id": 2,
|
| 8 |
+
"video_name": "2_240_ori81.mp4",
|
| 9 |
+
"aesthetic_score": 0.6802743077278137
|
| 10 |
+
},
|
| 11 |
+
{
|
| 12 |
+
"id": 239,
|
| 13 |
+
"video_name": "239_120_ori129.mp4",
|
| 14 |
+
"aesthetic_score": 0.632578432559967
|
| 15 |
+
}
|
| 16 |
+
]
|
| 17 |
+
}
|
Helios/eval/playground/results/toy-video/drifting_aesthetic_results.json
ADDED
|
@@ -0,0 +1,22 @@
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|
|
|
|
| 1 |
+
{
|
| 2 |
+
"metric": "drifting_aesthetic",
|
| 3 |
+
"description": "Start-end contrast of aesthetic (first/last 15% frames)",
|
| 4 |
+
"average_drift_score": 0.027865678071975708,
|
| 5 |
+
"num_videos": 2,
|
| 6 |
+
"per_video_results": [
|
| 7 |
+
{
|
| 8 |
+
"id": 2,
|
| 9 |
+
"video_name": "2_240_ori81.mp4",
|
| 10 |
+
"drift_aesthetic_score": 0.002410709857940674,
|
| 11 |
+
"start_aesthetic_score": 0.6802361011505127,
|
| 12 |
+
"end_aesthetic_score": 0.677825391292572
|
| 13 |
+
},
|
| 14 |
+
{
|
| 15 |
+
"id": 239,
|
| 16 |
+
"video_name": "239_120_ori129.mp4",
|
| 17 |
+
"drift_aesthetic_score": 0.05332064628601074,
|
| 18 |
+
"start_aesthetic_score": 0.6535003185272217,
|
| 19 |
+
"end_aesthetic_score": 0.6001796722412109
|
| 20 |
+
}
|
| 21 |
+
]
|
| 22 |
+
}
|
Helios/eval/playground/results/toy-video/drifting_motion_smoothness_results.json
ADDED
|
@@ -0,0 +1,22 @@
|
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|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"metric": "drifting_motion_smoothness",
|
| 3 |
+
"description": "Start-end contrast of motion smoothness (first/last 15% frames)",
|
| 4 |
+
"average_drift_score": 0.0009624073235373065,
|
| 5 |
+
"num_videos": 2,
|
| 6 |
+
"per_video_results": [
|
| 7 |
+
{
|
| 8 |
+
"id": 2,
|
| 9 |
+
"video_name": "2_240_ori81.mp4",
|
| 10 |
+
"drift_motion_smoothness_score": 0.0016874511579993978,
|
| 11 |
+
"start_motion_smoothness_score": 0.9880413336530265,
|
| 12 |
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"end_motion_smoothness_score": 0.9897287848110259
|
| 13 |
+
},
|
| 14 |
+
{
|
| 15 |
+
"id": 239,
|
| 16 |
+
"video_name": "239_120_ori129.mp4",
|
| 17 |
+
"drift_motion_smoothness_score": 0.0002373634890752152,
|
| 18 |
+
"start_motion_smoothness_score": 0.9948747067013198,
|
| 19 |
+
"end_motion_smoothness_score": 0.995112070190395
|
| 20 |
+
}
|
| 21 |
+
]
|
| 22 |
+
}
|
Helios/eval/playground/results/toy-video/drifting_naturalness_results.json
ADDED
|
@@ -0,0 +1,27 @@
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"metric": "drifting_naturalness",
|
| 3 |
+
"description": "Start-end contrast of naturalness (first/last 15% frames)",
|
| 4 |
+
"average_drift_score": 0.0,
|
| 5 |
+
"num_videos": 2,
|
| 6 |
+
"model_name": "gpt-5.2-2025-12-11",
|
| 7 |
+
"per_video_results": [
|
| 8 |
+
{
|
| 9 |
+
"id": 2,
|
| 10 |
+
"video_name": "2_240_ori81.mp4",
|
| 11 |
+
"drift_naturalness_score": 0.0,
|
| 12 |
+
"start_naturalness_score": 0.0,
|
| 13 |
+
"end_naturalness_score": 0.0,
|
| 14 |
+
"start_raw_score": "1",
|
| 15 |
+
"end_raw_score": "1"
|
| 16 |
+
},
|
| 17 |
+
{
|
| 18 |
+
"id": 239,
|
| 19 |
+
"video_name": "239_120_ori129.mp4",
|
| 20 |
+
"drift_naturalness_score": 0.0,
|
| 21 |
+
"start_naturalness_score": 0.75,
|
| 22 |
+
"end_naturalness_score": 0.75,
|
| 23 |
+
"start_raw_score": "4",
|
| 24 |
+
"end_raw_score": "4"
|
| 25 |
+
}
|
| 26 |
+
]
|
| 27 |
+
}
|
Helios/eval/playground/results/toy-video/drifting_semantic_results.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"metric": "drifting_semantic",
|
| 3 |
+
"description": "Start-end contrast of semantic consistency (first/last 15% frames)",
|
| 4 |
+
"average_drift_score": 0.006637156009674072,
|
| 5 |
+
"num_videos": 2,
|
| 6 |
+
"per_video_results": [
|
| 7 |
+
{
|
| 8 |
+
"id": 2,
|
| 9 |
+
"video_name": "2_240_ori81.mp4",
|
| 10 |
+
"prompt": "A stunning mid-afternoon landscape photograph with a low camera angle, showcasing several giant wooly mammoths treading through a snowy meadow. Their long, wooly fur gently billows in the brisk wind as they move, creating a sense of natural movement. Snow-covered trees and dramatic snow-capped mountains loom in the distance, adding to the majestic setting. Wispy clouds and a high sun cast a warm glow over the scene, enhancing the serene and awe-inspiring atmosphere. The depth of field brings out the detailed textures of the mammoths and the snowy environment, capturing every nuance of these prehistoric giants in breathtaking clarity.",
|
| 11 |
+
"drift_semantic_score": 0.0044051408767700195,
|
| 12 |
+
"start_semantic_score": 0.2939550578594208,
|
| 13 |
+
"end_semantic_score": 0.2983601987361908
|
| 14 |
+
},
|
| 15 |
+
{
|
| 16 |
+
"id": 239,
|
| 17 |
+
"video_name": "239_120_ori129.mp4",
|
| 18 |
+
"prompt": "An old man in blue jeans and a white T-shirt takes a leisurely stroll along a bustling street in Mumbai, India, during a breathtaking sunset. He walks with a gentle sway, his weathered face reflecting the warm hues of the setting sun. His hands rest casually in his pockets, and he appears content and at peace. The background features a vibrant mix of colorful buildings, street vendors, and pedestrians, with the sky painted in shades of orange, pink, and purple. The photo has a nostalgic and documentary style, capturing the essence of a serene moment amidst the city's energy. A medium shot with a soft focus on the old man.",
|
| 19 |
+
"drift_semantic_score": 0.008869171142578125,
|
| 20 |
+
"start_semantic_score": 0.2703956365585327,
|
| 21 |
+
"end_semantic_score": 0.2615264654159546
|
| 22 |
+
}
|
| 23 |
+
]
|
| 24 |
+
}
|
Helios/eval/playground/results/toy-video/merged_results.json
ADDED
|
@@ -0,0 +1,74 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"rating_scale": 10,
|
| 3 |
+
"summary": {
|
| 4 |
+
"non_drifting": {
|
| 5 |
+
"aesthetic": {
|
| 6 |
+
"name": "Aesthetic",
|
| 7 |
+
"raw_score": 0.6564263701438904,
|
| 8 |
+
"normalized_score": 0.6564263701438904,
|
| 9 |
+
"rating": 9,
|
| 10 |
+
"num_videos": 2
|
| 11 |
+
},
|
| 12 |
+
"motion_amplitude": {
|
| 13 |
+
"name": "Motion Amplitude",
|
| 14 |
+
"raw_score": 0.12899818271398544,
|
| 15 |
+
"normalized_score": 0.12899818271398544,
|
| 16 |
+
"rating": 3,
|
| 17 |
+
"num_videos": 2
|
| 18 |
+
},
|
| 19 |
+
"motion_smoothness": {
|
| 20 |
+
"name": "Motion Smoothness",
|
| 21 |
+
"raw_score": 0.9922347277689807,
|
| 22 |
+
"normalized_score": 0.9922347277689807,
|
| 23 |
+
"rating": 10,
|
| 24 |
+
"num_videos": 2
|
| 25 |
+
},
|
| 26 |
+
"naturalness": {
|
| 27 |
+
"name": "Naturalness",
|
| 28 |
+
"raw_score": 0.5,
|
| 29 |
+
"normalized_score": 0.5,
|
| 30 |
+
"rating": 7,
|
| 31 |
+
"num_videos": 2
|
| 32 |
+
},
|
| 33 |
+
"semantic": {
|
| 34 |
+
"name": "Semantic",
|
| 35 |
+
"raw_score": 0.2817579507827759,
|
| 36 |
+
"normalized_score": 0.2817579507827759,
|
| 37 |
+
"rating": 8,
|
| 38 |
+
"num_videos": 2
|
| 39 |
+
}
|
| 40 |
+
},
|
| 41 |
+
"drifting": {
|
| 42 |
+
"drifting_aesthetic": {
|
| 43 |
+
"name": "Drifting Aesthetic",
|
| 44 |
+
"raw_score": 0.027865678071975708,
|
| 45 |
+
"normalized_score": 0.027865678071975708,
|
| 46 |
+
"rating": 8,
|
| 47 |
+
"num_videos": 2
|
| 48 |
+
},
|
| 49 |
+
"drifting_motion_smoothness": {
|
| 50 |
+
"name": "Drifting Motion Smoothness",
|
| 51 |
+
"raw_score": 0.0009624073235373065,
|
| 52 |
+
"normalized_score": 0.0009624073235373065,
|
| 53 |
+
"rating": 10,
|
| 54 |
+
"num_videos": 2
|
| 55 |
+
},
|
| 56 |
+
"drifting_naturalness": {
|
| 57 |
+
"name": "Drifting Naturalness",
|
| 58 |
+
"raw_score": 0.0,
|
| 59 |
+
"normalized_score": 0.0,
|
| 60 |
+
"rating": 10,
|
| 61 |
+
"num_videos": 2
|
| 62 |
+
},
|
| 63 |
+
"drifting_semantic": {
|
| 64 |
+
"name": "Drifting Semantic",
|
| 65 |
+
"raw_score": 0.006637156009674072,
|
| 66 |
+
"normalized_score": 0.006637156009674072,
|
| 67 |
+
"rating": 10,
|
| 68 |
+
"num_videos": 2
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
"total_weighted_rating": 8.247
|
| 72 |
+
},
|
| 73 |
+
"per_video": {}
|
| 74 |
+
}
|
Helios/eval/playground/results/toy-video/motion_amplitude_results.json
ADDED
|
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"metric": "motion_fb",
|
| 3 |
+
"average_score": 0.12899818271398544,
|
| 4 |
+
"num_videos": 2,
|
| 5 |
+
"per_video_results": [
|
| 6 |
+
{
|
| 7 |
+
"id": 2,
|
| 8 |
+
"video_name": "2_240_ori81.mp4",
|
| 9 |
+
"motion_fb": 0.19912056624889374
|
| 10 |
+
},
|
| 11 |
+
{
|
| 12 |
+
"id": 239,
|
| 13 |
+
"video_name": "239_120_ori129.mp4",
|
| 14 |
+
"motion_fb": 0.05887579917907715
|
| 15 |
+
}
|
| 16 |
+
]
|
| 17 |
+
}
|
Helios/eval/playground/results/toy-video/motion_smoothness_results.json
ADDED
|
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"metric": "motion_smoothness",
|
| 3 |
+
"average_score": 0.9922347277689807,
|
| 4 |
+
"num_videos": 2,
|
| 5 |
+
"per_video_results": [
|
| 6 |
+
{
|
| 7 |
+
"id": 2,
|
| 8 |
+
"video_name": "2_240_ori81.mp4",
|
| 9 |
+
"motion_smoothness_score": 0.9896801291593404
|
| 10 |
+
},
|
| 11 |
+
{
|
| 12 |
+
"id": 239,
|
| 13 |
+
"video_name": "239_120_ori129.mp4",
|
| 14 |
+
"motion_smoothness_score": 0.9947893263786209
|
| 15 |
+
}
|
| 16 |
+
]
|
| 17 |
+
}
|
Helios/eval/playground/results/toy-video/naturalness_results.json
ADDED
|
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"metric": "naturalness",
|
| 3 |
+
"average_score": 0.5,
|
| 4 |
+
"num_videos": 2,
|
| 5 |
+
"model_name": "gpt-5.2-2025-12-11",
|
| 6 |
+
"num_frames_per_video": 16,
|
| 7 |
+
"per_video_results": [
|
| 8 |
+
{
|
| 9 |
+
"id": 2,
|
| 10 |
+
"video_name": "2_240_ori81.mp4",
|
| 11 |
+
"naturalness_score": 0.25,
|
| 12 |
+
"raw_score": "2"
|
| 13 |
+
},
|
| 14 |
+
{
|
| 15 |
+
"id": 239,
|
| 16 |
+
"video_name": "239_120_ori129.mp4",
|
| 17 |
+
"naturalness_score": 0.75,
|
| 18 |
+
"raw_score": "4"
|
| 19 |
+
}
|
| 20 |
+
]
|
| 21 |
+
}
|
Helios/eval/playground/results/toy-video/semantic_results.json
ADDED
|
@@ -0,0 +1,19 @@
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"metric": "semantic",
|
| 3 |
+
"average_score": 0.2817579507827759,
|
| 4 |
+
"num_videos": 2,
|
| 5 |
+
"per_video_results": [
|
| 6 |
+
{
|
| 7 |
+
"id": 2,
|
| 8 |
+
"video_name": "2_240_ori81.mp4",
|
| 9 |
+
"prompt": "A stunning mid-afternoon landscape photograph with a low camera angle, showcasing several giant wooly mammoths treading through a snowy meadow. Their long, wooly fur gently billows in the brisk wind as they move, creating a sense of natural movement. Snow-covered trees and dramatic snow-capped mountains loom in the distance, adding to the majestic setting. Wispy clouds and a high sun cast a warm glow over the scene, enhancing the serene and awe-inspiring atmosphere. The depth of field brings out the detailed textures of the mammoths and the snowy environment, capturing every nuance of these prehistoric giants in breathtaking clarity.",
|
| 10 |
+
"semantic_score": 0.29471662640571594
|
| 11 |
+
},
|
| 12 |
+
{
|
| 13 |
+
"id": 239,
|
| 14 |
+
"video_name": "239_120_ori129.mp4",
|
| 15 |
+
"prompt": "An old man in blue jeans and a white T-shirt takes a leisurely stroll along a bustling street in Mumbai, India, during a breathtaking sunset. He walks with a gentle sway, his weathered face reflecting the warm hues of the setting sun. His hands rest casually in his pockets, and he appears content and at peace. The background features a vibrant mix of colorful buildings, street vendors, and pedestrians, with the sky painted in shades of orange, pink, and purple. The photo has a nostalgic and documentary style, capturing the essence of a serene moment amidst the city's energy. A medium shot with a soft focus on the old man.",
|
| 16 |
+
"semantic_score": 0.2687992751598358
|
| 17 |
+
}
|
| 18 |
+
]
|
| 19 |
+
}
|
Helios/eval/utils/convert_json_to_excel.py
ADDED
|
@@ -0,0 +1,136 @@
|
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|
| 1 |
+
import argparse
|
| 2 |
+
import json
|
| 3 |
+
|
| 4 |
+
import pandas as pd
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
CUSTOM_ORDER = [
|
| 8 |
+
"total_weighted_rating",
|
| 9 |
+
"aesthetic",
|
| 10 |
+
"motion_amplitude",
|
| 11 |
+
"motion_smoothness",
|
| 12 |
+
"semantic",
|
| 13 |
+
"naturalness",
|
| 14 |
+
"drifting_aesthetic",
|
| 15 |
+
"drifting_motion_smoothness",
|
| 16 |
+
"drifting_semantic",
|
| 17 |
+
"drifting_naturalness",
|
| 18 |
+
]
|
| 19 |
+
|
| 20 |
+
SELECTED_METRICS = [
|
| 21 |
+
"total_weighted_rating",
|
| 22 |
+
"aesthetic",
|
| 23 |
+
"motion_amplitude",
|
| 24 |
+
"motion_smoothness",
|
| 25 |
+
"semantic",
|
| 26 |
+
"naturalness",
|
| 27 |
+
]
|
| 28 |
+
|
| 29 |
+
|
| 30 |
+
def json_to_excel(json_path, excel_path=None, use_selected_metrics=False, show_raw_values=False, score_type=""):
|
| 31 |
+
with open(json_path, "r") as f:
|
| 32 |
+
data = json.load(f)
|
| 33 |
+
|
| 34 |
+
models_data = data["models"]
|
| 35 |
+
df = pd.DataFrame.from_dict(models_data, orient="index")
|
| 36 |
+
|
| 37 |
+
df.reset_index(inplace=True)
|
| 38 |
+
df.rename(columns={"index": "model_name"}, inplace=True)
|
| 39 |
+
|
| 40 |
+
if use_selected_metrics:
|
| 41 |
+
available_cols = ["model_name"] + [col for col in SELECTED_METRICS if col in df.columns]
|
| 42 |
+
df = df[available_cols]
|
| 43 |
+
print(f"Selected {len(available_cols) - 1} metrics from available metrics")
|
| 44 |
+
|
| 45 |
+
valid_order = ["model_name"] + [col for col in CUSTOM_ORDER if col in df.columns]
|
| 46 |
+
df = df[valid_order]
|
| 47 |
+
print(f"Kept {len(valid_order) - 1} metrics as specified in CUSTOM_ORDER")
|
| 48 |
+
|
| 49 |
+
if excel_path is None:
|
| 50 |
+
excel_path = json_path.rsplit(".", 1)[0] + f"_{score_type}" + ".xlsx"
|
| 51 |
+
|
| 52 |
+
with pd.ExcelWriter(excel_path, engine="openpyxl") as writer:
|
| 53 |
+
df.to_excel(writer, sheet_name="Models", index=False)
|
| 54 |
+
|
| 55 |
+
metadata = pd.DataFrame(
|
| 56 |
+
{
|
| 57 |
+
"Property": ["timestamp", "num_models", "num_metrics", "filtered", "format"],
|
| 58 |
+
"Value": [
|
| 59 |
+
data.get("timestamp", "N/A"),
|
| 60 |
+
data.get("num_models", len(models_data)),
|
| 61 |
+
len(df.columns) - 1,
|
| 62 |
+
"Yes" if use_selected_metrics else "No",
|
| 63 |
+
"Raw Values" if show_raw_values else "Percentage",
|
| 64 |
+
],
|
| 65 |
+
}
|
| 66 |
+
)
|
| 67 |
+
metadata.to_excel(writer, sheet_name="Metadata", index=False)
|
| 68 |
+
|
| 69 |
+
worksheet = writer.sheets["Models"]
|
| 70 |
+
for idx, col in enumerate(df.columns):
|
| 71 |
+
max_length = max(df[col].astype(str).apply(len).max(), len(col))
|
| 72 |
+
if idx < 26:
|
| 73 |
+
col_letter = chr(65 + idx)
|
| 74 |
+
else:
|
| 75 |
+
col_letter = chr(65 + idx // 26 - 1) + chr(65 + idx % 26)
|
| 76 |
+
worksheet.column_dimensions[col_letter].width = min(max_length + 2, 50)
|
| 77 |
+
|
| 78 |
+
if col != "model_name" and pd.api.types.is_numeric_dtype(df[col]):
|
| 79 |
+
for row in range(2, len(df) + 2): # Start from row 2 (after header)
|
| 80 |
+
cell = worksheet[f"{col_letter}{row}"]
|
| 81 |
+
if cell.value is not None:
|
| 82 |
+
if col == "total_weighted_rating":
|
| 83 |
+
cell.number_format = "0.00"
|
| 84 |
+
elif show_raw_values:
|
| 85 |
+
cell.number_format = "0"
|
| 86 |
+
else:
|
| 87 |
+
cell.value = cell.value * 100
|
| 88 |
+
cell.number_format = '0.00"%"'
|
| 89 |
+
|
| 90 |
+
print(f"Conversion successful! Output file: {excel_path}")
|
| 91 |
+
print(f"Processed {len(df)} models with {len(df.columns) - 1} metrics")
|
| 92 |
+
print(f"Format: {'Raw values' if show_raw_values else 'Percentage'}")
|
| 93 |
+
|
| 94 |
+
return excel_path
|
| 95 |
+
|
| 96 |
+
|
| 97 |
+
if __name__ == "__main__":
|
| 98 |
+
parser = argparse.ArgumentParser()
|
| 99 |
+
|
| 100 |
+
parser.add_argument("--json_file", type=str, required=True, help="Input JSON file path")
|
| 101 |
+
parser.add_argument(
|
| 102 |
+
"--excel_file",
|
| 103 |
+
type=str,
|
| 104 |
+
required=True,
|
| 105 |
+
help="Output Excel file path (optional, defaults to input filename.xlsx)",
|
| 106 |
+
)
|
| 107 |
+
parser.add_argument("--filter", action="store_true", help="Use only metrics defined in SELECTED_METRICS list")
|
| 108 |
+
parser.add_argument(
|
| 109 |
+
"--score_type",
|
| 110 |
+
type=str,
|
| 111 |
+
choices=["raw", "normalized", "rating"],
|
| 112 |
+
default="rating",
|
| 113 |
+
help="Type of scores to use: 'raw', 'normalized', or 'rating'",
|
| 114 |
+
)
|
| 115 |
+
|
| 116 |
+
args = parser.parse_args()
|
| 117 |
+
|
| 118 |
+
if args.score_type == "rating":
|
| 119 |
+
raw_value = True
|
| 120 |
+
else:
|
| 121 |
+
raw_value = False
|
| 122 |
+
|
| 123 |
+
try:
|
| 124 |
+
json_to_excel(
|
| 125 |
+
args.json_file,
|
| 126 |
+
args.excel_file,
|
| 127 |
+
use_selected_metrics=args.filter,
|
| 128 |
+
show_raw_values=raw_value,
|
| 129 |
+
score_type=args.score_type,
|
| 130 |
+
)
|
| 131 |
+
except FileNotFoundError:
|
| 132 |
+
print(f"Error: File not found {args.json_file}")
|
| 133 |
+
except json.JSONDecodeError:
|
| 134 |
+
print(f"Error: {args.json_file} is not a valid JSON file")
|
| 135 |
+
except Exception as e:
|
| 136 |
+
print(f"Error: {e}")
|
Helios/eval/utils/extract_short_from_long.py
ADDED
|
@@ -0,0 +1,97 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import argparse
|
| 2 |
+
import os
|
| 3 |
+
import subprocess
|
| 4 |
+
from pathlib import Path
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
def extract_first_n_frames(input_root, output_root, num_frames=81):
|
| 8 |
+
input_path = Path(input_root)
|
| 9 |
+
output_path = Path(output_root)
|
| 10 |
+
|
| 11 |
+
for subfolder in input_path.iterdir():
|
| 12 |
+
if not subfolder.is_dir():
|
| 13 |
+
continue
|
| 14 |
+
|
| 15 |
+
print(f"Processing folder: {subfolder.name}")
|
| 16 |
+
|
| 17 |
+
output_subfolder = output_path / subfolder.name
|
| 18 |
+
output_subfolder.mkdir(parents=True, exist_ok=True)
|
| 19 |
+
|
| 20 |
+
video_files = list(subfolder.glob("*.mp4"))
|
| 21 |
+
|
| 22 |
+
for i, video_file in enumerate(video_files, 1):
|
| 23 |
+
original_name = video_file.stem
|
| 24 |
+
if "_ori" in original_name:
|
| 25 |
+
new_name = original_name.rsplit("_ori", 1)[0] + f"_ori{num_frames}.mp4"
|
| 26 |
+
else:
|
| 27 |
+
new_name = video_file.name
|
| 28 |
+
|
| 29 |
+
output_file = output_subfolder / new_name
|
| 30 |
+
if os.path.exists(output_file):
|
| 31 |
+
print(f"Skipping existing file: {output_file}")
|
| 32 |
+
continue
|
| 33 |
+
|
| 34 |
+
cmd = [
|
| 35 |
+
"ffmpeg",
|
| 36 |
+
"-i",
|
| 37 |
+
str(video_file),
|
| 38 |
+
"-vframes",
|
| 39 |
+
str(num_frames), # Extract only first N frames
|
| 40 |
+
"-map",
|
| 41 |
+
"0", # Copy all streams (video + audio)
|
| 42 |
+
"-c",
|
| 43 |
+
"copy", # Try direct copy (fastest, preserves all parameters)
|
| 44 |
+
"-y",
|
| 45 |
+
str(output_file),
|
| 46 |
+
]
|
| 47 |
+
|
| 48 |
+
try:
|
| 49 |
+
result = subprocess.run(cmd, capture_output=True)
|
| 50 |
+
|
| 51 |
+
if result.returncode != 0:
|
| 52 |
+
print(f" Direct copy failed for {video_file.name}, re-encoding...")
|
| 53 |
+
cmd = [
|
| 54 |
+
"ffmpeg",
|
| 55 |
+
"-i",
|
| 56 |
+
str(video_file),
|
| 57 |
+
"-vframes",
|
| 58 |
+
str(num_frames),
|
| 59 |
+
"-c:v",
|
| 60 |
+
"libx264", # Re-encode video
|
| 61 |
+
"-qp",
|
| 62 |
+
"0", # Lossless quality
|
| 63 |
+
"-c:a",
|
| 64 |
+
"copy", # Copy audio directly
|
| 65 |
+
"-map",
|
| 66 |
+
"0", # Copy all streams
|
| 67 |
+
"-y",
|
| 68 |
+
str(output_file),
|
| 69 |
+
]
|
| 70 |
+
subprocess.run(cmd, check=True, capture_output=True)
|
| 71 |
+
|
| 72 |
+
print(f" [{i}/{len(video_files)}] {video_file.name} -> {new_name}")
|
| 73 |
+
except subprocess.CalledProcessError as e:
|
| 74 |
+
print(f" Error processing {video_file.name}: {e}")
|
| 75 |
+
continue
|
| 76 |
+
|
| 77 |
+
|
| 78 |
+
if __name__ == "__main__":
|
| 79 |
+
parser = argparse.ArgumentParser(description="Extract first N frames from videos")
|
| 80 |
+
parser.add_argument(
|
| 81 |
+
"--input",
|
| 82 |
+
type=str,
|
| 83 |
+
default="long",
|
| 84 |
+
help="Input root directory (default: current directory)",
|
| 85 |
+
)
|
| 86 |
+
parser.add_argument(
|
| 87 |
+
"--output",
|
| 88 |
+
type=str,
|
| 89 |
+
default="short/0_from_long",
|
| 90 |
+
help="Output root directory (default: ./output_frames)",
|
| 91 |
+
)
|
| 92 |
+
parser.add_argument("--frames", type=int, default=81, help="Number of frames to extract (default: 81)")
|
| 93 |
+
|
| 94 |
+
args = parser.parse_args()
|
| 95 |
+
|
| 96 |
+
extract_first_n_frames(args.input, args.output, args.frames)
|
| 97 |
+
print("\nDone!")
|
Helios/eval/utils/third_party/ViCLIP/__init__.py
ADDED
|
File without changes
|
Helios/eval/utils/third_party/ViCLIP/simple_tokenizer.py
ADDED
|
@@ -0,0 +1,147 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import gzip
|
| 2 |
+
import html
|
| 3 |
+
import os
|
| 4 |
+
import subprocess
|
| 5 |
+
from functools import lru_cache
|
| 6 |
+
|
| 7 |
+
import ftfy
|
| 8 |
+
import regex as re
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
def default_bpe():
|
| 12 |
+
tokenizer_file = os.path.join("checkpoints", "ViCLIP/bpe_simple_vocab_16e6.txt.gz")
|
| 13 |
+
if not os.path.exists(tokenizer_file):
|
| 14 |
+
print(f"Downloading ViCLIP tokenizer to {tokenizer_file}")
|
| 15 |
+
wget_command = [
|
| 16 |
+
"wget",
|
| 17 |
+
"https://raw.githubusercontent.com/openai/CLIP/main/clip/bpe_simple_vocab_16e6.txt.gz",
|
| 18 |
+
"-P",
|
| 19 |
+
os.path.dirname(tokenizer_file),
|
| 20 |
+
]
|
| 21 |
+
subprocess.run(wget_command)
|
| 22 |
+
return tokenizer_file
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
@lru_cache()
|
| 26 |
+
def bytes_to_unicode():
|
| 27 |
+
"""
|
| 28 |
+
Returns list of utf-8 byte and a corresponding list of unicode strings.
|
| 29 |
+
The reversible bpe codes work on unicode strings.
|
| 30 |
+
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
|
| 31 |
+
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
|
| 32 |
+
This is a signficant percentage of your normal, say, 32K bpe vocab.
|
| 33 |
+
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
|
| 34 |
+
And avoids mapping to whitespace/control characters the bpe code barfs on.
|
| 35 |
+
"""
|
| 36 |
+
bs = (
|
| 37 |
+
list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
|
| 38 |
+
)
|
| 39 |
+
cs = bs[:]
|
| 40 |
+
n = 0
|
| 41 |
+
for b in range(2**8):
|
| 42 |
+
if b not in bs:
|
| 43 |
+
bs.append(b)
|
| 44 |
+
cs.append(2**8 + n)
|
| 45 |
+
n += 1
|
| 46 |
+
cs = [chr(n) for n in cs]
|
| 47 |
+
return dict(zip(bs, cs))
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+
def get_pairs(word):
|
| 51 |
+
"""Return set of symbol pairs in a word.
|
| 52 |
+
Word is represented as tuple of symbols (symbols being variable-length strings).
|
| 53 |
+
"""
|
| 54 |
+
pairs = set()
|
| 55 |
+
prev_char = word[0]
|
| 56 |
+
for char in word[1:]:
|
| 57 |
+
pairs.add((prev_char, char))
|
| 58 |
+
prev_char = char
|
| 59 |
+
return pairs
|
| 60 |
+
|
| 61 |
+
|
| 62 |
+
def basic_clean(text):
|
| 63 |
+
text = ftfy.fix_text(text)
|
| 64 |
+
text = html.unescape(html.unescape(text))
|
| 65 |
+
return text.strip()
|
| 66 |
+
|
| 67 |
+
|
| 68 |
+
def whitespace_clean(text):
|
| 69 |
+
text = re.sub(r"\s+", " ", text)
|
| 70 |
+
text = text.strip()
|
| 71 |
+
return text
|
| 72 |
+
|
| 73 |
+
|
| 74 |
+
class SimpleTokenizer(object):
|
| 75 |
+
def __init__(self, bpe_path: str = default_bpe()):
|
| 76 |
+
self.byte_encoder = bytes_to_unicode()
|
| 77 |
+
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
|
| 78 |
+
merges = gzip.open(bpe_path).read().decode("utf-8").split("\n")
|
| 79 |
+
merges = merges[1 : 49152 - 256 - 2 + 1]
|
| 80 |
+
merges = [tuple(merge.split()) for merge in merges]
|
| 81 |
+
vocab = list(bytes_to_unicode().values())
|
| 82 |
+
vocab = vocab + [v + "</w>" for v in vocab]
|
| 83 |
+
for merge in merges:
|
| 84 |
+
vocab.append("".join(merge))
|
| 85 |
+
vocab.extend(["<|startoftext|>", "<|endoftext|>"])
|
| 86 |
+
self.encoder = dict(zip(vocab, range(len(vocab))))
|
| 87 |
+
self.decoder = {v: k for k, v in self.encoder.items()}
|
| 88 |
+
self.bpe_ranks = dict(zip(merges, range(len(merges))))
|
| 89 |
+
self.cache = {"<|startoftext|>": "<|startoftext|>", "<|endoftext|>": "<|endoftext|>"}
|
| 90 |
+
self.pat = re.compile(
|
| 91 |
+
r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
|
| 92 |
+
re.IGNORECASE,
|
| 93 |
+
)
|
| 94 |
+
|
| 95 |
+
def bpe(self, token):
|
| 96 |
+
if token in self.cache:
|
| 97 |
+
return self.cache[token]
|
| 98 |
+
word = tuple(token[:-1]) + (token[-1] + "</w>",)
|
| 99 |
+
pairs = get_pairs(word)
|
| 100 |
+
|
| 101 |
+
if not pairs:
|
| 102 |
+
return token + "</w>"
|
| 103 |
+
|
| 104 |
+
while True:
|
| 105 |
+
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
|
| 106 |
+
if bigram not in self.bpe_ranks:
|
| 107 |
+
break
|
| 108 |
+
first, second = bigram
|
| 109 |
+
new_word = []
|
| 110 |
+
i = 0
|
| 111 |
+
while i < len(word):
|
| 112 |
+
try:
|
| 113 |
+
j = word.index(first, i)
|
| 114 |
+
new_word.extend(word[i:j])
|
| 115 |
+
i = j
|
| 116 |
+
except Exception:
|
| 117 |
+
new_word.extend(word[i:])
|
| 118 |
+
break
|
| 119 |
+
|
| 120 |
+
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
|
| 121 |
+
new_word.append(first + second)
|
| 122 |
+
i += 2
|
| 123 |
+
else:
|
| 124 |
+
new_word.append(word[i])
|
| 125 |
+
i += 1
|
| 126 |
+
new_word = tuple(new_word)
|
| 127 |
+
word = new_word
|
| 128 |
+
if len(word) == 1:
|
| 129 |
+
break
|
| 130 |
+
else:
|
| 131 |
+
pairs = get_pairs(word)
|
| 132 |
+
word = " ".join(word)
|
| 133 |
+
self.cache[token] = word
|
| 134 |
+
return word
|
| 135 |
+
|
| 136 |
+
def encode(self, text):
|
| 137 |
+
bpe_tokens = []
|
| 138 |
+
text = whitespace_clean(basic_clean(text)).lower()
|
| 139 |
+
for token in re.findall(self.pat, text):
|
| 140 |
+
token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
|
| 141 |
+
bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" "))
|
| 142 |
+
return bpe_tokens
|
| 143 |
+
|
| 144 |
+
def decode(self, tokens):
|
| 145 |
+
text = "".join([self.decoder[token] for token in tokens])
|
| 146 |
+
text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors="replace").replace("</w>", " ")
|
| 147 |
+
return text
|
Helios/eval/utils/third_party/ViCLIP/viclip.py
ADDED
|
@@ -0,0 +1,216 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import logging
|
| 2 |
+
import os
|
| 3 |
+
|
| 4 |
+
import torch
|
| 5 |
+
from torch import nn
|
| 6 |
+
|
| 7 |
+
from .simple_tokenizer import SimpleTokenizer as _Tokenizer
|
| 8 |
+
from .viclip_text import clip_text_l14
|
| 9 |
+
from .viclip_vision import clip_joint_l14
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
logger = logging.getLogger(__name__)
|
| 13 |
+
|
| 14 |
+
|
| 15 |
+
class ViCLIP(nn.Module):
|
| 16 |
+
"""docstring for ViCLIP"""
|
| 17 |
+
|
| 18 |
+
def __init__(
|
| 19 |
+
self,
|
| 20 |
+
tokenizer=None,
|
| 21 |
+
pretrain=os.path.join(os.path.dirname(os.path.abspath(__file__)), "ViClip-InternVid-10M-FLT.pth"),
|
| 22 |
+
freeze_text=True,
|
| 23 |
+
):
|
| 24 |
+
super(ViCLIP, self).__init__()
|
| 25 |
+
if tokenizer:
|
| 26 |
+
self.tokenizer = tokenizer
|
| 27 |
+
else:
|
| 28 |
+
self.tokenizer = _Tokenizer()
|
| 29 |
+
self.max_txt_l = 32
|
| 30 |
+
|
| 31 |
+
self.vision_encoder_name = "vit_l14"
|
| 32 |
+
|
| 33 |
+
self.vision_encoder_pretrained = False
|
| 34 |
+
self.inputs_image_res = 224
|
| 35 |
+
self.vision_encoder_kernel_size = 1
|
| 36 |
+
self.vision_encoder_center = True
|
| 37 |
+
self.video_input_num_frames = 8
|
| 38 |
+
self.vision_encoder_drop_path_rate = 0.1
|
| 39 |
+
self.vision_encoder_checkpoint_num = 24
|
| 40 |
+
self.is_pretrain = pretrain
|
| 41 |
+
self.vision_width = 1024
|
| 42 |
+
self.text_width = 768
|
| 43 |
+
self.embed_dim = 768
|
| 44 |
+
self.masking_prob = 0.9
|
| 45 |
+
|
| 46 |
+
self.text_encoder_name = "vit_l14"
|
| 47 |
+
self.text_encoder_pretrained = False #'bert-base-uncased'
|
| 48 |
+
self.text_encoder_d_model = 768
|
| 49 |
+
|
| 50 |
+
self.text_encoder_vocab_size = 49408
|
| 51 |
+
|
| 52 |
+
# create modules.
|
| 53 |
+
self.vision_encoder = self.build_vision_encoder()
|
| 54 |
+
self.text_encoder = self.build_text_encoder()
|
| 55 |
+
|
| 56 |
+
self.temp = nn.parameter.Parameter(torch.ones([]) * 1 / 100.0)
|
| 57 |
+
self.temp_min = 1 / 100.0
|
| 58 |
+
|
| 59 |
+
if pretrain:
|
| 60 |
+
logger.info(f"Load pretrained weights from {pretrain}")
|
| 61 |
+
state_dict = torch.load(pretrain, map_location="cpu", weights_only=False)["model"]
|
| 62 |
+
self.load_state_dict(state_dict)
|
| 63 |
+
|
| 64 |
+
# Freeze weights
|
| 65 |
+
if freeze_text:
|
| 66 |
+
self.freeze_text()
|
| 67 |
+
|
| 68 |
+
def freeze_text(self):
|
| 69 |
+
"""freeze text encoder"""
|
| 70 |
+
for p in self.text_encoder.parameters():
|
| 71 |
+
p.requires_grad = False
|
| 72 |
+
|
| 73 |
+
def no_weight_decay(self):
|
| 74 |
+
ret = {"temp"}
|
| 75 |
+
ret.update({"vision_encoder." + k for k in self.vision_encoder.no_weight_decay()})
|
| 76 |
+
ret.update({"text_encoder." + k for k in self.text_encoder.no_weight_decay()})
|
| 77 |
+
|
| 78 |
+
return ret
|
| 79 |
+
|
| 80 |
+
def forward(self, image, text, raw_text, idx, log_generation=None, return_sims=False):
|
| 81 |
+
"""forward and calculate loss.
|
| 82 |
+
|
| 83 |
+
Args:
|
| 84 |
+
image (torch.Tensor): The input images. Shape: [B,T,C,H,W].
|
| 85 |
+
text (dict): TODO
|
| 86 |
+
idx (torch.Tensor): TODO
|
| 87 |
+
|
| 88 |
+
Returns: TODO
|
| 89 |
+
|
| 90 |
+
"""
|
| 91 |
+
self.clip_contrastive_temperature()
|
| 92 |
+
|
| 93 |
+
vision_embeds = self.encode_vision(image)
|
| 94 |
+
text_embeds = self.encode_text(raw_text)
|
| 95 |
+
if return_sims:
|
| 96 |
+
sims = torch.nn.functional.normalize(vision_embeds, dim=-1) @ torch.nn.functional.normalize(
|
| 97 |
+
text_embeds, dim=-1
|
| 98 |
+
).transpose(0, 1)
|
| 99 |
+
return sims
|
| 100 |
+
|
| 101 |
+
# calculate loss
|
| 102 |
+
|
| 103 |
+
## VTC loss
|
| 104 |
+
loss_vtc = self.clip_loss.vtc_loss(vision_embeds, text_embeds, idx, self.temp, all_gather=True)
|
| 105 |
+
|
| 106 |
+
return {
|
| 107 |
+
"loss_vtc": loss_vtc,
|
| 108 |
+
}
|
| 109 |
+
|
| 110 |
+
def encode_vision(self, image, test=False):
|
| 111 |
+
"""encode image / videos as features.
|
| 112 |
+
|
| 113 |
+
Args:
|
| 114 |
+
image (torch.Tensor): The input images.
|
| 115 |
+
test (bool): Whether testing.
|
| 116 |
+
|
| 117 |
+
Returns: tuple.
|
| 118 |
+
- vision_embeds (torch.Tensor): The features of all patches. Shape: [B,T,L,C].
|
| 119 |
+
- pooled_vision_embeds (torch.Tensor): The pooled features. Shape: [B,T,C].
|
| 120 |
+
|
| 121 |
+
"""
|
| 122 |
+
if image.ndim == 5:
|
| 123 |
+
image = image.permute(0, 2, 1, 3, 4).contiguous()
|
| 124 |
+
else:
|
| 125 |
+
image = image.unsqueeze(2)
|
| 126 |
+
|
| 127 |
+
if not test and self.masking_prob > 0.0:
|
| 128 |
+
return self.vision_encoder(image, masking_prob=self.masking_prob)
|
| 129 |
+
|
| 130 |
+
return self.vision_encoder(image)
|
| 131 |
+
|
| 132 |
+
def encode_text(self, text):
|
| 133 |
+
"""encode text.
|
| 134 |
+
Args:
|
| 135 |
+
text (dict): The output of huggingface's `PreTrainedTokenizer`. contains keys:
|
| 136 |
+
- input_ids (torch.Tensor): Token ids to be fed to a model. Shape: [B,L].
|
| 137 |
+
- attention_mask (torch.Tensor): The mask indicate padded tokens. Shape: [B,L]. 0 is padded token.
|
| 138 |
+
- other keys refer to "https://huggingface.co/docs/transformers/v4.21.2/en/main_classes/tokenizer#transformers.PreTrainedTokenizer.__call__".
|
| 139 |
+
Returns: tuple.
|
| 140 |
+
- text_embeds (torch.Tensor): The features of all tokens. Shape: [B,L,C].
|
| 141 |
+
- pooled_text_embeds (torch.Tensor): The pooled features. Shape: [B,C].
|
| 142 |
+
|
| 143 |
+
"""
|
| 144 |
+
device = next(self.text_encoder.parameters()).device
|
| 145 |
+
text = self.text_encoder.tokenize(text, context_length=self.max_txt_l).to(device)
|
| 146 |
+
text_embeds = self.text_encoder(text)
|
| 147 |
+
return text_embeds
|
| 148 |
+
|
| 149 |
+
@torch.no_grad()
|
| 150 |
+
def clip_contrastive_temperature(self, min_val=0.001, max_val=0.5):
|
| 151 |
+
"""Seems only used during pre-training"""
|
| 152 |
+
self.temp.clamp_(min=self.temp_min)
|
| 153 |
+
|
| 154 |
+
def build_vision_encoder(self):
|
| 155 |
+
"""build vision encoder
|
| 156 |
+
Returns: (vision_encoder, vision_layernorm). Each is a `nn.Module`.
|
| 157 |
+
|
| 158 |
+
"""
|
| 159 |
+
encoder_name = self.vision_encoder_name
|
| 160 |
+
if encoder_name != "vit_l14":
|
| 161 |
+
raise ValueError(f"Not implemented: {encoder_name}")
|
| 162 |
+
vision_encoder = clip_joint_l14(
|
| 163 |
+
pretrained=self.vision_encoder_pretrained,
|
| 164 |
+
input_resolution=self.inputs_image_res,
|
| 165 |
+
kernel_size=self.vision_encoder_kernel_size,
|
| 166 |
+
center=self.vision_encoder_center,
|
| 167 |
+
num_frames=self.video_input_num_frames,
|
| 168 |
+
drop_path=self.vision_encoder_drop_path_rate,
|
| 169 |
+
checkpoint_num=self.vision_encoder_checkpoint_num,
|
| 170 |
+
)
|
| 171 |
+
return vision_encoder
|
| 172 |
+
|
| 173 |
+
def build_text_encoder(self):
|
| 174 |
+
"""build text_encoder and possiblly video-to-text multimodal fusion encoder.
|
| 175 |
+
Returns: nn.Module. The text encoder
|
| 176 |
+
|
| 177 |
+
"""
|
| 178 |
+
encoder_name = self.text_encoder_name
|
| 179 |
+
if encoder_name != "vit_l14":
|
| 180 |
+
raise ValueError(f"Not implemented: {encoder_name}")
|
| 181 |
+
text_encoder = clip_text_l14(
|
| 182 |
+
pretrained=self.text_encoder_pretrained,
|
| 183 |
+
embed_dim=self.text_encoder_d_model,
|
| 184 |
+
context_length=self.max_txt_l,
|
| 185 |
+
vocab_size=self.text_encoder_vocab_size,
|
| 186 |
+
checkpoint_num=0,
|
| 187 |
+
)
|
| 188 |
+
|
| 189 |
+
return text_encoder
|
| 190 |
+
|
| 191 |
+
def get_text_encoder(self):
|
| 192 |
+
"""get text encoder, used for text and cross-modal encoding"""
|
| 193 |
+
encoder = self.text_encoder
|
| 194 |
+
return encoder.bert if hasattr(encoder, "bert") else encoder
|
| 195 |
+
|
| 196 |
+
def get_text_features(self, input_text, tokenizer, text_feature_dict={}):
|
| 197 |
+
if input_text in text_feature_dict:
|
| 198 |
+
return text_feature_dict[input_text]
|
| 199 |
+
text_template = f"{input_text}"
|
| 200 |
+
with torch.no_grad():
|
| 201 |
+
# text_token = tokenizer.encode(text_template).cuda()
|
| 202 |
+
text_features = self.encode_text(text_template).float()
|
| 203 |
+
text_features /= text_features.norm(dim=-1, keepdim=True)
|
| 204 |
+
text_feature_dict[input_text] = text_features
|
| 205 |
+
return text_features
|
| 206 |
+
|
| 207 |
+
def get_vid_features(self, input_frames):
|
| 208 |
+
with torch.no_grad():
|
| 209 |
+
clip_feat = self.encode_vision(input_frames, test=True).float()
|
| 210 |
+
clip_feat /= clip_feat.norm(dim=-1, keepdim=True)
|
| 211 |
+
return clip_feat
|
| 212 |
+
|
| 213 |
+
def get_predict_label(self, clip_feature, text_feats_tensor, top=5):
|
| 214 |
+
label_probs = (100.0 * clip_feature @ text_feats_tensor.T).softmax(dim=-1)
|
| 215 |
+
top_probs, top_labels = label_probs.cpu().topk(top, dim=-1)
|
| 216 |
+
return top_probs, top_labels
|
Helios/eval/utils/third_party/ViCLIP/viclip_text.py
ADDED
|
@@ -0,0 +1,260 @@
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import functools
|
| 2 |
+
import logging
|
| 3 |
+
import os
|
| 4 |
+
from collections import OrderedDict
|
| 5 |
+
|
| 6 |
+
import torch
|
| 7 |
+
import torch.nn.functional as F
|
| 8 |
+
import torch.utils.checkpoint as checkpoint
|
| 9 |
+
from pkg_resources import packaging
|
| 10 |
+
from torch import nn
|
| 11 |
+
|
| 12 |
+
from .simple_tokenizer import SimpleTokenizer as _Tokenizer
|
| 13 |
+
|
| 14 |
+
|
| 15 |
+
logger = logging.getLogger(__name__)
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
MODEL_PATH = "https://huggingface.co/laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K"
|
| 19 |
+
_MODELS = {
|
| 20 |
+
"ViT-L/14": os.path.join(MODEL_PATH, "vit_l14_text.pth"),
|
| 21 |
+
}
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
class LayerNorm(nn.LayerNorm):
|
| 25 |
+
"""Subclass torch's LayerNorm to handle fp16."""
|
| 26 |
+
|
| 27 |
+
def forward(self, x: torch.Tensor):
|
| 28 |
+
orig_type = x.dtype
|
| 29 |
+
ret = super().forward(x.type(torch.float32))
|
| 30 |
+
return ret.type(orig_type)
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
class QuickGELU(nn.Module):
|
| 34 |
+
def forward(self, x: torch.Tensor):
|
| 35 |
+
return x * torch.sigmoid(1.702 * x)
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
class ResidualAttentionBlock(nn.Module):
|
| 39 |
+
def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
|
| 40 |
+
super().__init__()
|
| 41 |
+
|
| 42 |
+
self.attn = nn.MultiheadAttention(d_model, n_head)
|
| 43 |
+
self.ln_1 = LayerNorm(d_model)
|
| 44 |
+
self.mlp = nn.Sequential(
|
| 45 |
+
OrderedDict(
|
| 46 |
+
[
|
| 47 |
+
("c_fc", nn.Linear(d_model, d_model * 4)),
|
| 48 |
+
("gelu", QuickGELU()),
|
| 49 |
+
("c_proj", nn.Linear(d_model * 4, d_model)),
|
| 50 |
+
]
|
| 51 |
+
)
|
| 52 |
+
)
|
| 53 |
+
self.ln_2 = LayerNorm(d_model)
|
| 54 |
+
self.attn_mask = attn_mask
|
| 55 |
+
|
| 56 |
+
def attention(self, x: torch.Tensor):
|
| 57 |
+
self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
|
| 58 |
+
return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
|
| 59 |
+
|
| 60 |
+
def forward(self, x: torch.Tensor):
|
| 61 |
+
x = x + self.attention(self.ln_1(x))
|
| 62 |
+
x = x + self.mlp(self.ln_2(x))
|
| 63 |
+
return x
|
| 64 |
+
|
| 65 |
+
|
| 66 |
+
class Transformer(nn.Module):
|
| 67 |
+
def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None, checkpoint_num: int = 0):
|
| 68 |
+
super().__init__()
|
| 69 |
+
self.width = width
|
| 70 |
+
self.layers = layers
|
| 71 |
+
self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
|
| 72 |
+
|
| 73 |
+
self.checkpoint_num = checkpoint_num
|
| 74 |
+
|
| 75 |
+
def forward(self, x: torch.Tensor):
|
| 76 |
+
if self.checkpoint_num > 0:
|
| 77 |
+
segments = min(self.checkpoint_num, len(self.resblocks))
|
| 78 |
+
return checkpoint.checkpoint_sequential(self.resblocks, segments, x)
|
| 79 |
+
else:
|
| 80 |
+
return self.resblocks(x)
|
| 81 |
+
|
| 82 |
+
|
| 83 |
+
class CLIP_TEXT(nn.Module):
|
| 84 |
+
def __init__(
|
| 85 |
+
self,
|
| 86 |
+
embed_dim: int,
|
| 87 |
+
context_length: int,
|
| 88 |
+
vocab_size: int,
|
| 89 |
+
transformer_width: int,
|
| 90 |
+
transformer_heads: int,
|
| 91 |
+
transformer_layers: int,
|
| 92 |
+
checkpoint_num: int,
|
| 93 |
+
):
|
| 94 |
+
super().__init__()
|
| 95 |
+
|
| 96 |
+
self.context_length = context_length
|
| 97 |
+
self._tokenizer = _Tokenizer()
|
| 98 |
+
|
| 99 |
+
self.transformer = Transformer(
|
| 100 |
+
width=transformer_width,
|
| 101 |
+
layers=transformer_layers,
|
| 102 |
+
heads=transformer_heads,
|
| 103 |
+
attn_mask=self.build_attention_mask(),
|
| 104 |
+
checkpoint_num=checkpoint_num,
|
| 105 |
+
)
|
| 106 |
+
|
| 107 |
+
self.vocab_size = vocab_size
|
| 108 |
+
self.token_embedding = nn.Embedding(vocab_size, transformer_width)
|
| 109 |
+
self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
|
| 110 |
+
self.ln_final = LayerNorm(transformer_width)
|
| 111 |
+
|
| 112 |
+
self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
|
| 113 |
+
|
| 114 |
+
def no_weight_decay(self):
|
| 115 |
+
return {"token_embedding", "positional_embedding"}
|
| 116 |
+
|
| 117 |
+
@functools.lru_cache(maxsize=None)
|
| 118 |
+
def build_attention_mask(self):
|
| 119 |
+
# lazily create causal attention mask, with full attention between the vision tokens
|
| 120 |
+
# pytorch uses additive attention mask; fill with -inf
|
| 121 |
+
mask = torch.empty(self.context_length, self.context_length)
|
| 122 |
+
mask.fill_(float("-inf"))
|
| 123 |
+
mask.triu_(1) # zero out the lower diagonal
|
| 124 |
+
return mask
|
| 125 |
+
|
| 126 |
+
def tokenize(self, texts, context_length=77, truncate=True):
|
| 127 |
+
"""
|
| 128 |
+
Returns the tokenized representation of given input string(s)
|
| 129 |
+
Parameters
|
| 130 |
+
----------
|
| 131 |
+
texts : Union[str, List[str]]
|
| 132 |
+
An input string or a list of input strings to tokenize
|
| 133 |
+
context_length : int
|
| 134 |
+
The context length to use; all CLIP models use 77 as the context length
|
| 135 |
+
truncate: bool
|
| 136 |
+
Whether to truncate the text in case its encoding is longer than the context length
|
| 137 |
+
Returns
|
| 138 |
+
-------
|
| 139 |
+
A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
|
| 140 |
+
We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
|
| 141 |
+
"""
|
| 142 |
+
if isinstance(texts, str):
|
| 143 |
+
texts = [texts]
|
| 144 |
+
|
| 145 |
+
sot_token = self._tokenizer.encoder["<|startoftext|>"]
|
| 146 |
+
eot_token = self._tokenizer.encoder["<|endoftext|>"]
|
| 147 |
+
all_tokens = [[sot_token] + self._tokenizer.encode(text) + [eot_token] for text in texts]
|
| 148 |
+
if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"):
|
| 149 |
+
result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
|
| 150 |
+
else:
|
| 151 |
+
result = torch.zeros(len(all_tokens), context_length, dtype=torch.int)
|
| 152 |
+
|
| 153 |
+
for i, tokens in enumerate(all_tokens):
|
| 154 |
+
if len(tokens) > context_length:
|
| 155 |
+
if truncate:
|
| 156 |
+
tokens = tokens[:context_length]
|
| 157 |
+
tokens[-1] = eot_token
|
| 158 |
+
else:
|
| 159 |
+
raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
|
| 160 |
+
result[i, : len(tokens)] = torch.tensor(tokens)
|
| 161 |
+
|
| 162 |
+
return result
|
| 163 |
+
|
| 164 |
+
def forward(self, text):
|
| 165 |
+
x = self.token_embedding(text) # [batch_size, n_ctx, d_model]
|
| 166 |
+
|
| 167 |
+
x = x + self.positional_embedding
|
| 168 |
+
x = x.permute(1, 0, 2) # NLD -> LND
|
| 169 |
+
x = self.transformer(x)
|
| 170 |
+
x = x.permute(1, 0, 2) # LND -> NLD
|
| 171 |
+
x = self.ln_final(x)
|
| 172 |
+
|
| 173 |
+
# x.shape = [batch_size, n_ctx, transformer.width]
|
| 174 |
+
# take features from the eot embedding (eot_token is the highest number in each sequence)
|
| 175 |
+
x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
|
| 176 |
+
|
| 177 |
+
return x
|
| 178 |
+
|
| 179 |
+
|
| 180 |
+
def clip_text_b16(
|
| 181 |
+
embed_dim=512,
|
| 182 |
+
context_length=77,
|
| 183 |
+
vocab_size=49408,
|
| 184 |
+
transformer_width=512,
|
| 185 |
+
transformer_heads=8,
|
| 186 |
+
transformer_layers=12,
|
| 187 |
+
):
|
| 188 |
+
raise NotImplementedError
|
| 189 |
+
model = CLIP_TEXT(embed_dim, context_length, vocab_size, transformer_width, transformer_heads, transformer_layers)
|
| 190 |
+
pretrained = _MODELS["ViT-B/16"]
|
| 191 |
+
logger.info(f"Load pretrained weights from {pretrained}")
|
| 192 |
+
state_dict = torch.load(pretrained, map_location="cpu")
|
| 193 |
+
model.load_state_dict(state_dict, strict=False)
|
| 194 |
+
return model.eval()
|
| 195 |
+
|
| 196 |
+
|
| 197 |
+
def clip_text_l14(
|
| 198 |
+
embed_dim=768,
|
| 199 |
+
context_length=77,
|
| 200 |
+
vocab_size=49408,
|
| 201 |
+
transformer_width=768,
|
| 202 |
+
transformer_heads=12,
|
| 203 |
+
transformer_layers=12,
|
| 204 |
+
checkpoint_num=0,
|
| 205 |
+
pretrained=True,
|
| 206 |
+
):
|
| 207 |
+
model = CLIP_TEXT(
|
| 208 |
+
embed_dim,
|
| 209 |
+
context_length,
|
| 210 |
+
vocab_size,
|
| 211 |
+
transformer_width,
|
| 212 |
+
transformer_heads,
|
| 213 |
+
transformer_layers,
|
| 214 |
+
checkpoint_num,
|
| 215 |
+
)
|
| 216 |
+
if pretrained:
|
| 217 |
+
if isinstance(pretrained, str) and pretrained != "bert-base-uncased":
|
| 218 |
+
pretrained = _MODELS[pretrained]
|
| 219 |
+
else:
|
| 220 |
+
pretrained = _MODELS["ViT-L/14"]
|
| 221 |
+
logger.info(f"Load pretrained weights from {pretrained}")
|
| 222 |
+
state_dict = torch.load(pretrained, map_location="cpu")
|
| 223 |
+
if context_length != state_dict["positional_embedding"].size(0):
|
| 224 |
+
# assert context_length < state_dict["positional_embedding"].size(0), "Cannot increase context length."
|
| 225 |
+
print(f"Resize positional embedding from {state_dict['positional_embedding'].size(0)} to {context_length}")
|
| 226 |
+
if context_length < state_dict["positional_embedding"].size(0):
|
| 227 |
+
state_dict["positional_embedding"] = state_dict["positional_embedding"][:context_length]
|
| 228 |
+
else:
|
| 229 |
+
state_dict["positional_embedding"] = F.pad(
|
| 230 |
+
state_dict["positional_embedding"],
|
| 231 |
+
(0, 0, 0, context_length - state_dict["positional_embedding"].size(0)),
|
| 232 |
+
value=0,
|
| 233 |
+
)
|
| 234 |
+
|
| 235 |
+
message = model.load_state_dict(state_dict, strict=False)
|
| 236 |
+
print(f"Load pretrained weights from {pretrained}: {message}")
|
| 237 |
+
return model.eval()
|
| 238 |
+
|
| 239 |
+
|
| 240 |
+
def clip_text_l14_336(
|
| 241 |
+
embed_dim=768,
|
| 242 |
+
context_length=77,
|
| 243 |
+
vocab_size=49408,
|
| 244 |
+
transformer_width=768,
|
| 245 |
+
transformer_heads=12,
|
| 246 |
+
transformer_layers=12,
|
| 247 |
+
):
|
| 248 |
+
raise NotImplementedError
|
| 249 |
+
model = CLIP_TEXT(embed_dim, context_length, vocab_size, transformer_width, transformer_heads, transformer_layers)
|
| 250 |
+
pretrained = _MODELS["ViT-L/14_336"]
|
| 251 |
+
logger.info(f"Load pretrained weights from {pretrained}")
|
| 252 |
+
state_dict = torch.load(pretrained, map_location="cpu")
|
| 253 |
+
model.load_state_dict(state_dict, strict=False)
|
| 254 |
+
return model.eval()
|
| 255 |
+
|
| 256 |
+
|
| 257 |
+
def build_clip(config):
|
| 258 |
+
model_cls = config.text_encoder.clip_teacher
|
| 259 |
+
model = eval(model_cls)()
|
| 260 |
+
return model
|
Helios/eval/utils/third_party/ViCLIP/viclip_vision.py
ADDED
|
@@ -0,0 +1,364 @@
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|
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|
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|
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|
|
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|
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|
|
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|
|
|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python
|
| 2 |
+
import logging
|
| 3 |
+
import os
|
| 4 |
+
from collections import OrderedDict
|
| 5 |
+
|
| 6 |
+
import torch
|
| 7 |
+
import torch.utils.checkpoint as checkpoint
|
| 8 |
+
from einops import rearrange
|
| 9 |
+
from timm.layers import DropPath
|
| 10 |
+
from timm.models import register_model
|
| 11 |
+
from torch import nn
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
logger = logging.getLogger(__name__)
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
def load_temp_embed_with_mismatch(temp_embed_old, temp_embed_new, add_zero=True):
|
| 18 |
+
"""
|
| 19 |
+
Add/Remove extra temporal_embeddings as needed.
|
| 20 |
+
https://arxiv.org/abs/2104.00650 shows adding zero paddings works.
|
| 21 |
+
|
| 22 |
+
temp_embed_old: (1, num_frames_old, 1, d)
|
| 23 |
+
temp_embed_new: (1, num_frames_new, 1, d)
|
| 24 |
+
add_zero: bool, if True, add zero, else, interpolate trained embeddings.
|
| 25 |
+
"""
|
| 26 |
+
# TODO zero pad
|
| 27 |
+
num_frms_new = temp_embed_new.shape[1]
|
| 28 |
+
num_frms_old = temp_embed_old.shape[1]
|
| 29 |
+
logger.info(f"Load temporal_embeddings, lengths: {num_frms_old}-->{num_frms_new}")
|
| 30 |
+
if num_frms_new > num_frms_old:
|
| 31 |
+
if add_zero:
|
| 32 |
+
temp_embed_new[:, :num_frms_old] = temp_embed_old # untrained embeddings are zeros.
|
| 33 |
+
else:
|
| 34 |
+
pass
|
| 35 |
+
# temp_embed_new = interpolate_temporal_pos_embed(temp_embed_old, num_frms_new)
|
| 36 |
+
elif num_frms_new < num_frms_old:
|
| 37 |
+
temp_embed_new = temp_embed_old[:, :num_frms_new]
|
| 38 |
+
else: # =
|
| 39 |
+
temp_embed_new = temp_embed_old
|
| 40 |
+
return temp_embed_new
|
| 41 |
+
|
| 42 |
+
|
| 43 |
+
MODEL_PATH = "https://pjlab-gvm-data.oss-cn-shanghai.aliyuncs.com/internvideo/viclip/"
|
| 44 |
+
_MODELS = {
|
| 45 |
+
"ViT-L/14": os.path.join(MODEL_PATH, "ViClip-InternVid-10M-FLT.pth"),
|
| 46 |
+
}
|
| 47 |
+
|
| 48 |
+
|
| 49 |
+
class QuickGELU(nn.Module):
|
| 50 |
+
def forward(self, x):
|
| 51 |
+
return x * torch.sigmoid(1.702 * x)
|
| 52 |
+
|
| 53 |
+
|
| 54 |
+
class ResidualAttentionBlock(nn.Module):
|
| 55 |
+
def __init__(self, d_model, n_head, drop_path=0.0, attn_mask=None, dropout=0.0):
|
| 56 |
+
super().__init__()
|
| 57 |
+
|
| 58 |
+
self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
| 59 |
+
self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
| 60 |
+
self.attn = nn.MultiheadAttention(d_model, n_head, dropout=dropout)
|
| 61 |
+
self.ln_1 = nn.LayerNorm(d_model)
|
| 62 |
+
self.mlp = nn.Sequential(
|
| 63 |
+
OrderedDict(
|
| 64 |
+
[
|
| 65 |
+
("c_fc", nn.Linear(d_model, d_model * 4)),
|
| 66 |
+
("gelu", QuickGELU()),
|
| 67 |
+
("drop1", nn.Dropout(dropout)),
|
| 68 |
+
("c_proj", nn.Linear(d_model * 4, d_model)),
|
| 69 |
+
("drop2", nn.Dropout(dropout)),
|
| 70 |
+
]
|
| 71 |
+
)
|
| 72 |
+
)
|
| 73 |
+
self.ln_2 = nn.LayerNorm(d_model)
|
| 74 |
+
self.attn_mask = attn_mask
|
| 75 |
+
|
| 76 |
+
def attention(self, x):
|
| 77 |
+
self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
|
| 78 |
+
return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
|
| 79 |
+
|
| 80 |
+
def forward(self, x):
|
| 81 |
+
x = x + self.drop_path1(self.attention(self.ln_1(x)))
|
| 82 |
+
x = x + self.drop_path2(self.mlp(self.ln_2(x)))
|
| 83 |
+
return x
|
| 84 |
+
|
| 85 |
+
|
| 86 |
+
class Transformer(nn.Module):
|
| 87 |
+
def __init__(self, width, layers, heads, drop_path=0.0, checkpoint_num=0, dropout=0.0):
|
| 88 |
+
super().__init__()
|
| 89 |
+
dpr = [x.item() for x in torch.linspace(0, drop_path, layers)]
|
| 90 |
+
self.resblocks = nn.ModuleList()
|
| 91 |
+
for idx in range(layers):
|
| 92 |
+
self.resblocks.append(ResidualAttentionBlock(width, heads, drop_path=dpr[idx], dropout=dropout))
|
| 93 |
+
self.checkpoint_num = checkpoint_num
|
| 94 |
+
|
| 95 |
+
def forward(self, x):
|
| 96 |
+
for idx, blk in enumerate(self.resblocks):
|
| 97 |
+
if idx < self.checkpoint_num:
|
| 98 |
+
x = checkpoint.checkpoint(blk, x, use_reentrant=False)
|
| 99 |
+
else:
|
| 100 |
+
x = blk(x)
|
| 101 |
+
return x
|
| 102 |
+
|
| 103 |
+
|
| 104 |
+
class VisionTransformer(nn.Module):
|
| 105 |
+
def __init__(
|
| 106 |
+
self,
|
| 107 |
+
input_resolution,
|
| 108 |
+
patch_size,
|
| 109 |
+
width,
|
| 110 |
+
layers,
|
| 111 |
+
heads,
|
| 112 |
+
output_dim=None,
|
| 113 |
+
kernel_size=1,
|
| 114 |
+
num_frames=8,
|
| 115 |
+
drop_path=0,
|
| 116 |
+
checkpoint_num=0,
|
| 117 |
+
dropout=0.0,
|
| 118 |
+
temp_embed=True,
|
| 119 |
+
):
|
| 120 |
+
super().__init__()
|
| 121 |
+
self.output_dim = output_dim
|
| 122 |
+
self.conv1 = nn.Conv3d(
|
| 123 |
+
3,
|
| 124 |
+
width,
|
| 125 |
+
(kernel_size, patch_size, patch_size),
|
| 126 |
+
(kernel_size, patch_size, patch_size),
|
| 127 |
+
(0, 0, 0),
|
| 128 |
+
bias=False,
|
| 129 |
+
)
|
| 130 |
+
|
| 131 |
+
scale = width**-0.5
|
| 132 |
+
self.class_embedding = nn.Parameter(scale * torch.randn(width))
|
| 133 |
+
self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
|
| 134 |
+
self.ln_pre = nn.LayerNorm(width)
|
| 135 |
+
if temp_embed:
|
| 136 |
+
self.temporal_positional_embedding = nn.Parameter(torch.zeros(1, num_frames, width))
|
| 137 |
+
|
| 138 |
+
self.transformer = Transformer(
|
| 139 |
+
width, layers, heads, drop_path=drop_path, checkpoint_num=checkpoint_num, dropout=dropout
|
| 140 |
+
)
|
| 141 |
+
|
| 142 |
+
self.ln_post = nn.LayerNorm(width)
|
| 143 |
+
if output_dim is not None:
|
| 144 |
+
self.proj = nn.Parameter(torch.empty(width, output_dim))
|
| 145 |
+
else:
|
| 146 |
+
self.proj = None
|
| 147 |
+
|
| 148 |
+
self.dropout = nn.Dropout(dropout)
|
| 149 |
+
|
| 150 |
+
def get_num_layers(self):
|
| 151 |
+
return len(self.transformer.resblocks)
|
| 152 |
+
|
| 153 |
+
@torch.jit.ignore
|
| 154 |
+
def no_weight_decay(self):
|
| 155 |
+
return {"positional_embedding", "class_embedding", "temporal_positional_embedding"}
|
| 156 |
+
|
| 157 |
+
def mask_tokens(self, inputs, masking_prob=0.0):
|
| 158 |
+
B, L, _ = inputs.shape
|
| 159 |
+
|
| 160 |
+
# This is different from text as we are masking a fix number of tokens
|
| 161 |
+
Lm = int(masking_prob * L)
|
| 162 |
+
masked_indices = torch.zeros(B, L)
|
| 163 |
+
indices = torch.argsort(torch.rand_like(masked_indices), dim=-1)[:, :Lm]
|
| 164 |
+
batch_indices = torch.arange(masked_indices.shape[0]).unsqueeze(-1).expand_as(indices)
|
| 165 |
+
masked_indices[batch_indices, indices] = 1
|
| 166 |
+
|
| 167 |
+
masked_indices = masked_indices.bool()
|
| 168 |
+
|
| 169 |
+
return inputs[~masked_indices].reshape(B, -1, inputs.shape[-1])
|
| 170 |
+
|
| 171 |
+
def forward(self, x, masking_prob=0.0):
|
| 172 |
+
x = self.conv1(x) # shape = [*, width, grid, grid]
|
| 173 |
+
B, C, T, H, W = x.shape
|
| 174 |
+
x = x.permute(0, 2, 3, 4, 1).reshape(B * T, H * W, C)
|
| 175 |
+
|
| 176 |
+
x = torch.cat(
|
| 177 |
+
[
|
| 178 |
+
self.class_embedding.to(x.dtype)
|
| 179 |
+
+ torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),
|
| 180 |
+
x,
|
| 181 |
+
],
|
| 182 |
+
dim=1,
|
| 183 |
+
) # shape = [*, grid ** 2 + 1, width]
|
| 184 |
+
x = x + self.positional_embedding.to(x.dtype)
|
| 185 |
+
|
| 186 |
+
# temporal pos
|
| 187 |
+
cls_tokens = x[:B, :1, :]
|
| 188 |
+
x = x[:, 1:]
|
| 189 |
+
x = rearrange(x, "(b t) n m -> (b n) t m", b=B, t=T)
|
| 190 |
+
if hasattr(self, "temporal_positional_embedding"):
|
| 191 |
+
if x.size(1) == 1:
|
| 192 |
+
# This is a workaround for unused parameter issue
|
| 193 |
+
x = x + self.temporal_positional_embedding.mean(1)
|
| 194 |
+
else:
|
| 195 |
+
x = x + self.temporal_positional_embedding
|
| 196 |
+
x = rearrange(x, "(b n) t m -> b (n t) m", b=B, t=T)
|
| 197 |
+
|
| 198 |
+
if masking_prob > 0.0:
|
| 199 |
+
x = self.mask_tokens(x, masking_prob)
|
| 200 |
+
|
| 201 |
+
x = torch.cat((cls_tokens, x), dim=1)
|
| 202 |
+
|
| 203 |
+
x = self.ln_pre(x)
|
| 204 |
+
|
| 205 |
+
x = x.permute(1, 0, 2) # BND -> NBD
|
| 206 |
+
x = self.transformer(x)
|
| 207 |
+
|
| 208 |
+
x = self.ln_post(x)
|
| 209 |
+
|
| 210 |
+
if self.proj is not None:
|
| 211 |
+
x = self.dropout(x[0]) @ self.proj
|
| 212 |
+
else:
|
| 213 |
+
x = x.permute(1, 0, 2) # NBD -> BND
|
| 214 |
+
|
| 215 |
+
return x
|
| 216 |
+
|
| 217 |
+
|
| 218 |
+
def inflate_weight(weight_2d, time_dim, center=True):
|
| 219 |
+
logger.info(f"Init center: {center}")
|
| 220 |
+
if center:
|
| 221 |
+
weight_3d = torch.zeros(*weight_2d.shape)
|
| 222 |
+
weight_3d = weight_3d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
|
| 223 |
+
middle_idx = time_dim // 2
|
| 224 |
+
weight_3d[:, :, middle_idx, :, :] = weight_2d
|
| 225 |
+
else:
|
| 226 |
+
weight_3d = weight_2d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
|
| 227 |
+
weight_3d = weight_3d / time_dim
|
| 228 |
+
return weight_3d
|
| 229 |
+
|
| 230 |
+
|
| 231 |
+
def load_state_dict(model, state_dict, input_resolution=224, patch_size=16, center=True):
|
| 232 |
+
state_dict_3d = model.state_dict()
|
| 233 |
+
for k in state_dict.keys():
|
| 234 |
+
if k in state_dict_3d.keys() and state_dict[k].shape != state_dict_3d[k].shape:
|
| 235 |
+
if len(state_dict_3d[k].shape) <= 2:
|
| 236 |
+
logger.info(f"Ignore: {k}")
|
| 237 |
+
continue
|
| 238 |
+
logger.info(f"Inflate: {k}, {state_dict[k].shape} => {state_dict_3d[k].shape}")
|
| 239 |
+
time_dim = state_dict_3d[k].shape[2]
|
| 240 |
+
state_dict[k] = inflate_weight(state_dict[k], time_dim, center=center)
|
| 241 |
+
|
| 242 |
+
pos_embed_checkpoint = state_dict["positional_embedding"]
|
| 243 |
+
embedding_size = pos_embed_checkpoint.shape[-1]
|
| 244 |
+
num_patches = (input_resolution // patch_size) ** 2
|
| 245 |
+
orig_size = int((pos_embed_checkpoint.shape[-2] - 1) ** 0.5)
|
| 246 |
+
new_size = int(num_patches**0.5)
|
| 247 |
+
if orig_size != new_size:
|
| 248 |
+
logger.info(f"Pos_emb from {orig_size} to {new_size}")
|
| 249 |
+
extra_tokens = pos_embed_checkpoint[:1]
|
| 250 |
+
pos_tokens = pos_embed_checkpoint[1:]
|
| 251 |
+
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
|
| 252 |
+
pos_tokens = torch.nn.functional.interpolate(
|
| 253 |
+
pos_tokens, size=(new_size, new_size), mode="bicubic", align_corners=False
|
| 254 |
+
)
|
| 255 |
+
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(0, 2)
|
| 256 |
+
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=0)
|
| 257 |
+
state_dict["positional_embedding"] = new_pos_embed
|
| 258 |
+
|
| 259 |
+
message = model.load_state_dict(state_dict, strict=False)
|
| 260 |
+
logger.info(f"Load pretrained weights: {message}")
|
| 261 |
+
|
| 262 |
+
|
| 263 |
+
@register_model
|
| 264 |
+
def clip_joint_b16(pretrained=True, input_resolution=224, kernel_size=1, center=True, num_frames=8, drop_path=0.0):
|
| 265 |
+
model = VisionTransformer(
|
| 266 |
+
input_resolution=input_resolution,
|
| 267 |
+
patch_size=16,
|
| 268 |
+
width=768,
|
| 269 |
+
layers=12,
|
| 270 |
+
heads=12,
|
| 271 |
+
output_dim=512,
|
| 272 |
+
kernel_size=kernel_size,
|
| 273 |
+
num_frames=num_frames,
|
| 274 |
+
drop_path=drop_path,
|
| 275 |
+
)
|
| 276 |
+
raise NotImplementedError
|
| 277 |
+
if pretrained:
|
| 278 |
+
logger.info("load pretrained weights")
|
| 279 |
+
state_dict = torch.load(_MODELS["ViT-B/16"], map_location="cpu")
|
| 280 |
+
load_state_dict(model, state_dict, input_resolution=input_resolution, patch_size=16, center=center)
|
| 281 |
+
return model.eval()
|
| 282 |
+
|
| 283 |
+
|
| 284 |
+
@register_model
|
| 285 |
+
def clip_joint_l14(
|
| 286 |
+
pretrained=False,
|
| 287 |
+
input_resolution=224,
|
| 288 |
+
kernel_size=1,
|
| 289 |
+
center=True,
|
| 290 |
+
num_frames=8,
|
| 291 |
+
drop_path=0.0,
|
| 292 |
+
checkpoint_num=0,
|
| 293 |
+
dropout=0.0,
|
| 294 |
+
):
|
| 295 |
+
model = VisionTransformer(
|
| 296 |
+
input_resolution=input_resolution,
|
| 297 |
+
patch_size=14,
|
| 298 |
+
width=1024,
|
| 299 |
+
layers=24,
|
| 300 |
+
heads=16,
|
| 301 |
+
output_dim=768,
|
| 302 |
+
kernel_size=kernel_size,
|
| 303 |
+
num_frames=num_frames,
|
| 304 |
+
drop_path=drop_path,
|
| 305 |
+
checkpoint_num=checkpoint_num,
|
| 306 |
+
dropout=dropout,
|
| 307 |
+
)
|
| 308 |
+
if pretrained:
|
| 309 |
+
if isinstance(pretrained, str):
|
| 310 |
+
model_name = pretrained
|
| 311 |
+
else:
|
| 312 |
+
model_name = "ViT-L/14"
|
| 313 |
+
logger.info("load pretrained weights")
|
| 314 |
+
state_dict = torch.load(_MODELS[model_name], map_location="cpu")
|
| 315 |
+
load_state_dict(model, state_dict, input_resolution=input_resolution, patch_size=14, center=center)
|
| 316 |
+
return model.eval()
|
| 317 |
+
|
| 318 |
+
|
| 319 |
+
@register_model
|
| 320 |
+
def clip_joint_l14_336(pretrained=True, input_resolution=336, kernel_size=1, center=True, num_frames=8, drop_path=0.0):
|
| 321 |
+
raise NotImplementedError
|
| 322 |
+
model = VisionTransformer(
|
| 323 |
+
input_resolution=input_resolution,
|
| 324 |
+
patch_size=14,
|
| 325 |
+
width=1024,
|
| 326 |
+
layers=24,
|
| 327 |
+
heads=16,
|
| 328 |
+
output_dim=768,
|
| 329 |
+
kernel_size=kernel_size,
|
| 330 |
+
num_frames=num_frames,
|
| 331 |
+
drop_path=drop_path,
|
| 332 |
+
)
|
| 333 |
+
if pretrained:
|
| 334 |
+
logger.info("load pretrained weights")
|
| 335 |
+
state_dict = torch.load(_MODELS["ViT-L/14_336"], map_location="cpu")
|
| 336 |
+
load_state_dict(model, state_dict, input_resolution=input_resolution, patch_size=14, center=center)
|
| 337 |
+
return model.eval()
|
| 338 |
+
|
| 339 |
+
|
| 340 |
+
def interpolate_pos_embed_vit(state_dict, new_model):
|
| 341 |
+
key = "vision_encoder.temporal_positional_embedding"
|
| 342 |
+
if key in state_dict:
|
| 343 |
+
vision_temp_embed_new = new_model.state_dict()[key]
|
| 344 |
+
vision_temp_embed_new = vision_temp_embed_new.unsqueeze(2) # [1, n, d] -> [1, n, 1, d]
|
| 345 |
+
vision_temp_embed_old = state_dict[key]
|
| 346 |
+
vision_temp_embed_old = vision_temp_embed_old.unsqueeze(2)
|
| 347 |
+
|
| 348 |
+
state_dict[key] = load_temp_embed_with_mismatch(
|
| 349 |
+
vision_temp_embed_old, vision_temp_embed_new, add_zero=False
|
| 350 |
+
).squeeze(2)
|
| 351 |
+
|
| 352 |
+
key = "text_encoder.positional_embedding"
|
| 353 |
+
if key in state_dict:
|
| 354 |
+
text_temp_embed_new = new_model.state_dict()[key]
|
| 355 |
+
text_temp_embed_new = text_temp_embed_new.unsqueeze(0).unsqueeze(2) # [n, d] -> [1, n, 1, d]
|
| 356 |
+
text_temp_embed_old = state_dict[key]
|
| 357 |
+
text_temp_embed_old = text_temp_embed_old.unsqueeze(0).unsqueeze(2)
|
| 358 |
+
|
| 359 |
+
state_dict[key] = (
|
| 360 |
+
load_temp_embed_with_mismatch(text_temp_embed_old, text_temp_embed_new, add_zero=False)
|
| 361 |
+
.squeeze(2)
|
| 362 |
+
.squeeze(0)
|
| 363 |
+
)
|
| 364 |
+
return state_dict
|
Helios/eval/utils/third_party/amt/LICENSE
ADDED
|
@@ -0,0 +1,176 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
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|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
## creative commons
|
| 2 |
+
|
| 3 |
+
# Attribution-NonCommercial 4.0 International
|
| 4 |
+
|
| 5 |
+
Creative Commons Corporation (“Creative Commons”) is not a law firm and does not provide legal services or legal advice. Distribution of Creative Commons public licenses does not create a lawyer-client or other relationship. Creative Commons makes its licenses and related information available on an “as-is” basis. Creative Commons gives no warranties regarding its licenses, any material licensed under their terms and conditions, or any related information. Creative Commons disclaims all liability for damages resulting from their use to the fullest extent possible.
|
| 6 |
+
|
| 7 |
+
### Using Creative Commons Public Licenses
|
| 8 |
+
|
| 9 |
+
Creative Commons public licenses provide a standard set of terms and conditions that creators and other rights holders may use to share original works of authorship and other material subject to copyright and certain other rights specified in the public license below. The following considerations are for informational purposes only, are not exhaustive, and do not form part of our licenses.
|
| 10 |
+
|
| 11 |
+
* __Considerations for licensors:__ Our public licenses are intended for use by those authorized to give the public permission to use material in ways otherwise restricted by copyright and certain other rights. Our licenses are irrevocable. Licensors should read and understand the terms and conditions of the license they choose before applying it. Licensors should also secure all rights necessary before applying our licenses so that the public can reuse the material as expected. Licensors should clearly mark any material not subject to the license. This includes other CC-licensed material, or material used under an exception or limitation to copyright. [More considerations for licensors](http://wiki.creativecommons.org/Considerations_for_licensors_and_licensees#Considerations_for_licensors).
|
| 12 |
+
|
| 13 |
+
* __Considerations for the public:__ By using one of our public licenses, a licensor grants the public permission to use the licensed material under specified terms and conditions. If the licensor’s permission is not necessary for any reason–for example, because of any applicable exception or limitation to copyright–then that use is not regulated by the license. Our licenses grant only permissions under copyright and certain other rights that a licensor has authority to grant. Use of the licensed material may still be restricted for other reasons, including because others have copyright or other rights in the material. A licensor may make special requests, such as asking that all changes be marked or described. Although not required by our licenses, you are encouraged to respect those requests where reasonable. [More considerations for the public](http://wiki.creativecommons.org/Considerations_for_licensors_and_licensees#Considerations_for_licensees).
|
| 14 |
+
|
| 15 |
+
## Creative Commons Attribution-NonCommercial 4.0 International Public License
|
| 16 |
+
|
| 17 |
+
By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions.
|
| 18 |
+
|
| 19 |
+
### Section 1 – Definitions.
|
| 20 |
+
|
| 21 |
+
a. __Adapted Material__ means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image.
|
| 22 |
+
|
| 23 |
+
b. __Adapter's License__ means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public License.
|
| 24 |
+
|
| 25 |
+
c. __Copyright and Similar Rights__ means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights.
|
| 26 |
+
|
| 27 |
+
d. __Effective Technological Measures__ means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements.
|
| 28 |
+
|
| 29 |
+
e. __Exceptions and Limitations__ means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material.
|
| 30 |
+
|
| 31 |
+
f. __Licensed Material__ means the artistic or literary work, database, or other material to which the Licensor applied this Public License.
|
| 32 |
+
|
| 33 |
+
g. __Licensed Rights__ means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license.
|
| 34 |
+
|
| 35 |
+
h. __Licensor__ means the individual(s) or entity(ies) granting rights under this Public License.
|
| 36 |
+
|
| 37 |
+
i. __NonCommercial__ means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange.
|
| 38 |
+
|
| 39 |
+
j. __Share__ means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them.
|
| 40 |
+
|
| 41 |
+
k. __Sui Generis Database Rights__ means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world.
|
| 42 |
+
|
| 43 |
+
l. __You__ means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning.
|
| 44 |
+
|
| 45 |
+
### Section 2 – Scope.
|
| 46 |
+
|
| 47 |
+
a. ___License grant.___
|
| 48 |
+
|
| 49 |
+
1. Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to:
|
| 50 |
+
|
| 51 |
+
A. reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and
|
| 52 |
+
|
| 53 |
+
B. produce, reproduce, and Share Adapted Material for NonCommercial purposes only.
|
| 54 |
+
|
| 55 |
+
2. __Exceptions and Limitations.__ For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions.
|
| 56 |
+
|
| 57 |
+
3. __Term.__ The term of this Public License is specified in Section 6(a).
|
| 58 |
+
|
| 59 |
+
4. __Media and formats; technical modifications allowed.__ The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material.
|
| 60 |
+
|
| 61 |
+
5. __Downstream recipients.__
|
| 62 |
+
|
| 63 |
+
A. __Offer from the Licensor – Licensed Material.__ Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License.
|
| 64 |
+
|
| 65 |
+
B. __No downstream restrictions.__ You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material.
|
| 66 |
+
|
| 67 |
+
6. __No endorsement.__ Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i).
|
| 68 |
+
|
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>
|
| 161 |
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> Creative Commons may be contacted at creativecommons.org
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|
| 164 |
+
### Commercial licensing opportunities
|
| 165 |
+
For commercial uses of the Model & Software, please send email to cmm[AT]nankai.edu.cn
|
| 166 |
+
|
| 167 |
+
Citation:
|
| 168 |
+
|
| 169 |
+
@inproceedings{licvpr23amt,
|
| 170 |
+
title = {AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation},
|
| 171 |
+
author = {Li, Zhen and Zhu, Zuo-Liang and Han, Ling-Hao and Hou, Qibin and Guo, Chun-Le and Cheng, Ming-Ming},
|
| 172 |
+
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
|
| 173 |
+
year = {2023}
|
| 174 |
+
}
|
| 175 |
+
|
| 176 |
+
Copyright (c) 2023 MCG-NKU
|
Helios/eval/utils/third_party/amt/README.md
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|
| 1 |
+
# AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
This repository contains the official implementation of the following paper:
|
| 5 |
+
> **AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation**<br>
|
| 6 |
+
> [Zhen Li](https://paper99.github.io/)<sup>\*</sup>, [Zuo-Liang Zhu](https://nk-cs-zzl.github.io/)<sup>\*</sup>, [Ling-Hao Han](https://scholar.google.com/citations?user=0ooNdgUAAAAJ&hl=en), [Qibin Hou](https://scholar.google.com/citations?hl=en&user=fF8OFV8AAAAJ&view_op=list_works), [Chun-Le Guo](https://scholar.google.com/citations?hl=en&user=RZLYwR0AAAAJ), [Ming-Ming Cheng](https://mmcheng.net/cmm)<br>
|
| 7 |
+
> (\* denotes equal contribution) <br>
|
| 8 |
+
> Nankai University <br>
|
| 9 |
+
> In CVPR 2023<br>
|
| 10 |
+
|
| 11 |
+
[[Paper](https://arxiv.org/abs/2304.09790)]
|
| 12 |
+
[[Project Page](https://nk-cs-zzl.github.io/projects/amt/index.html)]
|
| 13 |
+
[[Web demos](#web-demos)]
|
| 14 |
+
[Video]
|
| 15 |
+
|
| 16 |
+
AMT is a **lightweight, fast, and accurate** algorithm for Frame Interpolation.
|
| 17 |
+
It aims to provide practical solutions for **video generation** from **a few given frames (at least two frames)**.
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
* More examples can be found in our [project page](https://nk-cs-zzl.github.io/projects/amt/index.html).
|
| 21 |
+
|
| 22 |
+
## Web demos
|
| 23 |
+
Integrated into [Hugging Face Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [](https://huggingface.co/spaces/NKU-AMT/AMT)
|
| 24 |
+
|
| 25 |
+
Try AMT to interpolate between two or more images at [](https://colab.research.google.com/drive/1IeVO5BmLouhRh6fL2z_y18kgubotoaBq?usp=sharing)
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
## Change Log
|
| 29 |
+
- **Apr 20, 2023**: Our code is publicly available.
|
| 30 |
+
|
| 31 |
+
|
| 32 |
+
## Method Overview
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
For technical details, please refer to the [method.md](docs/method.md) file, or read the full report on [arXiv](https://arxiv.org/abs/2304.09790).
|
| 36 |
+
|
| 37 |
+
## Dependencies and Installation
|
| 38 |
+
1. Clone Repo
|
| 39 |
+
|
| 40 |
+
```bash
|
| 41 |
+
git clone https://github.com/MCG-NKU/AMT.git
|
| 42 |
+
```
|
| 43 |
+
|
| 44 |
+
2. Create Conda Environment and Install Dependencies
|
| 45 |
+
|
| 46 |
+
```bash
|
| 47 |
+
conda env create -f environment.yaml
|
| 48 |
+
conda activate amt
|
| 49 |
+
```
|
| 50 |
+
3. Download pretrained models for demos from [Pretrained Models](#pretrained-models) and place them to the `pretrained` folder
|
| 51 |
+
|
| 52 |
+
## Quick Demo
|
| 53 |
+
|
| 54 |
+
**Note that the selected pretrained model (`[CKPT_PATH]`) needs to match the config file (`[CFG]`).**
|
| 55 |
+
|
| 56 |
+
> Creating a video demo, increasing $n$ will slow down the motion in the video. (With $m$ input frames, `[N_ITER]` $=n$ corresponds to $2^n\times (m-1)+1$ output frames.)
|
| 57 |
+
|
| 58 |
+
|
| 59 |
+
```bash
|
| 60 |
+
python demos/demo_2x.py -c [CFG] -p [CKPT] -n [N_ITER] -i [INPUT] -o [OUT_PATH] -r [FRAME_RATE]
|
| 61 |
+
# e.g. [INPUT]
|
| 62 |
+
# -i could be a video / a regular expression / a folder contains multiple images
|
| 63 |
+
# -i demo.mp4 (video)/img_*.png (regular expression)/img0.png img1.png (images)/demo_input (folder)
|
| 64 |
+
|
| 65 |
+
# e.g. a simple usage
|
| 66 |
+
python demos/demo_2x.py -c cfgs/AMT-S.yaml -p pretrained/amt-s.pth -n 6 -i assets/quick_demo/img0.png assets/quick_demo/img1.png
|
| 67 |
+
|
| 68 |
+
```
|
| 69 |
+
|
| 70 |
+
+ Note: Please enable `--save_images` for saving the output images (Save speed will be slowed down if there are too many output images)
|
| 71 |
+
+ Input type supported: `a video` / `a regular expression` / `multiple images` / `a folder containing input frames`.
|
| 72 |
+
+ Results are in the `[OUT_PATH]` (default is `results/2x`) folder.
|
| 73 |
+
|
| 74 |
+
## Pretrained Models
|
| 75 |
+
|
| 76 |
+
<p id="Pretrained"></p>
|
| 77 |
+
|
| 78 |
+
<table>
|
| 79 |
+
<thead>
|
| 80 |
+
<tr>
|
| 81 |
+
<th> Dataset </th>
|
| 82 |
+
<th> :link: Download Links </th>
|
| 83 |
+
<th> Config file </th>
|
| 84 |
+
<th> Trained on </th>
|
| 85 |
+
<th> Arbitrary/Fixed </th>
|
| 86 |
+
</tr>
|
| 87 |
+
</thead>
|
| 88 |
+
<tbody>
|
| 89 |
+
<tr>
|
| 90 |
+
<td>AMT-S</td>
|
| 91 |
+
<th> [<a href="https://drive.google.com/file/d/1WmOKmQmd6pnLpID8EpUe-TddFpJuavrL/view?usp=share_link">Google Driver</a>][<a href="https://pan.baidu.com/s/1yGaNLeb9TG5-81t0skrOUA?pwd=f66n">Baidu Cloud</a>][<a href="https://huggingface.co/lalala125/AMT/resolve/main/amt-s.pth">Hugging Face</a>] </th>
|
| 92 |
+
<th> [<a href="cfgs/AMT-S.yaml">cfgs/AMT-S</a>] </th>
|
| 93 |
+
<th>Vimeo90k</th>
|
| 94 |
+
<th>Fixed</th>
|
| 95 |
+
</tr>
|
| 96 |
+
<tr>
|
| 97 |
+
<td>AMT-L</td>
|
| 98 |
+
<th>[<a href="https://drive.google.com/file/d/1UyhYpAQLXMjFA55rlFZ0kdiSVTL7oU-z/view?usp=share_link">Google Driver</a>][<a href="https://pan.baidu.com/s/1qI4fBgS405Bd4Wn1R3Gbeg?pwd=nbne">Baidu Cloud</a>][<a href="https://huggingface.co/lalala125/AMT/resolve/main/amt-l.pth">Hugging Face</a>]</th>
|
| 99 |
+
<th> [<a href="cfgs/AMT-L.yaml">cfgs/AMT-L</a>] </th>
|
| 100 |
+
<th>Vimeo90k</th>
|
| 101 |
+
<th>Fixed</th>
|
| 102 |
+
</tr>
|
| 103 |
+
<tr>
|
| 104 |
+
<td>AMT-G</td>
|
| 105 |
+
<th>[<a href="https://drive.google.com/file/d/1yieLtKh4ei3gOrLN1LhKSP_9157Q-mtP/view?usp=share_link">Google Driver</a>][<a href="https://pan.baidu.com/s/1AjmQVziQut1bXgQnDcDKvA?pwd=caf6">Baidu Cloud</a>][<a href="https://huggingface.co/lalala125/AMT/resolve/main/amt-g.pth">Hugging Face</a>] </th>
|
| 106 |
+
<th> [<a href="cfgs/AMT-G.yaml">cfgs/AMT-G</a>] </th>
|
| 107 |
+
<th>Vimeo90k</th>
|
| 108 |
+
<th>Fixed</th>
|
| 109 |
+
</tr>
|
| 110 |
+
<tr>
|
| 111 |
+
<td>AMT-S</td>
|
| 112 |
+
<th>[<a href="https://drive.google.com/file/d/1f1xAF0EDm-rjDdny8_aLyeedfM0QL4-C/view?usp=share_link">Google Driver</a>][<a href="https://pan.baidu.com/s/1eZtoULyduQM8AkXeYEBOEw?pwd=8hy3">Baidu Cloud</a>][<a href="https://huggingface.co/lalala125/AMT/resolve/main/gopro_amt-s.pth">Hugging Face</a>] </th>
|
| 113 |
+
<th> [<a href="cfgs/AMT-S_gopro.yaml">cfgs/AMT-S_gopro</a>] </th>
|
| 114 |
+
<th>GoPro</th>
|
| 115 |
+
<th>Arbitrary</th>
|
| 116 |
+
</tr>
|
| 117 |
+
</tbody>
|
| 118 |
+
</table>
|
| 119 |
+
|
| 120 |
+
## Training and Evaluation
|
| 121 |
+
|
| 122 |
+
Please refer to [develop.md](docs/develop.md) to learn how to benchmark the AMT and how to train a new AMT model from scratch.
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| 123 |
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| 124 |
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| 125 |
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## Citation
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| 126 |
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If you find our repo useful for your research, please consider citing our paper:
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| 127 |
+
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| 128 |
+
```bibtex
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| 129 |
+
@inproceedings{licvpr23amt,
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| 130 |
+
title={AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation},
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| 131 |
+
author={Li, Zhen and Zhu, Zuo-Liang and Han, Ling-Hao and Hou, Qibin and Guo, Chun-Le and Cheng, Ming-Ming},
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| 132 |
+
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
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| 133 |
+
year={2023}
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| 134 |
+
}
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| 135 |
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```
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| 136 |
+
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| 137 |
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| 138 |
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## License
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| 139 |
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This code is licensed under the [Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/) for non-commercial use only.
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| 140 |
+
Please note that any commercial use of this code requires formal permission prior to use.
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| 141 |
+
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| 142 |
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## Contact
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| 143 |
+
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| 144 |
+
For technical questions, please contact `zhenli1031[AT]gmail.com` and `nkuzhuzl[AT]gmail.com`.
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| 145 |
+
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| 146 |
+
For commercial licensing, please contact `cmm[AT]nankai.edu.cn`
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| 147 |
+
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| 148 |
+
## Acknowledgement
|
| 149 |
+
|
| 150 |
+
We thank Jia-Wen Xiao, Zheng-Peng Duan, Rui-Qi Wu, and Xin Jin for proof reading.
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| 151 |
+
We thank [Zhewei Huang](https://github.com/hzwer) for his suggestions.
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| 152 |
+
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| 153 |
+
Here are some great resources we benefit from:
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| 154 |
+
|
| 155 |
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- [IFRNet](https://github.com/ltkong218/IFRNet) and [RIFE](https://github.com/megvii-research/ECCV2022-RIFE) for data processing, benchmarking, and loss designs.
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| 156 |
+
- [RAFT](https://github.com/princeton-vl/RAFT), [M2M-VFI](https://github.com/feinanshan/M2M_VFI), and [GMFlow](https://github.com/haofeixu/gmflow) for inspirations.
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| 157 |
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- [FILM](https://github.com/google-research/frame-interpolation) for Web demo reference.
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| 158 |
+
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| 159 |
+
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| 160 |
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**If you develop/use AMT in your projects, welcome to let us know. We will list your projects in this repository.**
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| 162 |
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We also thank all of our contributors.
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| 164 |
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<a href="https://github.com/MCG-NKU/AMT/graphs/contributors">
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<img src="https://contrib.rocks/image?repo=MCG-NKU/AMT" />
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| 166 |
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</a>
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| 167 |
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Helios/eval/utils/third_party/amt/__init__.py
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File without changes
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Helios/eval/utils/third_party/amt/benchmarks/__init__.py
ADDED
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File without changes
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