End of training

lr_scheduler/lr_scheduler.pt

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model_index.json

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+{
+  "_class_name": "UTTIPipeline",
+  "_diffusers_version": "0.25.0.dev0",
+  "scheduler": [
+    "sde_torch_param",
+    "TorchSDE_PARAM"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ]
+}

optimizer/optimizer.pt

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scheduler/scheduler_config.json

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+{
+  "_class_name": "TorchSDE_PARAM",
+  "_config_class_name": "SDEPolynomialConfig",
+  "_diffusers_version": "0.25.0.dev0",
+  "data_dimension": 3072
+}

scheduler/scheduler_config.py

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+import jax.numpy as jnp
+import jax
+import torch
+from dataclasses import dataclass
+import sympy
+import sympy as sp
+from sympy import Matrix, Symbol
+import math
+from sde_redefined_param import SDEDimension
+@dataclass
+class SDEPolynomialConfig:
+    name = "Custom"
+
+    initial_variable_value = 0
+    max_variable_value = 1# math.inf
+    min_sample_value = 1e-6
+
+    variable = Symbol('t', nonnegative=True, real=True, domain=sympy.Interval(initial_variable_value, max_variable_value, left_open=False, right_open=False))
+    drift_dimension = SDEDimension.SCALAR 
+    diffusion_dimension = SDEDimension.SCALAR
+    diffusion_matrix_dimension = SDEDimension.SCALAR 
+
+    drift_degree = 20
+    diffusion_degree = 20
+
+    drift_parameters = Matrix([sympy.symbols(f"f:{drift_degree}", real=True, nonzero=True)])
+
+    diffusion_parameters = Matrix([sympy.symbols(f"l:{diffusion_degree}", real=True, nonzero=True)])
+
+
+    @property
+    def drift(self): 
+        transformed_variable = self.variable
+        return -sympy.Abs(sum(sympy.HadamardProduct(Matrix([[transformed_variable**i for i in range(1,self.drift_degree+1)]]), self.drift_parameters).doit()))
+        
+
+    @property
+    def diffusion(self):
+        return self.variable**(sum(sympy.HadamardProduct(Matrix([[self.variable**i for i in range(0,self.diffusion_degree)]]),self.diffusion_parameters.applyfunc(lambda x: x**2)).doit()))
+
+    # TODO (KLAUS) : in the SDE SAMPLING CHANGING Q impacts how we sample z ~ N(0, Q*(delta t))
+    diffusion_matrix = 1 
+
+
+    module = 'jax'
+
+    drift_integral_form=True
+    diffusion_integral_form=True
+    diffusion_integral_decomposition = 'cholesky' # ldl
+
+
+
+    target = "epsilon" # x0
+    non_symbolic_parameters = {'drift': torch.ones(drift_degree), 'diffusion': torch.ones(diffusion_degree)}

scheduler/sdeparameters.pt

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text_encoder/config.json

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+{
+  "_name_or_path": "AltLuv/pokemon-test-tti",
+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "quick_gelu",
+  "hidden_size": 768,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "projection_dim": 512,
+  "torch_dtype": "float16",
+  "transformers_version": "4.35.2",
+  "vocab_size": 49408
+}

text_encoder/model.safetensors

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tokenizer/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer/tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "49406": {
+      "content": "<|startoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "49407": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}

unet/config.json

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+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.25.0.dev0",
+  "act_fn": "silu",
+  "addition_embed_type": null,
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": null,
+  "attention_head_dim": 8,
+  "attention_type": "default",
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "class_embeddings_concat": false,
+  "conv_in_kernel": 3,
+  "conv_out_kernel": 3,
+  "cross_attention_dim": 768,
+  "cross_attention_norm": null,
+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dropout": 0.0,
+  "dual_cross_attention": false,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 3,
+  "layers_per_block": 2,
+  "mid_block_only_cross_attention": null,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "out_channels": 3,
+  "projection_class_embeddings_input_dim": null,
+  "resnet_out_scale_factor": 1.0,
+  "resnet_skip_time_act": false,
+  "resnet_time_scale_shift": "default",
+  "reverse_transformer_layers_per_block": null,
+  "sample_size": 32,
+  "time_cond_proj_dim": null,
+  "time_embedding_act_fn": null,
+  "time_embedding_dim": null,
+  "time_embedding_type": "positional",
+  "timestep_post_act": null,
+  "transformer_layers_per_block": 1,
+  "up_block_types": [
+    "UpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D"
+  ],
+  "upcast_attention": false,
+  "use_linear_projection": false
+}

unet/diffusion_pytorch_model.safetensors

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