End of training

README.md

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+
+---
+license: creativeml-openrail-m
+base_model: kandinsky-community/kandinsky-2-2-prior
+datasets:
+- kbharat7/DogChestXrayDatasetNew
+tags:
+- kandinsky
+- text-to-image
+- diffusers
+- diffusers-training
+inference: true
+---
+    
+# Finetuning - aditya11997/test_prior
+
+This pipeline was finetuned from **kandinsky-community/kandinsky-2-2-prior** on the **kbharat7/DogChestXrayDatasetNew** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['dogxraysmall']: 
+
+![val_imgs_grid](./val_imgs_grid.png)
+
+
+## Pipeline usage
+
+You can use the pipeline like so:
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipe_prior = DiffusionPipeline.from_pretrained("aditya11997/test_prior", torch_dtype=torch.float16)
+pipe_t2i = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16)
+prompt = "dogxraysmall"
+image_embeds, negative_image_embeds = pipe_prior(prompt, guidance_scale=1.0).to_tuple()
+image = pipe_t2i(image_embeds=image_embeds, negative_image_embeds=negative_image_embeds).images[0]
+image.save("my_image.png")
+```
+
+## Training info
+
+These are the key hyperparameters used during training:
+
+* Epochs: 1
+* Learning rate: 1e-05
+* Batch size: 1
+* Gradient accumulation steps: 4
+* Image resolution: 768
+* Mixed-precision: None
+
+
+More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/aditya11997/text2image-fine-tune/runs/9j7m0fr8).

image_encoder/config.json

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+{
+  "_name_or_path": "kandinsky-community/kandinsky-2-2-prior",
+  "architectures": [
+    "CLIPVisionModelWithProjection"
+  ],
+  "attention_dropout": 0.0,
+  "dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_size": 1664,
+  "image_size": 224,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "layer_norm_eps": 1e-05,
+  "model_type": "clip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 48,
+  "patch_size": 14,
+  "projection_dim": 1280,
+  "torch_dtype": "float16",
+  "transformers_version": "4.38.2"
+}

image_encoder/model.safetensors

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image_processor/preprocessor_config.json

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+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  }
+}

logs/text2image-fine-tune/1709712401.761387/events.out.tfevents.1709712401.777f50584738.1857.1

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logs/text2image-fine-tune/1709712401.7630112/hparams.yml

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+adam_beta1: 0.9
+adam_beta2: 0.999
+adam_epsilon: 1.0e-08
+adam_weight_decay: 0.0
+allow_tf32: false
+cache_dir: null
+caption_column: text
+checkpointing_steps: 500
+checkpoints_total_limit: 3
+dataloader_num_workers: 0
+dataset_config_name: null
+dataset_name: kbharat7/DogChestXrayDatasetNew
+gradient_accumulation_steps: 4
+hub_model_id: null
+hub_token: null
+image_column: image
+learning_rate: 1.0e-05
+local_rank: -1
+logging_dir: logs
+lr_scheduler: constant
+lr_warmup_steps: 500
+max_grad_norm: 1.0
+max_train_samples: null
+max_train_steps: 10
+mixed_precision: null
+num_train_epochs: 1
+output_dir: test_prior
+pretrained_decoder_model_name_or_path: kandinsky-community/kandinsky-2-2-decoder
+pretrained_prior_model_name_or_path: kandinsky-community/kandinsky-2-2-prior
+push_to_hub: true
+report_to: tensorboard
+resolution: 768
+resume_from_checkpoint: null
+seed: null
+snr_gamma: null
+tracker_project_name: text2image-fine-tune
+train_batch_size: 1
+train_data_dir: null
+use_8bit_adam: false
+use_ema: false
+validation_epochs: 5

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model_index.json

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+{
+  "_class_name": "KandinskyV22PriorPipeline",
+  "_diffusers_version": "0.27.0.dev0",
+  "image_encoder": [
+    "transformers",
+    "CLIPVisionModelWithProjection"
+  ],
+  "image_processor": [
+    "transformers",
+    "CLIPImageProcessor"
+  ],
+  "prior": [
+    "diffusers",
+    "PriorTransformer"
+  ],
+  "scheduler": [
+    "diffusers",
+    "UnCLIPScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModelWithProjection"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ]
+}

prior/config.json

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+{
+  "_class_name": "PriorTransformer",
+  "_diffusers_version": "0.27.0.dev0",
+  "_name_or_path": "kandinsky-community/kandinsky-2-2-prior",
+  "added_emb_type": "prd",
+  "additional_embeddings": 4,
+  "attention_head_dim": 64,
+  "clip_embed_dim": null,
+  "dropout": 0.0,
+  "embedding_dim": 1280,
+  "embedding_proj_dim": null,
+  "embedding_proj_norm_type": null,
+  "encoder_hid_proj_type": "linear",
+  "norm_in_type": null,
+  "num_attention_heads": 32,
+  "num_embeddings": 77,
+  "num_layers": 20,
+  "time_embed_act_fn": "silu",
+  "time_embed_dim": null
+}

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scheduler/scheduler_config.json

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+  "_class_name": "UnCLIPScheduler",
+  "_diffusers_version": "0.27.0.dev0",
+  "beta_schedule": "squaredcos_cap_v2",
+  "clip_sample": true,
+  "clip_sample_range": 10.0,
+  "num_train_timesteps": 1000,
+  "prediction_type": "sample",
+  "variance_type": "fixed_small_log"
+}

text_encoder/config.json

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+{
+  "_name_or_path": "kandinsky-community/kandinsky-2-2-prior",
+  "architectures": [
+    "CLIPTextModelWithProjection"
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+  "initializer_range": 0.02,
+  "intermediate_size": 5120,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 20,
+  "num_hidden_layers": 32,
+  "pad_token_id": 1,
+  "projection_dim": 1280,
+  "torch_dtype": "float16",
+  "transformers_version": "4.38.2",
+  "vocab_size": 49408
+}

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tokenizer/special_tokens_map.json

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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
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tokenizer/tokenizer_config.json

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+}