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Browse files- configs/inference/controlnet_c_3b_canny.yaml +14 -0
- configs/inference/controlnet_c_3b_identity.yaml +17 -0
- configs/inference/controlnet_c_3b_inpainting.yaml +15 -0
- configs/inference/controlnet_c_3b_sr.yaml +15 -0
- configs/inference/lora_c_3b.yaml +15 -0
- configs/inference/stage_b_1b.yaml +13 -0
- configs/inference/stage_b_3b.yaml +13 -0
- configs/inference/stage_c_1b.yaml +7 -0
- configs/inference/stage_c_3b.yaml +7 -0
- configs/training/cfg_control_lr.yaml +48 -0
- configs/training/lora_personalization.yaml +38 -0
- configs/training/t2i.yaml +29 -0
    	
        configs/inference/controlnet_c_3b_canny.yaml
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            # GLOBAL STUFF
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            model_version: 3.6B
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            dtype: bfloat16
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            # ControlNet specific
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            controlnet_blocks: [0, 4, 8, 12, 51, 55, 59, 63]
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            controlnet_filter: CannyFilter
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            controlnet_filter_params: 
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              resize: 224
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            effnet_checkpoint_path: models/effnet_encoder.safetensors
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            previewer_checkpoint_path: models/previewer.safetensors
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            generator_checkpoint_path: models/stage_c_bf16.safetensors
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            controlnet_checkpoint_path: models/canny.safetensors
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        configs/inference/controlnet_c_3b_identity.yaml
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            # GLOBAL STUFF
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            model_version: 3.6B
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            dtype: bfloat16
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            # ControlNet specific
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            controlnet_bottleneck_mode: 'simple'
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            controlnet_blocks: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]
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            controlnet_filter: IdentityFilter
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            controlnet_filter_params: 
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              max_faces: 4
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              p_drop: 0.00
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              p_full: 0.0
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            effnet_checkpoint_path: models/effnet_encoder.safetensors
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            previewer_checkpoint_path: models/previewer.safetensors
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            generator_checkpoint_path: models/stage_c_bf16.safetensors
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            controlnet_checkpoint_path: 
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        configs/inference/controlnet_c_3b_inpainting.yaml
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            # GLOBAL STUFF
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            model_version: 3.6B
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            dtype: bfloat16
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            # ControlNet specific
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            controlnet_blocks: [0, 4, 8, 12, 51, 55, 59, 63]
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            controlnet_filter: InpaintFilter
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            controlnet_filter_params: 
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              thresold: [0.04, 0.4]
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              p_outpaint: 0.4
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            effnet_checkpoint_path: models/effnet_encoder.safetensors
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            previewer_checkpoint_path: models/previewer.safetensors
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            generator_checkpoint_path: models/stage_c_bf16.safetensors
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            controlnet_checkpoint_path: models/inpainting.safetensors
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        configs/inference/controlnet_c_3b_sr.yaml
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            # GLOBAL STUFF
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            model_version: 3.6B
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            dtype: bfloat16
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            # ControlNet specific
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            controlnet_bottleneck_mode: 'large'
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            controlnet_blocks: [0, 4, 8, 12, 51, 55, 59, 63]
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            controlnet_filter: SREffnetFilter
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            controlnet_filter_params: 
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              scale_factor: 0.5
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            effnet_checkpoint_path: models/effnet_encoder.safetensors
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            previewer_checkpoint_path: models/previewer.safetensors
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            generator_checkpoint_path: models/stage_c_bf16.safetensors
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            controlnet_checkpoint_path: models/super_resolution.safetensors
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        configs/inference/lora_c_3b.yaml
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            # GLOBAL STUFF
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            model_version: 3.6B
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            dtype: bfloat16
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            # LoRA specific
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            module_filters: ['.attn']
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            rank: 4
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            train_tokens:
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              # - ['^snail', null] # token starts with "snail" -> "snail" & "snails", don't need to be reinitialized
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              - ['[fernando]', '^dog</w>'] # custom token [snail], initialize as avg of snail & snails
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            effnet_checkpoint_path: models/effnet_encoder.safetensors
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            previewer_checkpoint_path: models/previewer.safetensors
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            generator_checkpoint_path: models/stage_c_bf16.safetensors
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            lora_checkpoint_path: models/lora_fernando_10k.safetensors
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        configs/inference/stage_b_1b.yaml
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            # GLOBAL STUFF
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            model_version: 700M
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            dtype: bfloat16
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            # For demonstration purposes in reconstruct_images.ipynb
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            webdataset_path: path to your dataset
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            batch_size: 1
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            image_size: 2048
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            grad_accum_steps: 1
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            effnet_checkpoint_path: models/effnet_encoder.safetensors
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            stage_a_checkpoint_path: models/stage_a.safetensors
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            generator_checkpoint_path: models/stage_b_lite_bf16.safetensors
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        configs/inference/stage_b_3b.yaml
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            # GLOBAL STUFF
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            model_version: 3B
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            dtype: bfloat16
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            # For demonstration purposes in reconstruct_images.ipynb
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            webdataset_path: path to your dataset
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            batch_size: 4
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            image_size: 1024
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            grad_accum_steps: 1
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            effnet_checkpoint_path: path to effnet of stablecascade / effnet_encoder.safetensors
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            stage_a_checkpoint_path: path to effnet of stablecascade stage a decoder/stage_a.safetensors
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            generator_checkpoint_path: path to effnet of stablecascade stage b decoer heavy version bf16/stage_b_lite_bf16.safetensors
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        configs/inference/stage_c_1b.yaml
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            # GLOBAL STUFF
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            model_version: 1B
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            dtype: bfloat16
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            effnet_checkpoint_path: path to effnet of stablecascade / effnet_encoder.safetensors
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            previewer_checkpoint_path: path to previewer of stablecascade/previewer.safetensors
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            generator_checkpoint_path: path to generator of stablecascade stage c lite version bf16 /stage_c_lite_bf16.safetensors
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        configs/inference/stage_c_3b.yaml
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            # GLOBAL STUFF
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            model_version: 3.6B
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            dtype: bfloat16
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            effnet_checkpoint_path: models/effnet_encoder.safetensors
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            previewer_checkpoint_path: models/previewer.safetensors
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            generator_checkpoint_path: models/stage_c_bf16.safetensors
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        configs/training/cfg_control_lr.yaml
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            # GLOBAL STUFF
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            experiment_id: Ultrapixel_controlnet
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            checkpoint_path: checkpoint output path
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            output_path:  visual results output path
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            model_version: 3.6B
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            dtype: float32
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            # # WandB
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            # wandb_project: StableCascade
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            # wandb_entity: wandb_username
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            #module_filters: ['.depthwise', '.mapper', '.attn', '.channelwise' ]
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            #rank: 32
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            # TRAINING PARAMS
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            lr: 1.0e-4
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            batch_size: 12
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            #image_size: [1536, 2048, 2560, 3072, 4096]
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            image_size: [1024, 2048, 2560, 3072, 3584, 3840, 4096, 4608]
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            #image_size: [  1024, 1536, 2048, 2560,  3072, 3584, 3840, 4096, 4608]
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            #image_size: [  1024, 1280]
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            multi_aspect_ratio: [1/1, 1/2, 1/3, 2/3, 3/4, 1/5, 2/5, 3/5, 4/5, 1/6, 5/6, 9/16]
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            grad_accum_steps: 2
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            updates: 40000
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            backup_every: 5000
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            save_every: 256
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            warmup_updates: 1
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            use_fsdp: True
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            # ControlNet specific
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            controlnet_blocks: [0, 4, 8, 12, 51, 55, 59, 63]
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            controlnet_filter: CannyFilter
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            controlnet_filter_params: 
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              resize: 224
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            # offset_noise: 0.1
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            # GDF
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            adaptive_loss_weight: True
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            ema_start_iters: 10
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            ema_iters: 50
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            ema_beta: 0.9
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            webdataset_path: path to your training dataset
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            effnet_checkpoint_path: models/effnet_encoder.safetensors
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            previewer_checkpoint_path: models/previewer.safetensors
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            generator_checkpoint_path: models/stage_c_bf16.safetensors
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            controlnet_checkpoint_path: models/canny.safetensors
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        configs/training/lora_personalization.yaml
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            # GLOBAL STUFF
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            experiment_id: roubao_cat_personalized
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            checkpoint_path: checkpoint output path
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            output_path:  visual results output path
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            model_version: 3.6B
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            dtype: float32
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            module_filters: [ '.attn']
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            rank: 4
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            train_tokens:
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              # - ['^snail', null] # token starts with "snail" -> "snail" & "snails", don't need to be reinitialized
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              - ['[roubaobao]', '^cat</w>'] # custom token [snail], initialize as avg of snail & snails
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            # TRAINING PARAMS
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            lr: 1.0e-4
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            batch_size: 4
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            image_size: [1024, 2048, 2560, 3072, 3584, 3840, 4096, 4608]
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            multi_aspect_ratio: [1/1, 1/2, 1/3, 2/3, 3/4, 1/5, 2/5, 3/5, 4/5, 1/6, 5/6, 9/16]
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            grad_accum_steps: 2
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            updates: 40000
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            backup_every: 5000
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            save_every: 512
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            warmup_updates: 1
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            use_ddp: True
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            # GDF
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            adaptive_loss_weight: True
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            tmp_prompt: a photo of a cat [roubaobao]
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            webdataset_path: path to your personalized training dataset
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            effnet_checkpoint_path:  models/effnet_encoder.safetensors
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            previewer_checkpoint_path:  models/previewer.safetensors
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            generator_checkpoint_path:  models/stage_c_bf16.safetensors
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            ultrapixel_path: models/ultrapixel_t2i.safetensors
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        configs/training/t2i.yaml
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            # GLOBAL STUFF
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            experiment_id: ultrapixel_t2i
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            #strc_fixlrt_norm3_lite_1024_hrft_newdata
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            checkpoint_path: checkpoint output path   #output model directory
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            output_path: visual results output path       #experiment output directory
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            model_version: 3.6B    # finetune large  stage c model of stablecascade
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            dtype: float32
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            # TRAINING PARAMS
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            lr: 1.0e-4
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            batch_size: 4   # gpu_number * num_per_gpu * grad_accum_steps
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            image_size: [1024, 2048, 2560, 3072, 3584, 3840, 4096, 4608]  # possible image resolution
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            multi_aspect_ratio: [1/1, 1/2, 1/3, 2/3, 3/4, 1/5, 2/5, 3/5, 4/5, 1/6, 5/6, 9/16]
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            grad_accum_steps: 2
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            updates: 40000
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            backup_every: 5000
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            save_every: 256
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| 19 | 
            +
            warmup_updates: 1
         | 
| 20 | 
            +
            use_ddp: True
         | 
| 21 | 
            +
             | 
| 22 | 
            +
            # GDF
         | 
| 23 | 
            +
            adaptive_loss_weight: True
         | 
| 24 | 
            +
             | 
| 25 | 
            +
             | 
| 26 | 
            +
            webdataset_path: path to your personalized training dataset
         | 
| 27 | 
            +
            effnet_checkpoint_path: models/effnet_encoder.safetensors
         | 
| 28 | 
            +
            previewer_checkpoint_path: models/previewer.safetensors
         | 
| 29 | 
            +
            generator_checkpoint_path: models/stage_c_bf16.safetensors
         |