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  1. .gitattributes +2 -0
  2. Encoding/Image/CLIP-ViT-H-fp16.safetensors +3 -0
  3. Encoding/Image/sigclip_vision_patch14_384.safetensors +3 -0
  4. Encoding/Text/CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors +3 -0
  5. Encoding/Text/CLIP-ViT-bigG-14-laion2B-39B-b160k.safetensors +3 -0
  6. Encoding/Text/clip_g.safetensors +3 -0
  7. Encoding/Text/clip_l.safetensors +3 -0
  8. Encoding/Text/stable-diffusion-2-1-clip-fp16.safetensors +3 -0
  9. Encoding/Text/t5xxl_fp16.safetensors +3 -0
  10. Encoding/Text/umt5-xxl-enc-bf16.safetensors +3 -0
  11. I2I/Face Restoring/Codeformer/codeformer-v0.1.0.pth +3 -0
  12. I2I/Face Restoring/GFPGAN/._GFPGANv1.4.pth +3 -0
  13. I2I/Face Restoring/GFPGAN/._parsing_parsenet.pth +3 -0
  14. I2I/Face Restoring/GFPGAN/GFPGANv1.4.pth +3 -0
  15. I2I/Face Restoring/GFPGAN/parsing_parsenet.pth +3 -0
  16. I2I/Face Swapping/InstantID/ip-adapter.bin +3 -0
  17. I2I/Face Swapping/Inswapper/inswapper_128.onnx +3 -0
  18. I2I/Face Swapping/PuLID/ip-adapter_pulid_sdxl_fp16.safetensors +3 -0
  19. I2I/Face Swapping/antelopev2/1k3d68.onnx +3 -0
  20. I2I/Face Swapping/antelopev2/2d106det.onnx +3 -0
  21. I2I/Face Swapping/antelopev2/genderage.onnx +3 -0
  22. I2I/Face Swapping/antelopev2/glintr100.onnx +3 -0
  23. I2I/Face Swapping/antelopev2/scrfd_10g_bnkps.onnx +3 -0
  24. I2I/Face Swapping/buffalo_l/1k3d68.onnx +3 -0
  25. I2I/Face Swapping/buffalo_l/2d106det.onnx +3 -0
  26. I2I/Face Swapping/buffalo_l/det_10g.onnx +3 -0
  27. I2I/Face Swapping/buffalo_l/genderage.onnx +3 -0
  28. I2I/Face Swapping/buffalo_l/w600k_r50.onnx +3 -0
  29. Upscaling/Image/CCSR/real-world_ccsr-fp32.safetensors +3 -0
  30. Upscaling/Image/ESRGAN/._4x-UltraSharp.pth +3 -0
  31. Upscaling/Image/ESRGAN/._4x_UniversalUpscalerV2-Neutral_115000_swaG.pth +3 -0
  32. Upscaling/Image/ESRGAN/._4x_UniversalUpscalerV2-Sharp_101000_G.pth +3 -0
  33. Upscaling/Image/ESRGAN/._x4-upscaler-ema.safetensors +3 -0
  34. Upscaling/Image/ESRGAN/._x4-upscaler-ema.yaml +0 -0
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  36. Upscaling/Image/ESRGAN/4x_UniversalUpscalerV2-Neutral_115000_swaG.pth +3 -0
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  39. Upscaling/Image/ESRGAN/x4-upscaler-ema.safetensors +3 -0
  40. Upscaling/Image/ESRGAN/x4-upscaler-ema.yaml +75 -0
  41. Upscaling/Image/LDSR/._model.ckpt +3 -0
  42. Upscaling/Image/LDSR/model.ckpt +3 -0
  43. Upscaling/Image/RealESRGAN/._RealESRGAN_x4plus.pth +3 -0
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  48. Upscaling/Image/SUPIR/SUPIR-v0Q_fp16.safetensors +3 -0
  49. VAE/Image/SD3.5L_VAE.safetensors +3 -0
  50. VAE/Image/difconsistencyRAWVAE_v10Safestensor.safetensors +3 -0
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+ model:
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+ base_learning_rate: 1.0e-04
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+ target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion
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+ params:
5
+ parameterization: "v"
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+ low_scale_key: "lr"
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+ linear_start: 0.0001
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+ linear_end: 0.02
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+ num_timesteps_cond: 1
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+ log_every_t: 200
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+ timesteps: 1000
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+ first_stage_key: "jpg"
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+ cond_stage_key: "txt"
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+ image_size: 128
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+ channels: 4
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+ cond_stage_trainable: false
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+ conditioning_key: "hybrid-adm"
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+ monitor: val/loss_simple_ema
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+ scale_factor: 0.08333
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+ use_ema: False
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+
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+ low_scale_config:
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+ target: ldm.modules.diffusionmodules.upscaling.ImageConcatWithNoiseAugmentation
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+ params:
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+ noise_schedule_config: # image space
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+ linear_start: 0.0001
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+ linear_end: 0.02
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+ max_noise_level: 350
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+
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+ unet_config:
31
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
32
+ params:
33
+ use_checkpoint: True
34
+ num_classes: 1000 # timesteps for noise conditioning (here constant, just need one)
35
+ image_size: 128
36
+ in_channels: 7
37
+ out_channels: 4
38
+ model_channels: 256
39
+ attention_resolutions: [ 2,4,8]
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+ num_res_blocks: 2
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+ channel_mult: [ 1, 2, 2, 4]
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+ disable_self_attentions: [True, True, True, False]
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+ disable_middle_self_attn: False
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+ num_heads: 8
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+ use_spatial_transformer: True
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+ transformer_depth: 1
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+ context_dim: 1024
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+ legacy: False
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+ use_linear_in_transformer: True
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+
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+ first_stage_config:
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+ target: ldm.models.autoencoder.AutoencoderKL
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+ params:
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+ embed_dim: 4
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+ ddconfig:
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+ # attn_type: "vanilla-xformers" this model needs efficient attention to be feasible on HR data, also the decoder seems to break in half precision (UNet is fine though)
57
+ double_z: True
58
+ z_channels: 4
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+ resolution: 256
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+ in_channels: 3
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+ out_ch: 3
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+ ch: 128
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+ ch_mult: [ 1,2,4 ] # num_down = len(ch_mult)-1
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+ num_res_blocks: 2
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+ attn_resolutions: [ ]
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+ dropout: 0.0
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+
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+ lossconfig:
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+ target: torch.nn.Identity
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+
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+ cond_stage_config:
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+ target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
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+ params:
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+ freeze: True
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+ layer: "penultimate"
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