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fffiloni commited on
Commit
021663d
1 Parent(s): a3e74b4

Update app.py

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Files changed (1) hide show
  1. app.py +5 -50
app.py CHANGED
@@ -1,49 +1,4 @@
1
- import subprocess
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- # Remove existing submodule
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- subprocess.run(["git", "submodule", "deinit", "-f", "--", "PASD"])
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- subprocess.run(["git", "rm", "-f", "PASD"])
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- subprocess.run(["rm", "-rf", ".git/modules/PASD"])
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-
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- # Add submodule
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- subprocess.run(["git", "submodule", "add", "https://github.com/fffiloni/PASD.git", "PASD"])
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- subprocess.run(["git", "submodule", "update", "--init", "--recursive"])
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-
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- # Ensure submodule is up-to-date
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- subprocess.run(["git", "submodule", "update", "--recursive", "--remote"])
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-
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- import torch
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- import types
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- torch.cuda.get_device_capability = lambda *args, **kwargs: (8, 6)
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- torch.cuda.get_device_properties = lambda *args, **kwargs: types.SimpleNamespace(name='NVIDIA A10G', major=8, minor=6, total_memory=23836033024, multi_processor_count=80)
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-
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- import huggingface_hub
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- huggingface_hub.snapshot_download(
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- repo_id='camenduru/PASD',
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- allow_patterns=[
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- 'pasd/**',
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- 'pasd_light/**',
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- 'pasd_light_rrdb/**',
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- 'pasd_rrdb/**',
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- ],
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- local_dir='PASD/runs',
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- local_dir_use_symlinks=False,
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- )
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- huggingface_hub.hf_hub_download(
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- repo_id='camenduru/PASD',
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- filename='majicmixRealistic_v6.safetensors',
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- local_dir='PASD/checkpoints/personalized_models',
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- local_dir_use_symlinks=False,
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- )
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- huggingface_hub.hf_hub_download(
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- repo_id='akhaliq/RetinaFace-R50',
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- filename='RetinaFace-R50.pth',
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- local_dir='PASD/annotator/ckpts',
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- local_dir_use_symlinks=False,
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- )
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-
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- import sys;
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- #sys.path.append('./PASD')
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  import spaces
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  import os
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  import datetime
@@ -78,10 +33,10 @@ else:
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  from models.pasd.unet_2d_condition import UNet2DConditionModel
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  from models.pasd.controlnet import ControlNetModel
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- pretrained_model_path = "runwayml/stable-diffusion-v1-5"
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- ckpt_path = "PASD/runs/pasd/checkpoint-100000"
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  #dreambooth_lora_path = "checkpoints/personalized_models/toonyou_beta3.safetensors"
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- dreambooth_lora_path = "PASD/checkpoints/personalized_models/majicmixRealistic_v6.safetensors"
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  #dreambooth_lora_path = "checkpoints/personalized_models/Realistic_Vision_V5.1.safetensors"
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  weight_dtype = torch.float16
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  device = "cuda"
@@ -90,7 +45,7 @@ scheduler = UniPCMultistepScheduler.from_pretrained(pretrained_model_path, subfo
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  text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder")
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  tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
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  vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
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- feature_extractor = CLIPImageProcessor.from_pretrained(pretrained_model_path, subfolder="feature_extractor")
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  unet = UNet2DConditionModel.from_pretrained(ckpt_path, subfolder="unet")
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  controlnet = ControlNetModel.from_pretrained(ckpt_path, subfolder="controlnet")
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  vae.requires_grad_(False)
@@ -237,7 +192,7 @@ with gr.Blocks(css=css) as demo:
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  """)
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  with gr.Row():
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  with gr.Column():
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- input_image = gr.Image(type="filepath", sources=["upload"], value="PASD/samples/frog.png")
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  prompt_in = gr.Textbox(label="Prompt", value="Frog")
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  with gr.Accordion(label="Advanced settings", open=False):
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  added_prompt = gr.Textbox(label="Added Prompt", value='clean, high-resolution, 8k, best quality, masterpiece')
 
 
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  import spaces
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  import os
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  import datetime
 
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  from models.pasd.unet_2d_condition import UNet2DConditionModel
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  from models.pasd.controlnet import ControlNetModel
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+ pretrained_model_path = "checkpoints/stable-diffusion-v1-5"
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+ ckpt_path = "runs/pasd/checkpoint-100000"
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  #dreambooth_lora_path = "checkpoints/personalized_models/toonyou_beta3.safetensors"
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+ dreambooth_lora_path = "checkpoints/personalized_models/majicmixRealistic_v6.safetensors"
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  #dreambooth_lora_path = "checkpoints/personalized_models/Realistic_Vision_V5.1.safetensors"
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  weight_dtype = torch.float16
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  device = "cuda"
 
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  text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder")
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  tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
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  vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
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+ feature_extractor = CLIPImageProcessor.from_pretrained(f"{pretrained_model_path}/feature_extractor")
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  unet = UNet2DConditionModel.from_pretrained(ckpt_path, subfolder="unet")
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  controlnet = ControlNetModel.from_pretrained(ckpt_path, subfolder="controlnet")
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  vae.requires_grad_(False)
 
192
  """)
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  with gr.Row():
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  with gr.Column():
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+ input_image = gr.Image(type="filepath", sources=["upload"], value="samples/frog.png")
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  prompt_in = gr.Textbox(label="Prompt", value="Frog")
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  with gr.Accordion(label="Advanced settings", open=False):
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  added_prompt = gr.Textbox(label="Added Prompt", value='clean, high-resolution, 8k, best quality, masterpiece')