Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -1,49 +1,4 @@
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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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# 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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# Ensure submodule is up-to-date
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subprocess.run(["git", "submodule", "update", "--recursive", "--remote"])
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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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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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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
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@@ -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 = "
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ckpt_path = "
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#dreambooth_lora_path = "checkpoints/personalized_models/toonyou_beta3.safetensors"
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dreambooth_lora_path = "
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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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@@ -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
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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)
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@@ -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="
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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)
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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="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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