Spaces:
Configuration error
Configuration error
Erwann Millon
commited on
Commit
•
28c5269
1
Parent(s):
e37b9e5
refactoring and change default path
Browse files- app.py +2 -6
- loaders.py +1 -1
app.py
CHANGED
@@ -5,7 +5,7 @@ import sys
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import wandb
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import torch
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from presets import
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sys.path.append("taming-transformers")
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@@ -36,7 +36,7 @@ def set_img_from_example(state, img):
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def get_cleared_mask():
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return gr.Image.update(value=None)
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class StateWrapper:
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"""This extremely ugly code is a hacky fix to allow
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def create_gif(state, *args, **kwargs):
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return state, state[0].create_gif(*args, **kwargs)
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def apply_asian_vector(state, *args, **kwargs):
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@@ -191,15 +191,11 @@ with gr.Blocks(css="styles.css") as demo:
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clear.click(StateWrapper.clear_transforms, inputs=[state], outputs=[state, out, mask])
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asian_weight.change(StateWrapper.apply_asian_vector, inputs=[state, asian_weight], outputs=[state, out, mask])
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lip_size.change(StateWrapper.apply_lip_vector, inputs=[state, lip_size], outputs=[state, out, mask])
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# hair_green_purple.change(StateWrapper.apply_gp_vector, inputs=[state, hair_green_purple], outputs=[state, out, mask])
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blue_eyes.change(StateWrapper.apply_rb_vector, inputs=[state, blue_eyes], outputs=[state, out, mask])
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blend_weight.change(StateWrapper.blend, inputs=[state, blend_weight], outputs=[state, out, mask])
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# requantize.change(StateWrapper.update_requant, inputs=[state, requantize], outputs=[state, out, mask])
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base_img.change(StateWrapper.update_images, inputs=[state, base_img, blend_img, blend_weight], outputs=[state, out, mask])
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blend_img.change(StateWrapper.update_images, inputs=[state, base_img, blend_img, blend_weight], outputs=[state, out, mask])
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# small_local.click(set_small_local, outputs=[iterations, learning_rate, lpips_weight, reconstruction_steps])
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# major_local.click(set_major_local, outputs=[iterations, learning_rate, lpips_weight, reconstruction_steps])
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# major_global.click(set_major_global, outputs=[iterations, learning_rate, lpips_weight, reconstruction_steps])
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apply_prompts.click(StateWrapper.apply_prompts, inputs=[state, positive_prompts, negative_prompts, learning_rate, iterations, lpips_weight, reconstruction_steps], outputs=[state, out, mask])
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rewind.change(StateWrapper.rewind, inputs=[state, rewind], outputs=[state, out, mask])
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set_mask.click(StateWrapper.set_mask, inputs=[state, mask], outputs=[state, testim])
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import wandb
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import torch
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+
from presets import set_preset
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sys.path.append("taming-transformers")
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def get_cleared_mask():
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return gr.Image.update(value=None)
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class StateWrapper:
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"""This extremely ugly code is a hacky fix to allow concurrent users on HF Spaces without instantiating new models for each user."""
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def create_gif(state, *args, **kwargs):
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return state, state[0].create_gif(*args, **kwargs)
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def apply_asian_vector(state, *args, **kwargs):
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clear.click(StateWrapper.clear_transforms, inputs=[state], outputs=[state, out, mask])
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asian_weight.change(StateWrapper.apply_asian_vector, inputs=[state, asian_weight], outputs=[state, out, mask])
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lip_size.change(StateWrapper.apply_lip_vector, inputs=[state, lip_size], outputs=[state, out, mask])
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blue_eyes.change(StateWrapper.apply_rb_vector, inputs=[state, blue_eyes], outputs=[state, out, mask])
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blend_weight.change(StateWrapper.blend, inputs=[state, blend_weight], outputs=[state, out, mask])
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# requantize.change(StateWrapper.update_requant, inputs=[state, requantize], outputs=[state, out, mask])
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base_img.change(StateWrapper.update_images, inputs=[state, base_img, blend_img, blend_weight], outputs=[state, out, mask])
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blend_img.change(StateWrapper.update_images, inputs=[state, base_img, blend_img, blend_weight], outputs=[state, out, mask])
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apply_prompts.click(StateWrapper.apply_prompts, inputs=[state, positive_prompts, negative_prompts, learning_rate, iterations, lpips_weight, reconstruction_steps], outputs=[state, out, mask])
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rewind.change(StateWrapper.rewind, inputs=[state, rewind], outputs=[state, out, mask])
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set_mask.click(StateWrapper.set_mask, inputs=[state, mask], outputs=[state, testim])
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loaders.py
CHANGED
@@ -17,7 +17,7 @@ def load_config(config_path, display=False):
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def load_default(device):
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conf_path = "./celeba_vqgan/
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config = load_config(conf_path, display=False)
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model = taming.models.vqgan.VQModel(**config.model.params)
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sd = torch.load("./celeba_vqgan/vqgan_only.pt", map_location=device)
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def load_default(device):
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conf_path = "./celeba_vqgan/vqgan_only.yaml"
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config = load_config(conf_path, display=False)
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model = taming.models.vqgan.VQModel(**config.model.params)
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sd = torch.load("./celeba_vqgan/vqgan_only.pt", map_location=device)
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