Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -12,13 +12,13 @@ from utils import load_models, save_model_w2w, save_model_for_diffusers
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from sampling import sample_weights
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from huggingface_hub import snapshot_download
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global device
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global generator
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global unet
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global vae
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global text_encoder
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global tokenizer
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global noise_scheduler
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device = "cuda:0"
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generator = torch.Generator(device=device)
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@@ -32,24 +32,23 @@ df = torch.load(f"{models_path}/identity_df.pt")
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weight_dimensions = torch.load(f"{models_path}/weight_dimensions.pt")
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unet, vae, text_encoder, tokenizer, noise_scheduler = load_models(device)
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global
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global network
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unet, _, _, _, _ = load_models(device)
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network = sample_weights(unet, proj, mean, std, v[:, :1000], device, factor = 1.00)
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def inference( prompt, negative_prompt, guidance_scale, ddim_steps, seed):
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global device
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global generator
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global unet
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global vae
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global text_encoder
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global tokenizer
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global noise_scheduler
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generator = generator.manual_seed(seed)
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latents = torch.randn(
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(1, unet.in_channels, 512 // 8, 512 // 8),
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@@ -57,7 +56,6 @@ def inference( prompt, negative_prompt, guidance_scale, ddim_steps, seed):
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device = device
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).bfloat16()
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text_input = tokenizer(prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt")
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text_embeddings = text_encoder(text_input.input_ids.to(device))[0]
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@@ -119,7 +117,7 @@ with gr.Blocks() as demo:
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with gr.Column():
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gallery = gr.Gallery(label="Generated Images")
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sample.click(fn=sample_model)
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submit.click(fn=inference,
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inputs=[prompt, negative_prompt, cfg, steps, seed],
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from sampling import sample_weights
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from huggingface_hub import snapshot_download
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#global device
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#global generator
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#global unet
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#global vae
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#global text_encoder
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#global tokenizer
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#global noise_scheduler
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device = "cuda:0"
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generator = torch.Generator(device=device)
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weight_dimensions = torch.load(f"{models_path}/weight_dimensions.pt")
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unet, vae, text_encoder, tokenizer, noise_scheduler = load_models(device)
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network = sample_weights(unet, proj, mean, std, v[:, :1000], device, factor = 1.00)
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#global network
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#def sample_model():
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# global unet
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# del unet
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# global network
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# unet, _, _, _, _ = load_models(device)
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def inference( prompt, negative_prompt, guidance_scale, ddim_steps, seed):
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#global device
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#global generator
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#global unet
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#global vae
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#global text_encoder
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#global tokenizer
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#global noise_scheduler
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generator = generator.manual_seed(seed)
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latents = torch.randn(
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(1, unet.in_channels, 512 // 8, 512 // 8),
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device = device
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).bfloat16()
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text_input = tokenizer(prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt")
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text_embeddings = text_encoder(text_input.input_ids.to(device))[0]
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with gr.Column():
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gallery = gr.Gallery(label="Generated Images")
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#sample.click(fn=sample_model)
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submit.click(fn=inference,
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inputs=[prompt, negative_prompt, cfg, steps, seed],
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