Spaces:
Running
on
Zero
Running
on
Zero
update
Browse files- hf_demo.py +2 -2
hf_demo.py
CHANGED
@@ -42,7 +42,7 @@ def demo_inference_gen(adapter_choice:str, prompt:str, samples:int=1,seed:int=0,
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prompts = [prompt]*samples
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infer_loader = get_validation_dataloader(prompts,num_workers=0)
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-
network = get_lora_network(pipe.unet, adapter_path)["network"]
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pred_images = inference(network, pipe.tokenizer, pipe.text_encoder, pipe.vae, pipe.unet, pipe.scheduler, infer_loader,
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height=512, width=512, scales=[1.0],
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save_dir=None, seed=seed,steps=steps, guidance_scale=guidance_scale,
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@@ -52,7 +52,7 @@ def demo_inference_gen(adapter_choice:str, prompt:str, samples:int=1,seed:int=0,
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@spaces.GPU
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def demo_inference_stylization(adapter_path:str, prompts:list, image:list, start_noise=800,seed:int=0):
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infer_loader = get_validation_dataloader(prompts, image,num_workers=0)
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-
network = get_lora_network(pipe.unet, adapter_path,
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pred_images = inference(network, pipe.tokenizer, pipe.text_encoder, pipe.vae, pipe.unet, pipe.scheduler, infer_loader,
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height=512, width=512, scales=[0.,1.],
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save_dir=None, seed=seed,steps=20, guidance_scale=7.5,
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prompts = [prompt]*samples
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infer_loader = get_validation_dataloader(prompts,num_workers=0)
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+
network = get_lora_network(pipe.unet, adapter_path, weight_dtype=dtype)["network"]
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pred_images = inference(network, pipe.tokenizer, pipe.text_encoder, pipe.vae, pipe.unet, pipe.scheduler, infer_loader,
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height=512, width=512, scales=[1.0],
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save_dir=None, seed=seed,steps=steps, guidance_scale=guidance_scale,
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@spaces.GPU
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def demo_inference_stylization(adapter_path:str, prompts:list, image:list, start_noise=800,seed:int=0):
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infer_loader = get_validation_dataloader(prompts, image,num_workers=0)
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+
network = get_lora_network(pipe.unet, adapter_path, weight_dtype=dtype)["network"]
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pred_images = inference(network, pipe.tokenizer, pipe.text_encoder, pipe.vae, pipe.unet, pipe.scheduler, infer_loader,
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height=512, width=512, scales=[0.,1.],
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save_dir=None, seed=seed,steps=20, guidance_scale=7.5,
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