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e2c1d93
1
Parent(s):
7a09818
Update app.py
Browse files
app.py
CHANGED
@@ -7,6 +7,7 @@ import spaces
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from diffusers import DiffusionPipeline
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import copy
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import random
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# Load LoRAs from JSON file
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with open('loras.json', 'r') as f:
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@@ -19,6 +20,23 @@ pipe.to("cuda")
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MAX_SEED = 2**32-1
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def update_selection(evt: gr.SelectData):
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selected_lora = loras[evt.index]
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new_placeholder = f"Type a prompt for {selected_lora['title']}"
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@@ -40,30 +58,34 @@ def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, wid
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trigger_word = selected_lora["trigger_word"]
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# Load LoRA weights
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# Set random seed for reproducibility
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yield image, seed
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css = '''
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from diffusers import DiffusionPipeline
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import copy
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import random
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import time
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# Load LoRAs from JSON file
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with open('loras.json', 'r') as f:
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MAX_SEED = 2**32-1
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class calculateDuration:
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def __init__(self, activity_name=""):
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self.activity_name = activity_name
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def __enter__(self):
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self.start_time = time.time()
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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self.end_time = time.time()
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self.elapsed_time = self.end_time - self.start_time
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if self.activity_name:
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print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
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else:
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print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
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def update_selection(evt: gr.SelectData):
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selected_lora = loras[evt.index]
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new_placeholder = f"Type a prompt for {selected_lora['title']}"
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trigger_word = selected_lora["trigger_word"]
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# Load LoRA weights
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with calculateDuration("Loading LoRA weights"):
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if "weights" in selected_lora:
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pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
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else:
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pipe.load_lora_weights(lora_path)
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# Set random seed for reproducibility
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with calculateDuration("Randomizing seed"):
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator(device="cuda").manual_seed(seed)
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with calculateDuration("Generating image"):
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# Generate image
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image = pipe(
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prompt=f"{prompt} {trigger_word}",
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num_inference_steps=steps,
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guidance_scale=cfg_scale,
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width=width,
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height=height,
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generator=generator,
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joint_attention_kwargs={"scale": lora_scale},
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).images[0]
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yield image, seed
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with calculateDuration("Unloading weights"):
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pipe.unload_lora_weights()
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css = '''
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