Update app.py
Browse files
app.py
CHANGED
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@@ -6,7 +6,6 @@ import numpy as np
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import os
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import spaces
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-
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if torch.cuda.is_available():
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device = "cuda"
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print("Using GPU")
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@@ -14,11 +13,9 @@ else:
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device = "cpu"
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print("Using CPU")
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# login hf token
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HF_TOKEN = os.getenv("HF_TOKEN")
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MAX_SEED = np.iinfo(np.int32).max
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CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"
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@@ -29,7 +26,6 @@ pipe.to(device)
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# Enable memory optimizations
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pipe.enable_attention_slicing()
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# Define the image generation function
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@spaces.GPU(duration=180)
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def generate_image(prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt, progress=gr.Progress(track_tqdm=True)):
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@@ -38,7 +34,6 @@ def generate_image(prompt, num_inference_steps, height, width, guidance_scale, s
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generator = torch.Generator().manual_seed(seed)
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-
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with torch.inference_mode():
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output = pipe(
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prompt=prompt,
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@@ -52,14 +47,12 @@ def generate_image(prompt, num_inference_steps, height, width, guidance_scale, s
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return output
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# Create the Gradio interface
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examples = [
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["A
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["
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["
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]
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css = '''
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@@ -69,19 +62,16 @@ h1{text-align:center}
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with gr.Blocks(css=css) as demo:
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with gr.Row():
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with gr.Column():
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gr.HTML(
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"""
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<h1 style='text-align: center'>
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FLUX.1-dev
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</h1>
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"""
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)
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gr.HTML(
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"""
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"""
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with gr.Group():
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with gr.Column():
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prompt = gr.Textbox(label="Prompt", info="Describe the image you want", placeholder="A cat...")
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@@ -96,7 +86,7 @@ with gr.Blocks(css=css) as demo:
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height = gr.Slider(label="Height", info="Height of the Image", minimum=256, maximum=1024, step=32, value=1024)
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with gr.Row():
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seed = gr.Slider(value=42, minimum=0, maximum=MAX_SEED, step=1, label="Seed", info="A starting point to initiate the generation process, put 0 for a random one")
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num_images_per_prompt = gr.Slider(label="Images Per Prompt", info="Number of Images to generate with the settings",minimum=1, maximum=4, step=1, value=2)
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gr.Examples(
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examples=examples,
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@@ -116,4 +106,4 @@ with gr.Blocks(css=css) as demo:
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outputs=[result],
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)
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demo.queue().launch(share=False)
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import os
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import spaces
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if torch.cuda.is_available():
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device = "cuda"
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print("Using GPU")
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device = "cpu"
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print("Using CPU")
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# login hf token
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HF_TOKEN = os.getenv("HF_TOKEN")
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MAX_SEED = np.iinfo(np.int32).max
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CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"
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# Enable memory optimizations
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pipe.enable_attention_slicing()
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# Define the image generation function
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@spaces.GPU(duration=180)
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def generate_image(prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt, progress=gr.Progress(track_tqdm=True)):
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generator = torch.Generator().manual_seed(seed)
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with torch.inference_mode():
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output = pipe(
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prompt=prompt,
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return output
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# Create the Gradio interface
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examples = [
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["A serene landscape with a sunset over mountains"],
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["A futuristic cityscape at night"],
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["A playful dog in a field of flowers"],
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]
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css = '''
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with gr.Blocks(css=css) as demo:
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with gr.Row():
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with gr.Column():
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gr.HTML(
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"""
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<h1 style='text-align: center'>
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FLUX.1-dev Image Generator
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</h1>
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<p style='text-align: center'>
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Use this tool to generate stunning images based on your descriptive text prompts.
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</p>
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"""
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)
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with gr.Group():
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with gr.Column():
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prompt = gr.Textbox(label="Prompt", info="Describe the image you want", placeholder="A cat...")
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height = gr.Slider(label="Height", info="Height of the Image", minimum=256, maximum=1024, step=32, value=1024)
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with gr.Row():
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seed = gr.Slider(value=42, minimum=0, maximum=MAX_SEED, step=1, label="Seed", info="A starting point to initiate the generation process, put 0 for a random one")
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num_images_per_prompt = gr.Slider(label="Images Per Prompt", info="Number of Images to generate with the settings", minimum=1, maximum=4, step=1, value=2)
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gr.Examples(
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examples=examples,
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outputs=[result],
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)
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demo.queue().launch(share=False)
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