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Add application file
Browse files- app.py +44 -0
- requirements.txt +5 -0
app.py
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import gradio as gr
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import torch
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from diffusers import DiffusionPipeline
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from diffusers import EulerDiscreteScheduler
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pipeline = DiffusionPipeline.from_pretrained("recoilme/ColorfulXL-Lightning",variant="fp16"#, torch_dtype=torch.float16
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, use_safetensors=True)#.to("cuda")
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pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config, timestep_spacing="trailing")
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def generate(prompt, negative_prompt, width, height, sample_steps):
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return pipeline(prompt=prompt, guidance_scale=0, negative_prompt="", width=width, height=height, num_inference_steps=sample_steps).images[0]
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with gr.Blocks() as interface:
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with gr.Column():
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with gr.Row():
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with gr.Column():
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prompt = gr.Textbox(label="Prompt", info="What do you want?", value="girl sitting on a small hill looking at night sky, back view, distant exploding moon, nights darkness, intricate circuits and sensors, photographic realism style, detailed textures, peacefulness, mysterious.", lines=4, interactive=True)
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with gr.Column():
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generate_button = gr.Button("Generate")
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output = gr.Image()
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with gr.Row():
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with gr.Accordion(label="Advanced Settings", open=False):
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with gr.Row():
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with gr.Column():
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width = gr.Slider(label="Width", info="The width in pixels of the generated image.", value=576, minimum=512, maximum=1280, step=64, interactive=True)
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height = gr.Slider(label="Height", info="The height in pixels of the generated image.", value=832, minimum=512, maximum=1280, step=64, interactive=True)
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with gr.Column():
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sampling_steps = gr.Slider(label="Sampling Steps", info="The number of denoising steps.", value=5, minimum=3, maximum=10, step=1, interactive=True)
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with gr.Row():
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about_text = """
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Based on: Stable Diffusion XL Image Generation interface built by Noa Roggendorff.
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You can enter a prompt and negative prompt, adjust the image size and sampling steps, and click the "Generate" button to generate an image.
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"""
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gr.Markdown(about_text)
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generate_button.click(fn=generate, inputs=[prompt, negative_prompt, width, height, sampling_steps], outputs=[output])
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if __name__ == "__main__":
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interface.launch()
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requirements.txt
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diffusers
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torch
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gradio
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accelerate
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transformers
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