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| import gradio as gr | |
| import numpy as np | |
| import random | |
| from diffusers import SemanticStableDiffusionPipeline | |
| import torch | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model_repo_id = "runwayml/stable-diffusion-v1-5" # Replace to the model you would like to use | |
| if torch.cuda.is_available(): | |
| torch_dtype = torch.float16 | |
| else: | |
| torch_dtype = torch.float32 | |
| pipe = SemanticStableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) | |
| pipe = pipe.to(device) | |
| out = pipe( | |
| prompt="a photo of the face of a woman", | |
| num_images_per_prompt=1, | |
| guidance_scale=7, | |
| editing_prompt=[ | |
| "smiling, smile", # Concepts to apply | |
| "glasses, wearing glasses", | |
| "curls, wavy hair, curly hair", | |
| "beard, full beard, mustache", | |
| ], | |
| reverse_editing_direction=[False, False, False, False], # Direction of guidance i.e. increase all concepts | |
| edit_warmup_steps=[10, 10, 10, 10], # Warmup period for each concept | |
| edit_guidance_scale=[4, 5, 5, 5.4], # Guidance scale for each concept | |
| edit_threshold=[ | |
| 0.99, | |
| 0.975, | |
| 0.925, | |
| 0.96, | |
| ], # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions | |
| edit_momentum_scale=0.3, # Momentum scale that will be added to the latent guidance | |
| edit_mom_beta=0.6, # Momentum beta | |
| edit_weights=[1, 1, 1, 1, 1], # Weights of the individual concepts against each other | |
| ) | |
| examples = [ | |
| "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", | |
| "An astronaut riding a green horse", | |
| "A delicious ceviche cheesecake slice", | |
| ] | |
| css = """ | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 640px; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(" # Text-to-Image Gradio Template") | |
| with gr.Row(): | |
| prompt = gr.Text( | |
| label="Prompt", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| container=False, | |
| ) | |
| run_button = gr.Button("Run", scale=0, variant="primary") | |
| result = gr.Image(label="Result", show_label=False) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| negative_prompt = gr.Text( | |
| label="Negative prompt", | |
| max_lines=1, | |
| placeholder="Enter a negative prompt", | |
| visible=False, | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Row(): | |
| width = gr.Slider( | |
| label="Width", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=1024, # Replace with defaults that work for your model | |
| ) | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=1024, # Replace with defaults that work for your model | |
| ) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0.0, | |
| maximum=10.0, | |
| step=0.1, | |
| value=0.0, # Replace with defaults that work for your model | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=50, | |
| step=1, | |
| value=2, # Replace with defaults that work for your model | |
| ) | |
| gr.Examples(examples=examples, inputs=[prompt]) | |
| gr.on( | |
| triggers=[run_button.click, prompt.submit], | |
| fn=infer, | |
| inputs=[ | |
| prompt, | |
| negative_prompt, | |
| seed, | |
| randomize_seed, | |
| width, | |
| height, | |
| guidance_scale, | |
| num_inference_steps, | |
| ], | |
| outputs=[result, seed], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |