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| import gradio as gr | |
| import torch | |
| from diffusers import DDIMScheduler, StableDiffusionPipeline | |
| stable_model_list = [ | |
| "runwayml/stable-diffusion-v1-5", | |
| "stabilityai/stable-diffusion-2-1", | |
| "sd-dreambooth-library/disco-diffusion-style", | |
| "prompthero/openjourney-v2", | |
| "andite/anything-v4.0", | |
| "Lykon/DreamShaper", | |
| "nitrosocke/Nitro-Diffusion", | |
| "dreamlike-art/dreamlike-diffusion-1.0", | |
| ] | |
| stable_prompt_list = ["a photo of a man.", "a photo of a girl."] | |
| stable_negative_prompt_list = ["bad, ugly", "deformed"] | |
| def stable_diffusion_text2img( | |
| model_path: str, | |
| prompt: str, | |
| negative_prompt: str, | |
| guidance_scale: int, | |
| num_inference_step: int, | |
| height: int, | |
| width: int, | |
| ): | |
| pipe = StableDiffusionPipeline.from_pretrained( | |
| model_path, safety_checker=None, torch_dtype=torch.float16 | |
| ).to("cuda") | |
| pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) | |
| pipe.enable_xformers_memory_efficient_attention() | |
| images = pipe( | |
| prompt, | |
| height=height, | |
| width=width, | |
| negative_prompt=negative_prompt, | |
| num_inference_steps=num_inference_step, | |
| guidance_scale=guidance_scale, | |
| ).images | |
| return images[0] | |
| def stable_diffusion_text2img_app(): | |
| with gr.Blocks(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| text2image_model_path = gr.Dropdown( | |
| choices=stable_model_list, | |
| value=stable_model_list[0], | |
| label="Text-Image Model Id", | |
| ) | |
| text2image_prompt = gr.Textbox( | |
| lines=1, value=stable_prompt_list[0], label="Prompt" | |
| ) | |
| text2image_negative_prompt = gr.Textbox( | |
| lines=1, | |
| value=stable_negative_prompt_list[0], | |
| label="Negative Prompt", | |
| ) | |
| with gr.Accordion("Advanced Options", open=False): | |
| text2image_guidance_scale = gr.Slider( | |
| minimum=0.1, | |
| maximum=15, | |
| step=0.1, | |
| value=7.5, | |
| label="Guidance Scale", | |
| ) | |
| text2image_num_inference_step = gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| value=50, | |
| label="Num Inference Step", | |
| ) | |
| text2image_height = gr.Slider( | |
| minimum=128, | |
| maximum=1280, | |
| step=32, | |
| value=512, | |
| label="Image Height", | |
| ) | |
| text2image_width = gr.Slider( | |
| minimum=128, | |
| maximum=1280, | |
| step=32, | |
| value=768, | |
| label="Image Width", | |
| ) | |
| text2image_predict = gr.Button(value="Generator") | |
| with gr.Column(): | |
| output_image = gr.Image(label="Output") | |
| gr.Examples( | |
| examples=[ | |
| [ | |
| stable_model_list[0], | |
| stable_prompt_list[0], | |
| stable_negative_prompt_list[0], | |
| 7.5, | |
| 50, | |
| 512, | |
| 768, | |
| ] | |
| ], | |
| inputs=[ | |
| text2image_model_path, | |
| text2image_prompt, | |
| text2image_negative_prompt, | |
| text2image_guidance_scale, | |
| text2image_num_inference_step, | |
| text2image_height, | |
| text2image_width, | |
| ], | |
| outputs=[output_image], | |
| cache_examples=False, | |
| fn=stable_diffusion_text2img, | |
| label="Text2Image Example", | |
| ) | |
| text2image_predict.click( | |
| fn=stable_diffusion_text2img, | |
| inputs=[ | |
| text2image_model_path, | |
| text2image_prompt, | |
| text2image_negative_prompt, | |
| text2image_guidance_scale, | |
| text2image_num_inference_step, | |
| text2image_height, | |
| text2image_width, | |
| ], | |
| outputs=output_image, | |
| ) | |