| import gradio as gr | |
| import torch | |
| from diffusers import StableDiffusionPipeline | |
| def image_generation(prompt): | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| pipeline = StableDiffusionPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-3-medium", | |
| torch_dtype=torch.float16 if device == "cuda" else torch.float32, | |
| ) | |
| #pipeline.to(device) | |
| pipeline.enable_model_cpu_offload() | |
| image = pipeline( | |
| prompt=prompt, | |
| negative_prompt="blurred, ugly, watermark, low resolution, blurry, nude", | |
| num_inference_steps=40, | |
| height=1024, | |
| width=1024, | |
| guidance_scale=8.0 | |
| ).images[0] | |
| return image | |
| interface = gr.Interface( | |
| fn=image_generation, | |
| inputs=gr.Textbox(lines=2, placeholder="Enter Your Prompt ..."), | |
| outputs=gr.Image(type="pil"), | |
| title="AI Text Generation By SD-3M" | |
| ) | |
| interface.launch() | |
| # import gradio as gr | |
| # import torch | |
| # from diffusers import StableDiffusers3Pipeline | |
| # def image_generation(prompt): | |
| # device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # pipeline = StableDiffusers3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", | |
| # torch_dtype=torch.float16 if device == "cuda" else torch.float32, | |
| # text_encoder_3 = None, | |
| # tokenizer_3 = None) | |
| # # pipeline.to(device) | |
| # pipeline.enable_model_cpu_offload() | |
| # image = pipeline( | |
| # prompt = prompt, | |
| # negative_prompt = "blurred, ugly, watermark, low resolution, blurry, nude", | |
| # num_inference_steps = 40, | |
| # height=1024, | |
| # width=1024, | |
| # guidance_scale=8.0 | |
| # ).images[0] | |
| # image.show() | |
| # interface= gr.interface( | |
| # fn=image_generation, | |
| # inputs = gr.Textbox(lines="2", placeholder="Enter Your Prompt ..."), | |
| # outputs = gr.Image(type="pil"), | |
| # title = "AI Text Generation By SD-3M" | |
| # ) | |
| # interface.launch() |