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| import gradio as gr | |
| import numpy as np | |
| import random | |
| from diffusers import StableDiffusionXLPipeline | |
| import torch | |
| from huggingface_hub import hf_hub_download | |
| # Set device: use "cuda" if available, otherwise "cpu" | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # Change the model to SDXL 1.0 base | |
| model_repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5" | |
| if torch.cuda.is_available(): | |
| torch_dtype = torch.float16 | |
| else: | |
| torch_dtype = torch.float32 | |
| # Load the SDXL 1.0 base pipeline with safetensors support. | |
| pipe = StableDiffusionXLPipeline.from_pretrained( | |
| model_repo_id, | |
| torch_dtype=torch_dtype, | |
| use_safetensors=True | |
| ) | |
| pipe = pipe.to(device) | |
| # Download your DreamCartoonLora weights from Hugging Face and load them into the pipeline. | |
| lora_path = hf_hub_download(repo_id="Leofreddare/CartoonFaceLora", filename="CartoonFaceLora.safetensors") | |
| print("Loaded CartoonFaceLora from:", lora_path) | |
| pipe.load_lora_weights(lora_path) | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = 1024 | |
| def infer( | |
| prompt, | |
| negative_prompt, | |
| seed, | |
| randomize_seed, | |
| width, | |
| height, | |
| guidance_scale, | |
| num_inference_steps, | |
| progress=gr.Progress(track_tqdm=True), | |
| ): | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| image = pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| width=width, | |
| height=height, | |
| generator=generator, | |
| ).images[0] | |
| return image, seed | |
| examples = [ | |
| "A dreamy cartoon landscape with vivid colors", | |
| "A futuristic city rendered in a cartoon style", | |
| "A magical forest with a cartoon twist", | |
| ] | |
| 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 with CartoonFaceLora on SDXL 1.0") | |
| 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) | |
| height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=7.5) | |
| num_inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=50, step=1, value=20) | |
| 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() | |