import gradio as gr import json import logging import torch from PIL import Image from diffusers import ( DiffusionPipeline, EulerDiscreteScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, KDPM2DiscreteScheduler, KDPM2AncestralDiscreteScheduler, EulerAncestralDiscreteScheduler, HeunDiscreteScheduler, LMSDiscreteScheduler, DEISMultistepScheduler, UniPCMultistepScheduler ) import spaces # Load LoRAs from JSON file with open('loras.json', 'r') as f: loras = json.load(f) # Initialize the base model base_model = "stabilityai/stable-diffusion-xl-base-1.0" pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.float16) pipe.to("cuda") def update_selection(evt: gr.SelectData): selected_lora = loras[evt.index] new_placeholder = f"Type a prompt for {selected_lora['title']}" lora_repo = selected_lora["repo"] updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨" return ( gr.update(placeholder=new_placeholder), updated_text, evt.index ) @spaces.GPU def run_lora(prompt, negative_prompt, cfg_scale, steps, selected_index, scheduler, seed, width, height, lora_scale): if selected_index is None: raise gr.Error("You must select a LoRA before proceeding.") selected_lora = loras[selected_index] lora_path = selected_lora["repo"] trigger_word = selected_lora["trigger_word"] # Load LoRA weights pipe.load_lora_weights(lora_path) # Set scheduler scheduler_config = pipe.scheduler.config if scheduler == "DPM++ 2M": pipe.scheduler = DPMSolverMultistepScheduler.from_config(scheduler_config) elif scheduler == "DPM++ 2M Karras": pipe.scheduler = DPMSolverMultistepScheduler.from_config(scheduler_config, use_karras_sigmas=True) elif scheduler == "DPM++ 2M SDE": pipe.scheduler = DPMSolverMultistepScheduler.from_config(scheduler_config, algorithm_type="sde-dpmsolver++") elif scheduler == "DPM++ 2M SDE Karras": pipe.scheduler = DPMSolverMultistepScheduler.from_config(scheduler_config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++") elif scheduler == "DPM++ SDE": pipe.scheduler = DPMSolverSinglestepScheduler.from_config(scheduler_config) elif scheduler == "DPM++ SDE Karras": pipe.scheduler = DPMSolverSinglestepScheduler.from_config(scheduler_config, use_karras_sigmas=True) elif scheduler == "DPM2": pipe.scheduler = KDPM2DiscreteScheduler.from_config(scheduler_config) elif scheduler == "DPM2 Karras": pipe.scheduler = KDPM2DiscreteScheduler.from_config(scheduler_config, use_karras_sigmas=True) elif scheduler == "DPM2 a": pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(scheduler_config) elif scheduler == "DPM2 a Karras": pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(scheduler_config, use_karras_sigmas=True) elif scheduler == "Euler": pipe.scheduler = EulerDiscreteScheduler.from_config(scheduler_config) elif scheduler == "Euler a": pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler_config) elif scheduler == "Heun": pipe.scheduler = HeunDiscreteScheduler.from_config(scheduler_config) elif scheduler == "LMS": pipe.scheduler = LMSDiscreteScheduler.from_config(scheduler_config) elif scheduler == "LMS Karras": pipe.scheduler = LMSDiscreteScheduler.from_config(scheduler_config, use_karras_sigmas=True) elif scheduler == "DEIS": pipe.scheduler = DEISMultistepScheduler.from_config(scheduler_config) elif scheduler == "UniPC": pipe.scheduler = UniPCMultistepScheduler.from_config(scheduler_config) # Set random seed for reproducibility generator = torch.Generator(device="cuda").manual_seed(seed) # Generate image image = pipe( prompt=f"{prompt} {trigger_word}", negative_prompt=negative_prompt, num_inference_steps=steps, guidance_scale=cfg_scale, width=width, height=height, generator=generator, cross_attention_kwargs={"scale": lora_scale}, ).images[0] # Unload LoRA weights pipe.unload_lora_weights() return image with gr.Blocks(theme=gr.themes.Soft()) as app: gr.Markdown("# artificialguybr LoRA Portfolio") gr.Markdown( "### This is my portfolio. Follow me on Twitter [@artificialguybr](https://twitter.com/artificialguybr).\n" "**Note**: Generation quality may vary. For best results, adjust the parameters.\n" "Special thanks to Hugging Face for their Diffusers library and Spaces platform." ) selected_index = gr.State(None) with gr.Row(): with gr.Column(scale=2): result = gr.Image(label="Generated Image", height=768) generate_button = gr.Button("Generate", variant="primary") with gr.Column(scale=1): gallery = gr.Gallery( [(item["image"], item["title"]) for item in loras], label="LoRA Gallery", allow_preview=False, columns=2 ) with gr.Row(): with gr.Column(): prompt_title = gr.Markdown("### Click on a LoRA in the gallery to select it") selected_info = gr.Markdown("") prompt = gr.Textbox(label="Prompt", lines=3, placeholder="Type a prompt after selecting a LoRA") negative_prompt = gr.Textbox(label="Negative Prompt", lines=2, value="low quality, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry") with gr.Column(): with gr.Row(): cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=7.5) steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=30) with gr.Row(): width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024) height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024) with gr.Row(): seed = gr.Slider(label="Seed", minimum=0, maximum=2**32-1, step=1, value=0, randomize=True) lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=1) scheduler = gr.Dropdown( label="Scheduler", choices=[ "DPM++ 2M", "DPM++ 2M Karras", "DPM++ 2M SDE", "DPM++ 2M SDE Karras", "DPM++ SDE", "DPM++ SDE Karras", "DPM2", "DPM2 Karras", "DPM2 a", "DPM2 a Karras", "Euler", "Euler a", "Heun", "LMS", "LMS Karras", "DEIS", "UniPC" ], value="DPM++ 2M SDE Karras" ) gallery.select(update_selection, outputs=[prompt, selected_info, selected_index]) generate_button.click( fn=run_lora, inputs=[prompt, negative_prompt, cfg_scale, steps, selected_index, scheduler, seed, width, height, lora_scale], outputs=[result] ) app.queue() app.launch()