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| import gradio as gr | |
| import torch | |
| from diffusers import ( | |
| StableDiffusionXLPipeline, | |
| EulerDiscreteScheduler, | |
| AutoencoderKL, | |
| DPMSolverSinglestepScheduler, | |
| ) | |
| # --- Configuration --- | |
| # The base model your LoRA was trained on. | |
| base_model_id = "stabilityai/stable-diffusion-xl-base-1.0" | |
| # --- The file is local, so we just need its name --- | |
| # The safetensors file is in the same directory as this script. | |
| lora_file_path = "emilyh.safetensors" | |
| # --- Load the Pipeline --- | |
| # Use a recommended VAE for SDXL | |
| vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) | |
| pipe = StableDiffusionXLPipeline.from_pretrained( | |
| base_model_id, | |
| vae=vae, | |
| torch_dtype=torch.float16, | |
| variant="fp16", | |
| use_safetensors=True | |
| ) | |
| # --- Load the local LoRA file --- | |
| # No download needed. We just load the local file directly. | |
| pipe.load_lora_weights(lora_file_path) | |
| # Move the pipeline to the GPU | |
| pipe.to("cuda") | |
| # --- Default Settings from your Recommendations --- | |
| default_positive_prompt = "masterpiece, best quality, ultra-detailed, realistic skin, intricate details, highres" | |
| default_negative_prompt = "low quality, worst quality, blurry, (deformed:1.3), extra fingers, cartoon, 3d, anime, bad anatomy" | |
| default_sampler = "DPM++ 2M Karras" | |
| default_cfg = 6.0 | |
| default_steps = 30 | |
| trigger_word = "emilyh" | |
| lora_tag_main = "<lora:emilyh:0.9>" | |
| # --- Define the Inference Function --- | |
| def generate_image(prompt, negative_prompt, sampler, steps, cfg, width, height, seed): | |
| """ | |
| Function to generate an image based on user inputs. | |
| """ | |
| # Combine the user prompt with the trigger word and LoRA tag | |
| full_prompt = f"{lora_tag_main}, {trigger_word}, {prompt}" | |
| # Set the scheduler (sampler) | |
| if sampler == "DPM++ 2M Karras": | |
| pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True) | |
| elif sampler == "DPM++ SDE Karras": | |
| pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++") | |
| else: # Default to DPM++ 2M Karras | |
| pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True) | |
| # Set seed for reproducibility | |
| generator = torch.Generator("cuda").manual_seed(seed) if seed != -1 else None | |
| # Generate the image | |
| image = pipe( | |
| prompt=full_prompt, | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=cfg, | |
| num_inference_steps=steps, | |
| generator=generator, | |
| cross_attention_kwargs={"scale": 0.9} | |
| ).images[0] | |
| return image | |
| # --- Create the Gradio Interface --- | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.Markdown("# `emilyh` LoRA Image Generator") | |
| gr.Markdown( | |
| "A Gradio interface for the `emilyh` LoRA. " | |
| "Based on the recommendations provided." | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt = gr.Textbox(label="Positive Prompt", value=default_positive_prompt, lines=3) | |
| negative_prompt = gr.Textbox(label="Negative Prompt", value=default_negative_prompt, lines=3) | |
| with gr.Row(): | |
| sampler = gr.Radio( | |
| label="Sampler", | |
| choices=["DPM++ 2M Karras", "DPM++ SDE Karras"], | |
| value=default_sampler, | |
| ) | |
| steps = gr.Slider(label="Steps", minimum=15, maximum=50, value=default_steps, step=1) | |
| cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=10.0, value=default_cfg, step=0.5) | |
| with gr.Row(): | |
| width = gr.Slider(label="Width", minimum=512, maximum=1024, value=1024, step=64) | |
| height = gr.Slider(label="Height", minimum=512, maximum=1024, value=1024, step=64) | |
| seed = gr.Slider(label="Seed", minimum=-1, maximum=999999999, step=1, value=-1, info="Use -1 for a random seed.") | |
| generate_button = gr.Button("Generate Image", variant="primary") | |
| with gr.Column(): | |
| output_image = gr.Image(label="Generated Image", type="pil") | |
| gr.Markdown( | |
| """ | |
| ### 🔧 Usage Guide | |
| * The trigger word `emilyh` and the LoRA tag `<lora:emilyh:0.9>` are automatically added to your prompt. | |
| * For best results, generate images in batches and choose the most consistent ones. | |
| * The LoRA captures the subject's appearance well across various poses and outfits. | |
| * A weight of 0.9 provides a good balance of likeness and flexibility. Using a weight closer to 1.0 can increase consistency but may cause stiffness. | |
| * This interface does not include ADetailer, which is recommended for final face refinement. | |
| """ | |
| ) | |
| generate_button.click( | |
| fn=generate_image, | |
| inputs=[prompt, negative_prompt, sampler, steps, cfg, width, height, seed], | |
| outputs=output_image | |
| ) | |
| demo.launch() |