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| import os | |
| import gc | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| import json | |
| import spaces | |
| import config | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| import logging | |
| from PIL import Image, PngImagePlugin | |
| from datetime import datetime | |
| import replicate | |
| from config import * | |
| from utils import * | |
| IS_COLAB=False | |
| """ | |
| Changes to base animagine-xl-3.1 log | |
| - Cut the wildcard | |
| - add in lora pipeline | |
| - use let get env variable | |
| - add in lora strenght variable | |
| """ | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| DESCRIPTION = "Animagine XL 3.1 X Galverse MAMA Replicate Repo " | |
| IS_COLAB = False | |
| #assert os.environ["REPLICATE_API_TOKEN"], "REPLICATE_API_TOKEN not set " | |
| MIN_IMAGE_SIZE = 512 | |
| MAX_IMAGE_SIZE = 2048 | |
| OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./outputs") | |
| def generate_replicate( | |
| prompt: str, | |
| negative_prompt: str = "", | |
| seed: int = 0, | |
| custom_width: int = 1024, | |
| custom_height: int = 1024, | |
| guidance_scale: float = 7.0, | |
| num_inference_steps: int = 28, | |
| sampler: str = "Euler a", | |
| aspect_ratio_selector: str = "896 x 1152", | |
| lora_strength: float = 0.7, | |
| style_selector: str = "(None)", | |
| quality_selector: str = "Standard v3.1", | |
| styles: str = "", | |
| quality_prompt: str = "", | |
| repo: str="galverse/mama-v1.5.1_leduyson:82d4539e72ec4473d1c34407a378815db55cb2eeb9639b898fcc7b4b67043973", | |
| lora_id: str= "galverse/mama-1.5", | |
| lora_style: str = "sks, galverse ", | |
| progress=gr.Progress(track_tqdm=True), | |
| ): | |
| np.random.seed(seed) | |
| width, height = aspect_ratio_handler( | |
| aspect_ratio_selector, | |
| custom_width, | |
| custom_height, | |
| ) | |
| # prompt = add_wildcard(prompt, wildcard_files) | |
| if quality_prompt: | |
| prompt, negative_prompt = preprocess_prompt( | |
| quality_prompt, quality_selector, prompt, negative_prompt, add_quality_tags | |
| ) | |
| if styles: | |
| prompt, negative_prompt = preprocess_prompt( | |
| styles, style_selector, prompt, negative_prompt | |
| ) | |
| width, height = preprocess_image_dimensions(width, height) | |
| metadata = { | |
| "prompt": prompt +", " + lora_style, | |
| "negative_prompt": negative_prompt, | |
| "resolution": f"{width} x {height}", | |
| "guidance_scale": guidance_scale, | |
| "lora_scale":lora_strength, | |
| "num_inference_steps": num_inference_steps, | |
| "seed": seed, | |
| "sampler": sampler, | |
| "width":width, | |
| "height":height, | |
| "num_outputs": 1, | |
| "guidance_scale":guidance_scale, | |
| "apply_watermark": True, | |
| "high_noise_frac": 0.8, | |
| "disable_safety_checker":True, | |
| "lora_id": lora_id, | |
| "seed":seed | |
| } | |
| images = replicate.run( | |
| repo, | |
| input=metadata | |
| ) | |
| image_paths = [ | |
| save_image_replicate(image, metadata, OUTPUT_DIR, IS_COLAB) | |
| for image in images | |
| ] | |
| for image_path in image_paths: | |
| logger.info(f"Image saved as {image_path} with metadata") | |
| return image_paths, metadata | |
| styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} | |
| quality_prompt = { | |
| k["name"]: (k["prompt"], k["negative_prompt"]) for k in quality_prompt_list | |
| } | |
| with gr.Blocks(css="style.css", theme="NoCrypt/miku@1.2.1") as demo: | |
| title = gr.HTML( | |
| f"""<h1><span>{DESCRIPTION}</span></h1>""", | |
| elem_id="title", | |
| ) | |
| gr.Markdown( | |
| f"""Gradio demo for Galverse MAMA 1.5 lora model, current repo [galverse/mama-v1.5.1_leduyson](https://replicate.com/galverse/mama-v1.5.1_leduyson)""", | |
| elem_id="subtitle", | |
| ) | |
| gr.DuplicateButton( | |
| value="Duplicate Space for private use", | |
| elem_id="duplicate-button", | |
| visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| with gr.Tab("Txt2img"): | |
| with gr.Group(): | |
| prompt = gr.Text( | |
| label="Prompt", | |
| max_lines=5, | |
| placeholder="Enter your prompt", | |
| ) | |
| negative_prompt = gr.Text( | |
| label="Negative Prompt", | |
| max_lines=5, | |
| placeholder="Enter a negative prompt", | |
| ) | |
| lora_strength = gr.Slider( | |
| label="Lora style strength", | |
| minimum=0, | |
| maximum=1, | |
| step=0.1, | |
| value=0.7, | |
| ) | |
| with gr.Accordion(label="Quality Tags", open=True): | |
| add_quality_tags = gr.Checkbox( | |
| label="Add Quality Tags", value=True | |
| ) | |
| quality_selector = gr.Dropdown( | |
| label="Quality Tags Presets", | |
| interactive=True, | |
| choices=list(quality_prompt.keys()), | |
| value="Standard v3.1", | |
| ) | |
| with gr.Tab("Advanced Settings"): | |
| with gr.Group(): | |
| style_selector = gr.Radio( | |
| label="Style Preset", | |
| container=True, | |
| interactive=True, | |
| choices=list(styles.keys()), | |
| value="(None)", | |
| ) | |
| with gr.Group(): | |
| aspect_ratio_selector = gr.Radio( | |
| label="Aspect Ratio", | |
| choices=aspect_ratios, | |
| value="896 x 1152", | |
| container=True, | |
| ) | |
| with gr.Group(visible=False) as custom_resolution: | |
| with gr.Row(): | |
| custom_width = gr.Slider( | |
| label="Width", | |
| minimum=MIN_IMAGE_SIZE, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=8, | |
| value=1024, | |
| ) | |
| custom_height = gr.Slider( | |
| label="Height", | |
| minimum=MIN_IMAGE_SIZE, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=8, | |
| value=1024, | |
| ) | |
| with gr.Group(): | |
| sampler = gr.Dropdown( | |
| label="Sampler", | |
| choices=sampler_list, | |
| interactive=True, | |
| value="DPMSolverMultistep", | |
| ) | |
| with gr.Group(): | |
| 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.Group(): | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=1, | |
| maximum=12, | |
| step=0.1, | |
| value=7.0, | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=50, | |
| step=1, | |
| value=28, | |
| ) | |
| with gr.Column(scale=3): | |
| with gr.Blocks(): | |
| run_button = gr.Button("Generate", variant="primary") | |
| result = gr.Gallery( | |
| label="Result", | |
| columns=1, | |
| height='100%', | |
| preview=True, | |
| show_label=False | |
| ) | |
| with gr.Accordion(label="Generation Parameters", open=False): | |
| gr_metadata = gr.JSON(label="metadata", show_label=False) | |
| aspect_ratio_selector.change( | |
| fn=lambda x: gr.update(visible=x == "Custom"), | |
| inputs=aspect_ratio_selector, | |
| outputs=custom_resolution, | |
| queue=False, | |
| api_name=False, | |
| ) | |
| gr.on( | |
| triggers=[ | |
| prompt.submit, | |
| negative_prompt.submit, | |
| run_button.click, | |
| ], | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=generate_replicate, | |
| inputs=[ | |
| prompt, | |
| negative_prompt, | |
| seed, | |
| custom_width, | |
| custom_height, | |
| guidance_scale, | |
| num_inference_steps, | |
| sampler, | |
| aspect_ratio_selector, | |
| lora_strength, | |
| style_selector, | |
| quality_selector, | |
| quality_prompt, | |
| ], | |
| outputs=[result, gr_metadata], | |
| api_name="run", | |
| ) | |
| demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB) |