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| # Imports | |
| import gradio as gr | |
| import random | |
| import spaces | |
| import torch | |
| import numpy | |
| import uuid | |
| import json | |
| import os | |
| from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler | |
| from PIL import Image | |
| # Pre-Initialize | |
| DEVICE = "auto" | |
| if DEVICE == "auto": | |
| DEVICE = "cuda" if torch.cuda.is_available() else "cpu" | |
| print(f"[SYSTEM] | Using {DEVICE} type compute device.") | |
| # Variables | |
| MAX_SEED = 9007199254740991 | |
| DEFAULT_INPUT = "" | |
| DEFAULT_NEGATIVE_INPUT = "" | |
| DEFAULT_HEIGHT = 1024 | |
| DEFAULT_WIDTH = 1024 | |
| REPO = "sd-community/sdxl-flash" | |
| REPO_WEIGHT = "ehristoforu/dalle-3-xl-v2" | |
| WEIGHT = "dalle-3-xl-lora-v2.safetensors" | |
| ADAPTER = "dalle" | |
| model = StableDiffusionXLPipeline.from_pretrained(REPO, torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False) | |
| model.scheduler = EulerAncestralDiscreteScheduler.from_config(model.scheduler.config) | |
| model.load_lora_weights(REPO_WEIGHT, weight_name=WEIGHT, adapter_name=ADAPTER) | |
| model.set_adapters(ADAPTER, adapter_weights=[0.7]) | |
| model.to(DEVICE) | |
| # Functions | |
| def get_seed(seed): | |
| seed = seed.strip() | |
| if seed.isdigit(): | |
| return int(seed) | |
| else: | |
| return random.randint(0, MAX_SEED) | |
| def generate(input=DEFAULT_INPUT, negative_input=DEFAULT_NEGATIVE_INPUT, height=DEFAULT_HEIGHT, width=DEFAULT_WIDTH, steps=1, guidance=0, seed=None): | |
| print(input, negative_input, height, width, steps, guidance, seed) | |
| pipe.to(DEVICE) | |
| seed = int(randomize_seed_fn(seed, randomize_seed)) | |
| parameters = { | |
| "prompt": prompt, | |
| "negative_prompt": negative_prompt, | |
| "height": height, | |
| "width": width, | |
| "num_inference_steps": steps, | |
| "guidance_scale": guidance_scale, | |
| "generator": torch.Generator().manual_seed(get_seed(seed)), | |
| "use_resolution_binning": True, | |
| "output_type":"pil", | |
| } | |
| images = pipe(**parameters).images | |
| image_paths = [save_image(img) for img in images] | |
| return image_paths | |
| # Initialize | |
| with gr.Blocks() as main: | |
| with gr.Column(): | |
| input = gr.Textbox(lines=1, value=DEFAULT_INPUT, label="Input") | |
| negative_input = gr.Textbox(lines=1, value=DEFAULT_NEGATIVE_INPUT, label="Input Negative") | |
| height = gr.Slider(minimum=1, maximum=2160, step=1, value=DEFAULT_HEIGHT, label="Height") | |
| width = gr.Slider(minimum=1, maximum=2160, step=1, value=DEFAULT_WIDTH, label="Width") | |
| steps = gr.Slider(minimum=0, maximum=100, step=1, value=1, label="Steps") | |
| guidance = gr.Slider(minimum=0, maximum=100, step=0.001, value=0, label = "Guidance") | |
| seed = gr.Textbox(lines=1, value="", label="Seed (Blank for random)") | |
| submit = gr.Button("▶") | |
| with gr.Column(): | |
| image = gr.Image(label="Image") | |
| submit.click(generate, inputs=[input, negative_input, height, width, steps, guidance, seed], outputs=[image]) | |
| main.launch(show_api=True) |