from __future__ import annotations import math import random import spaces import gradio as gr import numpy as np import torch from PIL import Image from diffusers import DiffusionPipeline, StableDiffusionXLPipeline, EDMEulerScheduler, StableDiffusionXLInstructPix2PixPipeline, AutoencoderKL from huggingface_hub import hf_hub_download from huggingface_hub import InferenceClient vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) pipe = StableDiffusionXLPipeline.from_pretrained("fluently/Fluently-XL-Final", torch_dtype=torch.float16, vae=vae) pipe.load_lora_weights("KingNish/Better-Image-XL-Lora", weight_name="example-03.safetensors", adapter_name="lora") pipe.set_adapters("lora") pipe.to("cuda") refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16") refiner.to("cuda") help_text = """ To optimize image results: - Adjust the **Image CFG weight** if the image isn't changing enough or is changing too much. Lower it to allow bigger changes, or raise it to preserve original details. - Modify the **Text CFG weight** to influence how closely the edit follows text instructions. Increase it to adhere more to the text, or decrease it for subtler changes. - Experiment with different **random seeds** and **CFG values** for varied outcomes. - **Rephrase your instructions** for potentially better results. - **Increase the number of steps** for enhanced edits. """ def set_timesteps_patched(self, num_inference_steps: int, device = None): self.num_inference_steps = num_inference_steps ramp = np.linspace(0, 1, self.num_inference_steps) sigmas = torch.linspace(math.log(self.config.sigma_min), math.log(self.config.sigma_max), len(ramp)).exp().flip(0) sigmas = (sigmas).to(dtype=torch.float32, device=device) self.timesteps = self.precondition_noise(sigmas) self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)]) self._step_index = None self._begin_index = None self.sigmas = self.sigmas.to("cpu") # Image Editor edit_file = hf_hub_download(repo_id="stabilityai/cosxl", filename="cosxl_edit.safetensors") EDMEulerScheduler.set_timesteps = set_timesteps_patched pipe_edit = StableDiffusionXLInstructPix2PixPipeline.from_single_file( edit_file, num_in_channels=8, is_cosxl_edit=True, vae=vae, torch_dtype=torch.float16 ) pipe_edit.scheduler = EDMEulerScheduler(sigma_min=0.002, sigma_max=120.0, sigma_data=1.0, prediction_type="v_prediction") pipe_edit.to("cuda") # Generator @spaces.GPU(duration=30, queue=False) def king(type , input_image , instruction: str , steps: int = 8, randomize_seed: bool = False, seed: int = 25, text_cfg_scale: float = 7.3, image_cfg_scale: float = 1.7, width: int = 1024, height: int = 1024, guidance_scale: float = 6, use_resolution_binning: bool = True, progress=gr.Progress(track_tqdm=True), ): if type=="Image Editing" : if randomize_seed: seed = random.randint(0, 99999) text_cfg_scale = text_cfg_scale image_cfg_scale = image_cfg_scale input_image = input_image steps=steps generator = torch.manual_seed(seed) output_image = pipe_edit( instruction, image=input_image, guidance_scale=text_cfg_scale, image_guidance_scale=image_cfg_scale, num_inference_steps=steps, generator=generator, output_type="latent", ).images refine = refiner( prompt=instruction, guidance_scale=guidance_scale, num_inference_steps=steps, image=output_image, generator=generator, ).images[0] return seed, refine else : if randomize_seed: seed = random.randint(0, 99999) generator = torch.Generator().manual_seed(seed) image = pipe( prompt = instruction, guidance_scale = guidance_scale, num_inference_steps = steps, width = (width), height = (height), generator = generator, output_type="latent", ).images refine = refiner( prompt=instruction, guidance_scale=guidance_scale, num_inference_steps=steps, image=image, generator=generator, ).images[0] return seed, refine client = InferenceClient() # Prompt classifier def response(instruction, input_image=None ): if input_image is None: output="Image Generation" else: try: text = instruction labels = ["Image Editing", "Image Generation"] classification = client.zero_shot_classification(text, labels, multi_label=True) output = classification[0] output = str(output) if "Editing" in output: output = "Image Editing" else: output = "Image Generation" except error: if input_image is None: output="Image Generation" else: output="Image Editing" return output css = ''' .gradio-container{max-width: 700px !important} h1{text-align:center} footer { visibility: hidden } ''' examples=[ [ "Image Generation", None, "A luxurious supercar with a unique design. The car should have a pearl white finish, and gold accents. 4k, realistic.", ], [ "Image Editing", "./supercar.png", "make it red", ], [ "Image Editing", "./red_car.png", "add some snow", ], [ "Image Generation", None, "An alien grasping a sign board contain word 'ALIEN' with Neon Glow, neon, futuristic, neonpunk, neon lights", ], [ "Image Generation", None, "Beautiful Eiffel Tower at Night", ], ] with gr.Blocks(css=css) as demo: gr.Markdown("# Image Generator Pro") with gr.Row(): instruction = gr.Textbox(lines=1, label="Instruction", interactive=True) with gr.Row(): with gr.Column(scale=1): type = gr.Dropdown(["Image Generation","Image Editing"], label="Task", value="Image Generation",interactive=True) with gr.Column(scale=1): generate_button = gr.Button("Generate") with gr.Row(): input_image = gr.Image(label="Image", type="pil", interactive=True) with gr.Row(): text_cfg_scale = gr.Number(value=7.3, step=0.1, label="Text CFG", interactive=True) image_cfg_scale = gr.Number(value=1.7, step=0.1,label="Image CFG", interactive=True) guidance_scale = gr.Number(value=6.0, step=0.1, label="Image Generation Guidance Scale", interactive=True) steps = gr.Number(value=25, step=1, label="Steps", interactive=True) randomize_seed = gr.Radio( ["Fix Seed", "Randomize Seed"], value="Randomize Seed", type="index", show_label=False, interactive=True, ) seed = gr.Number(value=1371, step=1, label="Seed", interactive=True) with gr.Row(): width = gr.Slider( label="Width", minimum=256, maximum=2048, step=64, value=1024) height = gr.Slider( label="Height", minimum=256, maximum=2048, step=64, value=1024) gr.Examples( examples=examples, inputs=[type,input_image, instruction], fn=king, outputs=[input_image], cache_examples=False, ) gr.Markdown(help_text) instruction.change(fn=response, inputs=[instruction,input_image], outputs=type, queue=False) input_image.upload(fn=response, inputs=[instruction,input_image], outputs=type, queue=False) gr.on(triggers=[ generate_button.click, instruction.submit ], fn=king, inputs=[type, input_image, instruction, steps, randomize_seed, seed, text_cfg_scale, image_cfg_scale, width, height, guidance_scale, ], outputs=[seed, input_image], ) demo.queue(max_size=99999).launch()