from __future__ import annotations import uuid import os import math import random import spaces import gradio as gr import torch from PIL import Image, ImageOps from diffusers import StableDiffusionInstructPix2PixPipeline from huggingface_hub import InferenceClient help_text = """ To optimize image editing 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. - For better facial details, especially if they're small, **crop the image** to enlarge the face's presence. """ model_id = "timbrooks/instruct-pix2pix" from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler if not torch.cuda.is_available(): DESCRIPTION += "\n

Running on CPU 🥶 This demo may not work on CPU.

" MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096")) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") if torch.cuda.is_available(): pipe = StableDiffusionXLPipeline.from_pretrained( "sd-community/sdxl-flash", torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False ) pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: if randomize_seed: seed = random.randint(0, 999999) return seed def resize_image(image, output_size=(512, 512)): # Calculate aspect ratios target_aspect = output_size[0] / output_size[1] # Aspect ratio of the desired size image_aspect = image.width / image.height # Aspect ratio of the original image # Resize then crop if the original image is larger if image_aspect > target_aspect: new_height = output_size[1] new_width = int(new_height * image_aspect) resized_image = image.resize((new_width, new_height), Image.LANCZOS) left = (new_width - output_size[0]) / 2 top = 0 right = (new_width + output_size[0]) / 2 bottom = output_size[1] else: new_width = output_size[0] new_height = int(new_width / image_aspect) resized_image = image.resize((new_width, new_height), Image.LANCZOS) left = 0 top = (new_height - output_size[1]) / 2 right = output_size[0] bottom = (new_height + output_size[1]) / 2 cropped_image = resized_image.crop((left, top, right, bottom)) return cropped_image pipe2 = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None).to("cuda") @spaces.GPU(duration=30, queue=False) def king(type = "Image Editing", input_image = None, instruction: str = "Eiffel tower", steps: int = 8, randomize_seed: bool = False, seed: int = 24, text_cfg_scale: float = 7.3, image_cfg_scale: float = 1.7, width: int = 1024, height: int = 1024, guidance_scale: float = 3, use_resolution_binning: bool = True, progress=gr.Progress(track_tqdm=True), ): if type=="Image Generation" : pipe.to(device) seed = int(randomize_seed_fn(seed, randomize_seed)) generator = torch.Generator().manual_seed(seed) options = { "prompt":instruction, "width":width, "height":height, "guidance_scale":guidance_scale, "num_inference_steps":steps, "generator":generator, "use_resolution_binning":use_resolution_binning, "output_type":"pil", } output_image = pipe(**options).images[0] return seed, output_image else: seed = int(randomize_seed_fn(seed, randomize_seed)) text_cfg_scale = text_cfg_scale image_cfg_scale = image_cfg_scale input_image = input_image steps=steps*6 generator = torch.manual_seed(seed) output_image = pipe2( instruction, image=input_image, guidance_scale=text_cfg_scale, image_guidance_scale=image_cfg_scale, num_inference_steps=steps, generator=generator).images[0] return seed, output_image def response(instruction, input_image=None): client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") generate_kwargs = dict( max_new_tokens=5, ) system="[SYSTEM] You will be provided with text, and your task is to classify task is image generation or image editing answer with only task do not say anything else and stop as soon as possible. [TEXT]" formatted_prompt = system + instruction + "[TASK]" stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) output = "" for response in stream: if not response.token.text == "": output += response.token.text if input_image is None: output="Image Generation" if "editing" in output: output = "Image Editing" else: output = "Image Generation" return output css = ''' .gradio-container{max-width: 600px !important} h1{text-align:center} footer { visibility: hidden } ''' def get_example(): case = [ [ "Image Generation", None, "A Super Car", ], [ "Image Editing", "./supercar.png", "make it red", ], [ "Image Editing", "./red_car.png", "add some snow", ], [ "Image Generation", None, "Ironman flying in front of Ststue of liberty", ], [ "Image Generation", None, "Beautiful Eiffel Tower at Night", ], ] return case with gr.Blocks(css=css) as demo: gr.Markdown("# Image Generator Pro") with gr.Row(): with gr.Column(scale=4): instruction = gr.Textbox(lines=1, label="Instruction", interactive=True) with gr.Column(scale=1): generate_button = gr.Button("Generate") with gr.Column(scale=1): type = gr.Dropdown(["Image Generation","Image Editing"], label="Task", value="Image Generation",interactive=True, visible=False) 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) steps = gr.Number(value=8, precision=0, 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, precision=0, label="Seed", interactive=True) gr.Examples( examples=get_example(), inputs=[type,input_image, instruction], fn=king, outputs=[input_image], cache_examples=True, ) gr.Markdown(help_text) instruction.change(fn=response, inputs=[instruction,input_image], outputs=type, queue=False) generate_button.click( fn=king, inputs=[type, input_image, instruction, steps, randomize_seed, seed, text_cfg_scale, image_cfg_scale, ], outputs=[seed, input_image], ) demo.queue(max_size=99999).launch()