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| 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, 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") | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| processor = BlipProcessor.from_pretrained("unography/blip-long-cap") | |
| model = BlipForConditionalGeneration.from_pretrained("unography/blip-long-cap", torch_dtype=torch.float16).to("cuda") | |
| # Generator | |
| 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" : | |
| raw_image = Image.open(input_image).convert('RGB') | |
| inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16) | |
| out = model.generate(**inputs, min_length=10, max_length=20) | |
| caption = processor.decode(out[0], skip_special_tokens=True) | |
| client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") | |
| system_instructions1 = "<s>[SYSTEM] Your task is to modify prompt by USER with edit text, and create new prompt for image generation, reply with prompt only, Your task is to reply with final prompt only. [USER]" | |
| formatted_prompt = f"{system_instructions1} {caption} [EDIT] {instruction} [FINAL_PROMPT]" | |
| stream = client1.text_generation(formatted_prompt, max_new_tokens=50, stream=True, details=True, return_full_text=False) | |
| instructions = "".join([response.token.text for response in stream if response.token.text != "</s>"]) | |
| print(instructions) | |
| 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( | |
| instructions, image=raw_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=instructions, | |
| 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='filepath', 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() |