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Running
on
Zero
| import gradio as gr | |
| import numpy as np | |
| import random | |
| import spaces | |
| import torch | |
| from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL | |
| from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast | |
| from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images | |
| dtype = torch.bfloat16 | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device) | |
| good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device) | |
| pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device) | |
| torch.cuda.empty_cache() | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = 2048 | |
| pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe) | |
| article_text = """ | |
| <div style="text-align: center;"> | |
| <p>Enjoying the tool? Buy me a coffee and get exclusive prompt guides!</p> | |
| <p><i>Instantly unlock helpful tips for creating better prompts!</i></p> | |
| <div style="display: flex; justify-content: center;"> | |
| <a href="https://piczify.lemonsqueezy.com/buy/0f5206fa-68e8-42f6-9ca8-4f80c587c83e"> | |
| <img src="https://www.buymeacoffee.com/assets/img/custom_images/yellow_img.png" | |
| alt="Buy Me a Coffee" | |
| style="height: 40px; width: auto; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2); border-radius: 10px;"> | |
| </a> | |
| </div> | |
| </div> | |
| """ | |
| def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, lora_id=None, lora_scale=0.95, progress=gr.Progress(track_tqdm=True)): | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| generator = torch.Generator().manual_seed(seed) | |
| # for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images( | |
| # prompt=prompt, | |
| # guidance_scale=guidance_scale, | |
| # num_inference_steps=num_inference_steps, | |
| # width=width, | |
| # height=height, | |
| # generator=generator, | |
| # output_type="pil", | |
| # good_vae=good_vae, | |
| # ): | |
| # yield img, seed | |
| # Handle LoRA loading | |
| # Load LoRA weights and prepare joint_attention_kwargs | |
| if lora_id: | |
| pipe.unload_lora_weights() | |
| pipe.load_lora_weights(lora_id) | |
| joint_attention_kwargs = {"scale": lora_scale} | |
| else: | |
| joint_attention_kwargs = None | |
| try: | |
| # Call the custom pipeline function with the correct keyword argument | |
| for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images( | |
| prompt=prompt, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| width=width, | |
| height=height, | |
| generator=generator, | |
| output_type="pil", | |
| good_vae=good_vae, # Assuming good_vae is defined elsewhere | |
| joint_attention_kwargs=joint_attention_kwargs, # Fixed parameter name | |
| ): | |
| yield img, seed | |
| finally: | |
| # Unload LoRA weights if they were loaded | |
| if lora_id: | |
| pipe.unload_lora_weights() | |
| # def query(lora_id, prompt, steps=28, cfg_scale=3.5, randomize_seed=True, seed=-1, width=1024, height=1024): | |
| # if prompt == "" or prompt == None: | |
| # return None | |
| # if lora_id.strip() == "" or lora_id == None: | |
| # lora_id = "black-forest-labs/FLUX.1-dev" | |
| # key = random.randint(0, 999) | |
| # API_URL = "https://api-inference.huggingface.co/models/"+ lora_id.strip() | |
| # API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")]) | |
| # headers = {"Authorization": f"Bearer {API_TOKEN}"} | |
| # # prompt = GoogleTranslator(source='ru', target='en').translate(prompt) | |
| # # print(f'\033[1mGeneration {key} translation:\033[0m {prompt}') | |
| # prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect." | |
| # # print(f'\033[1mGeneration {key}:\033[0m {prompt}') | |
| # # If seed is -1, generate a random seed and use it | |
| # if randomize_seed: | |
| # seed = random.randint(1, 4294967296) | |
| # payload = { | |
| # "inputs": prompt, | |
| # "steps": steps, | |
| # "cfg_scale": cfg_scale, | |
| # "seed": seed, | |
| # "parameters": { | |
| # "width": width, # Pass the width to the API | |
| # "height": height # Pass the height to the API | |
| # } | |
| # } | |
| # response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout) | |
| # if response.status_code != 200: | |
| # print(f"Error: Failed to get image. Response status: {response.status_code}") | |
| # print(f"Response content: {response.text}") | |
| # if response.status_code == 503: | |
| # raise gr.Error(f"{response.status_code} : The model is being loaded") | |
| # raise gr.Error(f"{response.status_code}") | |
| # try: | |
| # image_bytes = response.content | |
| # image = Image.open(io.BytesIO(image_bytes)) | |
| # print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})') | |
| # return image, seed, seed | |
| # except Exception as e: | |
| # print(f"Error when trying to open the image: {e}") | |
| # return None | |
| examples = [ | |
| "a tiny astronaut hatching from an egg on the moon", | |
| "a cat holding a sign that says hello world", | |
| "an anime illustration of a wiener schnitzel", | |
| ] | |
| css = """ | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 960px; | |
| } | |
| .generate-btn { | |
| background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%) !important; | |
| border: none !important; | |
| color: white !important; | |
| } | |
| .generate-btn:hover { | |
| transform: translateY(-2px); | |
| box-shadow: 0 5px 15px rgba(0,0,0,0.2); | |
| } | |
| """ | |
| with gr.Blocks(css=css) as app: | |
| gr.HTML("<center><h1>FLUX.1-Dev with LoRA support</h1></center>") | |
| with gr.Column(elem_id="col-container"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=3, elem_id="prompt-text-input") | |
| with gr.Row(): | |
| custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path (optional)", placeholder="multimodalart/vintage-ads-flux") | |
| with gr.Row(): | |
| with gr.Accordion("Advanced Settings", open=False): | |
| with gr.Row(): | |
| lora_scale = gr.Slider( | |
| label="LoRA Scale", | |
| minimum=0, | |
| maximum=2, | |
| step=0.01, | |
| value=0.95, | |
| ) | |
| width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=8) | |
| height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=8) | |
| seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967296, step=1) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Row(): | |
| steps = gr.Slider(label="Inference steps steps", value=28, minimum=1, maximum=100, step=1) | |
| cfg = gr.Slider(label="Guidance Scale", value=3.5, minimum=1, maximum=20, step=0.5) | |
| # method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"]) | |
| with gr.Row(): | |
| # text_button = gr.Button("Run", variant='primary', elem_id="gen-button") | |
| text_button = gr.Button("✨ Generate Image", variant='primary', elem_classes=["generate-btn"]) | |
| with gr.Column(): | |
| with gr.Row(): | |
| image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery") | |
| with gr.Row(): | |
| seed_output = gr.Textbox(label="Seed Used", show_copy_button = True) | |
| gr.Markdown(article_text) | |
| with gr.Column(): | |
| gr.Examples( | |
| examples = examples, | |
| inputs = [text_prompt], | |
| ) | |
| # text_button.click(query, inputs=[custom_lora, text_prompt, steps, cfg, randomize_seed, seed, width, height], outputs=[image_output,seed_output, seed]) | |
| text_button.click(infer, inputs=[text_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale], outputs=[image_output,seed_output, seed]) | |
| app.launch() | |