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import gradio as gr |
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import numpy as np |
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import random |
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import spaces |
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import torch |
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from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL |
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from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast |
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from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images |
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dtype = torch.bfloat16 |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device) |
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good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device) |
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pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device) |
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torch.cuda.empty_cache() |
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MAX_SEED = np.iinfo(np.int32).max |
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MAX_IMAGE_SIZE = 2048 |
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pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe) |
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@spaces.GPU() |
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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)): |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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generator = torch.Generator().manual_seed(seed) |
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if lora_id and lora_id.strip() != "": |
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pipe.unload_lora_weights() |
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pipe.load_lora_weights(lora_id.strip()) |
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joint_attention_kwargs = {"scale": lora_scale} |
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else: |
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joint_attention_kwargs = None |
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try: |
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for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images( |
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prompt=prompt, |
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guidance_scale=guidance_scale, |
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num_inference_steps=num_inference_steps, |
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width=width, |
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height=height, |
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generator=generator, |
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output_type="pil", |
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good_vae=good_vae, |
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joint_attention_kwargs=joint_attention_kwargs, |
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): |
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yield img, seed, seed |
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finally: |
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if lora_id: |
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pipe.unload_lora_weights() |
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examples = [ |
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"a tiny astronaut hatching from an egg on the moon", |
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"a cat holding a sign that says hello world", |
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"an anime illustration of a wiener schnitzel", |
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] |
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css = """ |
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#col-container { |
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margin: 0 auto; |
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max-width: 960px; |
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} |
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.generate-btn { |
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background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%) !important; |
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border: none !important; |
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color: white !important; |
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} |
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.generate-btn:hover { |
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transform: translateY(-2px); |
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box-shadow: 0 5px 15px rgba(0,0,0,0.2); |
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} |
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""" |
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with gr.Blocks(css=css) as app: |
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gr.HTML("<center><h1>FLUX.1-Dev with LoRA support</h1></center>") |
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with gr.Column(elem_id="col-container"): |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Row(): |
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text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=3, elem_id="prompt-text-input") |
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with gr.Row(): |
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custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path (optional)", placeholder="multimodalart/vintage-ads-flux") |
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with gr.Row(): |
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with gr.Accordion("Advanced Settings", open=False): |
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lora_scale = gr.Slider( |
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label="LoRA Scale", |
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minimum=0, |
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maximum=2, |
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step=0.01, |
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value=0.95, |
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) |
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with gr.Row(): |
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width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=8) |
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height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=8) |
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seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967296, step=1) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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with gr.Row(): |
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steps = gr.Slider(label="Inference steps steps", value=28, minimum=1, maximum=100, step=1) |
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cfg = gr.Slider(label="Guidance Scale", value=3.5, minimum=1, maximum=20, step=0.5) |
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with gr.Row(): |
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text_button = gr.Button("✨ Generate Image", variant='primary', elem_classes=["generate-btn"]) |
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with gr.Column(): |
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with gr.Row(): |
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image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery") |
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with gr.Row(): |
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seed_output = gr.Textbox(label="Seed Used", show_copy_button = True) |
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with gr.Column(): |
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gr.Examples( |
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examples = examples, |
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inputs = [text_prompt], |
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) |
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gr.on( |
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triggers=[text_button.click, text_prompt.submit], |
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fn = infer, |
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inputs=[text_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale], |
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outputs=[image_output,seed_output, seed] |
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) |
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app.launch(share=True) |
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