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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 FluxPipeline, FluxTransformer2DModel,FlowMatchEulerDiscreteScheduler, AutoencoderKL |
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from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast |
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dtype = torch.bfloat16 |
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device = "cuda" |
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sd3_repo = "stabilityai/stable-diffusion-3-medium-diffusers" |
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scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained (sd3_repo, subfolder="scheduler") |
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text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype) |
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tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype) |
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text_encoder_2 = T5EncoderModel.from_pretrained(sd3_repo, subfolder="text_encoder_3", torch_dtype=dtype) |
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tokenizer_2 = T5TokenizerFast.from_pretrained(sd3_repo, subfolder="tokenizer_3", torch_dtype=dtype) |
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vae = AutoencoderKL.from_pretrained("diffusers-internal-dev/FLUX.1-schnell", subfolder="vae", torch_dtype=dtype) |
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transformer = FluxTransformer2DModel.from_pretrained("diffusers-internal-dev/FLUX.1-schnell", subfolder="transformer", torch_dtype=dtype) |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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pipe = FluxPipeline( |
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scheduler=scheduler, |
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text_encoder=text_encoder, |
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tokenizer=tokenizer, |
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text_encoder_2=text_encoder_2, |
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tokenizer_2=tokenizer_2, |
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vae=vae, |
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transformer=transformer, |
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).to("cuda") |
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MAX_SEED = np.iinfo(np.int32).max |
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MAX_IMAGE_SIZE = 2048 |
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@spaces.GPU() |
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def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, 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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image = pipe( |
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prompt = prompt, |
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width = width, |
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height = height, |
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num_inference_steps = num_inference_steps, |
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generator = generator, |
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guidance_scale=0.0 |
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).images[0] |
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return image, seed |
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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: 520px; |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown(f"""# FLUX.1 [schnell] |
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12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation |
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[[blog](https://blackforestlabs.ai/2024/07/31/announcing-black-forest-labs/)] [[model](https://huggingface.co./black-forest-labs/FLUX.1-schnell)] |
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""") |
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with gr.Row(): |
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prompt = gr.Text( |
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label="Prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter your prompt", |
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container=False, |
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) |
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run_button = gr.Button("Run", scale=0) |
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result = gr.Image(label="Result", show_label=False) |
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with gr.Accordion("Advanced Settings", open=False): |
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seed = gr.Slider( |
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label="Seed", |
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minimum=0, |
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maximum=MAX_SEED, |
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step=1, |
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value=0, |
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) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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with gr.Row(): |
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width = gr.Slider( |
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label="Width", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=1024, |
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) |
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height = gr.Slider( |
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label="Height", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=1024, |
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) |
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with gr.Row(): |
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num_inference_steps = gr.Slider( |
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label="Number of inference steps", |
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minimum=1, |
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maximum=50, |
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step=1, |
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value=4, |
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) |
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gr.Examples( |
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examples = examples, |
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fn = infer, |
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inputs = [prompt], |
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outputs = [result, seed], |
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cache_examples="lazy" |
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) |
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gr.on( |
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triggers=[run_button.click, prompt.submit], |
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fn = infer, |
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inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps], |
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outputs = [result, seed] |
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) |
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demo.launch() |
