import gradio as gr import numpy as np import random from PIL import Image import spaces import torch from huggingface_hub import hf_hub_download from diffusers import FluxPriorReduxPipeline, FluxPipeline from diffusers.utils import load_image MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 2048 pipe = FluxPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev" , torch_dtype=torch.bfloat16 ).to("cuda") pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), lora_scale=0.125) pipe.fuse_lora(lora_scale=0.125) pipe.to(device="cuda", dtype=torch.bfloat16) pipe_prior_redux = FluxPriorReduxPipeline.from_pretrained( "black-forest-labs/FLUX.1-Redux-dev", text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer, text_encoder_2=pipe.text_encoder_2, tokenizer_2=pipe.tokenizer_2, torch_dtype=torch.bfloat16 ).to("cuda") examples = [[Image.open("mona_lisa.jpg"), "pink hair, at the beach", None, "", 0.035, 1., 1., 1., 1., 0, False], [Image.open("1665_Girl_with_a_Pearl_Earring.jpg"), "", Image.open("dali_example.jpg"), "", 0.08, .4, .6, .33, 1., 1912857110, False]] @spaces.GPU def infer(control_image, prompt, image_2, prompt_2, reference_scale= 0.03 , prompt_embeds_scale_1 =1, prompt_embeds_scale_2 =1, pooled_prompt_embeds_scale_1 =1, pooled_prompt_embeds_scale_2 =1, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)): if randomize_seed: seed = random.randint(0, MAX_SEED) if image_2 is not None: pipe_prior_output = pipe_prior_redux([control_image, image_2], prompt=[prompt, prompt_2], prompt_embeds_scale = [prompt_embeds_scale_1, prompt_embeds_scale_2], pooled_prompt_embeds_scale = [pooled_prompt_embeds_scale_1, pooled_prompt_embeds_scale_2]) else: pipe_prior_output = pipe_prior_redux(control_image, prompt=prompt, prompt_embeds_scale = [prompt_embeds_scale_1], pooled_prompt_embeds_scale = [pooled_prompt_embeds_scale_1]) cond_size = 729 hidden_size = 4096 max_sequence_length = 512 full_attention_size = max_sequence_length + hidden_size + cond_size attention_mask = torch.zeros( (full_attention_size, full_attention_size), device="cuda", dtype=torch.bfloat16 ) bias = torch.log( torch.tensor(reference_scale, dtype=torch.bfloat16, device="cuda").clamp(min=1e-5, max=1) ) attention_mask[:, max_sequence_length : max_sequence_length + cond_size] = bias joint_attention_kwargs=dict(attention_mask=attention_mask) images = pipe( guidance_scale=guidance_scale, width=width, height=height, num_inference_steps=num_inference_steps, generator=torch.Generator("cpu").manual_seed(seed), joint_attention_kwargs=joint_attention_kwargs, **pipe_prior_output, ).images[0] return images, seed css=""" #col-container { margin: 0 auto; max-width: 960px; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown(f"""# ⚡️ Fast FLUX.1 Redux [dev] ⚡️ An adapter for FLUX [dev] to create image variations combined with ByteDance [ Hyper FLUX 8 Steps LoRA](https://huggingface.co/ByteDance/Hyper-SD) 🏎️ Now with added support: - prompt input - attention masking for improved prompt adherence - multiple image interpolation [[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)] """) with gr.Row(): with gr.Column(): input_image = gr.Image(label="Image to create variations", type="pil") prompt = gr.Text( label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False, ) reference_scale = gr.Slider( info="lower to enhance prompt adherence", label="Masking Scale", minimum=0.01, maximum=0.08, step=0.001, value=0.03, ) run_button = gr.Button("Run") with gr.Column(): image_2 = gr.Image(label="2nd image to create interpolated variations", type="pil") prompt_2 = gr.Text( label="2nd Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False, ) result = gr.Image(label="Result", show_label=False) with gr.Accordion("Advanced Settings", open=False): seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Row(): prompt_embeds_scale_1 = gr.Slider( label="prompt embeds scale 1st image", minimum=0, maximum=1.5, step=0.01, value=1, ) prompt_embeds_scale_2 = gr.Slider( label="prompt embeds scale 2nd image", minimum=0, maximum=1.5, step=0.01, value=1, ) pooled_prompt_embeds_scale_1 = gr.Slider( label="pooled prompt embeds scale 1nd image", minimum=0, maximum=1.5, step=0.01, value=1, ) pooled_prompt_embeds_scale_2 = gr.Slider( label="pooled prompt embeds scale 2nd image", minimum=0, maximum=1.5, step=0.01, value=1, ) with gr.Row(): width = gr.Slider( label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, ) height = gr.Slider( label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, ) with gr.Row(): guidance_scale = gr.Slider( label="Guidance Scale", minimum=1, maximum=15, step=0.1, value=3.5, ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=1, maximum=30, step=1, value=8, ) gr.Examples( examples=examples, inputs=[input_image, prompt, image_2, prompt_2, reference_scale, prompt_embeds_scale_1, prompt_embeds_scale_2, pooled_prompt_embeds_scale_1, pooled_prompt_embeds_scale_2, seed, randomize_seed], outputs=[result, seed], fn=infer, ) gr.on( triggers=[run_button.click], fn = infer, inputs = [input_image, prompt, image_2, prompt_2, reference_scale, prompt_embeds_scale_1, prompt_embeds_scale_2, pooled_prompt_embeds_scale_1, pooled_prompt_embeds_scale_2, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], outputs = [result, seed] ) demo.launch()