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Running
on
Zero
| 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], | |
| [Image.open("1665_Girl_with_a_Pearl_Earring.jpg"), "", Image.open("dali_example.jpg"), "", 0.035, 1., 1., 1., 1., 0]] | |
| 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, | |
| 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=0.001, | |
| value=1, | |
| ) | |
| prompt_embeds_scale_2 = gr.Slider( | |
| label="prompt embeds scale 2nd image", | |
| minimum=0, | |
| maximum=1.5, | |
| step=0.001, | |
| value=1, | |
| ) | |
| pooled_prompt_embeds_scale_1 = gr.Slider( | |
| label="pooled prompt embeds scale 1nd image", | |
| minimum=0, | |
| maximum=1.5, | |
| step=0.001, | |
| value=1, | |
| ) | |
| pooled_prompt_embeds_scale_2 = gr.Slider( | |
| label="pooled prompt embeds scale 2nd image", | |
| minimum=0, | |
| maximum=1.5, | |
| step=0.001, | |
| 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], | |
| 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() |