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| import gradio as gr | |
| import spaces | |
| from clip_slider_pipeline import CLIPSliderFlux | |
| from diffusers import FluxPipeline | |
| import torch | |
| import numpy as np | |
| import cv2 | |
| from PIL import Image | |
| from diffusers.utils import load_image | |
| from diffusers.pipelines.flux.pipeline_flux_controlnet import FluxControlNetPipeline | |
| from diffusers.models.controlnet_flux import FluxControlNetModel | |
| def process_controlnet_img(image): | |
| controlnet_img = np.array(image) | |
| controlnet_img = cv2.Canny(controlnet_img, 100, 200) | |
| controlnet_img = HWC3(controlnet_img) | |
| controlnet_img = Image.fromarray(controlnet_img) | |
| # load pipelines | |
| pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", | |
| torch_dtype=torch.bfloat16) | |
| #pipe.enable_model_cpu_offload() | |
| clip_slider = CLIPSliderFlux(pipe, device=torch.device("cuda")) | |
| base_model = 'black-forest-labs/FLUX.1-schnell' | |
| controlnet_model = 'InstantX/FLUX.1-dev-Controlnet-Canny-alpha' | |
| # controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16) | |
| # pipe_controlnet = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16) | |
| # t5_slider_controlnet = T5SliderFlux(sd_pipe=pipe_controlnet,device=torch.device("cuda")) | |
| def generate(slider_x, prompt, seed, recalc_directions, iterations, steps, guidance_scale, | |
| x_concept_1, x_concept_2, | |
| avg_diff_x, | |
| img2img_type = None, img = None, | |
| controlnet_scale= None, ip_adapter_scale=None, | |
| ): | |
| # check if avg diff for directions need to be re-calculated | |
| print("slider_x", slider_x) | |
| print("x_concept_1", x_concept_1, "x_concept_2", x_concept_2) | |
| torch.manual_seed(seed) | |
| if not sorted(slider_x) == sorted([x_concept_1, x_concept_2]) or recalc_directions: | |
| avg_diff = clip_slider.find_latent_direction(slider_x[0], slider_x[1], num_iterations=iterations).to(torch.float16) | |
| x_concept_1, x_concept_2 = slider_x[0], slider_x[1] | |
| if img2img_type=="controlnet canny" and img is not None: | |
| control_img = process_controlnet_img(img) | |
| image = clip_slider.generate(prompt, guidance_scale=guidance_scale, image=control_img, controlnet_conditioning_scale =controlnet_scale, scale=0, scale_2nd=0, seed=seed, num_inference_steps=steps, avg_diff=avg_diff, avg_diff_2nd=avg_diff_2nd) | |
| elif img2img_type=="ip adapter" and img is not None: | |
| image = clip_slider.generate(prompt, guidance_scale=guidance_scale, ip_adapter_image=img, scale=0, scale_2nd=0, seed=seed, num_inference_steps=steps, avg_diff=avg_diff, avg_diff_2nd=avg_diff_2nd) | |
| else: # text to image | |
| image = clip_slider.generate(prompt, guidance_scale=guidance_scale, scale=0, scale_2nd=0, seed=seed, num_inference_steps=steps, avg_diff=avg_diff) | |
| #comma_concepts_x = ', '.join(slider_x) | |
| comma_concepts_x = f"{slider_x[1]}, {slider_x[0]}" | |
| avg_diff_x = avg_diff.cpu() | |
| return gr.update(label=comma_concepts_x, interactive=True), x_concept_1, x_concept_2, avg_diff_x, image | |
| def update_scales(x,prompt,seed, steps, guidance_scale, | |
| avg_diff_x, | |
| img2img_type = None, img = None, | |
| controlnet_scale= None, ip_adapter_scale=None,): | |
| avg_diff = avg_diff_x.cuda() | |
| torch.manual_seed(seed) | |
| if img2img_type=="controlnet canny" and img is not None: | |
| control_img = process_controlnet_img(img) | |
| image = t5_slider_controlnet.generate(prompt, guidance_scale=guidance_scale, image=control_img, controlnet_conditioning_scale =controlnet_scale, scale=x, seed=seed, num_inference_steps=steps, avg_diff=avg_diff) | |
| elif img2img_type=="ip adapter" and img is not None: | |
| image = clip_slider.generate(prompt, guidance_scale=guidance_scale, ip_adapter_image=img, scale=x,seed=seed, num_inference_steps=steps, avg_diff=avg_diff) | |
| else: | |
| image = clip_slider.generate(prompt, guidance_scale=guidance_scale, scale=x, seed=seed, num_inference_steps=steps, avg_diff=avg_diff) | |
| return image | |
| def update_x(x,y,prompt,seed, steps, | |
| avg_diff_x, avg_diff_y, | |
| img2img_type = None, | |
| img = None): | |
| avg_diff = avg_diff_x.cuda() | |
| avg_diff_2nd = avg_diff_y.cuda() | |
| image = clip_slider.generate(prompt, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd) | |
| return image | |
| def update_y(x,y,prompt,seed, steps, | |
| avg_diff_x, avg_diff_y, | |
| img2img_type = None, | |
| img = None): | |
| avg_diff = avg_diff_x.cuda() | |
| avg_diff_2nd = avg_diff_y.cuda() | |
| image = clip_slider.generate(prompt, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd) | |
| return image | |
| def reset_recalc_directions(): | |
| return True | |
| css = ''' | |
| #group { | |
| position: relative; | |
| width: 420px; | |
| height: 420px; | |
| margin-bottom: 20px; | |
| background-color: white | |
| } | |
| #x { | |
| position: absolute; | |
| bottom: 0; | |
| left: 25px; | |
| width: 400px; | |
| } | |
| #y { | |
| position: absolute; | |
| bottom: 20px; | |
| left: 67px; | |
| width: 400px; | |
| transform: rotate(-90deg); | |
| transform-origin: left bottom; | |
| } | |
| #image_out{position:absolute; width: 80%; right: 10px; top: 40px} | |
| ''' | |
| with gr.Blocks(css=css) as demo: | |
| gr.Markdown(""" | |
| <div style="text-align: center; font-size: 28px; font-weight: bold; margin-bottom: 20px;"> | |
| Latent Navigation | |
| </div> | |
| <div style="text-align: center; font-size: 16px; margin-bottom: 20px;"> | |
| Explorations in CLIP Space 🪐 | |
| </div> | |
| """) | |
| x_concept_1 = gr.State("") | |
| x_concept_2 = gr.State("") | |
| # y_concept_1 = gr.State("") | |
| # y_concept_2 = gr.State("") | |
| avg_diff_x = gr.State() | |
| #avg_diff_y = gr.State() | |
| recalc_directions = gr.State(False) | |
| #with gr.Tab("text2image"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| slider_x = gr.Dropdown(label="Slider concept range", allow_custom_value=True, multiselect=True, max_choices=2) | |
| #slider_y = gr.Dropdown(label="Slider Y concept range", allow_custom_value=True, multiselect=True, max_choices=2) | |
| prompt = gr.Textbox(label="Prompt") | |
| submit = gr.Button("find directions") | |
| with gr.Column(): | |
| with gr.Group(elem_id="group"): | |
| x = gr.Slider(minimum=-3, value=0, maximum=3.5, elem_id="x", interactive=False) | |
| #y = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="y", interactive=False) | |
| output_image = gr.Image(elem_id="image_out") | |
| # with gr.Row(): | |
| # generate_butt = gr.Button("generate") | |
| with gr.Accordion(label="advanced options", open=False): | |
| iterations = gr.Slider(label = "num iterations", minimum=0, value=200, maximum=400) | |
| steps = gr.Slider(label = "num inference steps", minimum=1, value=4, maximum=10) | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0.1, | |
| maximum=10.0, | |
| step=0.1, | |
| value=5, | |
| ) | |
| # correlation = gr.Slider( | |
| # label="correlation", | |
| # minimum=0.1, | |
| # maximum=1.0, | |
| # step=0.05, | |
| # value=0.6, | |
| # ) | |
| seed = gr.Slider(minimum=0, maximum=np.iinfo(np.int32).max, label="Seed", interactive=True, randomize=True) | |
| # with gr.Tab(label="image2image"): | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # image = gr.ImageEditor(type="pil", image_mode="L", crop_size=(512, 512)) | |
| # slider_x_a = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2) | |
| # slider_y_a = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2) | |
| # img2img_type = gr.Radio(["controlnet canny", "ip adapter"], label="", info="", visible=False, value="controlnet canny") | |
| # prompt_a = gr.Textbox(label="Prompt") | |
| # submit_a = gr.Button("Submit") | |
| # with gr.Column(): | |
| # with gr.Group(elem_id="group"): | |
| # x_a = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="x", interactive=False) | |
| # y_a = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="y", interactive=False) | |
| # output_image_a = gr.Image(elem_id="image_out") | |
| # with gr.Row(): | |
| # generate_butt_a = gr.Button("generate") | |
| # with gr.Accordion(label="advanced options", open=False): | |
| # iterations_a = gr.Slider(label = "num iterations", minimum=0, value=200, maximum=300) | |
| # steps_a = gr.Slider(label = "num inference steps", minimum=1, value=8, maximum=30) | |
| # guidance_scale_a = gr.Slider( | |
| # label="Guidance scale", | |
| # minimum=0.1, | |
| # maximum=10.0, | |
| # step=0.1, | |
| # value=5, | |
| # ) | |
| # controlnet_conditioning_scale = gr.Slider( | |
| # label="controlnet conditioning scale", | |
| # minimum=0.5, | |
| # maximum=5.0, | |
| # step=0.1, | |
| # value=0.7, | |
| # ) | |
| # ip_adapter_scale = gr.Slider( | |
| # label="ip adapter scale", | |
| # minimum=0.5, | |
| # maximum=5.0, | |
| # step=0.1, | |
| # value=0.8, | |
| # visible=False | |
| # ) | |
| # seed_a = gr.Slider(minimum=0, maximum=np.iinfo(np.int32).max, label="Seed", interactive=True, randomize=True) | |
| # submit.click(fn=generate, | |
| # inputs=[slider_x, slider_y, prompt, seed, iterations, steps, guidance_scale, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y], | |
| # outputs=[x, y, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y, output_image]) | |
| submit.click(fn=generate, | |
| inputs=[slider_x, prompt, seed, recalc_directions, iterations, steps, guidance_scale, x_concept_1, x_concept_2, avg_diff_x], | |
| outputs=[x, x_concept_1, x_concept_2, avg_diff_x, output_image]) | |
| iterations.change(fn=reset_recalc_directions, outputs=[recalc_directions]) | |
| seed.change(fn=reset_recalc_directions, outputs=[recalc_directions]) | |
| x.change(fn=update_scales, inputs=[x, prompt, seed, steps, guidance_scale, avg_diff_x], outputs=[output_image]) | |
| # generate_butt_a.click(fn=update_scales, inputs=[x_a,y_a, prompt_a, seed_a, steps_a, guidance_scale_a, avg_diff_x, avg_diff_y, img2img_type, image, controlnet_conditioning_scale, ip_adapter_scale], outputs=[output_image_a]) | |
| # submit_a.click(fn=generate, | |
| # inputs=[slider_x_a, slider_y_a, prompt_a, seed_a, iterations_a, steps_a, guidance_scale_a, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y, img2img_type, image, controlnet_conditioning_scale, ip_adapter_scale], | |
| # outputs=[x_a, y_a, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y, output_image_a]) | |
| if __name__ == "__main__": | |
| demo.launch() |