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| import gradio as gr | |
| from diffusers import ControlNetModel, EulerAncestralDiscreteScheduler | |
| import torch | |
| import numpy as np | |
| from PIL import Image, ImageFilter | |
| from extension import CustomStableDiffusionControlNetPipeline | |
| negative_prompt = "" | |
| device = torch.device('cuda') | |
| controlnet = ControlNetModel.from_pretrained("BlockDetail/PartialSketchControlNet", torch_dtype=torch.float16).to(device) | |
| pipe = CustomStableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", | |
| controlnet=controlnet, torch_dtype=torch.float16 | |
| ).to(device) | |
| pipe.safety_checker = None | |
| pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) | |
| threshold = 250 | |
| curr_num_samples = 2 | |
| all_gens = [] | |
| num_images = 5 | |
| with gr.Blocks() as demo: | |
| start_state = [] | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| stroke_type = gr.Radio(["Blocking", "Detail"], value="Detail", label="Stroke Type"), | |
| dilation_strength = gr.Slider(7, 117, value=65, step=2, label="Dilation Strength"), | |
| canvas = gr.Image(source="canvas", shape=(512, 512), tool="color-sketch", | |
| min_width=512, brush_radius = 2).style(width=512, height=512) | |
| prompt_box = gr.Textbox(width="50vw", label="Prompt") | |
| with gr.Row(): | |
| btn = gr.Button("Generate").style(width=100, height=80) | |
| btn2 = gr.Button("Reset").style(width=100, height=80) | |
| with gr.Column(): | |
| num_samples = gr.Slider(1, 5, value=2, step=1, label="Num Samples to Generate"), | |
| with gr.Tab("Renoised Images"): | |
| gallery0 = gr.Gallery(show_label=False, columns=[num_samples[0].value], rows=[2], object_fit="contain", height="auto", preview=True, interactive=False).style(width=512, height=512) | |
| with gr.Tab("Renoised Overlay"): | |
| gallery1 = gr.Gallery(show_label=False, columns=[num_samples[0].value], rows=[2], object_fit="contain", height="auto", preview=True, interactive=False).style(width=512, height=512) | |
| with gr.Tab("Pre-Renoise Images"): | |
| gallery2 = gr.Gallery(show_label=False, columns=[num_samples[0].value], rows=[2], object_fit="contain", height="auto", preview=True, interactive=False).style(width=512, height=512) | |
| with gr.Tab("Pre-Renoise Overlay"): | |
| gallery3 = gr.Gallery(show_label=False, columns=[num_samples[0].value], rows=[2], object_fit="contain", height="auto", preview=True, interactive=False).style(width=512, height=512) | |
| for k in range(num_images): | |
| start_state.append([None, None]) | |
| sketch_states = gr.State(start_state) | |
| checkbox_state = gr.State(True) | |
| def sketch(curr_sketch_image, dilation_mask, prompt, seed, num_steps, dilation): | |
| global curr_num_samples | |
| generator = torch.Generator(device="cuda:0") | |
| generator.manual_seed(seed) | |
| negative_prompt = "" | |
| guidance_scale = 7 | |
| controlnet_conditioning_scale = 1.0 | |
| images = pipe([prompt]*curr_num_samples, [curr_sketch_image.convert("RGB").point( lambda p: 256 if p > 128 else 0)]*curr_num_samples, guidance_scale=guidance_scale, controlnet_conditioning_scale = controlnet_conditioning_scale, negative_prompt = [negative_prompt] * curr_num_samples, num_inference_steps=num_steps, generator=generator, key_image=None, neg_mask=None).images | |
| # run blended renoising if blocking strokes are provided | |
| if dilation_mask is not None: | |
| new_images = pipe.collage([prompt] * curr_num_samples, images, [dilation_mask] * curr_num_samples, num_inference_steps=50, strength=0.8)["images"] | |
| else: | |
| new_images = images | |
| return images, new_images | |
| def run_sketching(prompt, curr_sketch, sketch_states, dilation, contour_dilation=11): | |
| seed = sketch_states[k][1] | |
| if seed is None: | |
| seed = np.random.randint(1000) | |
| sketch_states[k][1] = seed | |
| curr_sketch_image = Image.fromarray(curr_sketch[:, :, 0]).resize((512, 512)) | |
| curr_construction_image = Image.fromarray(255 - curr_sketch[:, :, 2] + curr_sketch[:, :, 0]) | |
| if np.sum(255 - np.array(curr_construction_image)) == 0: | |
| curr_construction_image = None | |
| curr_detail_image = Image.fromarray(curr_sketch[:, :, 2]).resize((512, 512)) | |
| if curr_construction_image is not None: | |
| dilation_mask = Image.fromarray(255 - np.array(curr_construction_image)).filter(ImageFilter.MaxFilter(dilation)) | |
| dilation_mask = dilation_mask.point( lambda p: 256 if p > 0 else 25).filter(ImageFilter.GaussianBlur(radius = 5)) | |
| neg_dilation_mask = Image.fromarray(255 - np.array(curr_detail_image)).filter(ImageFilter.MaxFilter(contour_dilation)) | |
| neg_dilation_mask = np.array(neg_dilation_mask.point( lambda p: 256 if p > 0 else 0)) | |
| dilation_mask = np.array(dilation_mask) | |
| dilation_mask[neg_dilation_mask > 0] = 25 | |
| dilation_mask = Image.fromarray(dilation_mask).filter(ImageFilter.GaussianBlur(radius = 5)) | |
| else: | |
| dilation_mask = None | |
| images, new_images = sketch(curr_sketch_image, dilation_mask, prompt, seed, num_steps = 40, dilation = dilation) | |
| save_sketch = np.array(Image.fromarray(curr_sketch).convert("RGBA")) | |
| save_sketch[:, :, 3][save_sketch[:, :, 0] > 128] = 0 | |
| overlays = [] | |
| for i in images: | |
| background = i.copy() | |
| background.putalpha(80) | |
| background = Image.alpha_composite(Image.fromarray(255 * np.ones((512, 512)).astype(np.uint8)).convert("RGBA"), background) | |
| overlay = Image.alpha_composite(background.resize((512, 512)), Image.fromarray(save_sketch).convert("RGBA")) | |
| overlays.append(overlay.convert("RGB")) | |
| new_overlays = [] | |
| for i in new_images: | |
| background = i.copy() | |
| background.putalpha(80) | |
| background = Image.alpha_composite(Image.fromarray(255 * np.ones((512, 512)).astype(np.uint8)).convert("RGBA"), background) | |
| overlay = Image.alpha_composite(background.resize((512, 512)), Image.fromarray(save_sketch).convert("RGBA")) | |
| new_overlays.append(overlay.convert("RGB")) | |
| global all_gens | |
| all_gens = new_images | |
| return new_images, new_overlays, images, overlays | |
| def reset(sketch_states): | |
| for k in range(len(sketch_states)): | |
| sketch_states[k] = [None, None] | |
| return None, sketch_states | |
| def change_color(stroke_type): | |
| if stroke_type == "Blocking": | |
| color = "#0000FF" | |
| else: | |
| color = "#000000" | |
| return gr.Image(source="canvas", shape=(512, 512), tool="color-sketch", | |
| min_width=512, brush_radius = 2, brush_color=color).style(width=400, height=400) | |
| def change_background(option): | |
| global all_gens | |
| if option == "None" or len(all_gens) == 0: | |
| return None | |
| elif option == "Sample 0": | |
| image_overlay = all_gens[0].copy() | |
| elif option == "Sample 1": | |
| image_overlay = all_gens[0].copy() | |
| else: | |
| return None | |
| image_overlay.putalpha(80) | |
| return image_overlay | |
| def change_num_samples(change): | |
| global curr_num_samples | |
| curr_num_samples = change | |
| return None | |
| btn.click(run_sketching, [prompt_box, canvas, sketch_states, dilation_strength[0]], [gallery0, gallery1, gallery2, gallery3]) | |
| btn2.click(reset, sketch_states, [canvas, sketch_states]) | |
| stroke_type[0].change(change_color, [stroke_type[0]], canvas) | |
| num_samples[0].change(change_num_samples, [num_samples[0]], None) | |
| demo.launch(share = True, debug = True) |