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| import gradio as gr | |
| import torch | |
| from PIL import Image, ImageDraw, ImageFont, ImageOps, ImageEnhance | |
| from quanto import qfloat8, quantize, freeze | |
| from diffusers import StableDiffusionControlNetPipeline, ControlNetModel | |
| from diffusers.utils import make_image_grid | |
| atkbold = ImageFont.truetype("Atkinson-Hyperlegible-Bold-102.otf",50) | |
| default_width = 1920 | |
| default_height = 1080 | |
| default_timesteps = 8 | |
| def mask_image_factory(mask_text="ASK FOR\nA SNACK", width=default_width, height=default_height): | |
| img = Image.new("L", (width, height), (0,)) | |
| draw = ImageDraw.Draw(img) | |
| draw.multiline_text( | |
| xy=(0,0), | |
| text=mask_text, | |
| fill=(255,), | |
| font=atkbold, | |
| align="center", | |
| spacing=0, | |
| ) | |
| cropped = img.crop(img.getbbox()) | |
| # Calculate aspect ratios | |
| image_aspect_ratio = width / height | |
| cropped_aspect_ratio = cropped.size[0] / cropped.size[1] | |
| # Determine which dimension of cropped.size is larger | |
| if cropped_aspect_ratio > image_aspect_ratio: | |
| # Calculate new dimensions for padding | |
| new_width = int(cropped.size[1] * image_aspect_ratio) | |
| new_height = cropped.size[1] | |
| else: | |
| new_width = cropped.size[0] | |
| new_height = int(cropped.size[0] / image_aspect_ratio) | |
| # Pad the image to the desired aspect ratio | |
| padded = ImageOps.pad(cropped, (new_width, new_height)) | |
| resized = padded.resize((width, height), resample=Image.Resampling.LANCZOS) | |
| return resized | |
| preferred_device = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu") | |
| # preferred_device = "cpu" | |
| preferred_dtype = torch.float32 | |
| controlnet = ControlNetModel.from_pretrained( | |
| "monster-labs/control_v1p_sd15_qrcode_monster", | |
| # "monster-labs/control_v1p_sdxl_qrcode_monster", | |
| subfolder="v2", | |
| torch_dtype=preferred_dtype, | |
| #torch_dtype=unet_preferred_dtype | |
| ).to(preferred_device) | |
| #quantize(controlnet, weights=qfloat8) | |
| #freeze(controlnet) | |
| ctlpipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "SimianLuo/LCM_Dreamshaper_v7", | |
| controlnet=controlnet, | |
| torch_dtype=preferred_dtype, | |
| safety_checker=None, | |
| ).to(preferred_device) | |
| #quantize(ctlpipe.unet, weights=qfloat8) | |
| #freeze(ctlpipe.unet) | |
| #quantize(ctlpipe.text_encoder, weights=qfloat8) | |
| #freeze(ctlpipe.text_encoder) | |
| def app(prompt, negative_prompt, mask_text, num_inference_steps, controlnet_conditioning_scale, width, height, seed, count): | |
| all_images = [ctlpipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| image=mask_image_factory(mask_text=mask_text, width=width, height=height), | |
| num_inference_steps=int(num_inference_steps), | |
| guidance_scale=8.0, | |
| controlnet_conditioning_scale=float(controlnet_conditioning_scale), | |
| generator=torch.manual_seed(int(seed + i)), | |
| height=height, | |
| width=width, | |
| ).images[0] for i in range(count)] | |
| if count == 1: | |
| cols = 1 | |
| rows = 1 | |
| elif count == 2: | |
| cols = 1 | |
| rows = 2 | |
| else: | |
| cols = 2 if count % 2 == 0 else 1 | |
| rows = count // cols | |
| return make_image_grid(all_images, cols=cols, rows=rows) | |
| app("corgis running in the park", "ugly, wrong", "ASK FOR\nA SNACK", 1, 1.0, default_height, default_width, 42, 1) | |
| iface = gr.Interface( | |
| app, | |
| [ | |
| gr.Textbox(label="Prompt", value="large happy dogs frolic on the seashore, tall cliffs, sun at the horizon, boulders, tide pools, golden hour"), | |
| gr.Textbox(label="Negative Prompt", value="ugly, wrong"), | |
| gr.Textbox(label="Mask Text", value="ASK FOR\nA SNACK"), | |
| gr.Number(label="Number of Inference Steps", value=default_timesteps, minimum=1, maximum=50, step=1), | |
| gr.Slider(label="ControlNet Conditioning Scale", value=0.6, minimum=-1.0, maximum=2.0, step=0.01), | |
| gr.Number(label="Width", value=default_width, minimum=256, maximum=2048, precision=0), | |
| gr.Number(label="Height", value=default_height, minimum=256, maximum=2048, precision=0), | |
| gr.Number(label="Random Number Seed", value=42, minimum=0, maximum=2**32-1, precision=0), | |
| gr.Radio(label="Number of Images to Generate with Subsequent Consecutive Seeds", choices=[1, 2, 4, 6, 10], value=2), | |
| ], | |
| "image", | |
| ) | |
| iface.launch() | |