Delete app_diff.py

app_diff.py

@@ -1,204 +0,0 @@
-import os
-from PIL import Image
-import json
-import random
-
-import cv2
-import einops
-import gradio as gr
-import numpy as np
-import torch
-
-from pytorch_lightning import seed_everything
-from annotator.util import resize_image, HWC3
-from torch.nn.functional import threshold, normalize, interpolate
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
-from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
-from einops import rearrange, repeat
-
-import argparse
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# parse= argparse.ArgumentParser()
-# parseadd_argument('--pretrained_model', type=str, default='runwayml/stable-diffusion-v1-5')
-# parseadd_argument('--controlnet', type=str, default='controlnet')
-# parseadd_argument('--precision', type=str, default='fp32')
-# = parseparse_)
-# pretrained_model = pretrained_model
-pretrained_model = 'runwayml/stable-diffusion-v1-5'
-controlnet = 'checkpoint-36000/controlnet'
-precision = 'bf16'
-
-# Check for different hardware architectures
-if torch.cuda.is_available():
-    device = "cuda"
-    # Check for xformers
-    try:
-        import xformers
-
-        enable_xformers = True
-    except ImportError:
-        enable_xformers = False
-elif torch.backends.mps.is_available():
-    device = "mps"
-else:
-    device = "cpu"
-
-print(f"Using device: {device}")
-
-# Load models
-if precision == 'fp32':
-    torch_dtype = torch.float32
-elif precision == 'fp16':
-    torch_dtype = torch.float16
-elif precision == 'bf16':
-    torch_dtype = torch.bfloat16
-else:
-    raise ValueError(f"Invalid precision: {precision}")
-
-controlnet = ControlNetModel.from_pretrained(controlnet, torch_dtype=torch_dtype)
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    pretrained_model, controlnet=controlnet, torch_dtype=torch_dtype
-)
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-pipe = pipe.to(device)
-
-# Apply optimizations based on hardware
-if device == "cuda":
-    pipe = pipe.to(device)
-    if enable_xformers:
-        pipe.enable_xformers_memory_efficient_attention()
-        print("xformers optimization enabled")
-elif device == "mps":
-    pipe = pipe.to(device)
-    pipe.enable_attention_slicing()
-    print("Attention slicing enabled for Apple Silicon")
-else:
-    # CPU-specific optimizations
-    pipe = pipe.to(device)
-    # pipe.enable_sequential_cpu_offload()
-    # pipe.enable_attention_slicing()
-
-feature_extractor = SegformerFeatureExtractor.from_pretrained("matei-dorian/segformer-b5-finetuned-human-parsing")
-segmodel = SegformerForSemanticSegmentation.from_pretrained("matei-dorian/segformer-b5-finetuned-human-parsing")
-
-
-def LGB_TO_RGB(gray_image, rgb_image):
-    # gray_image [H, W, 3]
-    # rgb_image [H, W, 3]
-
-    print("gray_image shape: ", gray_image.shape)
-    print("rgb_image shape: ", rgb_image.shape)
-
-    gray_image = cv2.cvtColor(gray_image, cv2.COLOR_RGB2GRAY)
-    lab_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2LAB)
-    lab_image[:, :, 0] = gray_image[:, :]
-
-    return cv2.cvtColor(lab_image, cv2.COLOR_LAB2RGB)
-
-
-@torch.inference_mode()
-def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, strength,
-            guidance_scale, seed, eta, threshold, save_memory=False):
-    with torch.no_grad():
-        img = resize_image(input_image, image_resolution)
-        H, W, C = img.shape
-        print("img shape: ", img.shape)
-        if C == 3:
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
-        control = torch.from_numpy(img).to(device).float()
-        control = control / 255.0
-        control = rearrange(control, 'h w c -> 1 c h w')
-        # control = repeat(control, 'b c h w -> b c h w', b=num_samples)
-        # control = rearrange(control, 'b h w c -> b c h w')
-
-        if a_prompt:
-            prompt = prompt + ', ' + a_prompt
-
-        if seed == -1:
-            seed = random.randint(0, 65535)
-        seed_everything(seed)
-
-        generator = torch.Generator(device=device).manual_seed(seed)
-        # Generate images
-        output = pipe(
-            num_images_per_prompt=num_samples,
-            prompt=prompt,
-            image=control.to(device),
-            negative_prompt=n_prompt,
-            num_inference_steps=ddim_steps,
-            guidance_scale=guidance_scale,
-            generator=generator,
-            eta=eta,
-            strength=strength,
-            output_type='np',
-
-        ).images
-
-        # output = einops.rearrange(output, 'b c h w -> b h w c')
-        output = (output * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
-
-        results = [output[i] for i in range(num_samples)]
-        results = [LGB_TO_RGB(img, result) for result in results]
-
-        # results의 각 이미지를 mask로 변환
-        masks = []
-        for result in results:
-            inputs = feature_extractor(images=result, return_tensors="pt")
-            outputs = segmodel(**inputs)
-            logits = outputs.logits
-            logits = logits.squeeze(0)
-            thresholded = torch.zeros_like(logits)
-            thresholded[logits > threshold] = 1
-            mask = thresholded[1:, :, :].sum(dim=0)
-            mask = mask.unsqueeze(0).unsqueeze(0)
-            mask = interpolate(mask, size=(H, W), mode='bilinear')
-            mask = mask.detach().numpy()
-            mask = np.squeeze(mask)
-            mask = np.where(mask > threshold, 1, 0)
-            masks.append(mask)
-
-        # results의 각 이미지를 mask를 이용해 mask가 0인 부분은 img 즉 흑백 이미지로 변환.
-        # img를 channel이 3인 rgb 이미지로 변환
-        final = [img * (1 - mask[:, :, None]) + result * mask[:, :, None] for result, mask in zip(results, masks)]
-
-    # mask to 255 img
-
-    mask_img = [mask * 255 for mask in masks]
-    return [img] + results + mask_img + final
-
-
-block = gr.Blocks().queue()
-with block:
-    with gr.Row():
-        gr.Markdown("## Control Stable Diffusion with Gray Image")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(sources=['upload'], type="numpy")
-            prompt = gr.Textbox(label="Prompt")
-            run_button = gr.Button(value="Run")
-            with gr.Accordion("Advanced options", open=False):
-                num_samples = gr.Slider(label="Images", minimum=1, maximum=1, value=1, step=1, visible=False)
-                # num_samples = 1
-                image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
-                strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
-                # guess_mode = gr.Checkbox(label='Guess Mode', value=False)
-                ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=20, value=20, step=1)
-                scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=1.0, step=0.1)
-                threshold = gr.Slider(label="Segmentation Threshold", minimum=0.1, maximum=0.9, value=0.5, step=0.05)
-                seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, value=-1, step=1)
-                eta = gr.Number(label="eta (DDIM)", value=0.0)
-                a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed')
-                n_prompt = gr.Textbox(label="Negative Prompt",
-                                      value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')
-        with gr.Column():
-            # result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
-            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery")
-    ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, strength, scale, seed,
-           eta, threshold]
-    run_button.click(fn=process, inputs=ips, outputs=[result_gallery], concurrency_limit=4)
-
-block.queue(max_size=100)
-block.launch(share=True)