Delete app.py

app.py

@@ -1,120 +0,0 @@
-from share import *
-import config
-import os
-import cv2
-import einops
-import gradio as gr
-import numpy as np
-import torch
-import random
-
-from pytorch_lightning import seed_everything
-from annotator.util import resize_image, HWC3
-from annotator.uniformer import UniformerDetector
-from cldm.model import create_model, load_state_dict
-from cldm.ddim_hacked import DDIMSampler
-
-from PIL import Image, ImageChops
-from IPython.display import display
-
-# os.environ["no_proxy"] = "localhost,127.0.0.1,::1"
-
-apply_uniformer = UniformerDetector()
-
-model = create_model('./models/cldm_v15.yaml').cpu()
-
-model.load_state_dict(load_state_dict('./models/control_sks_crack_ppl.ckpt', location='cuda'))
-model.load_state_dict(load_state_dict('./models/sks_crack500_epoch_19.ckpt', location='cuda'))
-
-model = model.cuda()
-ddim_sampler = DDIMSampler(model)
-
-def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, 
-            ddim_steps, guess_mode, strength, scale, seed, eta):
-    with torch.no_grad():
-        input_image = np.array(input_image)
-        input_image = input_image.astype('uint8')
-
-        input_image = HWC3(input_image)
-        detected_map = apply_uniformer(resize_image(input_image, detect_resolution))
-        img = resize_image(input_image, image_resolution)
-        H, W, C = img.shape
-
-        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_NEAREST)
-
-        control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
-        control = torch.stack([control for _ in range(num_samples)], dim=0)
-        control = einops.rearrange(control, 'b h w c -> b c h w').clone()
-
-        if seed == -1:
-            seed = random.randint(0, 65535)
-        seed_everything(seed)
-
-        if config.save_memory:
-            model.low_vram_shift(is_diffusing=False)
-
-        cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
-        un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
-        shape = (4, H // 8, W // 8)
-
-        if config.save_memory:
-            model.low_vram_shift(is_diffusing=True)
-
-        model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)  # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
-        samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
-                                                     shape, cond, verbose=False, eta=eta,
-                                                     unconditional_guidance_scale=scale,
-                                                     unconditional_conditioning=un_cond)
-
-        if config.save_memory:
-            model.low_vram_shift(is_diffusing=False)
-
-        x_samples = model.decode_first_stage(samples)
-        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
-
-        results = [x_samples[i] for i in range(num_samples)]
-    return [detected_map] + results
-
-block = gr.Blocks().queue()
-with block:
-    with gr.Row():
-        gr.Markdown("## Control Stable Diffusion with Segmentation Maps")
-    with gr.Row():
-        with gr.Column():
-            with gr.Tabs(elem_id="mode_img2img") as tab_list:
-                with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
-                    with gr.Row().style(height=500):
-                        with gr.Column():
-                            init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=480)
-                            init_run_button = gr.Button(label="Run")
-
-                with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
-                    with gr.Row().style(height=500):
-                        with gr.Column():
-                            sketch = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="canvas", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=480)
-                            sketch_run_button = gr.Button(label="Run")
-            with gr.Row():
-                prompt = gr.Textbox(label="Prompt")
-
-            with gr.Row():
-                with gr.Accordion("Advanced options", open=False):
-                    num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=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)
-                    detect_resolution = gr.Slider(label="Segmentation Resolution", minimum=128, maximum=1024, value=512, step=1)
-                    ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
-                    scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
-                    seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
-                    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')
-    init_ips = [init_img, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta]
-    sketch_ips = [sketch, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta]
-    init_run_button.click(fn=process, inputs=init_ips, outputs=[result_gallery])
-    sketch_run_button.click(fn=process, inputs=sketch_ips, outputs=[result_gallery])
-
-block.launch(server_name='127.0.0.1', share=True)