Delete gradio_canny2image_zh.py

gradio_canny2image_zh.py

@@ -1,126 +0,0 @@
-from diffusers import utils
-from diffusers.utils import deprecation_utils
-from diffusers.models import cross_attention
-utils.deprecate = lambda *arg, **kwargs: None
-deprecation_utils.deprecate = lambda *arg, **kwargs: None
-cross_attention.deprecate = lambda *arg, **kwargs: None
-
-import os
-import sys
-'''
-MAIN_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
-sys.path.insert(0, MAIN_DIR)
-os.chdir(MAIN_DIR)
-'''
-
-import gradio as gr
-import numpy as np
-import torch
-import random
-
-from annotator.util import resize_image, HWC3
-from annotator.canny import CannyDetector
-from diffusers.models.unet_2d_condition import UNet2DConditionModel
-from diffusers.pipelines import DiffusionPipeline
-from diffusers.schedulers import DPMSolverMultistepScheduler
-from models import ControlLoRA, ControlLoRACrossAttnProcessor
-
-apply_canny = CannyDetector()
-
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-pipeline = DiffusionPipeline.from_pretrained(
-    'IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1', safety_checker=None
-)
-pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
-pipeline = pipeline.to(device)
-unet: UNet2DConditionModel = pipeline.unet
-
-#ckpt_path = "ckpts/sd-diffusiondb-canny-model-control-lora-zh"
-ckpt_path = "svjack/canny-control-lora-zh"
-control_lora = ControlLoRA.from_pretrained(ckpt_path)
-control_lora = control_lora.to(device)
-
-# load control lora attention processors
-lora_attn_procs = {}
-lora_layers_list = list([list(layer_list) for layer_list in control_lora.lora_layers])
-n_ch = len(unet.config.block_out_channels)
-control_ids = [i for i in range(n_ch)]
-for name in pipeline.unet.attn_processors.keys():
-    cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
-    if name.startswith("mid_block"):
-        control_id = control_ids[-1]
-    elif name.startswith("up_blocks"):
-        block_id = int(name[len("up_blocks.")])
-        control_id = list(reversed(control_ids))[block_id]
-    elif name.startswith("down_blocks"):
-        block_id = int(name[len("down_blocks.")])
-        control_id = control_ids[block_id]
-
-    lora_layers = lora_layers_list[control_id]
-    if len(lora_layers) != 0:
-        lora_layer: ControlLoRACrossAttnProcessor = lora_layers.pop(0)
-        lora_attn_procs[name] = lora_layer
-
-unet.set_attn_processor(lora_attn_procs)
-
-
-def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, sample_steps, scale, seed, eta, low_threshold, high_threshold):
-    with torch.no_grad():
-        img = resize_image(HWC3(input_image), image_resolution)
-        H, W, C = img.shape
-
-        detected_map = apply_canny(img, low_threshold, high_threshold)
-        detected_map = HWC3(detected_map)
-
-        control = torch.from_numpy(detected_map[...,::-1].copy().transpose([2,0,1])).float().to(device)[None] / 127.5 - 1
-        _ = control_lora(control).control_states
-
-        if seed == -1:
-            seed = random.randint(0, 65535)
-
-        # run inference
-        generator = torch.Generator(device=device).manual_seed(seed)
-        images = []
-        for i in range(num_samples):
-            _ = control_lora(control).control_states
-            image = pipeline(
-                prompt + ', ' + a_prompt, negative_prompt=n_prompt,
-                num_inference_steps=sample_steps, guidance_scale=scale, eta=eta,
-                generator=generator, height=H, width=W).images[0]
-            images.append(np.asarray(image))
-
-        results = images
-    return [255 - detected_map] + results
-
-
-block = gr.Blocks().queue()
-with block:
-    with gr.Row():
-        gr.Markdown("## Control Stable Diffusion with Canny Edge Maps")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="numpy")
-            prompt = gr.Textbox(label="Prompt")
-            run_button = gr.Button(label="Run")
-            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=256)
-                low_threshold = gr.Slider(label="Canny low threshold", minimum=1, maximum=255, value=100, step=1)
-                high_threshold = gr.Slider(label="Canny high threshold", minimum=1, maximum=255, value=200, step=1)
-                sample_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", value=0.0)
-                a_prompt = gr.Textbox(label="Added Prompt", value='详细的模拟混合媒体拼贴画，帆布质地的当代艺术风格，朋克艺术，逼真主义，感性的身体，表现主义，极简主���。杰作，完美的组成，逼真的美丽的脸')
-                n_prompt = gr.Textbox(label="Negative Prompt",
-                                      value='低质量，模糊，混乱')
-        with gr.Column():
-            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
-    ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, sample_steps, scale, seed, eta, low_threshold, high_threshold]
-    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
-
-
-block.launch(server_name='0.0.0.0')
-
-#### block.launch(server_name='172.16.202.228', share=True)