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my_gradio.py
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my_gradio.py
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from share import *
import config
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.canny import CannyDetector
from annotator.midas import MidasDetector
from annotator.hed import HEDdetector, nms
from annotator.mlsd import MLSDdetector
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
apply_canny = CannyDetector()
A_PROMPT_DEFAULT = "best quality, extremely detailed"
N_PROMPT_DEFAULT = "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality"
def apply_color(image, color_map):
image = cv2.cvtColor(image, cv2.COLOR_RGB2LAB)
color_map = cv2.cvtColor(color_map, cv2.COLOR_RGB2LAB)
l, _, _ = cv2.split(image)
_, a, b = cv2.split(color_map)
merged = cv2.merge([l, a, b])
result = cv2.cvtColor(merged, cv2.COLOR_LAB2RGB)
return result
def run_sampler(
input_image: np.ndarray,
prompt: str,
a_prompt: str = A_PROMPT_DEFAULT,
n_prompt: str = N_PROMPT_DEFAULT,
num_samples: int = 1,
image_resolution: int = 512,
ddim_steps=20,
guess_mode=False,
strength=1.0,
scale=9.0,
seed: int = -1,
eta=0.0,
show_progress: bool = True
):
model = create_model('/Users/ayushnaique28/vs_code/Python/color-gen/models/cldm_v21.yaml').cpu()
model.load_state_dict(load_state_dict('/Users/ayushnaique28/vs_code/Python/color-gen/models/gokul.ckpt', location="mps"))
# Print model's state_dict
# print("Model's state_dict:")
# for param_tensor in model.state_dict():
# print(param_tensor, "\t", model.state_dict()[param_tensor].size())
model = model.to(torch.device("mps"))
ddim_sampler = DDIMSampler(model)
with torch.no_grad():
if torch.cuda.is_available():
model = model.cuda()
input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB) # Convert BGR to RGB if necessary
lab = cv2.cvtColor(input_image, cv2.COLOR_RGB2LAB)
input_image = lab[:,:,0]
input_image = input_image.astype(np.uint8) # Normalize the image to [0, 1]
img = resize_image(HWC3(input_image), image_resolution)
H, W, C = img.shape
# detected_map = apply_canny(img, 100, 200)
# detected_map = HWC3(detected_map)
control = torch.from_numpy(img.copy()).to(torch.float32).to(torch.device("mps")) / 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,
show_progress=show_progress,
)
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)
)
# x_samples
# print("HELLOOOOOO\n")
results = [x_samples[i] for i in range(num_samples)]
colored_results = [apply_color(img, result) for result in results]
return colored_results + results + [img] + [lab]
block = gr.Blocks().queue()
with block:
with gr.Row():
gr.Markdown("## TEST GRADIO")
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=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)
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", rows=2, height='auto')
ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta]
run_button.click(fn=run_sampler, inputs=ips, outputs=[result_gallery])
block.launch(server_name='0.0.0.0')