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update_example entity control

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mi804
2024-12-31 14:04:28 +08:00
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apps/gradio/entity_level_control.py Normal file
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import gradio as gr
from diffsynth import ModelManager, FluxImagePipeline
import os, torch
from PIL import Image
import numpy as np
from PIL import ImageDraw, ImageFont
import random
import json
lora_checkpoint_path = 'models/lora/entity_control/model_bf16.safetensors'
save_masks_dir = 'workdirs/tmp_mask'
def save_mask_prompts(masks, mask_prompts, global_prompt, seed=0, random_dir='0000000'):
save_dir = os.path.join(save_masks_dir, random_dir)
os.makedirs(save_dir, exist_ok=True)
for i, mask in enumerate(masks):
save_path = os.path.join(save_dir, f'{i}.png')
mask.save(save_path)
sample = {
"global_prompt": global_prompt,
"mask_prompts": mask_prompts,
"seed": seed,
}
with open(os.path.join(save_dir, f"prompts.json"), 'w') as f:
json.dump(sample, f, indent=4)
def visualize_masks(image, masks, mask_prompts, font_size=35, use_random_colors=False):
# Create a blank image for overlays
overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
colors = [
(165, 238, 173, 80),
(76, 102, 221, 80),
(221, 160, 77, 80),
(204, 93, 71, 80),
(145, 187, 149, 80),
(134, 141, 172, 80),
(157, 137, 109, 80),
(153, 104, 95, 80),
(165, 238, 173, 80),
(76, 102, 221, 80),
(221, 160, 77, 80),
(204, 93, 71, 80),
(145, 187, 149, 80),
(134, 141, 172, 80),
(157, 137, 109, 80),
(153, 104, 95, 80),
]
# Generate random colors for each mask
if use_random_colors:
colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))]
# Font settings
try:
font = ImageFont.truetype("arial", font_size) # Adjust as needed
except IOError:
font = ImageFont.load_default(font_size)
# Overlay each mask onto the overlay image
for mask, mask_prompt, color in zip(masks, mask_prompts, colors):
# Convert mask to RGBA mode
mask_rgba = mask.convert('RGBA')
mask_data = mask_rgba.getdata()
new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data]
mask_rgba.putdata(new_data)
# Draw the mask prompt text on the mask
draw = ImageDraw.Draw(mask_rgba)
mask_bbox = mask.getbbox() # Get the bounding box of the mask
text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position
draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font)
# Alpha composite the overlay with this mask
overlay = Image.alpha_composite(overlay, mask_rgba)
# Composite the overlay onto the original image
result = Image.alpha_composite(image.convert('RGBA'), overlay)
return result
config = {
"model_config": {
"FLUX": {
"model_folder": "models/FLUX",
"pipeline_class": FluxImagePipeline,
"default_parameters": {
"cfg_scale": 3.0,
"embedded_guidance": 3.5,
"num_inference_steps": 30,
}
},
},
"max_num_painter_layers": 8,
"max_num_model_cache": 1,
}
def load_model_list(model_type):
if model_type is None:
return []
folder = config["model_config"][model_type]["model_folder"]
file_list = [i for i in os.listdir(folder) if i.endswith(".safetensors")]
if model_type in ["HunyuanDiT", "Kolors", "FLUX"]:
file_list += [i for i in os.listdir(folder) if os.path.isdir(os.path.join(folder, i))]
file_list = sorted(file_list)
return file_list
model_dict = {}
def load_model(model_type, model_path):
global model_dict
model_key = f"{model_type}:{model_path}"
if model_key in model_dict:
return model_dict[model_key]
model_path = os.path.join(config["model_config"][model_type]["model_folder"], model_path)
model_manager = ModelManager()
if model_type == "FLUX":
model_manager.torch_dtype = torch.bfloat16
file_list = [
os.path.join(model_path, "text_encoder/model.safetensors"),
os.path.join(model_path, "text_encoder_2"),
]
for file_name in os.listdir(model_path):
if file_name.endswith(".safetensors"):
file_list.append(os.path.join(model_path, file_name))
model_manager.load_models(file_list)
model_manager.load_lora(lora_checkpoint_path, lora_alpha=1.)
else:
model_manager.load_model(model_path)
pipe = config["model_config"][model_type]["pipeline_class"].from_model_manager(model_manager)
while len(model_dict) + 1 > config["max_num_model_cache"]:
key = next(iter(model_dict.keys()))
model_manager_to_release, _ = model_dict[key]
model_manager_to_release.to("cpu")
del model_dict[key]
torch.cuda.empty_cache()
model_dict[model_key] = model_manager, pipe
return model_manager, pipe
with gr.Blocks() as app:
gr.Markdown("""
# 实体级控制文生图模型EliGen
**UI说明**
1. 点击Load model读取模型，然后左侧界面为文生图输入参数；右侧Painter为局部控制区域绘制区域，每个局部控制条件由其Local prompt和绘制的mask组成，支持精准控制文生图和Inpainting两种模式。
2. **精准控制生图模式：** 输入Globalprompt；激活并绘制一个或多个局部控制条件，点击Generate生成图像; Global Prompt推荐包含每个Local Prompt。
3. **Inpainting模式：** 你可以上传图像，或者将上一步生成的图像设置为Inpaint Input Image，采用类似的方式输入局部控制条件，进行局部重绘。
4. 尽情创造！
""")
gr.Markdown("""
# Entity-Level Controlled Text-to-Image Model: EliGen
**UI Instructions**
1. Click "Load model" to load the model. The left interface is for text-to-image input parameters; the right "Painter" is the area for drawing local control regions. Each local control condition consists of its Local Prompt and the drawn mask, supporting both precise control text-to-image and Inpainting modes.
2. **Precise Control Image Generation Mode:** Enter the Global Prompt; activate and draw one or more local control conditions, then click "Generate" to create the image. It is recommended that the Global Prompt includes all Local Prompts.
3. **Inpainting Mode:** You can upload an image or set the image generated in the previous step as the "Inpaint Input Image." Use a similar method to input local control conditions for local redrawing.
4. Enjoy!
""")
with gr.Row():
random_mask_dir = gr.State('')
with gr.Column(scale=382, min_width=100):
model_type = gr.State('FLUX')
model_path = gr.State('FLUX.1-dev')
with gr.Accordion(label="Model"):
load_model_button = gr.Button(value="Load model")
with gr.Accordion(label="Global prompt"):
prompt = gr.Textbox(label="Prompt", lines=3)
negative_prompt = gr.Textbox(label="Negative prompt", value="worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,", lines=1)
cfg_scale = gr.Slider(minimum=1.0, maximum=10.0, value=7.0, step=0.1, interactive=True, label="Classifier-free guidance scale")
embedded_guidance = gr.Slider(minimum=0.0, maximum=10.0, value=0.0, step=0.1, interactive=True, label="Embedded guidance scale")
with gr.Accordion(label="Inference Options"):
num_inference_steps = gr.Slider(minimum=1, maximum=100, value=20, step=1, interactive=True, label="Inference steps")
height = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Height")
width = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Width")
return_with_mask = gr.Checkbox(value=True, interactive=True, label="show result with mask painting")
with gr.Column():
use_fixed_seed = gr.Checkbox(value=True, interactive=False, label="Use fixed seed")
seed = gr.Number(minimum=0, maximum=10**9, value=0, interactive=True, label="Random seed", show_label=False)
with gr.Accordion(label="Inpaint Input Image (Testing)"):
input_image = gr.Image(sources=None, show_label=False, interactive=True, type="pil")
background_weight = gr.Slider(minimum=0.0, maximum=1000., value=0., step=1, interactive=False, label="background_weight")
with gr.Column():
reset_input_button = gr.Button(value="Reset Inpaint Input")
send_input_to_painter = gr.Button(value="Set as painter's background")
@gr.on(inputs=[input_image], outputs=[input_image], triggers=reset_input_button.click)
def reset_input_image(input_image):
return None
@gr.on(
inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask],
outputs=[prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, load_model_button, random_mask_dir],
triggers=load_model_button.click
)
def model_path_to_default_params(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask):
load_model(model_type, model_path)
cfg_scale = config["model_config"][model_type]["default_parameters"].get("cfg_scale", cfg_scale)
embedded_guidance = config["model_config"][model_type]["default_parameters"].get("embedded_guidance", embedded_guidance)
num_inference_steps = config["model_config"][model_type]["default_parameters"].get("num_inference_steps", num_inference_steps)
height = config["model_config"][model_type]["default_parameters"].get("height", height)
width = config["model_config"][model_type]["default_parameters"].get("width", width)
return_with_mask = config["model_config"][model_type]["default_parameters"].get("return_with_mask", return_with_mask)
return prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, gr.update(value="Loaded FLUX"), gr.State(f'{random.randint(0, 1000000):08d}')
with gr.Column(scale=618, min_width=100):
with gr.Accordion(label="Painter"):
enable_local_prompt_list = []
local_prompt_list = []
mask_scale_list = []
canvas_list = []
for painter_layer_id in range(config["max_num_painter_layers"]):
with gr.Tab(label=f"Layer {painter_layer_id}"):
enable_local_prompt = gr.Checkbox(label="Enable", value=False, key=f"enable_local_prompt_{painter_layer_id}")
local_prompt = gr.Textbox(label="Local prompt", key=f"local_prompt_{painter_layer_id}")
mask_scale = gr.Slider(minimum=0.0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Mask scale", key=f"mask_scale_{painter_layer_id}")
canvas = gr.ImageEditor(canvas_size=(512, 1), sources=None, layers=False, interactive=True, image_mode="RGBA",
brush=gr.Brush(default_size=30, default_color="#000000", colors=["#000000"]),
label="Painter", key=f"canvas_{painter_layer_id}")
@gr.on(inputs=[height, width, canvas], outputs=canvas, triggers=[height.change, width.change, canvas.clear, enable_local_prompt.change], show_progress="hidden")
def resize_canvas(height, width, canvas):
h, w = canvas["background"].shape[:2]
if h != height or width != w:
return np.ones((height, width, 3), dtype=np.uint8) * 255
else:
return canvas
enable_local_prompt_list.append(enable_local_prompt)
local_prompt_list.append(local_prompt)
mask_scale_list.append(mask_scale)
canvas_list.append(canvas)
with gr.Accordion(label="Results"):
run_button = gr.Button(value="Generate", variant="primary")
output_image = gr.Image(sources=None, show_label=False, interactive=False, type="pil")
with gr.Row():
with gr.Column():
output_to_painter_button = gr.Button(value="Set as painter's background")
with gr.Column():
output_to_input_button = gr.Button(value="Set as input image")
real_output = gr.State(None)
mask_out = gr.State(None)
@gr.on(
inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir] + enable_local_prompt_list + local_prompt_list + mask_scale_list + canvas_list,
outputs=[output_image, real_output, mask_out],
triggers=run_button.click
)
def generate_image(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir, *args, progress=gr.Progress()):
print("save to:", random_mask_dir.value)
_, pipe = load_model(model_type, model_path)
input_params = {
"prompt": prompt,
"negative_prompt": negative_prompt,
"cfg_scale": cfg_scale,
"num_inference_steps": num_inference_steps,
"height": height,
"width": width,
"progress_bar_cmd": progress.tqdm,
}
if isinstance(pipe, FluxImagePipeline):
input_params["embedded_guidance"] = embedded_guidance
if input_image is not None:
input_params["inpaint_input"] = input_image.resize((width, height)).convert("RGB")
enable_local_prompt_list, local_prompt_list, mask_scale_list, canvas_list = (
args[0 * config["max_num_painter_layers"]: 1 * config["max_num_painter_layers"]],
args[1 * config["max_num_painter_layers"]: 2 * config["max_num_painter_layers"]],
args[2 * config["max_num_painter_layers"]: 3 * config["max_num_painter_layers"]],
args[3 * config["max_num_painter_layers"]: 4 * config["max_num_painter_layers"]]
)
local_prompts, masks, mask_scales = [], [], []
for enable_local_prompt, local_prompt, mask_scale, canvas in zip(
enable_local_prompt_list, local_prompt_list, mask_scale_list, canvas_list
):
if enable_local_prompt:
local_prompts.append(local_prompt)
masks.append(Image.fromarray(canvas["layers"][0][:, :, -1]).convert("RGB"))
mask_scales.append(mask_scale)
entity_masks = None if len(masks) == 0 else masks
entity_prompts = None if len(local_prompts) == 0 else local_prompts
input_params.update({
"entity_prompts": entity_prompts,
"entity_masks": entity_masks,
})
torch.manual_seed(seed)
image = pipe(**input_params)
# visualize masks
masks = [mask.resize(image.size) for mask in masks]
image_with_mask = visualize_masks(image, masks, local_prompts)
# save_mask_prompts(masks, local_prompts, prompt, seed, random_mask_dir.value)
real_output = gr.State(image)
mask_out = gr.State(image_with_mask)
if return_with_mask:
return image_with_mask, real_output, mask_out
return image, real_output, mask_out
@gr.on(inputs=[input_image] + canvas_list, outputs=canvas_list, triggers=send_input_to_painter.click)
def send_input_to_painter_background(input_image, *canvas_list):
if input_image is None:
return tuple(canvas_list)
for canvas in canvas_list:
h, w = canvas["background"].shape[:2]
canvas["background"] = input_image.resize((w, h))
return tuple(canvas_list)
@gr.on(inputs=[real_output] + canvas_list, outputs=canvas_list, triggers=output_to_painter_button.click)
def send_output_to_painter_background(real_output, *canvas_list):
if real_output is None:
return tuple(canvas_list)
for canvas in canvas_list:
h, w = canvas["background"].shape[:2]
canvas["background"] = real_output.value.resize((w, h))
return tuple(canvas_list)
@gr.on(inputs=[return_with_mask, real_output, mask_out], outputs=[output_image], triggers=[return_with_mask.change], show_progress="hidden")
def show_output(return_with_mask, real_output, mask_out):
if return_with_mask:
return mask_out.value
else:
return real_output.value
@gr.on(inputs=[real_output], outputs=[input_image], triggers=output_to_input_button.click)
def send_output_to_pipe_input(real_output):
return real_output.value
app.launch()