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modify yaml and workflow

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This commit is contained in:
maochaojie
2024-11-21 15:41:45 +08:00
parent a683061c6f
commit 7d7943fed3
7 changed files with 140 additions and 106 deletions
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scepter/methods/studio/chatbot/models/ace_0.6b_1024.yaml
+1
View File
@@ -1,5 +1,6 @@
NAME: ACE_0.6B_1024
IS_DEFAULT: False
USE_DYNAMIC_MODEL: False
DEFAULT_PARAS:
PARAS:
#
scepter/methods/studio/chatbot/models/ace_0.6b_1024_refiner.yaml
+2 -1
View File
@@ -1,5 +1,6 @@
NAME: ACE_0.6B_1024_REFINER
IS_DEFAULT: True
IS_DEFAULT: False
USE_DYNAMIC_MODEL: False
DEFAULT_PARAS:
PARAS:
#
scepter/methods/studio/chatbot/models/ace_0.6b_512.yaml
+2 -1
View File
@@ -1,5 +1,6 @@
NAME: ACE_0.6B_512
IS_DEFAULT: False
IS_DEFAULT: True
USE_DYNAMIC_MODEL: False
DEFAULT_PARAS:
PARAS:
#
scepter/modules/inference/ace_inference.py
+31 -20
View File
@@ -87,12 +87,16 @@ class TextEmbedding(nn.Module):
class RefinerInference(DiffusionInference):
def init_from_cfg(self, cfg):
self.use_dynamic_model = cfg.get('USE_DYNAMIC_MODEL', True)
super().init_from_cfg(cfg)
self.diffusion = DIFFUSIONS.build(cfg.MODEL.DIFFUSION, logger=self.logger) \
if cfg.MODEL.have('DIFFUSION') else None
self.max_seq_length = cfg.MODEL.get("MAX_SEQ_LENGTH", 4096)
assert self.diffusion is not None
if not self.use_dynamic_model:
self.dynamic_load(self.first_stage_model, 'first_stage_model')
self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
self.dynamic_load(self.diffusion_model, 'diffusion_model')
@torch.no_grad()
def encode_first_stage(self, x, **kwargs):
_, dtype = self.get_function_info(self.first_stage_model, 'encode')
@@ -152,17 +156,17 @@ class RefinerInference(DiffusionInference):
noise.append(noise_)
noise, x_shapes = pack_imagelist_into_tensor(noise)
if reverse_scale > 0:
self.dynamic_load(self.first_stage_model, 'first_stage_model')
if self.use_dynamic_model: self.dynamic_load(self.first_stage_model, 'first_stage_model')
x_samples = [x.unsqueeze(0) for x in x_samples]
x_start = self.encode_first_stage(x_samples, **kwargs)
self.dynamic_unload(self.first_stage_model,
if self.use_dynamic_model: self.dynamic_unload(self.first_stage_model,
'first_stage_model',
skip_loaded=True)
x_start, _ = pack_imagelist_into_tensor(x_start)
else:
x_start = None
# cond stage
self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
if self.use_dynamic_model: self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
function_name, dtype = self.get_function_info(self.cond_stage_model)
with torch.autocast('cuda',
enabled=dtype == 'float16',
@@ -170,12 +174,12 @@ class RefinerInference(DiffusionInference):
ctx = getattr(get_model(self.cond_stage_model),
function_name)(prompt)
ctx["x_shapes"] = x_shapes
self.dynamic_unload(self.cond_stage_model,
if self.use_dynamic_model: self.dynamic_unload(self.cond_stage_model,
'cond_stage_model',
skip_loaded=True)
self.dynamic_load(self.diffusion_model, 'diffusion_model')
if self.use_dynamic_model: self.dynamic_load(self.diffusion_model, 'diffusion_model')
# UNet use input n_prompt
function_name, dtype = self.get_function_info(
self.diffusion_model)
@@ -203,12 +207,12 @@ class RefinerInference(DiffusionInference):
x=x_start,
**kwargs).float()
latent = unpack_tensor_into_imagelist(latent, x_shapes)
self.dynamic_unload(self.diffusion_model,
if self.use_dynamic_model: self.dynamic_unload(self.diffusion_model,
'diffusion_model',
skip_loaded=True)
self.dynamic_load(self.first_stage_model, 'first_stage_model')
if self.use_dynamic_model: self.dynamic_load(self.first_stage_model, 'first_stage_model')
x_samples = self.decode_first_stage(latent)
self.dynamic_unload(self.first_stage_model,
if self.use_dynamic_model: self.dynamic_unload(self.first_stage_model,
'first_stage_model',
skip_loaded=True)
return x_samples
@@ -227,6 +231,7 @@ class ACEInference(DiffusionInference):
def init_from_cfg(self, cfg):
self.name = cfg.NAME
self.is_default = cfg.get('IS_DEFAULT', False)
self.use_dynamic_model = cfg.get('USE_DYNAMIC_MODEL', True)
module_paras = self.load_default(cfg.get('DEFAULT_PARAS', None))
assert cfg.have('MODEL')
@@ -250,6 +255,7 @@ class ACEInference(DiffusionInference):
# self.refiner_prompt = cfg.get('REFINER_PROMPT', "")
self.ace_prompt = cfg.get("ACE_PROMPT", [])
if self.refiner_model_cfg:
self.refiner_model_cfg.USE_DYNAMIC_MODEL = self.use_dynamic_model
self.refiner_module = RefinerInference(self.logger)
self.refiner_module.init_from_cfg(self.refiner_model_cfg)
else:
@@ -277,6 +283,10 @@ class ACEInference(DiffusionInference):
self.size_factor = cfg.get('SIZE_FACTOR', 8)
self.decoder_bias = cfg.get('DECODER_BIAS', 0)
self.default_n_prompt = cfg.get('DEFAULT_N_PROMPT', '')
if not self.use_dynamic_model:
self.dynamic_load(self.first_stage_model, 'first_stage_model')
self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
self.dynamic_load(self.diffusion_model, 'diffusion_model')
@torch.no_grad()
def encode_first_stage(self, x, **kwargs):
@@ -388,9 +398,9 @@ class ACEInference(DiffusionInference):
if use_ace and (not is_txt_image or refiner_scale <= 0):
ctx, null_ctx = {}, {}
# Get Noise Shape
self.dynamic_load(self.first_stage_model, 'first_stage_model')
if self.use_dynamic_model: self.dynamic_load(self.first_stage_model, 'first_stage_model')
x = self.encode_first_stage(image)
self.dynamic_unload(self.first_stage_model,
if self.use_dynamic_model: self.dynamic_unload(self.first_stage_model,
'first_stage_model',
skip_loaded=True)
noise = [
@@ -406,7 +416,7 @@ class ACEInference(DiffusionInference):
ctx['x_mask'] = null_ctx['x_mask'] = cond_mask
# Encode Prompt
self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
if self.use_dynamic_model: self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
function_name, dtype = self.get_function_info(self.cond_stage_model)
cont, cont_mask = getattr(get_model(self.cond_stage_model),
function_name)(prompt)
@@ -416,14 +426,14 @@ class ACEInference(DiffusionInference):
function_name)(n_prompt)
null_cont, null_cont_mask = self.cond_stage_embeddings(
prompt, edit_image, null_cont, null_cont_mask)
self.dynamic_unload(self.cond_stage_model,
if self.use_dynamic_model: self.dynamic_unload(self.cond_stage_model,
'cond_stage_model',
skip_loaded=False)
ctx['crossattn'] = cont
null_ctx['crossattn'] = null_cont
# Encode Edit Images
self.dynamic_load(self.first_stage_model, 'first_stage_model')
if self.use_dynamic_model: self.dynamic_load(self.first_stage_model, 'first_stage_model')
edit_image = [to_device(i, strict=False) for i in edit_image]
edit_image_mask = [to_device(i, strict=False) for i in edit_image_mask]
e_img, e_mask = [], []
@@ -434,14 +444,14 @@ class ACEInference(DiffusionInference):
m = [None] * len(u)
e_img.append(self.encode_first_stage(u, **kwargs))
e_mask.append([self.interpolate_func(i) for i in m])
self.dynamic_unload(self.first_stage_model,
if self.use_dynamic_model: self.dynamic_unload(self.first_stage_model,
'first_stage_model',
skip_loaded=True)
null_ctx['edit'] = ctx['edit'] = e_img
null_ctx['edit_mask'] = ctx['edit_mask'] = e_mask
# Diffusion Process
self.dynamic_load(self.diffusion_model, 'diffusion_model')
if self.use_dynamic_model: self.dynamic_load(self.diffusion_model, 'diffusion_model')
function_name, dtype = self.get_function_info(self.diffusion_model)
with torch.autocast('cuda',
enabled=dtype in ('float16', 'bfloat16'),
@@ -482,15 +492,15 @@ class ACEInference(DiffusionInference):
guide_rescale=guide_rescale,
return_intermediate=None,
**kwargs)
self.dynamic_unload(self.diffusion_model,
if self.use_dynamic_model: self.dynamic_unload(self.diffusion_model,
'diffusion_model',
skip_loaded=False)
# Decode to Pixel Space
self.dynamic_load(self.first_stage_model, 'first_stage_model')
if self.use_dynamic_model: self.dynamic_load(self.first_stage_model, 'first_stage_model')
samples = unpack_tensor_into_imagelist(latent, x_shapes)
x_samples = self.decode_first_stage(samples)
self.dynamic_unload(self.first_stage_model,
if self.use_dynamic_model: self.dynamic_unload(self.first_stage_model,
'first_stage_model',
skip_loaded=False)
x_samples = [x.squeeze(0) for x in x_samples]
@@ -509,7 +519,8 @@ class ACEInference(DiffusionInference):
x_samples = self.refiner_module.refine(x_samples,
reverse_scale = input_refine_scale,
prompt= input_refine_prompt,
seed=seed)
seed=seed,
use_dynamic_model=self.use_dynamic_model)
imgs = [
torch.clamp((x_i.float() + 1.0) / 2.0 + self.decoder_bias / 255,
scepter/studio/chatbot/chatbot.py
+22 -8
View File
@@ -55,8 +55,10 @@ class ChatBotUI(object):
from diffusers.utils import export_to_video
except Exception as e:
print(f"Import diffusers failed, please install or upgrade diffusers. Error information: {e}")
cfg = Config(cfg_file=cfg_general_file)
if isinstance(cfg_general_file, str):
cfg = Config(cfg_file=cfg_general_file)
else:
cfg = cfg_general_file
cfg.WORK_DIR = os.path.join(root_work_dir, cfg.WORK_DIR)
if not FS.exists(cfg.WORK_DIR):
FS.make_dir(cfg.WORK_DIR)
@@ -75,7 +77,7 @@ class ChatBotUI(object):
self.model_choices[model_name] = model_cfg
print('Models: ', self.model_choices.keys())
assert len(self.model_choices) > 0
if self.default_model_name == "": self.default_model_name = self.model_choices.keys()[0]
if self.default_model_name == "": self.default_model_name = list(self.model_choices.keys())[0]
self.model_name = self.default_model_name
self.pipe = ACEInference()
self.pipe.init_from_cfg(self.model_choices[self.default_model_name])
@@ -383,7 +385,7 @@ class ChatBotUI(object):
label='Rescale')
self.refiner_scale = gr.Slider(minimum=-0.1,
maximum=1.0,
value=self.pipe.input.get("refiner_scale", 0.5),
value=self.pipe.input.get("refiner_scale", -1),
visible=self.pipe.input.get("refiner_scale", None) is not None,
label='Refiner Scale')
self.seed = gr.Slider(minimum=-1,
@@ -530,6 +532,7 @@ class ChatBotUI(object):
lock.acquire()
del self.pipe
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
self.pipe = ACEInference()
self.pipe.init_from_cfg(self.model_choices[model_name])
self.model_name = model_name
@@ -555,7 +558,7 @@ class ChatBotUI(object):
value=self.pipe.input.get("refiner_prompt", ""),
visible=self.pipe.input.get("refiner_prompt", None) is not None),
gr.Slider(
value=self.pipe.input.get("refiner_scale", 0.5),
value=self.pipe.input.get("refiner_scale", -1),
visible=self.pipe.input.get("refiner_scale", None) is not None
),
gr.Checkbox(
@@ -996,9 +999,13 @@ class ChatBotUI(object):
w = int(w / ratio)
img = img.resize((w, h))
edit_image.append(img)
if img_mask is not None:
img_mask = img_mask if np.sum(np.array(img_mask)) > 0 else None
edit_image_mask.append(
img_mask if img_mask is not None else None)
edit_task.append(task)
if ref1 is not None:
ref1 = ref1 if np.sum(np.array(ref1)) > 0 else None
if ref1 is not None:
edit_image.append(ref1)
edit_image_mask.append(None)
@@ -1034,8 +1041,13 @@ class ChatBotUI(object):
img_str = f'<img src="data:image/png;base64,{img_b64}" style="pointer-events: none;">'
history = [(prompt,
f'{pre_info} The generated image is:\n {img_str}')]
img_id = get_md5(img_b64)[:12]
save_path = os.path.join(self.cache_dir, f'{img_id}.png')
img.convert('RGB').save(save_path)
return self.get_history(history), gr.update(value=''), gr.update(
visible=False), gr.update(value=-1)
visible=False), gr.Image(value=save_path), gr.update(value=-1)
with self.eg:
self.example_task = gr.Text(label='Task Name',
@@ -1061,8 +1073,9 @@ class ChatBotUI(object):
self.example_task, self.example_image, self.example_mask,
self.example_ref_im1, self.text, self.seed
],
outputs=[self.chatbot, self.text, self.gallery, self.seed],
outputs=[self.chatbot, self.text, self.gallery, self.legacy_image_viewer, self.seed],
examples_per_page=4,
cache_examples=False,
run_on_click=True)
########################################
@@ -1390,7 +1403,7 @@ class ChatBotUI(object):
def run_gr(cfg):
with gr.Blocks() as demo:
chatbot = ChatBotUI(cfg)
chatbot.create_bot_ui()
chatbot.create_ui()
chatbot.set_callbacks()
demo.launch(server_name='0.0.0.0',
server_port=cfg.args.server_port,
@@ -1402,6 +1415,7 @@ if __name__ == '__main__':
parser.add_argument('--server_port',
dest='server_port',
help='',
type=int,
default=2345)
parser.add_argument('--root_path', dest='root_path', help='', default='')
cfg = Config(load=True, parser_ins=parser)
scepter/studio/chatbot/example.py
+69 -56
View File
@@ -3,6 +3,7 @@
import os
from scepter.modules.utils.file_system import FS
from PIL import Image
def download_image(image, local_path=None):
@@ -10,44 +11,56 @@ def download_image(image, local_path=None):
local_path = FS.get_from(image, local_path=local_path)
return local_path
def blank_image():
return Image.new('RGBA', (128, 128), (0, 0, 0, 0))
def get_examples(cache_dir):
print('Downloading Examples ...')
bl_img = blank_image()
examples = [
[
'Facial Editing',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/e33edc106953.png?raw=true',
os.path.join(cache_dir, 'examples/e33edc106953.png')), None,
None, '{image} let the man smile', 6666
os.path.join(cache_dir, 'examples/e33edc106953.png')), bl_img,
bl_img, '{image} let the man smile', 6666
],
[
'Facial Editing',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/5d2bcc91a3e9.png?raw=true',
os.path.join(cache_dir, 'examples/5d2bcc91a3e9.png')), None,
None, 'let the man in {image} wear sunglasses', 9999
os.path.join(cache_dir, 'examples/5d2bcc91a3e9.png')), bl_img,
bl_img, 'let the man in {image} wear sunglasses', 9999
],
[
'Facial Editing',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/5d2bcc91a3e9.png?raw=true',
os.path.join(cache_dir, 'examples/5d2bcc91a3e9.png')), bl_img,
bl_img, 'let the man in {image} wear sunglasses', 9999
],
[
'Facial Editing',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/3a52eac708bd.png?raw=true',
os.path.join(cache_dir, 'examples/3a52eac708bd.png')), None,
None, '{image} red hair', 9999
os.path.join(cache_dir, 'examples/3a52eac708bd.png')), bl_img,
bl_img, '{image} red hair', 9999
],
[
'Facial Editing',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/3f4dc464a0ea.png?raw=true',
os.path.join(cache_dir, 'examples/3f4dc464a0ea.png')), None,
None, '{image} let the man serious', 99999
os.path.join(cache_dir, 'examples/3f4dc464a0ea.png')), bl_img,
bl_img, '{image} let the man serious', 99999
],
[
'Controllable Generation',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/131ca90fd2a9.png?raw=true',
os.path.join(cache_dir,
'examples/131ca90fd2a9.png')), None, None,
'examples/131ca90fd2a9.png')), bl_img, bl_img,
'"A person sits contemplatively on the ground, surrounded by falling autumn leaves. Dressed in a green sweater and dark blue pants, they rest their chin on their hand, exuding a relaxed demeanor. Their stylish checkered slip-on shoes add a touch of flair, while a black purse lies in their lap. The backdrop of muted brown enhances the warm, cozy atmosphere of the scene." , generate the image that corresponds to the given scribble {image}.',
613725
],
@@ -59,7 +72,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/33e9f27c2c48_mask.png?raw=true',
os.path.join(cache_dir,
'examples/33e9f27c2c48_mask.png')), None,
'examples/33e9f27c2c48_mask.png')), bl_img,
'Put the text "C A T" at the position marked by mask in the {image}',
6666
],
@@ -67,7 +80,7 @@ def get_examples(cache_dir):
'Style Transfer',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/9e73e7eeef55.png?raw=true',
os.path.join(cache_dir, 'examples/9e73e7eeef55.png')), None,
os.path.join(cache_dir, 'examples/9e73e7eeef55.png')), bl_img,
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/2e02975293d6.png?raw=true',
os.path.join(cache_dir, 'examples/2e02975293d6.png')),
@@ -81,7 +94,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/f2b22c08be3f_mask.png?raw=true',
os.path.join(cache_dir,
'examples/f2b22c08be3f_mask.png')), None,
'examples/f2b22c08be3f_mask.png')), bl_img,
'Could the {image} be widened within the space designated by mask, while retaining the original?',
6666
],
@@ -89,57 +102,57 @@ def get_examples(cache_dir):
'Image Segmentation',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/db3ebaa81899.png?raw=true',
os.path.join(cache_dir, 'examples/db3ebaa81899.png')), None,
None, '{image} Segmentation', 6666
os.path.join(cache_dir, 'examples/db3ebaa81899.png')), bl_img,
bl_img, '{image} Segmentation', 6666
],
[
'Depth Estimation',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/f1927c4692ba.png?raw=true',
os.path.join(cache_dir, 'examples/f1927c4692ba.png')), None,
None, '{image} Depth Estimation', 6666
os.path.join(cache_dir, 'examples/f1927c4692ba.png')), bl_img,
bl_img, '{image} Depth Estimation', 6666
],
[
'Pose Estimation',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/014e5bf3b4d1.png?raw=true',
os.path.join(cache_dir, 'examples/014e5bf3b4d1.png')), None,
None, '{image} distinguish the poses of the figures', 999999
os.path.join(cache_dir, 'examples/014e5bf3b4d1.png')), bl_img,
bl_img, '{image} distinguish the poses of the figures', 999999
],
[
'Scribble Extraction',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/5f59a202f8ac.png?raw=true',
os.path.join(cache_dir, 'examples/5f59a202f8ac.png')), None,
None, 'Generate a scribble of {image}, please.', 6666
os.path.join(cache_dir, 'examples/5f59a202f8ac.png')), bl_img,
bl_img, 'Generate a scribble of {image}, please.', 6666
],
[
'Mosaic',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/3a2f52361eea.png?raw=true',
os.path.join(cache_dir, 'examples/3a2f52361eea.png')), None,
None, 'Adapt {image} into a mosaic representation.', 6666
os.path.join(cache_dir, 'examples/3a2f52361eea.png')), bl_img,
bl_img, 'Adapt {image} into a mosaic representation.', 6666
],
[
'Edge map Extraction',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/b9d1e519d6e5.png?raw=true',
os.path.join(cache_dir, 'examples/b9d1e519d6e5.png')), None,
None, 'Get the edge-enhanced result for {image}.', 6666
os.path.join(cache_dir, 'examples/b9d1e519d6e5.png')), bl_img,
bl_img, 'Get the edge-enhanced result for {image}.', 6666
],
[
'Grayscale',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/c4ebbe2ba29b.png?raw=true',
os.path.join(cache_dir, 'examples/c4ebbe2ba29b.png')), None,
None, 'transform {image} into a black and white one', 6666
os.path.join(cache_dir, 'examples/c4ebbe2ba29b.png')), bl_img,
bl_img, 'transform {image} into a black and white one', 6666
],
[
'Contour Extraction',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/19652d0f6c4b.png?raw=true',
os.path.join(cache_dir,
'examples/19652d0f6c4b.png')), None, None,
'examples/19652d0f6c4b.png')), bl_img, bl_img,
'Would you be able to make a contour picture from {image} for me?',
6666
],
@@ -148,7 +161,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/249cda2844b7.png?raw=true',
os.path.join(cache_dir,
'examples/249cda2844b7.png')), None, None,
'examples/249cda2844b7.png')), bl_img, bl_img,
'Following the segmentation outcome in mask of {image}, develop a real-life image using the explanatory note in "a mighty cat lying on the bed”.',
6666
],
@@ -157,7 +170,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/411f6c4b8e6c.png?raw=true',
os.path.join(cache_dir,
'examples/411f6c4b8e6c.png')), None, None,
'examples/411f6c4b8e6c.png')), bl_img, bl_img,
'use the depth map {image} and the text caption "a cut white cat" to create a corresponding graphic image',
999999
],
@@ -166,7 +179,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/a35c96ed137a.png?raw=true',
os.path.join(cache_dir,
'examples/a35c96ed137a.png')), None, None,
'examples/a35c96ed137a.png')), bl_img, bl_img,
'help translate this posture schema {image} into a colored image based on the context I provided "A beautiful woman Climbing the climbing wall, wearing a harness and climbing gear, skillfully maneuvering up the wall with her back to the camera, with a safety rope."',
3599999
],
@@ -175,7 +188,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/dcb2fc86f1ce.png?raw=true',
os.path.join(cache_dir,
'examples/dcb2fc86f1ce.png')), None, None,
'examples/dcb2fc86f1ce.png')), bl_img, bl_img,
'Transform and generate an image using mosaic {image} and "Monarch butterflies gracefully perch on vibrant purple flowers, showcasing their striking orange and black wings in a lush garden setting." description',
6666
],
@@ -184,7 +197,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/4cd4ee494962.png?raw=true',
os.path.join(cache_dir,
'examples/4cd4ee494962.png')), None, None,
'examples/4cd4ee494962.png')), bl_img, bl_img,
'make this {image} colorful as per the "beautiful sunflowers"',
6666
],
@@ -193,7 +206,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/a47e3a9cd166.png?raw=true',
os.path.join(cache_dir,
'examples/a47e3a9cd166.png')), None, None,
'examples/a47e3a9cd166.png')), bl_img, bl_img,
'Take the edge conscious {image} and the written guideline "A whimsical animated character is depicted holding a delectable cake adorned with blue and white frosting and a drizzle of chocolate. The character wears a yellow headband with a bow, matching a cozy yellow sweater. Her dark hair is styled in a braid, tied with a yellow ribbon. With a golden fork in hand, she stands ready to enjoy a slice, exuding an air of joyful anticipation. The scene is creatively rendered with a charming and playful aesthetic." and produce a realistic image.',
613725
],
@@ -202,7 +215,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/d890ed8a3ac2.png?raw=true',
os.path.join(cache_dir,
'examples/d890ed8a3ac2.png')), None, None,
'examples/d890ed8a3ac2.png')), bl_img, bl_img,
'creating a vivid image based on {image} and description "This image features a delicious rectangular tart with a flaky, golden-brown crust. The tart is topped with evenly sliced tomatoes, layered over a creamy cheese filling. Aromatic herbs are sprinkled on top, adding a touch of green and enhancing the visual appeal. The background includes a soft, textured fabric and scattered white flowers, creating an elegant and inviting presentation. Bright red tomatoes in the upper right corner hint at the fresh ingredients used in the dish."',
6666
],
@@ -211,7 +224,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/0844a686a179.png?raw=true',
os.path.join(cache_dir,
'examples/0844a686a179.png')), None, None,
'examples/0844a686a179.png')), bl_img, bl_img,
'Eliminate noise interference in {image} and maximize the crispness to obtain superior high-definition quality',
6666
],
@@ -223,7 +236,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/fa91b6b7e59b_mask.png?raw=true',
os.path.join(cache_dir,
'examples/fa91b6b7e59b_mask.png')), None,
'examples/fa91b6b7e59b_mask.png')), bl_img,
'Ensure to overhaul the parts of the {image} indicated by the mask.',
6666
],
@@ -235,7 +248,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/632899695b26_mask.png?raw=true',
os.path.join(cache_dir,
'examples/632899695b26_mask.png')), None,
'examples/632899695b26_mask.png')), bl_img,
'Refashion the mask portion of {image} in accordance with "A yellow egg with a smiling face painted on it"',
6666
],
@@ -244,7 +257,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/354d17594afe.png?raw=true',
os.path.join(cache_dir,
'examples/354d17594afe.png')), None, None,
'examples/354d17594afe.png')), bl_img, bl_img,
'{image} change the dog\'s posture to walking in the water, and change the background to green plants and a pond.',
6666
],
@@ -253,7 +266,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/38946455752b.png?raw=true',
os.path.join(cache_dir,
'examples/38946455752b.png')), None, None,
'examples/38946455752b.png')), bl_img, bl_img,
'{image} change the color of the dress from white to red and the model\'s hair color red brown to blonde.Other parts remain unchanged',
6669
],
@@ -262,7 +275,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/3ba5202f0cd8.png?raw=true',
os.path.join(cache_dir,
'examples/3ba5202f0cd8.png')), None, None,
'examples/3ba5202f0cd8.png')), bl_img, bl_img,
'Keep the same facial feature in @3ba5202f0cd8, change the woman\'s clothing from a Blue denim jacket to a white turtleneck sweater and adjust her posture so that she is supporting her chin with both hands. Other aspects, such as background, hairstyle, facial expression, etc, remain unchanged.',
99999
],
@@ -270,22 +283,22 @@ def get_examples(cache_dir):
'Facial Editing',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/369365b94725.png?raw=true',
os.path.join(cache_dir, 'examples/369365b94725.png')), None,
None, '{image} Make her looking at the camera', 6666
os.path.join(cache_dir, 'examples/369365b94725.png')), bl_img,
bl_img, '{image} Make her looking at the camera', 6666
],
[
'Facial Editing',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/92751f2e4a0e.png?raw=true',
os.path.join(cache_dir, 'examples/92751f2e4a0e.png')), None,
None, '{image} Remove the smile from his face', 9899999
os.path.join(cache_dir, 'examples/92751f2e4a0e.png')), bl_img,
bl_img, '{image} Remove the smile from his face', 9899999
],
[
'Remove Text',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/8530a6711b2e.png?raw=true',
os.path.join(cache_dir, 'examples/8530a6711b2e.png')), None,
None, 'Aim to remove any textual element in {image}', 6666
os.path.join(cache_dir, 'examples/8530a6711b2e.png')), bl_img,
bl_img, 'Aim to remove any textual element in {image}', 6666
],
[
'Remove Text',
@@ -295,7 +308,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/c4d7fb28f8f6_mask.png?raw=true',
os.path.join(cache_dir,
'examples/c4d7fb28f8f6_mask.png')), None,
'examples/c4d7fb28f8f6_mask.png')), bl_img,
'Rub out any text found in the mask sector of the {image}.', 6666
],
[
@@ -303,7 +316,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/e2f318fa5e5b.png?raw=true',
os.path.join(cache_dir,
'examples/e2f318fa5e5b.png')), None, None,
'examples/e2f318fa5e5b.png')), bl_img, bl_img,
'Remove the unicorn in this {image}, ensuring a smooth edit.',
99999
],
@@ -315,7 +328,7 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/1ae96d8aca00_mask.png?raw=true',
os.path.join(cache_dir, 'examples/1ae96d8aca00_mask.png')),
None, 'Discard the contents of the mask area from {image}.', 99999
bl_img, 'Discard the contents of the mask area from {image}.', 99999
],
[
'Add Object',
@@ -325,22 +338,22 @@ def get_examples(cache_dir):
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/80289f48e511_mask.png?raw=true',
os.path.join(cache_dir,
'examples/80289f48e511_mask.png')), None,
'examples/80289f48e511_mask.png')), bl_img,
'add a Hot Air Balloon into the {image}, per the mask', 613725
],
[
'Style Transfer',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/d725cb2009e8.png?raw=true',
os.path.join(cache_dir, 'examples/d725cb2009e8.png')), None,
None, 'Change the style of {image} to colored pencil style', 99999
os.path.join(cache_dir, 'examples/d725cb2009e8.png')), bl_img,
bl_img, 'Change the style of {image} to colored pencil style', 99999
],
[
'Style Transfer',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/e0f48b3fd010.png?raw=true',
os.path.join(cache_dir, 'examples/e0f48b3fd010.png')), None,
None, 'make {image} to Walt Disney Animation style', 99999
os.path.join(cache_dir, 'examples/e0f48b3fd010.png')), bl_img,
bl_img, 'make {image} to Walt Disney Animation style', 99999
],
[
'Try On',
@@ -359,8 +372,8 @@ def get_examples(cache_dir):
'Workflow',
download_image(
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/cb85353c004b.png?raw=true',
os.path.join(cache_dir, 'examples/cb85353c004b.png')), None,
None, '<workflow> ice cream {image}', 99999
os.path.join(cache_dir, 'examples/cb85353c004b.png')), bl_img,
bl_img, '<workflow> ice cream {image}', 99999
],
]
print('Finish. Start building UI ...')
scepter/workflow/model_node.py
+13 -20
View File
@@ -39,8 +39,8 @@ class ModelNode:
'mantras': ('CONDITIONING', ),
'tuners': ('CONDITIONING', ),
'controls': ('CONDITIONING', ),
'image': ('IMAGE',),
'mask': ('MASK',)
'image': ('IMAGE', ),
'mask': ('MASK', )
}
}
@@ -64,15 +64,17 @@ class ModelNode:
image = [TT.ToPILImage()(image.squeeze(0).permute(2, 0, 1))]
if mask is not None:
mask = [TT.ToPILImage()(mask.squeeze(0))]
data = self.format_parameters(model, model_source, prompt, negative_prompt,
parameters, mantras, tuners, controls, image, mask)
data = self.format_parameters(model, model_source, prompt,
negative_prompt, parameters, mantras,
tuners, controls, image, mask)
cfg = self.model_file.get(model)['config']
cfg = self.source_mapping(cfg, model_source)
self.init_infer(model, cfg)
if model.startswith('ACE'):
output = self.diff_infer(**data[0], **data[1])
output_image = torch.stack([ TT.ToTensor()(img) for img in output]).permute(0, 2, 3, 1).unsqueeze(0)
output_image = torch.stack([TT.ToTensor()(img) for img in output
]).permute(0, 2, 3, 1).unsqueeze(0)
else:
output = self.diff_infer(data[0], **data[1])
x = output['images'].permute(0, 2, 3, 1)
@@ -94,13 +96,13 @@ class ModelNode:
elif source == 'Local':
cfg_new = copy.deepcopy(cfg)
cfg_new.MODEL = cfg_new.MODEL_LOCAL
if hasattr(cfg_new, 'EFINER_MODEL_LOCAL'):
if hasattr(cfg_new, 'REFINER_MODEL_LOCAL'):
cfg_new.REFINER_MODEL = cfg_new.REFINER_MODEL_LOCAL
return cfg_new
elif source == 'HuggingFace':
cfg_new = copy.deepcopy(cfg)
cfg_new.MODEL = cfg_new.MODEL_HF
if hasattr(cfg_new, 'EFINER_MODEL_HF'):
if hasattr(cfg_new, 'REFINER_MODEL_HF'):
cfg_new.REFINER_MODEL = cfg_new.REFINER_MODEL_HF
return cfg_new
else:
@@ -147,17 +149,8 @@ class ModelNode:
self.pipeline[model_name] = diff_infer
self.diff_infer = diff_infer
def format_parameters(self,
model,
model_source,
prompt,
negative_prompt,
parameters,
mantras,
tuners,
controls,
image,
mask):
def format_parameters(self, model, model_source, prompt, negative_prompt,
parameters, mantras, tuners, controls, image, mask):
input_data = {'prompt': prompt, 'negative_prompt': negative_prompt}
input_params = {
'diffusion_model': self.model_file.get(model)['diffusion_model'],
@@ -167,10 +160,10 @@ class ModelNode:
}
if image is not None:
input_data.update({"image": image})
input_data.update({'image': image})
if mask is not None:
input_data.update({"mask": mask})
input_data.update({'mask': mask})
if parameters:
seed = parameters.pop('seed', -1)