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modify ace

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皓童
2025-01-07 14:15:44 +08:00
parent c0be2e6166
commit 05a74f03f6
2 changed files with 58 additions and 24 deletions
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demo.py
+57 -23
View File
@@ -134,23 +134,32 @@ class DemoUI(object):
elem_id='Reference_image'
)
with gr.Column(scale=1, visible=True) as self.edit_preprocess_panel:
with gr.Accordion(label='Related Input Image', open=False):
self.generation_info_preview = gr.Text(
lines=2,
)
self.edit_preprocess_preview = gr.Image(
height=600,
interactive=False,
type='pil',
elem_id='preprocess_image'
)
with gr.Row():
with gr.Accordion(label='Related Input Image', open=False):
self.edit_preprocess_preview = gr.Image(
height=600,
interactive=False,
type='pil',
elem_id='preprocess_image'
)
self.edit_preprocess_mask_preview = gr.Image(
height=600,
interactive=False,
type='pil',
elem_id='preprocess_image_mask'
)
self.edit_preprocess_mask_preview = gr.Image(
height=600,
interactive=False,
type='pil',
elem_id='preprocess_image_mask'
)
with gr.Row():
instruction = """
**Instruction**:
1. Please choose the Task Type based on the scenario of the generation task. We provide three types of generation capabilities: Portrait ID Preservation Generation(portrait),
Object ID Preservation Generation(subject), and Local Controlled Generation(local editing), which can be selected from the task dropdown menu.
2. When uploading images in the Reference Image section, the generated image will reference the ID information of that image. Please ensure that the ID information is clear.
In the Edit Image section, the uploaded image will maintain its structural and content information, and you must draw a mask area to specify the region to be regenerated.
3. When the task type is local editing, there are various editing types to choose from. Users can select different information preserving dimensions, such as edge information,
color information, and more. The pre-processing information can be viewed in the 'related input image' tab.
"""
self.instruction = gr.Markdown(value=instruction)
with gr.Row():
self.model_name_dd = gr.Dropdown(
choices=self.model_choices,
@@ -164,6 +173,10 @@ class DemoUI(object):
interactive=True,
value=self.edit_type_list[0],
label='Edit Type')
with gr.Row():
self.generation_info_preview = gr.Markdown(
label='System Log.',
show_label=True)
with gr.Row(variant='panel',
equal_height=True,
show_progress=False):
@@ -314,6 +327,8 @@ class DemoUI(object):
self.edit_type.change(change_edit_type, inputs=[self.edit_type], outputs=[self.repainting_scale])
def preprocess_input(ref_image, edit_image_dict, preprocess = None):
err_msg = ""
is_suc = True
if ref_image is not None:
ref_image = pillow_convert(ref_image, "RGB")
@@ -323,14 +338,19 @@ class DemoUI(object):
else:
edit_image = edit_image_dict["background"]
edit_mask = np.array(edit_image_dict["layers"][0])[:, :, 3]
if np.sum(edit_image) < 1:
if np.sum(np.array(edit_image)) < 1:
edit_image = None
edit_mask = None
elif np.sum(np.array(edit_mask)) < 1:
err_msg = "You must draw the repainting area for the edited image."
return None, None, None, False, err_msg
else:
edit_image = pillow_convert(edit_image, "RGB")
edit_mask = Image.fromarray(edit_mask).convert('L')
return edit_image, edit_mask, ref_image
if ref_image is None and edit_image is None:
err_msg = "Please provide the reference image or edited image."
return None, None, None, False, err_msg
return edit_image, edit_mask, ref_image, is_suc, err_msg
def run_chat(
prompt,
@@ -343,12 +363,26 @@ class DemoUI(object):
seed,
output_h,
output_w,
repainting_scale
repainting_scale,
progress=gr.Progress(track_tqdm=True)
):
model_path = self.task_model[task_type]["MODEL_PATH"]
edit_info = self.edit_type_dict[edit_type]
pre_edit_image, pre_edit_mask, pre_ref_image = preprocess_input(ref_image, edit_image)
if task_type in ["portrait", "subject"] and ref_image is None:
err_msg = "<mark>Please provide the reference image.</mark>"
return (gr.Image(), gr.Column(visible=True),
gr.Image(),
gr.Image(),
gr.Text(value=err_msg))
pre_edit_image, pre_edit_mask, pre_ref_image, is_suc, err_msg = preprocess_input(ref_image, edit_image)
if not is_suc:
err_msg = f"<mark>{err_msg}</mark>"
return (gr.Image(), gr.Column(visible=True),
gr.Image(),
gr.Image(),
gr.Text(value=err_msg))
pre_edit_image = edit_preprocess(edit_info, we.device_id, pre_edit_image, pre_edit_mask)
# edit_image["background"] = pre_edit_image
st = time.time()
@@ -403,7 +437,7 @@ class DemoUI(object):
queue=True)
def run_example(task_type, edit_type, prompt, ref_image, edit_image, edit_mask,
output_h, output_w, seed):
output_h, output_w, seed, progress=gr.Progress(track_tqdm=True)):
model_path = self.task_model[task_type]["MODEL_PATH"]
step = self.pipe.input.get("sample_steps", 20)
@@ -413,7 +447,7 @@ class DemoUI(object):
edit_image = self.construct_edit_image(edit_image, edit_mask)
pre_edit_image, pre_edit_mask, pre_ref_image = preprocess_input(ref_image, edit_image)
pre_edit_image, pre_edit_mask, pre_ref_image, _, _ = preprocess_input(ref_image, edit_image)
pre_edit_image = edit_preprocess(edit_info, we.device_id, pre_edit_image, pre_edit_mask)
edit_info = edit_info or {}
repainting_scale = edit_info.get("REPAINTING_SCALE", 1.0)
inference/ace_plus_diffusers.py
+1 -1
View File
@@ -32,7 +32,7 @@ class ACEPlusDiffuserInference():
local_folder = FS.get_dir_to_local_dir(cfg.MODEL.PRETRAINED_MODEL)
self.pipe = FluxFillPipeline.from_pretrained(local_folder, torch_dtype=torch.bfloat16).to("cuda")
self.pipe = FluxFillPipeline.from_pretrained(local_folder, torch_dtype=torch.bfloat16).to(we.device_id)
tokenizer_2 = T5TokenizerFast.from_pretrained(os.path.join(local_folder, "tokenizer_2"),
additional_special_tokens=["{image}"])