Spaces:
Running
Running
Upload app.py
Browse files
app.py
CHANGED
@@ -74,9 +74,6 @@ args = parser.parse_args()
|
|
74 |
|
75 |
args.device = "cuda"
|
76 |
|
77 |
-
base_path = 'feishen29/IMAGDressing-v1'
|
78 |
-
|
79 |
-
|
80 |
vae = AutoencoderKL.from_pretrained('stabilityai/sd-vae-ft-mse').to(dtype=torch.float16, device=args.device)
|
81 |
tokenizer = CLIPTokenizer.from_pretrained("SG161222/Realistic_Vision_V4.0_noVAE", subfolder="tokenizer")
|
82 |
text_encoder = CLIPTextModel.from_pretrained("SG161222/Realistic_Vision_V4.0_noVAE", subfolder="text_encoder").to(dtype=torch.float16, device=args.device)
|
@@ -292,24 +289,19 @@ def dress_process(garm_img, face_img, pose_img, prompt, cloth_guidance_scale, ca
|
|
292 |
# return result[OutputKeys.OUTPUT_IMG]
|
293 |
return output[0]
|
294 |
|
295 |
-
|
296 |
-
|
297 |
-
garm_list = os.listdir(os.path.join(example_path, "cloth", 'cloth'))
|
298 |
-
garm_list_path = [os.path.join(example_path, "cloth", 'cloth', garm) for garm in garm_list]
|
299 |
-
|
300 |
-
face_list = os.listdir(os.path.join(example_path, "face", 'face'))
|
301 |
-
face_list_path = [os.path.join(example_path, "face", 'face', face) for face in face_list]
|
302 |
-
|
303 |
-
pose_list = os.listdir(os.path.join(example_path, "pose", 'pose'))
|
304 |
-
pose_list_path = [os.path.join(example_path, "pose", 'pose', pose) for pose in pose_list]
|
305 |
|
|
|
|
|
306 |
|
|
|
|
|
307 |
|
308 |
-
|
|
|
309 |
|
310 |
-
def process_image(image):
|
311 |
|
312 |
-
return image
|
313 |
|
314 |
image_blocks = gr.Blocks().queue()
|
315 |
with image_blocks as demo:
|
@@ -335,8 +327,6 @@ with image_blocks as demo:
|
|
335 |
example = gr.Examples(
|
336 |
inputs=imgs,
|
337 |
examples_per_page=10,
|
338 |
-
fn=process_image,
|
339 |
-
outputs=imgs,
|
340 |
examples=face_list_path
|
341 |
)
|
342 |
with gr.Row():
|
@@ -350,8 +340,6 @@ with image_blocks as demo:
|
|
350 |
example = gr.Examples(
|
351 |
inputs=pose_img,
|
352 |
examples_per_page=8,
|
353 |
-
fn=process_image,
|
354 |
-
outputs=pose_img,
|
355 |
examples=pose_list_path)
|
356 |
|
357 |
# with gr.Column():
|
|
|
74 |
|
75 |
args.device = "cuda"
|
76 |
|
|
|
|
|
|
|
77 |
vae = AutoencoderKL.from_pretrained('stabilityai/sd-vae-ft-mse').to(dtype=torch.float16, device=args.device)
|
78 |
tokenizer = CLIPTokenizer.from_pretrained("SG161222/Realistic_Vision_V4.0_noVAE", subfolder="tokenizer")
|
79 |
text_encoder = CLIPTextModel.from_pretrained("SG161222/Realistic_Vision_V4.0_noVAE", subfolder="text_encoder").to(dtype=torch.float16, device=args.device)
|
|
|
289 |
# return result[OutputKeys.OUTPUT_IMG]
|
290 |
return output[0]
|
291 |
|
292 |
+
base_path = 'yisol/IDM-VTON'
|
293 |
+
example_path = os.path.join(os.path.dirname(__file__), 'example')
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
294 |
|
295 |
+
garm_list = os.listdir(os.path.join(example_path,"cloth"))
|
296 |
+
garm_list_path = [os.path.join(example_path,"cloth",garm) for garm in garm_list]
|
297 |
|
298 |
+
face_list = os.listdir(os.path.join(example_path,"face"))
|
299 |
+
face_list_path = [os.path.join(example_path,"face",face) for face in face_list]
|
300 |
|
301 |
+
pose_list = os.listdir(os.path.join(example_path,"pose"))
|
302 |
+
pose_list_path = [os.path.join(example_path,"pose",pose) for pose in pose_list]
|
303 |
|
|
|
304 |
|
|
|
305 |
|
306 |
image_blocks = gr.Blocks().queue()
|
307 |
with image_blocks as demo:
|
|
|
327 |
example = gr.Examples(
|
328 |
inputs=imgs,
|
329 |
examples_per_page=10,
|
|
|
|
|
330 |
examples=face_list_path
|
331 |
)
|
332 |
with gr.Row():
|
|
|
340 |
example = gr.Examples(
|
341 |
inputs=pose_img,
|
342 |
examples_per_page=8,
|
|
|
|
|
343 |
examples=pose_list_path)
|
344 |
|
345 |
# with gr.Column():
|