| Input (Celebrities) | +Animation 1: Playing Guitar | +Animation 2: Cooking | +Animation 3: Dancing | +
 |
+ + | + | + |
 |
+ + | + | + |
 |
+ + | + | + |
| Input (Memes) | +Animation | +Input | +Animation | +
| Yao Ming Laugh | Girl Burning House | + | |
 |
+ + |  |
+ + |
| Girl with a Pearl Earring | Great Gatsby | + | |
 |
+ + |  |
+ + |
| Initial Image: Playing Guitar | +ID-Animator: Playing Guitar | +AdaFace-Animate: Playing Guitar | +Initial Image Dancing | +ID-Animator: Dancing | +AdaFace-Animate: Dancing | +
 |
+ + | + |  |
+ + | + |
 |
+ + | + |  |
+ + | + |
 |
+ + | + |  |
+ + | + |
| Input (Memes) | +ID-Animator | +AdaFace-Animate | +
|
+ + | + |
|
+ + | + |
| Input (Celebrities) | +ID-Animator: Playing Guitar | +AdaFace-Animate: Playing Guitar | +ID-Animator: Dancing | +AdaFace-Animate: Dancing | +
|
+ + | + | + | + |
|
+ + | + | + | + |
|
+ + | + | + | + |
+ +❗️**What's New**❗️ +- Support switching between two model styles: **Realistic** and **Anime**. +- If you just changed the model style, the first image/video generation will take extra 20~30 seconds for loading new model weight. + +❗️**Tips**❗️ +- You can upload one or more subject images for generating ID-specific video. +- If the face dominates the video frames, try increasing the 'Weight of ID prompt in the negative prompt'. +- If the face loses focus, try increasing the guidance scale. +- If the motion is weird, e.g., the prompt is "... running", try increasing the number of sampling steps. +- Usage explanations and demos: [Readme](https://huggingface.co/spaces/adaface-neurips/adaface-animate/blob/main/README2.md). +- AdaFace Text-to-Image: + AdaFace +
+
+
+**TODO:**
+- ControlNet integration.
+ """
+ )
+
+ with gr.Row():
+ with gr.Column():
+ files = gr.File(
+ label="Drag / Select 1 or more photos of a person's face",
+ file_types=["image"],
+ file_count="multiple"
+ )
+ image_container = gr.Image(label="image container", sources="upload", type="numpy", height=256, visible=False)
+ uploaded_files_gallery = gr.Gallery(label="Subject images", visible=False, columns=3, rows=2, height=300)
+ with gr.Column(visible=False) as clear_button_column:
+ remove_and_reupload = gr.ClearButton(value="Remove and upload subject images", components=files, size="sm")
+
+ init_img_files = gr.File(
+ label="[Optional] Generate 4 images and select 1 image",
+ file_types=["image"],
+ file_count="multiple"
+ )
+ init_img_container = gr.Image(label="init image container", sources="upload", type="numpy", height=256, visible=False)
+ # Although there's only one image, we still use columns=3, to scale down the image size.
+ # Otherwise it will occupy the full width, and the gallery won't show the whole image.
+ uploaded_init_img_gallery = gr.Gallery(label="Init image", visible=False, columns=3, rows=1, height=200)
+ # placeholder is just hint, not the real value. So we use "value='0'" instead of "placeholder='0'".
+ init_img_selected_idx = gr.Textbox(label="Selected init image index", value="0", visible=False)
+
+ with gr.Column(visible=True) as init_gen_button_column:
+ gen_init = gr.Button(value="Generate 3 new init images")
+ with gr.Column(visible=False) as init_clear_button_column:
+ remove_init_and_reupload = gr.ClearButton(value="Upload an old init image", components=init_img_files, size="sm")
+
+ prompt = gr.Dropdown(label="Prompt",
+ info="Try something like 'man/woman walking on the beach'.",
+ value="((best quality)), ((masterpiece)), ((realistic)), highlighted hair, futuristic silver armor suit, confident stance, high-resolution, living room, smiling, head tilted, perfect smooth skin",
+ allow_custom_value=True,
+ filterable=False,
+ choices=[
+ "((best quality)), ((masterpiece)), ((realistic)), highlighted hair, futuristic silver armor suit, confident stance, high-resolution, living room, smiling, head tilted, perfect smooth skin",
+ "walking on the beach, sunset, orange sky, eye level shot",
+ "in a white apron and chef hat, garnishing a gourmet dish, full body view, long shot",
+ "dancing pose among folks in a park, waving hands",
+ "in iron man costume flying pose, the sky ablaze with hues of orange and purple, full body view, long shot",
+ "jedi wielding a lightsaber, star wars, full body view, eye level shot",
+ "playing guitar on a boat, ocean waves",
+ "with a passion for reading, curled up with a book in a cozy nook near a window",
+ #"running pose in a park, full body view, eye level shot",
+ "in superman costume flying pose, the sky ablaze with hues of orange and purple, full body view, long shot"
+ ])
+
+ init_image_strength = gr.Slider(
+ label="Init Image Strength",
+ info="How much the init image should influence each frame. 0: no influence (scenes are more dynamic), 3: strongest influence (scenes are more static).",
+ minimum=0,
+ maximum=1.5,
+ step=0.25,
+ value=1,
+ )
+ init_image_final_weight = gr.Slider(
+ label="Final Weight of the Init Image",
+ info="How much the init image should influence the end of the video",
+ minimum=0,
+ maximum=0.25,
+ step=0.025,
+ value=0.1,
+ )
+
+ model_style_type = gr.Dropdown(
+ label="Base Model Style Type",
+ info="Switching the base model type will take 10~20 seconds to reload the model",
+ value=args.model_style_type.capitalize(),
+ choices=["Realistic", "Anime"], #"Photorealistic"],
+ allow_custom_value=False,
+ filterable=False,
+ )
+ guidance_scale = gr.Slider(
+ label="Guidance scale",
+ info="If > 10, there may be artifacts.",
+ minimum=1.0,
+ maximum=12.0,
+ step=1,
+ value=args.guidance_scale,
+ )
+
+ do_neg_id_prompt_weight = gr.Slider(
+ label="Weight of ID prompt in the negative prompt",
+ minimum=0.0,
+ maximum=0.9,
+ step=0.1,
+ value=args.do_neg_id_prompt_weight,
+ visible=True
+ )
+
+ seed = gr.Slider(
+ label="Seed",
+ minimum=0,
+ maximum=MAX_SEED,
+ step=1,
+ value=985,
+ )
+ randomize_seed = gr.Checkbox(
+ label="Randomize seed",
+ value=True,
+ info="Uncheck for reproducible results")
+
+ negative_prompt = gr.Textbox(
+ label="Negative Prompt",
+ placeholder="low quality",
+ value="(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, bare breasts, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, long neck, UnrealisticDream",
+ )
+ num_steps = gr.Slider(
+ label="Number of sampling steps. More steps for better composition, but longer time.",
+ minimum=30,
+ maximum=70,
+ step=10,
+ value=40,
+ )
+
+ submit = gr.Button("Generate Video")
+
+ with gr.Accordion(open=False, label="Advanced Options"):
+ video_length = gr.Slider(
+ label="video_length",
+ info="Do not change; any values other than 16 will mess up the output video",
+ minimum=16,
+ maximum=21,
+ step=1,
+ value=16,
+ interactive=False,
+ visible=False,
+ )
+ is_adaface_enabled = gr.Checkbox(label="Enable AdaFace",
+ info="Enable AdaFace for better face details. If unchecked, it falls back to ID-Animator (https://huggingface.co/spaces/ID-Animator/ID-Animator).",
+ value=True)
+ adaface_ckpt_path = gr.Textbox(
+ label="AdaFace checkpoint path",
+ placeholder=args.adaface_ckpt_path,
+ value=args.adaface_ckpt_path,
+ )
+
+ adaface_power_scale = gr.Slider(
+ label="AdaFace Embedding Power Scale",
+ info="Increase this scale slightly only if the face is defocused or the face details are not clear",
+ minimum=0.8,
+ maximum=1.2,
+ step=0.1,
+ value=1,
+ )
+
+ image_embed_cfg_begin_scale = gr.Slider(
+ label="ID-Animator Image Embedding Initial Scale",
+ info="The scale of the ID-Animator image embedding (influencing coarse facial features and poses)",
+ minimum=0.6,
+ maximum=1.5,
+ step=0.1,
+ value=1.0,
+ )
+ image_embed_cfg_end_scale = gr.Slider(
+ label="ID-Animator Image Embedding Final Scale",
+ info="The scale of the ID-Animator image embedding (influencing coarse facial features and poses)",
+ minimum=0.3,
+ maximum=1.5,
+ step=0.1,
+ value=0.5,
+ )
+
+ id_animator_anneal_steps = gr.Slider(
+ label="ID-Animator Scale Anneal Steps",
+ minimum=0,
+ maximum=40,
+ step=1,
+ value=20,
+ visible=True,
+ )
+
+ attn_scale = gr.Slider(
+ label="ID-Animator Attention Processor Scale",
+ info="The scale of the ID embeddings on the attention (the higher, the more focus on the face, less on the background)" ,
+ minimum=0,
+ maximum=2,
+ step=0.1,
+ value=1,
+ )
+
+ with gr.Column():
+ result_video = gr.Video(label="Generated Animation", interactive=False)
+
+ files.upload(fn=swap_to_gallery, inputs=files, outputs=[uploaded_files_gallery, clear_button_column, files])
+ remove_and_reupload.click(fn=remove_back_to_files, outputs=[uploaded_files_gallery, clear_button_column, files, init_img_selected_idx])
+
+ init_img_files.upload(fn=swap_to_gallery, inputs=init_img_files,
+ outputs=[uploaded_init_img_gallery, init_clear_button_column, init_img_files])
+ remove_init_and_reupload.click(fn=remove_back_to_files,
+ outputs=[uploaded_init_img_gallery, init_clear_button_column,
+ init_img_files, init_img_selected_idx])
+ gen_init.click(fn=check_prompt_and_model_type,
+ inputs=[prompt, model_style_type],outputs=None).success(
+ fn=randomize_seed_fn,
+ inputs=[seed, randomize_seed],
+ outputs=seed,
+ queue=False,
+ api_name=False,
+ ).then(fn=gen_init_images, inputs=[uploaded_files_gallery, prompt,
+ guidance_scale, do_neg_id_prompt_weight],
+ outputs=[uploaded_init_img_gallery, init_img_files, init_clear_button_column])
+ uploaded_init_img_gallery.select(fn=get_clicked_image, inputs=None, outputs=init_img_selected_idx)
+
+ submit.click(fn=check_prompt_and_model_type,
+ inputs=[prompt, model_style_type],outputs=None).success(
+ fn=randomize_seed_fn,
+ inputs=[seed, randomize_seed],
+ outputs=seed,
+ queue=False,
+ api_name=False,
+ ).then(
+ fn=generate_video,
+ inputs=[image_container, files,
+ init_img_files, init_img_selected_idx, init_image_strength, init_image_final_weight,
+ prompt, negative_prompt, num_steps, video_length, guidance_scale, do_neg_id_prompt_weight,
+ seed, attn_scale, image_embed_cfg_begin_scale, image_embed_cfg_end_scale,
+ is_adaface_enabled, adaface_ckpt_path, adaface_power_scale, id_animator_anneal_steps],
+ outputs=[result_video]
+ )
+
+demo.launch(share=True, server_name=args.ip, ssl_verify=False)
diff --git a/assets/alita/alita armor orig.mp4 b/assets/alita/alita armor orig.mp4
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+version https://git-lfs.github.com/spec/v1
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diff --git a/assets/alita/alita armor.mp4 b/assets/alita/alita armor.mp4
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diff --git a/assets/alita/alita beach orig.mp4 b/assets/alita/alita beach orig.mp4
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+version https://git-lfs.github.com/spec/v1
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diff --git a/assets/alita/alita beach.mp4 b/assets/alita/alita beach.mp4
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+version https://git-lfs.github.com/spec/v1
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diff --git a/assets/alita/alita cooking orig.mp4 b/assets/alita/alita cooking orig.mp4
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+++ b/assets/alita/alita cooking orig.mp4
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+version https://git-lfs.github.com/spec/v1
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diff --git a/assets/alita/alita cooking.mp4 b/assets/alita/alita cooking.mp4
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+version https://git-lfs.github.com/spec/v1
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diff --git a/assets/alita/alita dancing orig.mp4 b/assets/alita/alita dancing orig.mp4
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+version https://git-lfs.github.com/spec/v1
+oid sha256:272d51d1f9da1bdd01d4a0f6f78636e4bafa81c38ae86eaf46940578138f0fea
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diff --git a/assets/alita/alita dancing.mp4 b/assets/alita/alita dancing.mp4
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+version https://git-lfs.github.com/spec/v1
+oid sha256:732233e36ebe71c0eca03645547d2c5625fa8de55bf099227f57b4f3090c095b
+size 255039
diff --git a/assets/alita/alita iron man orig.mp4 b/assets/alita/alita iron man orig.mp4
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+++ b/assets/alita/alita iron man orig.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 165630
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--- /dev/null
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:871e3fa5d6a65de07bd11fa40fe0d14da67d325b359a468e71b2be9814dd1837
+size 203639
diff --git a/assets/alita/alita lightsaber orig.mp4 b/assets/alita/alita lightsaber orig.mp4
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+version https://git-lfs.github.com/spec/v1
+oid sha256:af766df382f32fd0dc1eba204dddf4089d957c68b4a6a33678856c0b58531355
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+version https://git-lfs.github.com/spec/v1
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diff --git a/assets/alita/alita playing guitar orig.mp4 b/assets/alita/alita playing guitar orig.mp4
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+version https://git-lfs.github.com/spec/v1
+oid sha256:ad2bc5371e7a4379db46c462eaf4acb7dc9c9baa50c82679706a9943233f27ae
+size 230990
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+version https://git-lfs.github.com/spec/v1
+oid sha256:e637be23b654e68cdda64eded5fe15ec841f362557ef13f2e11484d8249342da
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diff --git a/assets/alita/alita reading orig.mp4 b/assets/alita/alita reading orig.mp4
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diff --git a/assets/alita/alita running orig.mp4 b/assets/alita/alita running orig.mp4
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diff --git a/assets/alita/alita running.mp4 b/assets/alita/alita running.mp4
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diff --git a/assets/alita/alita superman orig.mp4 b/assets/alita/alita superman orig.mp4
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+++ b/assets/alita/alita superman orig.mp4
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diff --git a/assets/alita/alita.jpg b/assets/alita/alita.jpg
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diff --git a/assets/alita/alita.txt b/assets/alita/alita.txt
new file mode 100644
index 0000000000000000000000000000000000000000..617565f0a1b1617bd66c7160e89773a3d94c489a
--- /dev/null
+++ b/assets/alita/alita.txt
@@ -0,0 +1,21 @@
+alita:
+
+woman ((best quality)), ((masterpiece)), ((realistic)), long highlighted hair, futuristic silver armor suit, confident stance, high-resolution, living room, smiling, head tilted, perfect smooth skin
+
+woman walking on the beach, sunset, orange sky, bright lighting, ocean waves seagulls, rippling water, serene atmosphere, eye level shot
+
+woman in a white apron and chef hat, garnishing a gourmet dish, full body view, long shot
+
+woman dancing pose among folks in a park, waving hands
+
+woman in iron man costume flying pose, the sky ablaze with hues of orange and purple, full body view, long shot
+
+woman jedi wielding a lightsaber, star wars, full body view, eye level shot
+
+woman playing guitar on a boat, ocean waves
+
+woman with a passion for reading, curled up with a book in a cozy nook near a window
+
+woman running pose in a park, eye level shot
+
+woman in superman costume flying pose, the sky ablaze with hues of orange and purple, full body view, long shot
diff --git a/assets/alita/init images/alita armor.jpg b/assets/alita/init images/alita armor.jpg
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diff --git a/assets/alita/init images/alita dancing.jpg b/assets/alita/init images/alita dancing.jpg
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+man in iron man costume flying pose, the sky ablaze with hues of orange and purple, distant view, long shot
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+man playing guitar on a boat, ocean waves
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+Jennifer Lawrence:
+
+woman ((best quality)), ((masterpiece)), ((realistic)), long highlighted hair, futuristic silver armor suit, confident stance, high-resolution, living room, smiling, head tilted, perfect smooth skin
+
+woman walking on the beach, sunset, orange sky, bright lighting, ocean waves seagulls, rippling water, serene atmosphere, eye level shot
+
+# "distant view" suppresses background, so use "full body view" instead.
+woman in a white apron and chef hat, garnishing a gourmet dish, full body view, long shot
+
+woman dancing pose among folks in a park, waving hands
+
+woman in iron man costume flying pose, the sky ablaze with hues of orange and purple, full body view, long shot
+
+woman jedi wielding a lightsaber, star wars, full body view, eye level shot
+
+woman playing guitar on a boat, ocean waves
+
+woman with a passion for reading, curled up with a book in a cozy nook near a window
+
+woman running pose in a park, eye level shot
+
+woman in superman costume flying pose, the sky ablaze with hues of orange and purple, full body view, long shot
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+Jensen Huang:
+
+man ((best quality)), ((masterpiece)), ((realistic)), highlighted hair, futuristic silver armor suit, confident stance, high-resolution, living room, smiling, head tilted, perfect smooth skin
+
+# Note the "face portrait of" prefix. Otherwise it's hard to generate an initial image highlighting the face.
+face portrait of man walking on the beach, sunset, orange sky, bright lighting, ocean waves seagulls, rippling water, serene atmosphere, eye level shot
+
+man in a white apron and chef hat, garnishing a gourmet dish, full body view, long shot
+
+man dancing pose among folks in a park, waving hands
+
+man in iron man costume flying pose, the sky ablaze with hues of orange and purple, full body view, long shot
+
+man jedi wielding a lightsaber, star wars, full body view, eye level shot
+
+man playing guitar on a boat, ocean waves
+
+man with a passion for reading, curled up with a book in a cozy nook near a window
+
+man running pose in a park, eye level shot
+
+man in superman costume flying pose, the sky ablaze with hues of orange and purple, full body view, long shot
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+Keanu Reeves:
+
+man ((best quality)), ((masterpiece)), ((realistic)), highlighted hair, futuristic silver armor suit, confident stance, high-resolution, living room, smiling, head tilted, perfect smooth skin
+
+man walking on the beach, sunset, orange sky, bright lighting, ocean waves seagulls, rippling water, serene atmosphere, eye level shot
+
+man in a white apron and chef hat, garnishing a gourmet dish, full body view, long shot
+
+man dancing pose among folks in a park, waving hands
+
+man in iron man costume flying pose, the sky ablaze with hues of orange and purple, full body view, long shot
+
+man jedi wielding a lightsaber, star wars, full body view, eye level shot
+
+man playing guitar on a boat, ocean waves
+
+man with a passion for reading, curled up with a book in a cozy nook near a window
+
+man running pose in a park, eye level shot
+
+man in superman costume flying pose, the sky ablaze with hues of orange and purple, full body view, long shot
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+angry man:
+angry man, gritting teeth
+
+boy fist:
+boy pumps one fist on a beach
+
+girl burning house:
+girl in front of a burning house, massive fire
+
+nick young confused:
+man confused
+
+side eyeing chloe:
+girl giving a sideways glance
+
+great gatsby:
+man cheers with a glass of wine
+
+yao ming laugh:
+man laughing embarrassedly
+
+girl with a pearl earring:
+woman in a yellow shirt
+
+flip palm:
+woman raises hand, eye level shot
+
+mona lisa:
+woman smiling, oil painting, white background
+(There are a lot of high-frequency noises introduced into the AdaFace video, so it looks less appealing than the ID-Animator video. But the authenticity is still higher.)
+
+harold:
+man walking on the beach, sunset, orange sky, bright lighting, ocean waves seagulls, rippling water, serene atmosphere, eye level shot
diff --git a/assets/memes/mona lisa smiling orig.mp4 b/assets/memes/mona lisa smiling orig.mp4
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diff --git a/assets/yann-lecun/yann lecun.txt b/assets/yann-lecun/yann lecun.txt
new file mode 100644
index 0000000000000000000000000000000000000000..82641d2573bdcba0585922b6fbfd2c9caf6302a9
--- /dev/null
+++ b/assets/yann-lecun/yann lecun.txt
@@ -0,0 +1,21 @@
+yann lecun:
+
+man ((best quality)), ((masterpiece)), ((realistic)), highlighted hair, futuristic silver armor suit, confident stance, high-resolution, living room, smiling, head tilted, perfect smooth skin
+
+man walking on the beach, sunset, orange sky, bright lighting, ocean waves seagulls, rippling water, serene atmosphere, eye level shot
+
+man in a white apron and chef hat, garnishing a gourmet dish, full body view, long shot
+
+man dancing pose among folks in a park, waving hands
+
+man in iron man costume flying pose, the sky ablaze with hues of orange and purple, full body view, long shot
+
+man jedi wielding a lightsaber, star wars, full body view, eye level shot
+
+man playing guitar on a boat, ocean waves
+
+man with a passion for reading, curled up with a book in a cozy nook near a window
+
+man running pose in a park, eye level shot
+
+man in superman costume flying pose, the sky ablaze with hues of orange and purple, full body view, long shot
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new file mode 100644
index 0000000000000000000000000000000000000000..e936b216293e314a31289c8f3b01bf19f14cd34b
--- /dev/null
+++ b/assets/zendaya/zendaya armor orig.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ea3f33aa32197352a46e30d777119ab335fe56a8bb29ba2051bf5e88ec7f62bc
+size 155944
diff --git a/assets/zendaya/zendaya armor.mp4 b/assets/zendaya/zendaya armor.mp4
new file mode 100644
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--- /dev/null
+++ b/assets/zendaya/zendaya armor.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 171952
diff --git a/assets/zendaya/zendaya beach orig.mp4 b/assets/zendaya/zendaya beach orig.mp4
new file mode 100644
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--- /dev/null
+++ b/assets/zendaya/zendaya beach orig.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ef82a7011431c8bc51de5fc63025ac1554c63c83960a89290d9d178396b8bad
+size 101044
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new file mode 100644
index 0000000000000000000000000000000000000000..e67eb0752f538eb272aebb951fc268655ccbad90
--- /dev/null
+++ b/assets/zendaya/zendaya beach.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:73739e7df58442bd753f8ece91d8e5943ea00e6a142e37594ef44ba1b7d1cab6
+size 108625
diff --git a/assets/zendaya/zendaya cooking orig.mp4 b/assets/zendaya/zendaya cooking orig.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..ed590e10252e5239a52813a123c3896e98080a10
--- /dev/null
+++ b/assets/zendaya/zendaya cooking orig.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:adb2c452a740572621a438d12bf3c242ffa0d4dfa74974933d0a0f684d1a1838
+size 126777
diff --git a/assets/zendaya/zendaya cooking.mp4 b/assets/zendaya/zendaya cooking.mp4
new file mode 100644
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--- /dev/null
+++ b/assets/zendaya/zendaya cooking.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:af2121ebd78b71ab6e6385cf6dd048df81890ad52c9502f625cddb6d0032c923
+size 131164
diff --git a/assets/zendaya/zendaya dancing orig.mp4 b/assets/zendaya/zendaya dancing orig.mp4
new file mode 100644
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--- /dev/null
+++ b/assets/zendaya/zendaya dancing orig.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f12095732e5fdfc30fe4f0e46ae3c9f4e7b75d1864f381cbd1f207936f88016
+size 155573
diff --git a/assets/zendaya/zendaya dancing.mp4 b/assets/zendaya/zendaya dancing.mp4
new file mode 100644
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--- /dev/null
+++ b/assets/zendaya/zendaya dancing.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ee9d32ef75feccdd83b6f7ba0f90faa9e507bd09545fa547c46934a255213690
+size 171515
diff --git a/assets/zendaya/zendaya iron man orig.mp4 b/assets/zendaya/zendaya iron man orig.mp4
new file mode 100644
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--- /dev/null
+++ b/assets/zendaya/zendaya iron man orig.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8dacd9d6981a29674a1db8007432cd7b47a2d96fac8cf1cb85ed6b993031a06a
+size 181763
diff --git a/assets/zendaya/zendaya iron man.mp4 b/assets/zendaya/zendaya iron man.mp4
new file mode 100644
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--- /dev/null
+++ b/assets/zendaya/zendaya iron man.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2cb1a2183bee12948a586246d4315a2d6b338da66510297377f881a6c6e71c93
+size 210492
diff --git a/assets/zendaya/zendaya lightsaber orig.mp4 b/assets/zendaya/zendaya lightsaber orig.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..750967ec4e6e509acc5d7c5217ff7e598e880da7
--- /dev/null
+++ b/assets/zendaya/zendaya lightsaber orig.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d2ed2998326c141e8e985a34e093f13ae2e56dec3046870fa69ee8e4111b2ba2
+size 100742
diff --git a/assets/zendaya/zendaya lightsaber.mp4 b/assets/zendaya/zendaya lightsaber.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..e8f3fe3245a59676869b2c1dabe8026c989629db
--- /dev/null
+++ b/assets/zendaya/zendaya lightsaber.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19b8fac7cf11bab553690477bc4003268347770ec9a414d3d5c74780a63cbdf8
+size 122322
diff --git a/assets/zendaya/zendaya playing guitar orig.mp4 b/assets/zendaya/zendaya playing guitar orig.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..af36d9950b4abd229ef95bec1820e90b8a14df32
--- /dev/null
+++ b/assets/zendaya/zendaya playing guitar orig.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba35c6c285cf49decf74bb19a79a838d761f6c89ca4d1f3f0f3927578aac05fd
+size 170955
diff --git a/assets/zendaya/zendaya playing guitar.mp4 b/assets/zendaya/zendaya playing guitar.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..1dc758d5e06997942dcf2e252f0ded7cd239d3c0
--- /dev/null
+++ b/assets/zendaya/zendaya playing guitar.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:efc439154a462bb960a8f726eeeb4dc291943bb89add56e6219f47d8da348d1b
+size 129033
diff --git a/assets/zendaya/zendaya reading orig.mp4 b/assets/zendaya/zendaya reading orig.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..08e9a9bf1e2dd06331fa9b4865f6a92c8d970725
--- /dev/null
+++ b/assets/zendaya/zendaya reading orig.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ea2057b0eff7d5fd2f54cefd225ed87215b1f369c7e61b25247e72e02ad57dc8
+size 177206
diff --git a/assets/zendaya/zendaya reading.mp4 b/assets/zendaya/zendaya reading.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..6b9821d75710de53faf582b72b27de9bcd4d6c95
--- /dev/null
+++ b/assets/zendaya/zendaya reading.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69f5d5ba945174bec2bd322707e37af5d00afee07bba2f7f0c0eaeb4901788e4
+size 187419
diff --git a/assets/zendaya/zendaya running orig.mp4 b/assets/zendaya/zendaya running orig.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..81742e87ac4e3d173273718a5a608f9c59253f35
--- /dev/null
+++ b/assets/zendaya/zendaya running orig.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1aaad7cf77fe344746164eaf6a5507f27d39fb6c364d777bdddd25e566e85e28
+size 136598
diff --git a/assets/zendaya/zendaya running.mp4 b/assets/zendaya/zendaya running.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..e7c199e87cd4234e04ec09e2999a41d870914ef0
--- /dev/null
+++ b/assets/zendaya/zendaya running.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b27e06c81da350dc196e11e96e81a20fddcb3461bc567bf57283ca45de45f612
+size 189955
diff --git a/assets/zendaya/zendaya superman orig.mp4 b/assets/zendaya/zendaya superman orig.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..8fffac12a2ed0e571ca3916b1e1fc2bebb6df375
--- /dev/null
+++ b/assets/zendaya/zendaya superman orig.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0a1639dbb9a7e43ba454c91c13b6a5e16a0ea2f8da17106a2a33a11aa5bfb25f
+size 128106
diff --git a/assets/zendaya/zendaya superman.mp4 b/assets/zendaya/zendaya superman.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..c60241b2fd7f8f4fbe9478c591268ae030093459
--- /dev/null
+++ b/assets/zendaya/zendaya superman.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ddabc88df8deb494a5d06c39f006b571512bdee2e205a3346ed953281baca563
+size 190335
diff --git a/assets/zendaya/zendaya.jpg b/assets/zendaya/zendaya.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..be0a2548be07156ef20527dd002ec672568e5a89
--- /dev/null
+++ b/assets/zendaya/zendaya.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:039e8ee0885ade1e61fe30aaf0066d20338b7d92eb23ceed8f436e8efa4eb7ad
+size 166734
diff --git a/assets/zendaya/zendaya.txt b/assets/zendaya/zendaya.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7cf75fdd8e1e7862bc60743acf651c131ad92cde
--- /dev/null
+++ b/assets/zendaya/zendaya.txt
@@ -0,0 +1,21 @@
+zendaya:
+
+woman ((best quality)), ((masterpiece)), ((realistic)), long highlighted hair, futuristic silver armor suit, confident stance, high-resolution, living room, smiling, head tilted, perfect smooth skin
+
+woman walking on the beach, sunset, orange sky, bright lighting, ocean waves seagulls, rippling water, serene atmosphere, eye level shot
+
+woman in a white apron and chef hat, garnishing a gourmet dish, full body view, long shot
+
+woman dancing pose among folks in a park, waving hands
+
+woman in iron man costume flying pose, the sky ablaze with hues of orange and purple, full body view, long shot
+
+woman jedi wielding a lightsaber, star wars, full body view, eye level shot
+
+woman playing guitar on a boat, ocean waves
+
+woman with a passion for reading, curled up with a book in a cozy nook near a window
+
+woman running pose in a park, eye level shot
+
+woman in superman costume flying pose, the sky ablaze with hues of orange and purple, full body view, long shot
diff --git a/demo/Monna.png b/demo/Monna.png
new file mode 100644
index 0000000000000000000000000000000000000000..487855597e2b34e8ae6a30c8e127d65568b58c6d
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diff --git a/demo/ann.png b/demo/ann.png
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diff --git a/demo/augustus.png b/demo/augustus.png
new file mode 100644
index 0000000000000000000000000000000000000000..f8817c9e4ef760514c9a535ff1e71054bd646ac1
--- /dev/null
+++ b/demo/augustus.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d0b81262bd8948038581294290cc30eb390bb67cbafeef0cd423b2c958b32f1c
+size 182617
diff --git a/demo/gakki.png b/demo/gakki.png
new file mode 100644
index 0000000000000000000000000000000000000000..95b2fe3b712bd401ef4c1b3ea676b532e9910533
--- /dev/null
+++ b/demo/gakki.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:955916c8a0dab8ecdbdd057ddcfcb776a55e3832bf06b073e541fc6707767cbd
+size 215814
diff --git a/demo/lecun.png b/demo/lecun.png
new file mode 100644
index 0000000000000000000000000000000000000000..7417130a6339dc8f2ed367a4f485c5f68d5ee267
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diff --git a/demo/qianlong.png b/demo/qianlong.png
new file mode 100644
index 0000000000000000000000000000000000000000..9e61dc09731dd86c78bb8cd28dc64464dc14ce8b
--- /dev/null
+++ b/demo/qianlong.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:32ad7c7aea1c733ba776394ef4c5eaaf72d0005474f609184e827730c0deaded
+size 238351
diff --git a/demo/zendaya.png b/demo/zendaya.png
new file mode 100644
index 0000000000000000000000000000000000000000..be0a2548be07156ef20527dd002ec672568e5a89
--- /dev/null
+++ b/demo/zendaya.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:039e8ee0885ade1e61fe30aaf0066d20338b7d92eb23ceed8f436e8efa4eb7ad
+size 166734
diff --git a/faceadapter/attention_processor.py b/faceadapter/attention_processor.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bfbca5e8a736286b6d7aae508094bbc2fdca5a7
--- /dev/null
+++ b/faceadapter/attention_processor.py
@@ -0,0 +1,367 @@
+# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class AttnProcessor(nn.Module):
+ r"""
+ Default processor for performing attention-related computations.
+ """
+
+ def __init__(
+ self,
+ hidden_size=None,
+ cross_attention_dim=None,
+ ):
+ super().__init__()
+
+ def __call__(
+ self,
+ attn,
+ hidden_states,
+ encoder_hidden_states=None,
+ attention_mask=None,
+ temb=None,
+ ):
+ residual = hidden_states
+
+ if attn.spatial_norm is not None:
+ hidden_states = attn.spatial_norm(hidden_states, temb)
+
+ input_ndim = hidden_states.ndim
+
+ if input_ndim == 4:
+ batch_size, channel, height, width = hidden_states.shape
+ hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+ batch_size, sequence_length, _ = (
+ hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+ )
+ attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+ if attn.group_norm is not None:
+ hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+ query = attn.to_q(hidden_states)
+
+ if encoder_hidden_states is None:
+ encoder_hidden_states = hidden_states
+ elif attn.norm_cross:
+ encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+ key = attn.to_k(encoder_hidden_states)
+ value = attn.to_v(encoder_hidden_states)
+
+ query = attn.head_to_batch_dim(query)
+ key = attn.head_to_batch_dim(key)
+ value = attn.head_to_batch_dim(value)
+
+ attention_probs = attn.get_attention_scores(query, key, attention_mask)
+ hidden_states = torch.bmm(attention_probs, value)
+ hidden_states = attn.batch_to_head_dim(hidden_states)
+
+ # linear proj
+ hidden_states = attn.to_out[0](hidden_states)
+ # dropout
+ hidden_states = attn.to_out[1](hidden_states)
+
+ if input_ndim == 4:
+ hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+ if attn.residual_connection:
+ hidden_states = hidden_states + residual
+
+ hidden_states = hidden_states / attn.rescale_output_factor
+
+ return hidden_states
+
+class AttnProcessor2_0(torch.nn.Module):
+ r"""
+ Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+ """
+
+ def __init__(
+ self,
+ hidden_size=None,
+ cross_attention_dim=None,
+ ):
+ super().__init__()
+ if not hasattr(F, "scaled_dot_product_attention"):
+ raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+ def __call__(
+ self,
+ attn,
+ hidden_states,
+ encoder_hidden_states=None,
+ attention_mask=None,
+ temb=None,
+ ):
+ residual = hidden_states
+
+ if attn.spatial_norm is not None:
+ hidden_states = attn.spatial_norm(hidden_states, temb)
+
+ input_ndim = hidden_states.ndim
+
+ if input_ndim == 4:
+ batch_size, channel, height, width = hidden_states.shape
+ hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+ batch_size, sequence_length, _ = (
+ hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+ )
+
+ if attention_mask is not None:
+ attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+ # scaled_dot_product_attention expects attention_mask shape to be
+ # (batch, heads, source_length, target_length)
+ attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+ if attn.group_norm is not None:
+ hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+ query = attn.to_q(hidden_states)
+
+ if encoder_hidden_states is None:
+ encoder_hidden_states = hidden_states
+ elif attn.norm_cross:
+ encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+ key = attn.to_k(encoder_hidden_states)
+ value = attn.to_v(encoder_hidden_states)
+
+ inner_dim = key.shape[-1]
+ head_dim = inner_dim // attn.heads
+
+ query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+ key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+ value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+ # the output of sdp = (batch, num_heads, seq_len, head_dim)
+ # TODO: add support for attn.scale when we move to Torch 2.1
+ hidden_states = F.scaled_dot_product_attention(
+ query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+ )
+
+ hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+ hidden_states = hidden_states.to(query.dtype)
+
+ # linear proj
+ hidden_states = attn.to_out[0](hidden_states)
+ # dropout
+ hidden_states = attn.to_out[1](hidden_states)
+
+ if input_ndim == 4:
+ hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+ if attn.residual_connection:
+ hidden_states = hidden_states + residual
+
+ hidden_states = hidden_states / attn.rescale_output_factor
+
+ return hidden_states
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from diffusers.models.lora import LoRALinearLayer
+
+
+
+from torch.utils.checkpoint import checkpoint
+
+class LoRAFaceAttnProcessor(nn.Module):
+ r"""
+ Attention processor for Face-Adapater.
+ Args:
+ hidden_size (`int`):
+ The hidden size of the attention layer.
+ cross_attention_dim (`int`):
+ The number of channels in the `encoder_hidden_states`.
+ scale (`float`, defaults to 1.0):
+ the weight scale of image prompt.
+ num_tokens (`int`, defaults to 4 when do face_adapter_plus it should be 16):
+ The context length of the image features.
+ """
+
+ def __init__(self, hidden_size, cross_attention_dim=None, rank=4, network_alpha=None, lora_scale=1.0, scale=1.0, num_tokens=4):
+ super().__init__()
+
+ self.rank = rank
+ self.lora_scale = lora_scale
+
+ self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
+ self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
+ self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
+ self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
+
+ self.hidden_size = hidden_size
+ self.cross_attention_dim = cross_attention_dim
+ self.scale = scale
+ self.num_tokens = num_tokens
+
+ self.to_k_face = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+ self.to_v_face = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+ self.gradient_checkpointing=False
+ def __call__(
+ self,
+ attn,
+ hidden_states,
+ encoder_hidden_states=None,
+ attention_mask=None,
+ temb=None,
+ ):
+ if self.gradient_checkpointing and self.training:
+ def create_custom_forward(module, return_dict=None):
+ def custom_forward(*inputs):
+ if return_dict is not None:
+ return module(*inputs, return_dict=return_dict)
+ else:
+ return module(*inputs)
+
+ return custom_forward
+ if attn.spatial_norm is not None:
+ hidden_states = attn.spatial_norm(hidden_states, temb)
+
+ input_ndim = hidden_states.ndim
+
+ if input_ndim == 4:
+ batch_size, channel, height, width = hidden_states.shape
+ hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+ batch_size, sequence_length, _ = (
+ hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+ )
+ attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+ if attn.group_norm is not None:
+ hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+ query = attn.to_q(hidden_states) + self.lora_scale *torch.utils.checkpoint.checkpoint(
+ create_custom_forward(self.to_q_lora),
+ hidden_states )
+ if encoder_hidden_states is None:
+ encoder_hidden_states = hidden_states
+ else:
+ # get encoder_hidden_states, face_hidden_states
+ end_pos = encoder_hidden_states.shape[1] - self.num_tokens
+ encoder_hidden_states, face_hidden_states = (
+ encoder_hidden_states[:, :end_pos, :],
+ encoder_hidden_states[:, end_pos:, :],
+ )
+ if attn.norm_cross:
+ encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+ key = attn.to_k(encoder_hidden_states) + self.lora_scale * torch.utils.checkpoint.checkpoint(
+ create_custom_forward(self.to_k_lora),
+ encoder_hidden_states )
+ value = attn.to_v(encoder_hidden_states) + self.lora_scale * torch.utils.checkpoint.checkpoint(
+ create_custom_forward(self.to_v_lora),
+ encoder_hidden_states )
+ query = attn.head_to_batch_dim(query)
+ key = attn.head_to_batch_dim(key)
+ value = attn.head_to_batch_dim(value)
+
+ attention_probs = attn.get_attention_scores(query, key, attention_mask)
+ hidden_states = torch.bmm(attention_probs, value)
+ hidden_states = attn.batch_to_head_dim(hidden_states)
+
+ # for face-adapter
+ face_key = self.to_k_face(face_hidden_states)
+ face_value = self.to_v_face(face_hidden_states)
+
+ face_key = attn.head_to_batch_dim(face_key)
+ face_value = attn.head_to_batch_dim(face_value)
+
+ face_attention_probs = attn.get_attention_scores(query, face_key, None)
+ self.attn_map = face_attention_probs
+ face_hidden_states = torch.bmm(face_attention_probs, face_value)
+ face_hidden_states = attn.batch_to_head_dim(face_hidden_states)
+
+ hidden_states = hidden_states + self.scale * face_hidden_states
+
+ # linear proj
+ hidden_states = attn.to_out[0](hidden_states) + self.lora_scale * self.to_out_lora(hidden_states)
+ # dropout
+ hidden_states = attn.to_out[1](hidden_states)
+
+ if input_ndim == 4:
+ hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+ if attn.residual_connection:
+ hidden_states = hidden_states + residual
+
+ hidden_states = hidden_states / attn.rescale_output_factor
+
+ else:
+ if attn.spatial_norm is not None:
+ hidden_states = attn.spatial_norm(hidden_states, temb)
+
+ input_ndim = hidden_states.ndim
+
+ if input_ndim == 4:
+ batch_size, channel, height, width = hidden_states.shape
+ hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+ batch_size, sequence_length, _ = (
+ hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+ )
+ attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+ if attn.group_norm is not None:
+ hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+ query = attn.to_q(hidden_states) + self.lora_scale * self.to_q_lora(hidden_states)
+
+ if encoder_hidden_states is None:
+ encoder_hidden_states = hidden_states
+ else:
+ # get encoder_hidden_states, face_hidden_states
+ end_pos = encoder_hidden_states.shape[1] - self.num_tokens
+ encoder_hidden_states, face_hidden_states = (
+ encoder_hidden_states[:, :end_pos, :],
+ encoder_hidden_states[:, end_pos:, :],
+ )
+ if attn.norm_cross:
+ encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+ key = attn.to_k(encoder_hidden_states) + self.lora_scale * self.to_k_lora(encoder_hidden_states)
+ value = attn.to_v(encoder_hidden_states) + self.lora_scale * self.to_v_lora(encoder_hidden_states)
+
+ query = attn.head_to_batch_dim(query)
+ key = attn.head_to_batch_dim(key)
+ value = attn.head_to_batch_dim(value)
+
+ attention_probs = attn.get_attention_scores(query, key, attention_mask)
+ hidden_states = torch.bmm(attention_probs, value)
+ hidden_states = attn.batch_to_head_dim(hidden_states)
+
+ # for face-adapter
+ face_key = self.to_k_face(face_hidden_states)
+ face_value = self.to_v_face(face_hidden_states)
+
+ face_key = attn.head_to_batch_dim(face_key)
+ face_value = attn.head_to_batch_dim(face_value)
+
+ face_attention_probs = attn.get_attention_scores(query, face_key, None)
+ self.attn_map = face_attention_probs
+ face_hidden_states = torch.bmm(face_attention_probs, face_value)
+ face_hidden_states = attn.batch_to_head_dim(face_hidden_states)
+
+ hidden_states = hidden_states + self.scale * face_hidden_states
+
+ # linear proj
+ hidden_states = attn.to_out[0](hidden_states) + self.lora_scale * self.to_out_lora(hidden_states)
+ # dropout
+ hidden_states = attn.to_out[1](hidden_states)
+
+ if input_ndim == 4:
+ hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+ if attn.residual_connection:
+ hidden_states = hidden_states + residual
+
+ hidden_states = hidden_states / attn.rescale_output_factor
+ return hidden_states
diff --git a/faceadapter/face_adapter.py b/faceadapter/face_adapter.py
new file mode 100644
index 0000000000000000000000000000000000000000..9591df45879a4463bb45751f77dd1bbbed9df494
--- /dev/null
+++ b/faceadapter/face_adapter.py
@@ -0,0 +1,429 @@
+from typing import List
+
+import torch
+from torch import nn
+from PIL import Image
+from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
+from .attention_processor import LoRAFaceAttnProcessor
+
+from .utils import is_torch2_available, get_generator
+
+if is_torch2_available():
+ from .attention_processor import (
+ AttnProcessor2_0 as AttnProcessor,
+ )
+else:
+ from .attention_processor import AttnProcessor
+from .resampler import Resampler
+
+
+class ImageProjModel(torch.nn.Module):
+ """Projection Model"""
+
+ def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
+ super().__init__()
+
+ self.generator = None
+ self.cross_attention_dim = cross_attention_dim
+ self.clip_extra_context_tokens = clip_extra_context_tokens
+ self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
+ self.norm = torch.nn.LayerNorm(cross_attention_dim)
+
+ def forward(self, image_embeds):
+ embeds = image_embeds
+ clip_extra_context_tokens = self.proj(embeds).reshape(
+ -1, self.clip_extra_context_tokens, self.cross_attention_dim
+ )
+ clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
+ return clip_extra_context_tokens
+
+
+class MLPProjModel(torch.nn.Module):
+ """SD model with image prompt"""
+ def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
+ super().__init__()
+
+ self.proj = torch.nn.Sequential(
+ torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
+ torch.nn.GELU(),
+ torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
+ torch.nn.LayerNorm(cross_attention_dim)
+ )
+
+ def forward(self, image_embeds):
+ clip_extra_context_tokens = self.proj(image_embeds)
+ return clip_extra_context_tokens
+
+
+class FaceAdapterLora:
+ def __init__(self, sd_pipe, image_encoder_path, id_ckpt, device, num_tokens=4,torch_type=torch.float32):
+ self.device = device
+ self.image_encoder_path = image_encoder_path
+ self.id_ckpt = id_ckpt
+ self.num_tokens = num_tokens
+ self.torch_type = torch_type
+
+ self.pipe = sd_pipe.to(self.device)
+ self.set_face_adapter()
+ # load image encoder
+ self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
+ self.device, dtype=self.torch_type
+ )
+ self.clip_image_processor = CLIPImageProcessor()
+ # image proj model
+ self.image_proj_model = self.init_proj()
+
+ self.load_face_adapter()
+
+ def to(self, device):
+ self.device = device
+ self.pipe = self.pipe.to(device)
+ self.image_encoder = self.image_encoder.to(device)
+ self.image_proj_model = self.image_proj_model.to(device)
+ return self
+
+ def init_proj(self):
+ image_proj_model = ImageProjModel(
+ cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
+ clip_embeddings_dim=self.image_encoder.config.projection_dim,
+ clip_extra_context_tokens=self.num_tokens,
+ ).to(self.device, dtype=self.torch_type)
+ return image_proj_model
+
+ def set_face_adapter(self):
+ unet = self.pipe.unet
+ attn_procs = {}
+ for name in unet.attn_processors.keys():
+ cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
+ if name.startswith("mid_block"):
+ hidden_size = unet.config.block_out_channels[-1]
+ elif name.startswith("up_blocks"):
+ block_id = int(name[len("up_blocks.")])
+ hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+ elif name.startswith("down_blocks"):
+ block_id = int(name[len("down_blocks.")])
+ hidden_size = unet.config.block_out_channels[block_id]
+ if cross_attention_dim is None:
+ attn_procs[name] = AttnProcessor().to(self.device, dtype=self.torch_type)
+ else:
+ attn_procs[name] = LoRAFaceAttnProcessor(
+ hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0, rank=128, num_tokens=self.num_tokens,
+ ).to(self.device, dtype=self.torch_type)
+ unet.set_attn_processor(attn_procs)
+ def load_face_adapter(self):
+ state_dict = torch.load(self.id_ckpt, map_location="cpu")
+ if 'state_dict' in state_dict:
+ state_dict = state_dict['state_dict']
+ image_proj_dict={}
+ face_adapter_proj={}
+ for k,v in state_dict.items():
+ if k.startswith("module.image_proj_model"):
+ image_proj_dict[k.replace("module.image_proj_model.", "")] = state_dict[k]
+ elif k.startswith("module.adapter_modules."):
+ face_adapter_proj[k.replace("module.adapter_modules.", "")] = state_dict[k]
+ elif k.startswith("image_proj_model"):
+ image_proj_dict[k.replace("image_proj_model.", "")] = state_dict[k]
+ elif k.startswith("adapter_modules."):
+ face_adapter_proj[k.replace("adapter_modules.", "")] = state_dict[k]
+ else:
+ print("ERROR!")
+ return
+ state_dict = {}
+ state_dict['image_proj'] = image_proj_dict
+ state_dict["face_adapter"] = face_adapter_proj
+ self.image_proj_model.load_state_dict(state_dict["image_proj"])
+ adapter_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
+ adapter_layers.load_state_dict(state_dict["face_adapter"],strict=False)
+ @torch.inference_mode()
+ def get_image_embeds(self, pil_image=None, clip_image_embeds=None):
+ if pil_image is not None:
+ if isinstance(pil_image, Image.Image):
+ pil_image = [pil_image]
+ clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
+ clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=self.torch_type)).image_embeds
+ else:
+ clip_image_embeds = clip_image_embeds.to(self.device, dtype=self.torch_type)
+ image_prompt_embeds = self.image_proj_model(clip_image_embeds)
+ uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(clip_image_embeds))
+ return image_prompt_embeds, uncond_image_prompt_embeds
+
+ # This scales the face-adapter face_hidden_states (attn output). attn_processor.scale: default 1.0.
+ # faceadapter/attention_processor.py:L283.
+ def set_attn_scale(self, attn_scale):
+ for attn_processor in self.pipe.unet.attn_processors.values():
+ if isinstance(attn_processor, LoRAFaceAttnProcessor):
+ attn_processor.scale = attn_scale
+
+ def generate(
+ self,
+ pil_image=None,
+ clip_image_embeds=None,
+ prompt=None,
+ negative_prompt=None,
+ attn_scale=1,
+ num_samples=4,
+ seed=None,
+ guidance_scale=7.5,
+ num_inference_steps=30,
+ **kwargs,
+ ):
+ self.set_attn_scale(attn_scale)
+
+ if pil_image is not None:
+ num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
+ else:
+ num_prompts = clip_image_embeds.size(0)
+
+ if prompt is None:
+ prompt = "best quality, high quality"
+ if negative_prompt is None:
+ negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
+
+ if not isinstance(prompt, List):
+ prompt = [prompt] * num_prompts
+ if not isinstance(negative_prompt, List):
+ negative_prompt = [negative_prompt] * num_prompts
+
+ image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(
+ pil_image=pil_image, clip_image_embeds=clip_image_embeds
+ )
+ bs_embed, seq_len, _ = image_prompt_embeds.shape
+ image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
+ image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
+ uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
+ uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
+
+ with torch.inference_mode():
+ prompt_embeds_, negative_prompt_embeds_ = self.pipe.encode_prompt(
+ prompt,
+ device=self.device,
+ num_images_per_prompt=num_samples,
+ do_classifier_free_guidance=True,
+ negative_prompt=negative_prompt,
+ )
+ prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
+ negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)
+
+ generator = get_generator(seed, self.device)
+
+ images = self.pipe(
+ prompt_embeds=prompt_embeds,
+ negative_prompt_embeds=negative_prompt_embeds,
+ guidance_scale=guidance_scale,
+ num_inference_steps=num_inference_steps,
+ generator=generator,
+ **kwargs,
+ ).images
+
+ return images
+
+
+class FaceAdapterPlusForVideoLora(FaceAdapterLora):
+ def init_proj(self):
+ image_proj_model = Resampler(
+ dim=self.pipe.unet.config.cross_attention_dim,
+ depth=4,
+ dim_head=64,
+ heads=12,
+ num_queries=self.num_tokens,
+ embedding_dim=self.image_encoder.config.hidden_size,
+ output_dim=self.pipe.unet.config.cross_attention_dim,
+ ff_mult=4,
+ ).to(self.device, dtype=self.torch_type)
+ return image_proj_model
+
+ @torch.inference_mode()
+ def get_image_embeds(self, pil_image=None, clip_image_embeds=None):
+ if isinstance(pil_image, Image.Image):
+ pil_image = [pil_image]
+ clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
+ clip_image = clip_image.to(self.device, dtype=self.torch_type)
+ clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
+ image_prompt_embeds = self.image_proj_model(clip_image_embeds)
+ uncond_clip_image_embeds = self.image_encoder(
+ torch.zeros_like(clip_image), output_hidden_states=True
+ ).hidden_states[-2]
+ uncond_image_prompt_embeds = self.image_proj_model(uncond_clip_image_embeds)
+ return image_prompt_embeds, uncond_image_prompt_embeds
+
+ def generate(
+ self,
+ pil_image=None,
+ init_image=None,
+ init_image_strength=1.,
+ clip_image_embeds=None,
+ prompt=None,
+ negative_prompt=None,
+ adaface_prompt_embeds=None,
+ adaface_power_scale=1.0,
+ attn_scale=1.0,
+ num_samples=1,
+ seed=None,
+ guidance_scale=4,
+ num_inference_steps=30,
+ id_animator_anneal_steps=0,
+ width=512,
+ height=512,
+ video_length=16,
+ image_embed_cfg_scales=[0.8, 0.3],
+ controlnet_images: torch.FloatTensor = None,
+ controlnet_image_index: list = [0],
+ **kwargs,
+ ):
+ self.set_attn_scale(attn_scale)
+ num_prompts=1
+
+ if prompt is None:
+ prompt = "best quality, high quality"
+ if negative_prompt is None:
+ negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
+
+ if not isinstance(prompt, List):
+ prompt = [prompt] * num_prompts
+ if not isinstance(negative_prompt, List):
+ negative_prompt = [negative_prompt] * num_prompts
+
+ num_prompt_img = len(pil_image)
+ total_image_prompt_embeds = 0
+ for i in range(num_prompt_img):
+ prompt_img = pil_image[i]
+ image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(
+ pil_image=prompt_img, clip_image_embeds=clip_image_embeds
+ )
+ bs_embed, seq_len, _ = image_prompt_embeds.shape
+ image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
+ image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
+ total_image_prompt_embeds += image_prompt_embeds
+
+ image_prompt_embeds = total_image_prompt_embeds / num_prompt_img
+ uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
+ uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
+
+ with torch.inference_mode():
+ # if do_classifier_free_guidance,
+ # duplicate unconditional embeddings for each generation per prompt, using mps friendly method.
+ # https://github.com/huggingface/diffusers/blob/70f8d4b488f03730ae3bc11d4d707bafe153d10d/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L469
+ prompt_embeds_, negative_prompt_embeds_ = self.pipe.encode_prompt(
+ prompt,
+ device=self.device,
+ num_videos_per_prompt=num_samples,
+ do_classifier_free_guidance=True,
+ negative_prompt=negative_prompt,
+ )
+
+ if adaface_prompt_embeds is not None and adaface_prompt_embeds[0] is not None:
+ negative_prompt_embeds0 = negative_prompt_embeds_
+ adaface_prompt_embeds, negative_prompt_embeds_ = adaface_prompt_embeds
+ # self.torch_type == torch.float16. adaface_prompt_embeds is torch.float32.
+ prompt_embeds_ = adaface_prompt_embeds.repeat(num_samples, 1, 1).to(dtype=self.torch_type)
+ negative_prompt_embeds_ = negative_prompt_embeds_.repeat(num_samples, 1, 1).to(dtype=self.torch_type)
+ if adaface_power_scale != 1.0:
+ prompt_embeds_ = prompt_embeds_ * adaface_power_scale - negative_prompt_embeds0 * (1 - adaface_power_scale)
+
+ # Note to balance image_prompt_embeds with uncond_image_prompt_embeds after scaling.
+ image_prompt_embeds_begin = image_prompt_embeds * image_embed_cfg_scales[0] + uncond_image_prompt_embeds * (1 - image_embed_cfg_scales[0])
+ image_prompt_embeds_end = image_prompt_embeds * image_embed_cfg_scales[1] + uncond_image_prompt_embeds * (1 - image_embed_cfg_scales[1])
+ # Disable annealing by setting prompt_embeds_begin the same as prompt_embeds_end.
+ prompt_embeds_begin = torch.cat([prompt_embeds_, image_prompt_embeds_begin], dim=1)
+ prompt_embeds_end = torch.cat([prompt_embeds_, image_prompt_embeds_end], dim=1)
+ # Scale down ID-Animator's face embeddings from prompt_embeds_begin to prompt_embeds_end,
+ # so that they don't dominate the generation.
+ prompt_embeds = (prompt_embeds_begin, prompt_embeds_end, id_animator_anneal_steps)
+ else:
+ # The conventional ID-Animator's way to apply face embeddings.
+ prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
+
+ # We still need uncond_image_prompt_embeds, otherwise the output is blank.
+ negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)
+
+ generator = get_generator(seed, self.device)
+
+ video = self.pipe(
+ init_image=init_image,
+ init_image_strength=init_image_strength,
+ prompt = "",
+ prompt_embeds = prompt_embeds,
+ negative_prompt_embeds=negative_prompt_embeds,
+ guidance_scale=guidance_scale,
+ num_inference_steps=num_inference_steps,
+ generator=generator,
+ width = width,
+ height=height,
+ video_length = video_length,
+ controlnet_images = controlnet_images,
+ controlnet_image_index=controlnet_image_index,
+ **kwargs,
+ ).videos
+
+ return video
+
+ def generate_video_edit(
+ self,
+ pil_image=None,
+ clip_image_embeds=None,
+ prompt=None,
+ negative_prompt=None,
+ attn_scale=1.0,
+ num_samples=1,
+ seed=None,
+ guidance_scale=7.5,
+ num_inference_steps=30,
+ width=512,
+ height=512,
+ video_length=16,
+ video_latents=None,
+ **kwargs,
+ ):
+ self.set_attn_scale(attn_scale)
+
+ if pil_image is not None:
+ num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
+ else:
+ num_prompts = clip_image_embeds.size(0)
+
+ if prompt is None:
+ prompt = "best quality, high quality"
+ if negative_prompt is None:
+ negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
+
+ if not isinstance(prompt, List):
+ prompt = [prompt] * num_prompts
+ if not isinstance(negative_prompt, List):
+ negative_prompt = [negative_prompt] * num_prompts
+
+ image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(
+ pil_image=pil_image, clip_image_embeds=clip_image_embeds
+ )
+ bs_embed, seq_len, _ = image_prompt_embeds.shape
+ image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
+ image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
+ uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
+ uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
+ with torch.inference_mode():
+ prompt_embeds_, negative_prompt_embeds_ = self.pipe.encode_prompt(
+ prompt,
+ device=self.device,
+ num_videos_per_prompt=num_samples,
+ do_classifier_free_guidance=True,
+ negative_prompt=negative_prompt,
+ )
+ prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
+ negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)
+
+ generator = get_generator(seed, self.device)
+
+ video = self.pipe.video_edit(
+ prompt = "",
+ prompt_embeds = prompt_embeds,
+ negative_prompt_embeds=negative_prompt_embeds,
+ guidance_scale=guidance_scale,
+ num_inference_steps=num_inference_steps,
+ generator=generator,
+ width = width,
+ height=height,
+ video_length = video_length,
+ latents=video_latents,
+ **kwargs,
+ ).videos
+
+ return video
\ No newline at end of file
diff --git a/faceadapter/resampler.py b/faceadapter/resampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4069063fed04008550bb9e5ed23616f766931a2
--- /dev/null
+++ b/faceadapter/resampler.py
@@ -0,0 +1,228 @@
+# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
+# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
+
+import math
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+from einops.layers.torch import Rearrange
+
+
+# FFN
+def FeedForward(dim, mult=4):
+ inner_dim = int(dim * mult)
+ return nn.Sequential(
+ nn.LayerNorm(dim),
+ nn.Linear(dim, inner_dim, bias=False),
+ nn.GELU(),
+ nn.Linear(inner_dim, dim, bias=False),
+ )
+
+
+def reshape_tensor(x, heads):
+ bs, length, width = x.shape
+ # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
+ x = x.view(bs, length, heads, -1)
+ # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
+ x = x.transpose(1, 2)
+ # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
+ x = x.reshape(bs, heads, length, -1)
+ return x
+
+
+class PerceiverAttention(nn.Module):
+ def __init__(self, *, dim, dim_head=64, heads=8):
+ super().__init__()
+ self.scale = dim_head**-0.5
+ self.dim_head = dim_head
+ self.heads = heads
+ inner_dim = dim_head * heads
+
+ self.norm1 = nn.LayerNorm(dim)
+ self.norm2 = nn.LayerNorm(dim)
+
+ self.to_q = nn.Linear(dim, inner_dim, bias=False)
+ self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+ self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+ def forward(self, x, latents):
+ """
+ Args:
+ x (torch.Tensor): image features
+ shape (b, n1, D)
+ latent (torch.Tensor): latent features
+ shape (b, n2, D)
+ """
+ x = self.norm1(x)
+ latents = self.norm2(latents)
+
+ b, l, _ = latents.shape
+
+ q = self.to_q(latents)
+ kv_input = torch.cat((x, latents), dim=-2)
+ k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+ q = reshape_tensor(q, self.heads)
+ k = reshape_tensor(k, self.heads)
+ v = reshape_tensor(v, self.heads)
+
+ # attention
+ scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+ weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards
+ weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+ out = weight @ v
+
+ out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+
+ return self.to_out(out)
+
+
+
+class ResamplerFaceID(nn.Module):
+ def __init__(
+ self,
+ dim=1024,
+ depth=8,
+ dim_head=64,
+ heads=16,
+ num_queries=8,
+ embedding_dim=768,
+ output_dim=1024,
+ ff_mult=4,
+ max_seq_len: int = 257, # CLIP tokens + CLS token
+ apply_pos_emb: bool = False,
+ num_latents_mean_pooled: int = 0, # number of latents derived from mean pooled representation of the sequence
+ ):
+ super().__init__()
+ self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None
+
+ self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
+
+ self.proj_in = nn.Linear(embedding_dim, dim)
+
+ self.proj_out = nn.Linear(dim, output_dim)
+ self.norm_out = nn.LayerNorm(output_dim)
+
+ self.to_latents_from_mean_pooled_seq = (
+ nn.Sequential(
+ nn.LayerNorm(dim),
+ nn.Linear(dim, dim * num_latents_mean_pooled),
+ Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled),
+ )
+ if num_latents_mean_pooled > 0
+ else None
+ )
+
+ self.layers = nn.ModuleList([])
+ for _ in range(depth):
+ self.layers.append(
+ nn.ModuleList(
+ [
+ PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+ FeedForward(dim=dim, mult=ff_mult),
+ ]
+ )
+ )
+
+ def forward(self, x):
+ if self.pos_emb is not None:
+ n, device = x.shape[1], x.device
+ pos_emb = self.pos_emb(torch.arange(n, device=device))
+ x = x + pos_emb
+ #(x:2,1,512)
+ #(latents:2,16,768)
+ # x= x.unsqueeze(1)
+ # x= x.repeat(x.size(0), latents.size(1), 1)
+ latents = self.latents.repeat(x.size(0), 1, 1)
+
+ x = self.proj_in(x)
+
+ if self.to_latents_from_mean_pooled_seq:
+ meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool))
+ meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq)
+ latents = torch.cat((meanpooled_latents, latents), dim=-2)
+
+ for attn, ff in self.layers:
+ latents = attn(x, latents) + latents
+ latents = ff(latents) + latents
+ latents = self.proj_out(latents)
+ return self.norm_out(latents)
+
+
+class Resampler(nn.Module):
+ def __init__(
+ self,
+ dim=1024,
+ depth=8,
+ dim_head=64,
+ heads=16,
+ num_queries=8,
+ embedding_dim=768,
+ output_dim=1024,
+ ff_mult=4,
+ max_seq_len: int = 257, # CLIP tokens + CLS token
+ apply_pos_emb: bool = False,
+ num_latents_mean_pooled: int = 0, # number of latents derived from mean pooled representation of the sequence
+ ):
+ super().__init__()
+ self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None
+
+ self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
+
+ self.proj_in = nn.Linear(embedding_dim, dim)
+
+ self.proj_out = nn.Linear(dim, output_dim)
+ self.norm_out = nn.LayerNorm(output_dim)
+
+ self.to_latents_from_mean_pooled_seq = (
+ nn.Sequential(
+ nn.LayerNorm(dim),
+ nn.Linear(dim, dim * num_latents_mean_pooled),
+ Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled),
+ )
+ if num_latents_mean_pooled > 0
+ else None
+ )
+
+ self.layers = nn.ModuleList([])
+ for _ in range(depth):
+ self.layers.append(
+ nn.ModuleList(
+ [
+ PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+ FeedForward(dim=dim, mult=ff_mult),
+ ]
+ )
+ )
+
+ def forward(self, x):
+ if self.pos_emb is not None:
+ n, device = x.shape[1], x.device
+ pos_emb = self.pos_emb(torch.arange(n, device=device))
+ x = x + pos_emb
+ latents = self.latents.repeat(x.size(0), 1, 1)
+
+ x = self.proj_in(x)
+
+ if self.to_latents_from_mean_pooled_seq:
+ meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool))
+ meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq)
+ latents = torch.cat((meanpooled_latents, latents), dim=-2)
+
+ for attn, ff in self.layers:
+ latents = attn(x, latents) + latents
+ latents = ff(latents) + latents
+ latents = self.proj_out(latents)
+ return self.norm_out(latents)
+
+
+def masked_mean(t, *, dim, mask=None):
+ if mask is None:
+ return t.mean(dim=dim)
+
+ denom = mask.sum(dim=dim, keepdim=True)
+ mask = rearrange(mask, "b n -> b n 1")
+ masked_t = t.masked_fill(~mask, 0.0)
+
+ return masked_t.sum(dim=dim) / denom.clamp(min=1e-5)
diff --git a/faceadapter/utils.py b/faceadapter/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a273358585962fdf383d0bb7a0e1c654b4999b8
--- /dev/null
+++ b/faceadapter/utils.py
@@ -0,0 +1,93 @@
+import torch
+import torch.nn.functional as F
+import numpy as np
+from PIL import Image
+
+attn_maps = {}
+def hook_fn(name):
+ def forward_hook(module, input, output):
+ if hasattr(module.processor, "attn_map"):
+ attn_maps[name] = module.processor.attn_map
+ del module.processor.attn_map
+
+ return forward_hook
+
+def register_cross_attention_hook(unet):
+ for name, module in unet.named_modules():
+ if name.split('.')[-1].startswith('attn2'):
+ module.register_forward_hook(hook_fn(name))
+
+ return unet
+
+def upscale(attn_map, target_size):
+ attn_map = torch.mean(attn_map, dim=0)
+ attn_map = attn_map.permute(1,0)
+ temp_size = None
+
+ for i in range(0,5):
+ scale = 2 ** i
+ if ( target_size[0] // scale ) * ( target_size[1] // scale) == attn_map.shape[1]*64:
+ temp_size = (target_size[0]//(scale*8), target_size[1]//(scale*8))
+ break
+
+ assert temp_size is not None, "temp_size cannot is None"
+
+ attn_map = attn_map.view(attn_map.shape[0], *temp_size)
+
+ attn_map = F.interpolate(
+ attn_map.unsqueeze(0).to(dtype=torch.float32),
+ size=target_size,
+ mode='bilinear',
+ align_corners=False
+ )[0]
+
+ attn_map = torch.softmax(attn_map, dim=0)
+ return attn_map
+def get_net_attn_map(image_size, batch_size=2, instance_or_negative=False, detach=True):
+
+ idx = 0 if instance_or_negative else 1
+ net_attn_maps = []
+
+ for name, attn_map in attn_maps.items():
+ attn_map = attn_map.cpu() if detach else attn_map
+ attn_map = torch.chunk(attn_map, batch_size)[idx].squeeze()
+ attn_map = upscale(attn_map, image_size)
+ net_attn_maps.append(attn_map)
+
+ net_attn_maps = torch.mean(torch.stack(net_attn_maps,dim=0),dim=0)
+
+ return net_attn_maps
+
+def attnmaps2images(net_attn_maps):
+
+ #total_attn_scores = 0
+ images = []
+
+ for attn_map in net_attn_maps:
+ attn_map = attn_map.cpu().numpy()
+ #total_attn_scores += attn_map.mean().item()
+
+ normalized_attn_map = (attn_map - np.min(attn_map)) / (np.max(attn_map) - np.min(attn_map)) * 255
+ normalized_attn_map = normalized_attn_map.astype(np.uint8)
+ #print("norm: ", normalized_attn_map.shape)
+ image = Image.fromarray(normalized_attn_map)
+
+ #image = fix_save_attn_map(attn_map)
+ images.append(image)
+
+ #print(total_attn_scores)
+ return images
+def is_torch2_available():
+ return hasattr(F, "scaled_dot_product_attention")
+
+def get_generator(seed, device):
+
+ if seed is not None:
+ if isinstance(seed, list):
+ generator = [torch.Generator(device).manual_seed(seed_item) for seed_item in seed]
+ else:
+ generator = torch.Generator(device).manual_seed(seed)
+ else:
+ generator = None
+
+ return generator
\ No newline at end of file
diff --git a/infer.py b/infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f2379d9dd8f826ed400330f7f40983e056d4e6a
--- /dev/null
+++ b/infer.py
@@ -0,0 +1,106 @@
+from diffusers import AutoencoderKL, DDIMScheduler
+import torch
+from transformers import CLIPTextModel, CLIPTokenizer
+from animatediff.models.unet import UNet3DConditionModel
+from omegaconf import OmegaConf
+from animatediff.pipelines.pipeline_animation import AnimationPipeline
+from animatediff.utils.util import load_weights
+from safetensors import safe_open
+from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint
+from faceadapter.face_adapter import FaceAdapterPlusForVideoLora
+
+model_style_type2base_model_path = {
+ "realistic": "models/rv51/realisticVisionV51_v51VAE_dste8.safetensors",
+ "anime": "models/aingdiffusion/aingdiffusion_v170_ar.safetensors",
+ "photorealistic": "models/sar/sar.safetensors" # LDM format. Needs to be converted.
+}
+
+def load_model(model_style_type="realistic", device="cuda"):
+ inference_config = "inference-v2.yaml"
+ sd_version = "animatediff/sd"
+ id_ckpt = "models/animator.ckpt"
+ image_encoder_path = "models/image_encoder"
+
+ base_model_path = model_style_type2base_model_path[model_style_type]
+
+ motion_module_path="models/v3_sd15_mm.ckpt"
+ motion_lora_path = "models/v3_sd15_adapter.ckpt"
+ inference_config = OmegaConf.load(inference_config)
+
+ tokenizer = CLIPTokenizer.from_pretrained(sd_version, subfolder="tokenizer",torch_dtype=torch.float16,
+ )
+ text_encoder = CLIPTextModel.from_pretrained(sd_version, subfolder="text_encoder",torch_dtype=torch.float16,
+ ).to(device=device)
+ vae = AutoencoderKL.from_pretrained(sd_version, subfolder="vae",torch_dtype=torch.float16,
+ ).to(device=device)
+ unet = UNet3DConditionModel.from_pretrained_2d(sd_version, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs)
+ ).to(device=device)
+ # unet.to(dtype=torch.float16) does not work on hf spaces.
+ unet = unet.half()
+
+ pipeline = AnimationPipeline(
+ vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
+ controlnet=None,
+ #beta_start=0.00085, beta_end=0.012, beta_schedule="linear",steps_offset=1
+ scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs)
+ # scheduler=DPMSolverMultistepScheduler(**OmegaConf.to_container(inference_config.DPMSolver_scheduler_kwargs)
+ # scheduler=EulerAncestralDiscreteScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs)
+ # scheduler=EulerAncestralDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="linear",steps_offset=1
+ ),
+ torch_dtype=torch.float16,
+ ).to(device=device)
+
+ pipeline = load_weights(
+ pipeline,
+ # motion module
+ motion_module_path = motion_module_path,
+ motion_module_lora_configs = [],
+ # domain adapter
+ adapter_lora_path = motion_lora_path,
+ adapter_lora_scale = 1,
+ # image layers
+ dreambooth_model_path = None,
+ lora_model_path = "",
+ lora_alpha = 0.8
+ ).to(device=device)
+
+ if base_model_path != "":
+ print(f"load dreambooth model from {base_model_path}")
+ dreambooth_state_dict = {}
+ with safe_open(base_model_path, framework="pt", device="cpu") as f:
+ for key in f.keys():
+ dreambooth_state_dict[key] = f.get_tensor(key)
+
+ converted_vae_checkpoint = convert_ldm_vae_checkpoint(dreambooth_state_dict, pipeline.vae.config)
+ # print(vae)
+ #vae ->to_q,to_k,to_v
+ # print(converted_vae_checkpoint)
+ convert_vae_keys = list(converted_vae_checkpoint.keys())
+ for key in convert_vae_keys:
+ if "encoder.mid_block.attentions" in key or "decoder.mid_block.attentions" in key:
+ new_key = None
+ if "key" in key:
+ new_key = key.replace("key","to_k")
+ elif "query" in key:
+ new_key = key.replace("query","to_q")
+ elif "value" in key:
+ new_key = key.replace("value","to_v")
+ elif "proj_attn" in key:
+ new_key = key.replace("proj_attn","to_out.0")
+ if new_key:
+ converted_vae_checkpoint[new_key] = converted_vae_checkpoint.pop(key)
+
+ pipeline.vae.load_state_dict(converted_vae_checkpoint)
+
+ converted_unet_checkpoint = convert_ldm_unet_checkpoint(dreambooth_state_dict, pipeline.unet.config)
+ pipeline.unet.load_state_dict(converted_unet_checkpoint, strict=False)
+
+ pipeline.text_encoder = convert_ldm_clip_checkpoint(dreambooth_state_dict, dtype=torch.float16).to(device=device)
+
+ del dreambooth_state_dict
+ pipeline = pipeline.to(torch.float16)
+ id_animator = FaceAdapterPlusForVideoLora(pipeline, image_encoder_path, id_ckpt, num_tokens=16,
+ device=torch.device(device), torch_type=torch.float16)
+
+ return id_animator
+
diff --git a/inference-v2.yaml b/inference-v2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5e36d052a8044e088b0f841e3dadf08f4eeb5a1c
--- /dev/null
+++ b/inference-v2.yaml
@@ -0,0 +1,28 @@
+unet_additional_kwargs:
+ use_inflated_groupnorm: true
+ unet_use_cross_frame_attention: false
+ unet_use_temporal_attention: false
+ use_motion_module: true
+ motion_module_resolutions: [1,2,4,8]
+ motion_module_mid_block: true
+ motion_module_decoder_only: false
+ motion_module_type: "Vanilla"
+
+ motion_module_kwargs:
+ num_attention_heads: 8
+ num_transformer_block: 1
+ attention_block_types: [ "Temporal_Self", "Temporal_Self" ]
+ temporal_position_encoding: true
+ temporal_position_encoding_max_len: 32
+ temporal_attention_dim_div: 1
+
+noise_scheduler_kwargs:
+ beta_start: 0.00085
+ beta_end: 0.012
+ beta_schedule: "linear"
+ steps_offset: 1
+ clip_sample: False
+
+DPMSolver_scheduler_kwargs:
+ algorithm_type: "dpmsolver++"
+ use_karras_sigmas: False
diff --git a/models/arc2face/arc2face/config.json b/models/arc2face/arc2face/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..b98bc620c69b69af13acbe9f56ddc0db18ca8a04
--- /dev/null
+++ b/models/arc2face/arc2face/config.json
@@ -0,0 +1,67 @@
+{
+ "_class_name": "UNet2DConditionModel",
+ "_diffusers_version": "0.22.0",
+ "act_fn": "silu",
+ "addition_embed_type": null,
+ "addition_embed_type_num_heads": 64,
+ "addition_time_embed_dim": null,
+ "attention_head_dim": 8,
+ "attention_type": "default",
+ "block_out_channels": [
+ 320,
+ 640,
+ 1280,
+ 1280
+ ],
+ "center_input_sample": false,
+ "class_embed_type": null,
+ "class_embeddings_concat": false,
+ "conv_in_kernel": 3,
+ "conv_out_kernel": 3,
+ "cross_attention_dim": 768,
+ "cross_attention_norm": null,
+ "down_block_types": [
+ "CrossAttnDownBlock2D",
+ "CrossAttnDownBlock2D",
+ "CrossAttnDownBlock2D",
+ "DownBlock2D"
+ ],
+ "downsample_padding": 1,
+ "dropout": 0.0,
+ "dual_cross_attention": false,
+ "encoder_hid_dim": null,
+ "encoder_hid_dim_type": null,
+ "flip_sin_to_cos": true,
+ "freq_shift": 0,
+ "in_channels": 4,
+ "layers_per_block": 2,
+ "mid_block_only_cross_attention": null,
+ "mid_block_scale_factor": 1,
+ "mid_block_type": "UNetMidBlock2DCrossAttn",
+ "norm_eps": 1e-05,
+ "norm_num_groups": 32,
+ "num_attention_heads": null,
+ "num_class_embeds": null,
+ "only_cross_attention": false,
+ "out_channels": 4,
+ "projection_class_embeddings_input_dim": null,
+ "resnet_out_scale_factor": 1.0,
+ "resnet_skip_time_act": false,
+ "resnet_time_scale_shift": "default",
+ "reverse_transformer_layers_per_block": null,
+ "sample_size": 64,
+ "time_cond_proj_dim": null,
+ "time_embedding_act_fn": null,
+ "time_embedding_dim": null,
+ "time_embedding_type": "positional",
+ "timestep_post_act": null,
+ "transformer_layers_per_block": 1,
+ "up_block_types": [
+ "UpBlock2D",
+ "CrossAttnUpBlock2D",
+ "CrossAttnUpBlock2D",
+ "CrossAttnUpBlock2D"
+ ],
+ "upcast_attention": false,
+ "use_linear_projection": false
+}
diff --git a/models/arc2face/encoder/config.json b/models/arc2face/encoder/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..49c600b99d5738877abb69598b678f2a0005a309
--- /dev/null
+++ b/models/arc2face/encoder/config.json
@@ -0,0 +1,24 @@
+{
+ "architectures": [
+ "CLIPTextModel"
+ ],
+ "attention_dropout": 0.0,
+ "bos_token_id": 0,
+ "dropout": 0.0,
+ "eos_token_id": 2,
+ "hidden_act": "quick_gelu",
+ "hidden_size": 768,
+ "initializer_factor": 1.0,
+ "initializer_range": 0.02,
+ "intermediate_size": 3072,
+ "layer_norm_eps": 1e-05,
+ "max_position_embeddings": 77,
+ "model_type": "clip_text_model",
+ "num_attention_heads": 12,
+ "num_hidden_layers": 12,
+ "pad_token_id": 1,
+ "projection_dim": 768,
+ "torch_dtype": "float32",
+ "transformers_version": "4.34.1",
+ "vocab_size": 49408
+}
diff --git a/models/image_encoder/config.json b/models/image_encoder/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..9c841dd0314e27141ed01a24df1734e407a95c4a
--- /dev/null
+++ b/models/image_encoder/config.json
@@ -0,0 +1,23 @@
+{
+ "_name_or_path": "./image_encoder",
+ "architectures": [
+ "CLIPVisionModelWithProjection"
+ ],
+ "attention_dropout": 0.0,
+ "dropout": 0.0,
+ "hidden_act": "gelu",
+ "hidden_size": 1280,
+ "image_size": 224,
+ "initializer_factor": 1.0,
+ "initializer_range": 0.02,
+ "intermediate_size": 5120,
+ "layer_norm_eps": 1e-05,
+ "model_type": "clip_vision_model",
+ "num_attention_heads": 16,
+ "num_channels": 3,
+ "num_hidden_layers": 32,
+ "patch_size": 14,
+ "projection_dim": 1024,
+ "torch_dtype": "float16",
+ "transformers_version": "4.28.0.dev0"
+}
diff --git a/models/insightface/models/antelopev2/2d106det.onnx b/models/insightface/models/antelopev2/2d106det.onnx
new file mode 100644
index 0000000000000000000000000000000000000000..cdb163d88b5f51396855ebc795e0114322c98b6b
--- /dev/null
+++ b/models/insightface/models/antelopev2/2d106det.onnx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f001b856447c413801ef5c42091ed0cd516fcd21f2d6b79635b1e733a7109dbf
+size 5030888
diff --git a/models/insightface/models/antelopev2/genderage.onnx b/models/insightface/models/antelopev2/genderage.onnx
new file mode 100644
index 0000000000000000000000000000000000000000..fcf638481cea978e99ddabd914ccd3b70c8401cb
--- /dev/null
+++ b/models/insightface/models/antelopev2/genderage.onnx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4fde69b1c810857b88c64a335084f1c3fe8f01246c9a191b48c7bb756d6652fb
+size 1322532
diff --git a/models/insightface/models/buffalo_l/2d106det.onnx b/models/insightface/models/buffalo_l/2d106det.onnx
new file mode 100644
index 0000000000000000000000000000000000000000..cdb163d88b5f51396855ebc795e0114322c98b6b
--- /dev/null
+++ b/models/insightface/models/buffalo_l/2d106det.onnx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f001b856447c413801ef5c42091ed0cd516fcd21f2d6b79635b1e733a7109dbf
+size 5030888
diff --git a/models/insightface/models/buffalo_l/genderage.onnx b/models/insightface/models/buffalo_l/genderage.onnx
new file mode 100644
index 0000000000000000000000000000000000000000..fcf638481cea978e99ddabd914ccd3b70c8401cb
--- /dev/null
+++ b/models/insightface/models/buffalo_l/genderage.onnx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4fde69b1c810857b88c64a335084f1c3fe8f01246c9a191b48c7bb756d6652fb
+size 1322532
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5ff450cb6210dc62ac4d6803106a338af9781bfb
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,17 @@
+diffusers==0.29.2
+torch
+torchvision
+imageio
+imageio-ffmpeg
+accelerate
+einops
+gradio
+transformers
+insightface
+omegaconf
+opencv-python
+onnx>=1.16.0
+onnxruntime
+safetensors
+spaces
+ftfy
diff --git a/scripts/avg_models.py b/scripts/avg_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea44d934acafe0967981edd16891117627f987fe
--- /dev/null
+++ b/scripts/avg_models.py
@@ -0,0 +1,118 @@
+#!/usr/bin/env python3
+""" Checkpoint Averaging Script
+
+This script averages all model weights for checkpoints in specified path that match
+the specified filter wildcard. All checkpoints must be from the exact same model.
+
+For any hope of decent results, the checkpoints should be from the same or child
+(via resumes) training session. This can be viewed as similar to maintaining running
+EMA (exponential moving average) of the model weights or performing SWA (stochastic
+weight averaging), but post-training.
+
+Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
+"""
+import torch
+import torch.nn as nn
+import argparse
+import os
+import glob
+import hashlib
+from collections import OrderedDict, defaultdict
+import re
+import copy
+from safetensors.torch import load_file as safetensors_load_file
+from safetensors.torch import save_file as safetensors_save_file
+
+parser = argparse.ArgumentParser(description='PyTorch Checkpoint Averager')
+parser.add_argument('--input', default='', nargs="+", type=str, metavar='PATHS',
+ help='path(s) to base input folder containing checkpoints')
+parser.add_argument('--output', type=str, default='avgmodel.pt', metavar='PATH',
+ help='output file name of the averaged checkpoint')
+parser.add_argument('--suffix', default='', type=str, metavar='WILDCARD',
+ help='checkpoint suffix')
+parser.add_argument('--min', type=int, default=500, help='Minimal iteration of checkpoints to average')
+parser.add_argument('--max', type=int, default=-1, help='Maximum iteration of checkpoints to average')
+
+def main():
+ args = parser.parse_args()
+ patterns = args.input
+ sel_checkpoint_filenames = []
+
+ for pattern in patterns:
+ if args.suffix is not None:
+ if not args.suffix.startswith('*'):
+ pattern += '*'
+ pattern += args.suffix
+
+ checkpoint_filenames = glob.glob(pattern, recursive=True)
+ if len(checkpoint_filenames) == 0:
+ print("WARNING: No checkpoints matching '{}' and iteration >= {} in '{}'".format(
+ pattern, args.min, args.input))
+ continue
+
+ sel_checkpoint_filenames += checkpoint_filenames
+
+ avg_ckpt = {}
+ avg_counts = {}
+ for i, c in enumerate(sel_checkpoint_filenames):
+ if c.endswith(".safetensors"):
+ checkpoint = safetensors_load_file(c)
+ else:
+ checkpoint = torch.load(c, map_location='cpu')
+ print(c)
+
+ for k in checkpoint:
+ # Skip ema weights
+ if k.startswith("model_ema."):
+ continue
+ if k not in avg_ckpt:
+ avg_ckpt[k] = checkpoint[k]
+ print(f"Copy {k}")
+ avg_counts[k] = 1
+ # Another occurrence of a previously seen nn.Module.
+ elif isinstance(checkpoint[k], nn.Module):
+ #print(f"nn.Module: {k}")
+ avg_state_dict = avg_ckpt[k].state_dict()
+ param_state_dict = checkpoint[k]
+ for m_k, m_v in param_state_dict.state_dict().items():
+ if m_k not in avg_state_dict:
+ avg_state_dict[m_k] = copy.copy(m_v)
+ print(f"Copy {k}.{m_k}")
+ else:
+ avg_state_dict[m_k].data += m_v
+ print(f"Accumulate {k}.{m_k}")
+ avg_ckpt[k].load_state_dict(avg_state_dict)
+ avg_counts[k] = 1
+ # Another occurrence of a previously seen nn.Parameter.
+ elif isinstance(checkpoint[k], (nn.Parameter, torch.Tensor)):
+ #print(f"nn.Parameter: {k}")
+ avg_ckpt[k].data += checkpoint[k].data
+ avg_counts[k] += 1
+ else:
+ print(f"NOT copying {type(checkpoint[k])}: {k}")
+ pass
+
+ for k in avg_ckpt:
+ # safetensors use torch.Tensor instead of nn.Parameter.
+ if isinstance(avg_ckpt[k], (nn.Parameter, torch.Tensor)):
+ print(f"Averaging nn.Parameter: {k}")
+ avg_ckpt[k].data /= avg_counts[k]
+ elif isinstance(avg_ckpt[k], nn.Module):
+ print(f"Averaging nn.Module: {k}")
+ avg_state_dict = avg_ckpt[k].state_dict()
+ for m_k, m_v in avg_state_dict.items():
+ m_v.data = (m_v.data / avg_counts[k]).to(m_v.data.dtype)
+ avg_ckpt[k].load_state_dict(avg_state_dict)
+ else:
+ print(f"NOT averaging {type(avg_ckpt[k])}: {k}")
+
+ if args.output.endswith(".safetensors"):
+ safetensors_save_file(avg_ckpt, args.output)
+ else:
+ torch.save(avg_ckpt, args.output)
+
+ print("=> Saved state_dict to '{}'".format(args.output))
+
+
+if __name__ == '__main__':
+ main()
diff --git a/scripts/repl_textencoder.py b/scripts/repl_textencoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea4372b49fcdda32e98e935c6cf482996129deca
--- /dev/null
+++ b/scripts/repl_textencoder.py
@@ -0,0 +1,61 @@
+import torch
+from safetensors.torch import load_file as safetensors_load_file
+from safetensors.torch import save_file as safetensors_save_file
+import sys, argparse
+
+def load_ckpt(ckpt_filepath):
+ print(f"Loading model from {ckpt_filepath}")
+ if ckpt_filepath.endswith(".safetensors"):
+ state_dict = safetensors_load_file(ckpt_filepath, device="cpu")
+ ckpt = None
+ else:
+ ckpt = torch.load(ckpt_filepath, map_location="cpu")
+ state_dict = ckpt["state_dict"]
+ return ckpt, state_dict
+
+def save_ckpt(ckpt, ckpt_state_dict, ckpt_filepath):
+ if ckpt_filepath.endswith(".safetensors"):
+ safetensors_save_file(ckpt_state_dict, ckpt_filepath)
+ else:
+ if ckpt is not None:
+ torch.save(ckpt, ckpt_filepath)
+ else:
+ torch.save(ckpt_state_dict, ckpt_filepath)
+
+ print(f"Saved to {ckpt_filepath}")
+
+def load_two_models(base_ckpt_filepath, te_ckpt_filepath):
+ base_ckpt, base_state_dict = load_ckpt(base_ckpt_filepath)
+ _, te_state_dict = load_ckpt(te_ckpt_filepath)
+ # Other fields in sd_ckpt are also needed when saving the checkpoint.
+ # So return the whole sd_ckpt.
+ return base_ckpt, base_state_dict, te_state_dict
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--base_ckpt", type=str, required=True, help="Path to the base checkpoint")
+parser.add_argument("--te_ckpt", type=str, required=True, help="Path to the checkpoint providing text encoder")
+parser.add_argument("--out_ckpt", type=str, required=True, help="Path to the output checkpoint")
+args = parser.parse_args()
+
+base_ckpt, base_state_dict, te_state_dict = load_two_models(args.base_ckpt, args.te_ckpt)
+# base_state_dict = sd_ckpt["state_dict"]
+
+repl_count = 0
+
+for k in base_state_dict:
+ if k.startswith("cond_stage_model."):
+ if k not in te_state_dict:
+ print(f"!!!! '{k}' not in TE checkpoint")
+ continue
+ if base_state_dict[k].shape != te_state_dict[k].shape:
+ print(f"!!!! '{k}' shape mismatch: {base_state_dict[k].shape} vs {te_state_dict[k].shape} !!!!")
+ continue
+ print(k)
+ base_state_dict[k] = te_state_dict[k]
+ repl_count += 1
+
+if repl_count > 0:
+ print(f"{repl_count} parameters replaced")
+ save_ckpt(base_ckpt, base_state_dict, args.out_ckpt)
+else:
+ print("ERROR: No parameter replaced")
diff --git a/scripts/repl_vae.py b/scripts/repl_vae.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a2c0c188a156d5f4040c038603a0b6f05214eb1
--- /dev/null
+++ b/scripts/repl_vae.py
@@ -0,0 +1,62 @@
+import torch
+from safetensors.torch import load_file as safetensors_load_file
+from safetensors.torch import save_file as safetensors_save_file
+import sys, argparse
+
+def load_ckpt(ckpt_filepath):
+ print(f"Loading model from {ckpt_filepath}")
+ if ckpt_filepath.endswith(".safetensors"):
+ state_dict = safetensors_load_file(ckpt_filepath, device="cpu")
+ ckpt = None
+ else:
+ ckpt = torch.load(ckpt_filepath, map_location="cpu")
+ state_dict = ckpt["state_dict"]
+ return ckpt, state_dict
+
+def save_ckpt(ckpt, ckpt_state_dict, ckpt_filepath):
+ if ckpt_filepath.endswith(".safetensors"):
+ safetensors_save_file(ckpt_state_dict, ckpt_filepath)
+ else:
+ if ckpt is not None:
+ torch.save(ckpt, ckpt_filepath)
+ else:
+ torch.save(ckpt_state_dict, ckpt_filepath)
+
+ print(f"Saved to {ckpt_filepath}")
+
+def load_two_models(base_ckpt_filepath, vae_ckpt_filepath):
+ base_ckpt, base_state_dict = load_ckpt(base_ckpt_filepath)
+ _, vae_state_dict = load_ckpt(vae_ckpt_filepath)
+ # Other fields in sd_ckpt are also needed when saving the checkpoint.
+ # So return the whole sd_ckpt.
+ return base_ckpt, base_state_dict, vae_state_dict
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--base_ckpt", type=str, required=True, help="Path to the base checkpoint")
+parser.add_argument("--vae_ckpt", type=str, required=True, help="Path to the checkpoint providing vae")
+parser.add_argument("--out_ckpt", type=str, required=True, help="Path to the output checkpoint")
+args = parser.parse_args()
+
+base_ckpt, base_state_dict, vae_state_dict = load_two_models(args.base_ckpt, args.vae_ckpt)
+# base_state_dict = sd_ckpt["state_dict"]
+
+repl_count = 0
+
+for k in base_state_dict:
+ if k.startswith("first_stage_model."):
+ k2 = k.replace("first_stage_model.", "")
+ if k2 not in vae_state_dict:
+ print(f"!!!! '{k2}' not in VAE checkpoint")
+ continue
+ if base_state_dict[k].shape != vae_state_dict[k2].shape:
+ print(f"!!!! '{k}' shape mismatch: {base_state_dict[k].shape} vs {vae_state_dict[k2].shape} !!!!")
+ continue
+ print(k)
+ base_state_dict[k] = vae_state_dict[k2]
+ repl_count += 1
+
+if repl_count > 0:
+ print(f"{repl_count} parameters replaced")
+ save_ckpt(base_ckpt, base_state_dict, args.out_ckpt)
+else:
+ print("ERROR: No parameter replaced")