Black and isort

scripts/demo/gradio_app.py

@@ -1,41 +1,42 @@
 # Adding this at the very top of app.py to make 'generative-models' directory discoverable
-import sys
 import os
-sys.path.append(os.path.join(os.path.dirname(__file__), 'generative-models'))
+import sys
+
+sys.path.append(os.path.join(os.path.dirname(__file__), "generative-models"))
 
 import math
+import random
+import uuid
 from glob import glob
 from pathlib import Path
 from typing import Optional
 
 import cv2
+import gradio as gr
 import numpy as np
 import torch
 from einops import rearrange, repeat
 from fire import Fire
+from huggingface_hub import hf_hub_download
 from omegaconf import OmegaConf
 from PIL import Image
 from torchvision.transforms import ToTensor
 
+from scripts.sampling.simple_video_sample import (
+    get_batch, get_unique_embedder_keys_from_conditioner, load_model)
 from scripts.util.detection.nsfw_and_watermark_dectection import \
     DeepFloydDataFiltering
 from sgm.inference.helpers import embed_watermark
 from sgm.util import default, instantiate_from_config
-from scripts.sampling.simple_video_sample import load_model, get_unique_embedder_keys_from_conditioner, get_batch
-
-import gradio as gr
-import uuid
-import random
-from huggingface_hub import hf_hub_download
 
 # To download all svd models
-#hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid-xt", filename="svd_xt.safetensors", local_dir="checkpoints") 
+# hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid-xt", filename="svd_xt.safetensors", local_dir="checkpoints")
-#hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid", filename="svd.safetensors", local_dir="checkpoints") 
+# hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid", filename="svd.safetensors", local_dir="checkpoints")
-#hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid-xt-1-1", filename="svd_xt_1_1.safetensors", local_dir="checkpoints") 
+# hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid-xt-1-1", filename="svd_xt_1_1.safetensors", local_dir="checkpoints")
 
 
 # Define the repo, local directory and filename
-repo_id="stabilityai/stable-video-diffusion-img2vid-xt-1-1" # replace with "stabilityai/stable-video-diffusion-img2vid-xt" or "stabilityai/stable-video-diffusion-img2vid" for other models  
+repo_id = "stabilityai/stable-video-diffusion-img2vid-xt-1-1"  # replace with "stabilityai/stable-video-diffusion-img2vid-xt" or "stabilityai/stable-video-diffusion-img2vid" for other models
 filename = "svd_xt_1_1.safetensors"  # replace with "svd_xt.safetensors" or "svd.safetensors" for other models
 local_dir = "checkpoints"
 local_file_path = os.path.join(local_dir, filename)
@@ -43,17 +44,13 @@ local_file_path = os.path.join(local_dir, filename)
 # Check if the file already exists
 if not os.path.exists(local_file_path):
     # If the file doesn't exist, download it
-    hf_hub_download(
+    hf_hub_download(repo_id=repo_id, filename=filename, local_dir=local_dir)
-        repo_id=repo_id, 
-        filename=filename,
-        local_dir=local_dir  
-    )
     print("File downloaded.")
 else:
     print("File already exists. No need to download.")
 
 
-version = "svd_xt_1_1" # replace with 'svd_xt' or 'svd' for other models 
+version = "svd_xt_1_1"  # replace with 'svd_xt' or 'svd' for other models
 device = "cuda"
 max_64_bit_int = 2**63 - 1
 
@@ -71,6 +68,7 @@ model, filter = load_model(
     num_steps,
 )
 
+
 def sample(
     input_path: str = "assets/test_image.png",  # Can either be image file or folder with image files
     seed: Optional[int] = None,
@@ -82,14 +80,14 @@ def sample(
     decoding_t: int = 7,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
     device: str = "cuda",
     output_folder: str = "outputs",
-    progress=gr.Progress(track_tqdm=True)
+    progress=gr.Progress(track_tqdm=True),
 ):
     """
     Simple script to generate a single sample conditioned on an image `input_path` or multiple images, one for each
     image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`.
     """
-    fps_id = int(fps_id ) #casting float slider values to int)
+    fps_id = int(fps_id)  # casting float slider values to int)
-    if(randomize_seed):
+    if randomize_seed:
         seed = random.randint(0, max_64_bit_int)
 
     torch.manual_seed(seed)
@@ -260,23 +258,50 @@ def resize_image(image_path, output_size=(1024, 576)):
 
     return cropped_image
 
-with gr.Blocks() as demo:
-  gr.Markdown('''# Community demo for Stable Video Diffusion - Img2Vid - XT ([model](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt), [paper](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets))
-#### Research release ([_non-commercial_](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/LICENSE)): generate `4s` vid from a single image at (`25 frames` at `6 fps`). Generation takes ~60s in an A100. [Join the waitlist for Stability's upcoming web experience](https://stability.ai/contact).
-  ''')
-  with gr.Row():
-    with gr.Column():
-        image = gr.Image(label="Upload your image", type="filepath")
-        generate_btn = gr.Button("Generate")
-    video = gr.Video()
-  with gr.Accordion("Advanced options", open=False):
-      seed = gr.Slider(label="Seed", value=42, randomize=True, minimum=0, maximum=max_64_bit_int, step=1)
-      randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-      motion_bucket_id = gr.Slider(label="Motion bucket id", info="Controls how much motion to add/remove from the image", value=127, minimum=1, maximum=255)
-      fps_id = gr.Slider(label="Frames per second", info="The length of your video in seconds will be 25/fps", value=6, minimum=5, maximum=30)
 
-  image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
+with gr.Blocks() as demo:
-  generate_btn.click(fn=sample, inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id], outputs=[video, seed], api_name="video")
+    gr.Markdown(
+        """# Community demo for Stable Video Diffusion - Img2Vid - XT ([model](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt), [paper](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets))
+#### Research release ([_non-commercial_](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/LICENSE)): generate `4s` vid from a single image at (`25 frames` at `6 fps`). Generation takes ~60s in an A100. [Join the waitlist for Stability's upcoming web experience](https://stability.ai/contact).
+  """
+    )
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(label="Upload your image", type="filepath")
+            generate_btn = gr.Button("Generate")
+        video = gr.Video()
+    with gr.Accordion("Advanced options", open=False):
+        seed = gr.Slider(
+            label="Seed",
+            value=42,
+            randomize=True,
+            minimum=0,
+            maximum=max_64_bit_int,
+            step=1,
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        motion_bucket_id = gr.Slider(
+            label="Motion bucket id",
+            info="Controls how much motion to add/remove from the image",
+            value=127,
+            minimum=1,
+            maximum=255,
+        )
+        fps_id = gr.Slider(
+            label="Frames per second",
+            info="The length of your video in seconds will be 25/fps",
+            value=6,
+            minimum=5,
+            maximum=30,
+        )
+
+    image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
+    generate_btn.click(
+        fn=sample,
+        inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id],
+        outputs=[video, seed],
+        api_name="video",
+    )
 
 if __name__ == "__main__":
     demo.queue(max_size=20)

scripts/demo/turbo.py

@@ -1,5 +1,6 @@
-from streamlit_helpers import *
 from st_keyup import st_keyup
+from streamlit_helpers import *
+
 from sgm.modules.diffusionmodules.sampling import EulerAncestralSampler
 
 VERSION2SPECS = {
@@ -203,8 +204,12 @@ if __name__ == "__main__":
         ),
     )
     sampler.n_sample_steps = n_steps
-    default_prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe."
+    default_prompt = (
-    prompt = st_keyup("Enter a value", value=default_prompt, debounce=300, key="interactive_text")
+        "A cinematic shot of a baby racoon wearing an intricate italian priest robe."
+    )
+    prompt = st_keyup(
+        "Enter a value", value=default_prompt, debounce=300, key="interactive_text"
+    )
 
     cols = st.columns([1, 5, 1])
     if mode != "skip":
@@ -217,7 +222,13 @@ if __name__ == "__main__":
 
         sampler.noise_sampler = SeededNoise(seed=st.session_state.seed)
         out = sample(
-            model, sampler, H=512, W=512, seed=st.session_state.seed, prompt=prompt, filter=state.get("filter")
+            model,
+            sampler,
+            H=512,
+            W=512,
+            seed=st.session_state.seed,
+            prompt=prompt,
+            filter=state.get("filter"),
         )
         with cols[1]:
             st.image(out[0])

sgm/modules/autoencoding/temporal_ae.py

@@ -3,14 +3,12 @@ from typing import Callable, Iterable, Union
 import torch
 from einops import rearrange, repeat
 
-from sgm.modules.diffusionmodules.model import (
+from sgm.modules.diffusionmodules.model import (XFORMERS_IS_AVAILABLE,
-    XFORMERS_IS_AVAILABLE,
+                                                AttnBlock, Decoder,
-    AttnBlock,
+                                                MemoryEfficientAttnBlock,
-    Decoder,
+                                                ResnetBlock)
-    MemoryEfficientAttnBlock,
+from sgm.modules.diffusionmodules.openaimodel import (ResBlock,
-    ResnetBlock,
+                                                      timestep_embedding)
-)
-from sgm.modules.diffusionmodules.openaimodel import ResBlock, timestep_embedding
 from sgm.modules.video_attention import VideoTransformerBlock
 from sgm.util import partialclass
 

sgm/modules/video_attention.py

@@ -1,7 +1,8 @@
 import torch
 
 from ..modules.attention import *
-from ..modules.diffusionmodules.util import AlphaBlender, linear, timestep_embedding
+from ..modules.diffusionmodules.util import (AlphaBlender, linear,
+                                             timestep_embedding)
 
 
 class TimeMixSequential(nn.Sequential):