Adding a gradio demo of SVD to be run locally (#144)

* Adding a gradio demo of SVD to be run locally

* Update gradio_app.py

* Create svd_xt_1_1.yaml

* Update pt2.txt

---------

Co-authored-by: Sumith Kulal <sumith1896@gmail.com>

README.md

@@ -24,6 +24,7 @@
       We use the standard image encoder from SD 2.1, but replace the decoder with a temporally-aware `deflickering decoder`.
     - [SVD-XT](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt): Same architecture as `SVD` but finetuned
       for 25 frame generation.
+    - You can run the community-build gradio demo locally by running `python -m scripts.demo.gradio_app`.  
     - We provide a streamlit demo `scripts/demo/video_sampling.py` and a standalone python script `scripts/sampling/simple_video_sample.py` for inference of both models.
     - Alongside the model, we release a [technical report](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets).
 

requirements/pt2.txt

@@ -37,4 +37,5 @@ wandb>=0.15.6
 webdataset>=0.2.33
 wheel>=0.41.0
 xformers>=0.0.20
+gradio
 streamlit-keyup==0.2.0

scripts/demo/gradio_app.py

@@ -0,0 +1,283 @@
+# 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 math
+from glob import glob
+from pathlib import Path
+from typing import Optional
+
+import cv2
+import numpy as np
+import torch
+from einops import rearrange, repeat
+from fire import Fire
+from omegaconf import OmegaConf
+from PIL import Image
+from torchvision.transforms import ToTensor
+
+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", 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") 
+
+
+# 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  
+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)
+
+# 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(
+        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 
+device = "cuda"
+max_64_bit_int = 2**63 - 1
+
+if version == "svd_xt_1_1":
+    num_frames = 25
+    num_steps = 30
+    model_config = "scripts/sampling/configs/svd_xt_1_1.yaml"
+else:
+    raise ValueError(f"Version {version} does not exist.")
+
+model, filter = load_model(
+    model_config,
+    device,
+    num_frames,
+    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,
+    randomize_seed: bool = True,
+    motion_bucket_id: int = 127,
+    fps_id: int = 6,
+    version: str = "svd_xt_1_1",
+    cond_aug: float = 0.02,
+    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)
+):
+    """
+    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)
+    if(randomize_seed):
+        seed = random.randint(0, max_64_bit_int)
+        
+    torch.manual_seed(seed)
+    
+    path = Path(input_path)
+    all_img_paths = []
+    if path.is_file():
+        if any([input_path.endswith(x) for x in ["jpg", "jpeg", "png"]]):
+            all_img_paths = [input_path]
+        else:
+            raise ValueError("Path is not valid image file.")
+    elif path.is_dir():
+        all_img_paths = sorted(
+            [
+                f
+                for f in path.iterdir()
+                if f.is_file() and f.suffix.lower() in [".jpg", ".jpeg", ".png"]
+            ]
+        )
+        if len(all_img_paths) == 0:
+            raise ValueError("Folder does not contain any images.")
+    else:
+        raise ValueError
+
+    for input_img_path in all_img_paths:
+        with Image.open(input_img_path) as image:
+            if image.mode == "RGBA":
+                image = image.convert("RGB")
+            w, h = image.size
+
+            if h % 64 != 0 or w % 64 != 0:
+                width, height = map(lambda x: x - x % 64, (w, h))
+                image = image.resize((width, height))
+                print(
+                    f"WARNING: Your image is of size {h}x{w} which is not divisible by 64. We are resizing to {height}x{width}!"
+                )
+
+            image = ToTensor()(image)
+            image = image * 2.0 - 1.0
+
+        image = image.unsqueeze(0).to(device)
+        H, W = image.shape[2:]
+        assert image.shape[1] == 3
+        F = 8
+        C = 4
+        shape = (num_frames, C, H // F, W // F)
+        if (H, W) != (576, 1024):
+            print(
+                "WARNING: The conditioning frame you provided is not 576x1024. This leads to suboptimal performance as model was only trained on 576x1024. Consider increasing `cond_aug`."
+            )
+        if motion_bucket_id > 255:
+            print(
+                "WARNING: High motion bucket! This may lead to suboptimal performance."
+            )
+
+        if fps_id < 5:
+            print("WARNING: Small fps value! This may lead to suboptimal performance.")
+
+        if fps_id > 30:
+            print("WARNING: Large fps value! This may lead to suboptimal performance.")
+
+        value_dict = {}
+        value_dict["motion_bucket_id"] = motion_bucket_id
+        value_dict["fps_id"] = fps_id
+        value_dict["cond_aug"] = cond_aug
+        value_dict["cond_frames_without_noise"] = image
+        value_dict["cond_frames"] = image + cond_aug * torch.randn_like(image)
+        value_dict["cond_aug"] = cond_aug
+
+        with torch.no_grad():
+            with torch.autocast(device):
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    [1, num_frames],
+                    T=num_frames,
+                    device=device,
+                )
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=[
+                        "cond_frames",
+                        "cond_frames_without_noise",
+                    ],
+                )
+
+                for k in ["crossattn", "concat"]:
+                    uc[k] = repeat(uc[k], "b ... -> b t ...", t=num_frames)
+                    uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=num_frames)
+                    c[k] = repeat(c[k], "b ... -> b t ...", t=num_frames)
+                    c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=num_frames)
+
+                randn = torch.randn(shape, device=device)
+
+                additional_model_inputs = {}
+                additional_model_inputs["image_only_indicator"] = torch.zeros(
+                    2, num_frames
+                ).to(device)
+                additional_model_inputs["num_video_frames"] = batch["num_video_frames"]
+
+                def denoiser(input, sigma, c):
+                    return model.denoiser(
+                        model.model, input, sigma, c, **additional_model_inputs
+                    )
+
+                samples_z = model.sampler(denoiser, randn, cond=c, uc=uc)
+                model.en_and_decode_n_samples_a_time = decoding_t
+                samples_x = model.decode_first_stage(samples_z)
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+
+                os.makedirs(output_folder, exist_ok=True)
+                base_count = len(glob(os.path.join(output_folder, "*.mp4")))
+                video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
+                writer = cv2.VideoWriter(
+                    video_path,
+                    cv2.VideoWriter_fourcc(*"mp4v"),
+                    fps_id + 1,
+                    (samples.shape[-1], samples.shape[-2]),
+                )
+
+                samples = embed_watermark(samples)
+                samples = filter(samples)
+                vid = (
+                    (rearrange(samples, "t c h w -> t h w c") * 255)
+                    .cpu()
+                    .numpy()
+                    .astype(np.uint8)
+                )
+                for frame in vid:
+                    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+                    writer.write(frame)
+                writer.release()
+        
+        return video_path, seed
+
+
+def resize_image(image_path, output_size=(1024, 576)):
+    image = Image.open(image_path)
+    # Calculate aspect ratios
+    target_aspect = output_size[0] / output_size[1]  # Aspect ratio of the desired size
+    image_aspect = image.width / image.height  # Aspect ratio of the original image
+
+    # Resize then crop if the original image is larger
+    if image_aspect > target_aspect:
+        # Resize the image to match the target height, maintaining aspect ratio
+        new_height = output_size[1]
+        new_width = int(new_height * image_aspect)
+        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
+        # Calculate coordinates for cropping
+        left = (new_width - output_size[0]) / 2
+        top = 0
+        right = (new_width + output_size[0]) / 2
+        bottom = output_size[1]
+    else:
+        # Resize the image to match the target width, maintaining aspect ratio
+        new_width = output_size[0]
+        new_height = int(new_width / image_aspect)
+        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
+        # Calculate coordinates for cropping
+        left = 0
+        top = (new_height - output_size[1]) / 2
+        right = output_size[0]
+        bottom = (new_height + output_size[1]) / 2
+
+    # Crop the image
+    cropped_image = resized_image.crop((left, top, right, bottom))
+
+    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)
+  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)
+    demo.launch(share=True)

scripts/sampling/configs/svd_xt_1_1.yaml

@@ -0,0 +1,146 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+    ckpt_path: checkpoints/svd_xt_1_1.safetensors
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 768
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - is_trainable: False
+          input_key: cond_frames_without_noise
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: fps_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: motion_bucket_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencodingEngine
+      params:
+        loss_config:
+          target: torch.nn.Identity
+        regularizer_config:
+          target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
+        encoder_config: 
+          target: sgm.modules.diffusionmodules.model.Encoder
+          params:
+            attn_type: vanilla
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [1, 2, 4, 4]
+            num_res_blocks: 2
+            attn_resolutions: []
+            dropout: 0.0
+        decoder_config:
+          target: sgm.modules.autoencoding.temporal_ae.VideoDecoder
+          params:
+            attn_type: vanilla
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [1, 2, 4, 4]
+            num_res_blocks: 2
+            attn_resolutions: []
+            dropout: 0.0
+            video_kernel_size: [3, 1, 1]
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+          params:
+            sigma_max: 700.0
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.LinearPredictionGuider
+          params:
+            max_scale: 3.0
+            min_scale: 1.5