update: space hardware

.gitignore

@@ -1,2 +1,4 @@
 .env
-__pycache__/
+__pycache__/
+*.mp4
+*.jpg

app.py

@@ -1,21 +1,57 @@
 import gradio as gr
 import torch
 import torchvision
-from diffusers import I2VGenXLPipeline
-from diffusers.utils.loading_utils import load_image
+from diffusers import I2VGenXLPipeline, DiffusionPipeline
+from torchvision.transforms.functional import to_tensor
 from PIL import Image
 
-device = torch.device("cpu")
-negative_prompt = "Distorted, discontinuous, Ugly, blurry, low resolution, motionless, static, disfigured, disconnected limbs, Ugly faces, incomplete arms"
-generator = torch.manual_seed(8888)
-pipeline = I2VGenXLPipeline.from_pretrained("ali-vilab/i2vgen-xl", torch_dtype=torch.float16, variant="fp16")
-pipeline.to(device)
-pipeline.enable_model_cpu_offload()
-pipeline.unet.enable_forward_chunking()
+if gr.NO_RELOAD:
+    n_steps = 40
+    high_noise_frac = 0.8
+    negative_prompt = "Distorted, discontinuous, Ugly, blurry, low resolution, motionless, static, disfigured, disconnected limbs, Ugly faces, incomplete arms"
+    generator = torch.manual_seed(8888)
 
-def generate(image: Image.Image, prompt: str):
-    image = image.convert("RGB")
-    frames = pipeline(
+    base = DiffusionPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-xl-base-1.0", 
+        torch_dtype=torch.float16, 
+        variant="fp16", 
+        use_safetensors=True,
+    )
+    # refiner = DiffusionPipeline.from_pretrained(
+    #     "stabilityai/stable-diffusion-xl-refiner-1.0",
+    #     text_encoder_2=base.text_encoder_2,
+    #     vae=base.vae,
+    #     torch_dtype=torch.float16,
+    #     use_safetensors=True,
+    #     variant="fp16",
+    # )
+    # refiner.to("cuda")
+    # base.to("cuda")
+    # refiner.enable_model_cpu_offload()
+    base.enable_model_cpu_offload()
+    pipeline = I2VGenXLPipeline.from_pretrained("ali-vilab/i2vgen-xl", torch_dtype=torch.float16, variant="fp16")
+    pipeline.enable_model_cpu_offload()
+    pipeline.unet.enable_forward_chunking()
+
+def generate(prompt: str):
+    image = base(
+        prompt=prompt,
+        num_inference_steps=n_steps,
+        # denoising_end=high_noise_frac,
+        # output_type="latent",
+    ).images[0]
+    # image = refiner(
+    #     prompt=prompt,
+    #     num_inference_steps=n_steps,
+    #     denoising_start=high_noise_frac,
+    #     image=image,
+    # ).images[0]
+    # print(image)
+    # print(type(image))
+    # print(image.size())
+    image.save("frame.jpg")
+    image = to_tensor(image)
+    frames: list[Image.Image] = pipeline(
         prompt=prompt,
         image=image,
         num_inference_steps=50,
@@ -24,12 +60,14 @@ def generate(image: Image.Image, prompt: str):
         generator=generator,
         decode_chunk_size=6,
     ).frames[0]
-    torchvision.io.write_video("video.mp4", frames, fps=16)
+    frames = [to_tensor(frame.convert("RGB")).mul(255).byte().permute(1, 2, 0) for frame in frames]
+    frames = torch.stack(frames)
+    torchvision.io.write_video("video.mp4", frames, fps=4)
     return "video.mp4"
 
 app = gr.Interface(
     fn=generate,
-    inputs=[gr.Image(type="pil"), "text"],
+    inputs=["text"],
     outputs=gr.Video()
 )
 

app_local.py

@@ -0,0 +1,73 @@
+import gradio as gr
+import torch
+import torchvision
+from diffusers import I2VGenXLPipeline, DiffusionPipeline
+from torchvision.transforms.functional import to_tensor
+from PIL import Image
+
+if gr.NO_RELOAD:
+    n_steps = 50
+    high_noise_frac = 0.8
+    negative_prompt = "Distorted, discontinuous, Ugly, blurry, low resolution, motionless, static, disfigured, disconnected limbs, Ugly faces, incomplete arms"
+    generator = torch.manual_seed(8888)
+
+    base = DiffusionPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-xl-base-1.0", 
+        torch_dtype=torch.float16, 
+        variant="fp16", 
+        use_safetensors=True,
+    )
+    refiner = DiffusionPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-xl-refiner-1.0",
+        text_encoder_2=base.text_encoder_2,
+        vae=base.vae,
+        torch_dtype=torch.float16,
+        use_safetensors=True,
+        variant="fp16",
+    )
+    pipeline = I2VGenXLPipeline.from_pretrained("ali-vilab/i2vgen-xl", torch_dtype=torch.float16, variant="fp16")
+
+    base.to("cuda")
+    refiner.to("cuda")
+    pipeline.to("cuda")
+
+    base.unet = torch.compile(base.unet, mode="reduce-overhead", fullgraph=True)
+    refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
+    pipeline.unet = torch.compile(pipeline.unet, mode="reduce-overhead", fullgraph=True)
+
+def generate(prompt: str):
+    image = base(
+        prompt=prompt,
+        num_inference_steps=n_steps,
+        denoising_end=high_noise_frac,
+        output_type="latent",
+    ).images[0]
+    image = refiner(
+        prompt=prompt,
+        num_inference_steps=n_steps,
+        denoising_start=high_noise_frac,
+        image=image,
+    ).images[0]
+    image = to_tensor(image)
+    frames: list[Image.Image] = pipeline(
+        prompt=prompt,
+        image=image,
+        num_inference_steps=50,
+        negative_prompt=negative_prompt,
+        guidance_scale=9.0,
+        generator=generator,
+    ).frames[0]
+    frames = [to_tensor(frame.convert("RGB")).mul(255).byte().permute(1, 2, 0) for frame in frames]
+    frames = torch.stack(frames)
+    torchvision.io.write_video("video.mp4", frames, fps=8)
+    return "video.mp4"
+
+app = gr.Interface(
+    fn=generate,
+    inputs=["text"],
+    outputs=gr.Video()
+)
+
+if __name__ == "__main__":
+    app.launch()
+