fix: local with spaces

app.py

@@ -6,7 +6,7 @@ from torchvision.transforms.functional import to_tensor
 from PIL import Image
 
 if gr.NO_RELOAD:
-    n_steps = 40
+    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)
@@ -17,39 +17,37 @@ if gr.NO_RELOAD:
         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()
+    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")
-    pipeline.enable_model_cpu_offload()
-    pipeline.unet.enable_forward_chunking()
+
+    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",
+        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 = 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,
@@ -58,11 +56,10 @@ def generate(prompt: str):
         negative_prompt=negative_prompt,
         guidance_scale=9.0,
         generator=generator,
-        decode_chunk_size=6,
     ).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=4)
+    torchvision.io.write_video("video.mp4", frames, fps=8)
     return "video.mp4"
 
 app = gr.Interface(

app_local.py

@@ -6,7 +6,7 @@ from torchvision.transforms.functional import to_tensor
 from PIL import Image
 
 if gr.NO_RELOAD:
-    n_steps = 50
+    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)
@@ -17,37 +17,39 @@ if gr.NO_RELOAD:
         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 = 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")
-
-    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)
+    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,
+        # 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,
@@ -56,10 +58,11 @@ def generate(prompt: str):
         negative_prompt=negative_prompt,
         guidance_scale=9.0,
         generator=generator,
+        decode_chunk_size=6,
     ).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)
+    torchvision.io.write_video("video.mp4", frames, fps=4)
     return "video.mp4"
 
 app = gr.Interface(