debug 12

app.py

@@ -10,31 +10,29 @@ def erzeuge(prompt):
     return pipeline(prompt).images  # [0]
 
 
-def erzeuge_komplex(prompt):
-    scheduler = DDPMScheduler.from_pretrained("google/ddpm-cat-256")
-    model = UNet2DModel.from_pretrained("google/ddpm-cat-256").to("cuda")
-    scheduler.set_timesteps(50)
+# def erzeuge_komplex(prompt):
+#     scheduler = DDPMScheduler.from_pretrained("google/ddpm-cat-256")
+#     model = UNet2DModel.from_pretrained("google/ddpm-cat-256").to("cuda")
+#     scheduler.set_timesteps(50)
 
-    sample_size = model.config.sample_size
-    noise = torch.randn((1, 3, sample_size, sample_size)).to("cuda")
-    input = noise
+#     sample_size = model.config.sample_size
+#     noise = torch.randn((1, 3, sample_size, sample_size)).to("cuda")
+#     input = noise
 
-    for t in scheduler.timesteps:
-        with torch.no_grad():
-            noisy_residual = model(input, t).sample
-            prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample
-            input = prev_noisy_sample
+#     for t in scheduler.timesteps:
+#         with torch.no_grad():
+#             noisy_residual = model(input, t).sample
+#             prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample
+#             input = prev_noisy_sample
 
-    image = (input / 2 + 0.5).clamp(0, 1)
-    image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
-    image = Image.fromarray((image * 255).round().astype("uint8"))
-    return image
+#     image = (input / 2 + 0.5).clamp(0, 1)
+#     image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
+#     image = Image.fromarray((image * 255).round().astype("uint8"))
+#     return image
 
 
-# pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2")
 # pipeline = DiffusionPipeline.from_pretrained("google/ddpm-cat-256")
 pipeline = DiffusionPipeline.from_pretrained("google/ddpm-celebahq-256")
-
 # pipeline.to("cuda")