debug 13

app.py

@@ -1,40 +1,17 @@
 import gradio as gr
 from diffusers import DiffusionPipeline
-import torch
-from diffusers import DDPMScheduler, UNet2DModel
-from PIL import Image
-import numpy as np
-
-
-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)
-
-#     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
-
-#     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
-
+# import torch
+# from diffusers import DDPMScheduler, UNet2DModel
+# from PIL import Image
+# import numpy as np
 
 # pipeline = DiffusionPipeline.from_pretrained("google/ddpm-cat-256")
 pipeline = DiffusionPipeline.from_pretrained("google/ddpm-celebahq-256")
 # pipeline.to("cuda")
 
+def erzeuge(prompt):
+    return pipeline(prompt).images  # [0]
+
 
 with gr.Blocks() as demo:
     with gr.Column(variant="panel"):
@@ -43,7 +20,7 @@ with gr.Blocks() as demo:
                 label="Deine Beschreibung:",
                 show_label=False,
                 max_lines=1,
-                placeholder="Bildbeschreibung",
+                placeholder="Bildbeschrei",
             )
             btn = gr.Button("erzeuge Bild")