debug 8

app.py

@@ -1,62 +1,95 @@
-import gradio as gr
-from diffusers import DiffusionPipeline
-import torch
-from diffusers import DDPMScheduler, UNet2DModel
-from PIL import Image
-import numpy as np
+# 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(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 = 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")
+
+
+# with gr.Blocks() as demo:
+#     with gr.Column(variant="panel"):
+#         with gr.Row(variant="compact"):
+#             text = gr.Textbox(
+#                 label="Deine Beschreibung:",
+#                 show_label=False,
+#                 max_lines=1,
+#                 placeholder="Bildbeschreibung",
+#             ).scale(
+#                 container=False,
+#             )
+#             btn = gr.Button("erzeuge Bild").style(full_width=False, min_width=100)
+
+#         gallery = gr.Gallery(
+#             label="Erzeugtes Bild", show_label=False, elem_id="gallery"
+#         ).style(columns=[2], rows=[2], object_fit="contain", height="auto")
+
+#     btn.click(erzeuge, inputs=[text], outputs=[gallery])
+#     text.submit(erzeuge, inputs=[text], outputs=[gallery])
+
+# if __name__ == "__main__":
+#     demo.launch()
+
 
+
+
+
+
+
+from diffusers import DiffusionPipeline
+from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
+import torch
+import gradio as gr
+import random
+
+pipeline = DiffusionPipeline.from_pretrained("google/ddpm-celebahq-256")
 # pipeline.to("cuda")
 
+def predict(steps, seed):
+    generator = torch.manual_seed(seed)
+    for i in range(1,steps):
+        yield pipeline(generator=generator, num_inference_steps=i).images[0]
 
-with gr.Blocks() as demo:
-    with gr.Column(variant="panel"):
-        with gr.Row(variant="compact"):
-            text = gr.Textbox(
-                label="Deine Beschreibung:",
-                show_label=False,
-                max_lines=1,
-                placeholder="Bildbeschreibung",
-            ).scale(
-                container=False,
-            )
-            btn = gr.Button("erzeuge Bild").style(full_width=False, min_width=100)
-
-        gallery = gr.Gallery(
-            label="Erzeugtes Bild", show_label=False, elem_id="gallery"
-        ).style(columns=[2], rows=[2], object_fit="contain", height="auto")
-
-    btn.click(erzeuge, inputs=[text], outputs=[gallery])
-    text.submit(erzeuge, inputs=[text], outputs=[gallery])
-
-if __name__ == "__main__":
-    demo.launch()
+random_seed = random.randint(0, 2147483647)
+gr.Interface(
+    predict,
+    inputs=[
+        gr.inputs.Slider(1, 100, label='Inference Steps', default=5, step=1),
+        gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1),
+    ],
+    outputs=gr.Image(shape=[128,128], type="pil", elem_id="output_image"),
+    css="#output_image{width: 256px}",
+    title="Unconditional butterflies",
+    description="图片���成器",
+).queue().launch()