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()