First commit

app.py

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+# !pip install diffusers
+from diffusers import DiffusionPipeline, DDIMPipeline, DDPMPipeline, PNDMPipeline
+import gradio as gr
+import PIL.Image
+import numpy as np
+import random
+import torch
+
+model_id = "google/ddpm-celebahq-256"
+
+# load model and scheduler
+ddpm = DDPMPipeline.from_pretrained(model_id)
+ddim = DDIMPipeline.from_pretrained(model_id)
+pndm = PNDMPipeline.from_pretrained(model_id)
+
+# run pipeline in inference (sample random noise and denoise)
+def predict(steps=100,seed=42,scheduler="ddim"):
+    generator = torch.manual_seed(seed)
+    if(scheduler == "ddim"):
+        image = ddim(generator=generator, num_inference_steps=steps) #does not work (returns random noise)
+        image = image["sample"]
+    elif(scheduler == "ddpm"):
+        image = ddpm(generator=generator) #works, but does not let me set the number of steps
+    elif(scheduler == "pndm"):
+        image = pndm(generator=generator, num_inference_steps=steps) #does not work, still detects its DDPM behind the scenes and does not run pndm steps
+        image = image["sample"]
+    
+    # process image to PIL
+    image_processed = image.cpu().permute(0, 2, 3, 1)
+    image_processed = (image_processed + 1.0) * 127.5
+    image_processed = image_processed.clamp(0, 255).numpy().astype(np.uint8)
+    return PIL.Image.fromarray(image_processed[0])
+
+
+random_seed = random.randint(0, 2147483647)
+gr.Interface(
+    predict,
+    inputs=[
+        gr.inputs.Slider(1, 1000, label='Inference Steps', default=1000, step=1),
+        gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed),
+        gr.inputs.Radio(["ddim", "ddpm", "pndm"], default="ddpm",label="Diffusion scheduler")
+    ],
+    outputs="image",
+).launch()

requirements.txt

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+git+https://github.com/huggingface/diffusers.git