iterative outputs

README.md

@@ -4,7 +4,7 @@ emoji: 🧨
 colorFrom: blue
 colorTo: pink
 sdk: gradio
-sdk_version: 3.0.26
+sdk_version: 3.2.1b0
 app_file: app.py
 pinned: false
 ---

app.py

@@ -7,10 +7,10 @@ import numpy as np
 
 pipeline = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
 
-def predict(steps=1, seed=42):
+def predict(steps, seed):
     generator = torch.manual_seed(seed)
-    images = pipeline(generator=generator, num_inference_steps=steps)["sample"]
-    return images[0]
+    for i in range(1,steps):
+        yield pipeline(generator=generator, num_inference_steps=i)["sample"][0]
 
 random_seed = random.randint(0, 2147483647)
 gr.Interface(
@@ -23,4 +23,4 @@ gr.Interface(
     css="#output_image{width: 256px}",
     title="ldm-celebahq-256 - 🧨 diffusers library",
     description="This Spaces contains an unconditional Latent Diffusion process for the <a href=\"https://huggingface.co/CompVis/ldm-celebahq-256\">ldm-celebahq-256</a> face generator model by <a href=\"https://huggingface.co/CompVis\">CompVis</a> using the <a href=\"https://github.com/huggingface/diffusers\">diffusers library</a>. The goal of this demo is to showcase the diffusers library capabilities. If you want the state-of-the-art experience with Latent Diffusion text-to-image check out the <a href=\"https://huggingface.co/spaces/multimodalart/latentdiffusion\">main Spaces</a>.",
-).launch()
+).queue().launch()