Initial commit

app.py

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+from diffusers import StableDiffusionPipeline
+from compel import Compel
+import gradio
+import torch
+
+model_id = "dream-textures/texture-diffusion"
+device = "cuda"
+dtype = torch.float16
+
+pipe = StableDiffusionPipeline.from_pretrained(
+  model_id, torch_dtype=dtype
+).to(device)
+#pipe = StableDiffusionPipeline.from_pretrained(model_id)
+
+compel_proc = Compel(
+  tokenizer=pipe.tokenizer,
+  text_encoder=pipe.text_encoder,
+  truncate_long_prompts=False,
+)
+
+def predict(
+  prompt: str,
+  generator: int,
+  num_inference_steps: int,
+  strength: float,
+  guidance_scale: float,
+):
+  generator = torch.manual_seed(generator)
+  prompt_embeds = compel_proc(prompt)
+
+  results = pipe(
+    prompt_embeds=prompt_embeds,
+    generator=generator,
+    guidance_scale=float(guidance_scale),
+    num_inference_steps=num_inference_steps,
+    output_type="pil",
+    strength=float(strength),
+  )
+
+  if len(results.images) > 0:
+    return results.images[0]
+  return None
+
+app = gradio.Interface(
+  fn=predict,
+  inputs=[
+    gradio.Textbox("pbr brick wall"), # prompt
+    gradio.Slider(0, 2147483647, 2159232, step=1), # generator
+    gradio.Slider(2, 15, 4, step=1), # num_inference_steps
+    gradio.Slider(0.0, 1.0, 0.5, step=0.01), # strength
+    gradio.Slider(0.0, 5.0, 0.2, step=0.01), # guidance_scale
+  ],
+  outputs=gradio.Image(type="pil")
+)
+app.launch()

requirements.txt

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+accelerate
+compel
+diffusers
+gradio
+torch