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Update README.md

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@@ -20,8 +20,7 @@ Version 3.4 continued training from [ProtoGen v2.2](https://huggingface.co/darks
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  ## Space
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  We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run dreamlike-diffusion-1.0:
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- [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/akhaliq/dreamlike-diffusion-1.0)
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-
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  ### CompVis
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  [Download ProtoGen_X3.4.ckpt) (5.98GB)](https://huggingface.co/darkstorm2150/Protogen_x3.4_Official_Release/blob/main/ProtoGen_X3.4.ckpt)
@@ -32,7 +31,7 @@ This model can be used just like any other Stable Diffusion model. For more info
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  please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
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  ```python
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- from diffusers import StableDiffusionPipeline
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  import torch
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  prompt = (
@@ -44,9 +43,10 @@ prompt = (
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  model_id = "darkstorm2150/Protogen_x3.4_Official_Release"
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  pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
 
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  pipe = pipe.to("cuda")
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- image = pipe(prompt).images[0]
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  image.save("./result.jpg")
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  ```
 
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  ## Space
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  We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run dreamlike-diffusion-1.0:
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+ [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/darkstorm2150/Stable-Diffusion-Protogen-webui)
 
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  ### CompVis
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  [Download ProtoGen_X3.4.ckpt) (5.98GB)](https://huggingface.co/darkstorm2150/Protogen_x3.4_Official_Release/blob/main/ProtoGen_X3.4.ckpt)
 
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  please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
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  ```python
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+ from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
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  import torch
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  prompt = (
 
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  model_id = "darkstorm2150/Protogen_x3.4_Official_Release"
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  pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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+ pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
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  pipe = pipe.to("cuda")
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+ image = pipe(prompt, num_inference_steps=25).images[0]
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  image.save("./result.jpg")
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  ```