dome272 multimodalart HF staff commited on
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Wording in the code example (#2)

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- Wording in the code example (142aa16b4cb6ed9f02fa825b0f9f42bd6d8bb69e)
- Update README.md (7b41fafe0af25f1dee86c1bfad9db38ccf548381)


Co-authored-by: Apolinário from multimodal AI art <[email protected]>

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  1. README.md +24 -20
README.md CHANGED
@@ -65,7 +65,9 @@ aesthetic prompts. Specifically, Stable Cascade (30 inference steps) was compare
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  steps), SDXL (50 inference steps), SDXL Turbo (1 inference step) and Würstchen v2 (30 inference steps).
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  ## Code Example
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- Iinstall `diffusers` from this branch while the PR is WIP.
 
 
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  ```shell
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  pip install git+https://github.com/kashif/diffusers.git@wuerstchen-v3
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  ```
@@ -75,31 +77,33 @@ import torch
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  from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
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  device = "cuda"
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- dtype = torch.bfloat16
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  num_images_per_prompt = 2
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- prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=dtype).to(device)
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- decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", torch_dtype=dtype).to(device)
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  prompt = "Anthropomorphic cat dressed as a pilot"
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  negative_prompt = ""
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- with torch.cuda.amp.autocast(dtype=dtype):
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- prior_output = prior(
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- prompt=prompt,
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- height=1024,
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- width=1024,
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- negative_prompt=negative_prompt,
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- guidance_scale=4.0,
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- num_images_per_prompt=num_images_per_prompt,
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- )
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- decoder_output = decoder(
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- image_embeddings=prior_output.image_embeddings,
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- prompt=prompt,
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- negative_prompt=negative_prompt,
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- guidance_scale=0.0,
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- output_type="pil",
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- ).images
 
 
 
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  ```
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  ## Uses
 
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  steps), SDXL (50 inference steps), SDXL Turbo (1 inference step) and Würstchen v2 (30 inference steps).
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  ## Code Example
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+
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+ **⚠️ Important**: For the code below to work, you have to install `diffusers` from this branch while the PR is WIP.
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+
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  ```shell
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  pip install git+https://github.com/kashif/diffusers.git@wuerstchen-v3
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  ```
 
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  from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
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  device = "cuda"
 
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  num_images_per_prompt = 2
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+ prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=torch.bfloat16).to(device)
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+ decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", torch_dtype=torch.float16).to(device)
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  prompt = "Anthropomorphic cat dressed as a pilot"
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  negative_prompt = ""
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+ prior_output = prior(
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+ prompt=prompt,
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+ height=1024,
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+ width=1024,
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+ negative_prompt=negative_prompt,
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+ guidance_scale=4.0,
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+ num_images_per_prompt=num_images_per_prompt,
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+ num_inference_steps=20
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+ )
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+ decoder_output = decoder(
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+ image_embeddings=prior_output.image_embeddings.half(),
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+ prompt=prompt,
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+ negative_prompt=negative_prompt,
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+ guidance_scale=0.0,
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+ output_type="pil",
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+ num_inference_steps=10
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+ ).images
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+
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+ #Now decoder_output is a list with your PIL images
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  ```
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  ## Uses