mirror of
https://github.com/huggingface/diffusers.git
synced 2026-01-27 17:22:53 +03:00
Steven Liu
GitHub
@@ -66,6 +66,8 @@
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title: Stable Diffusion XL
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- local: using-diffusers/controlnet
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title: ControlNet
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- local: using-diffusers/diffedit
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title: DiffEdit
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- local: using-diffusers/distilled_sd
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title: Distilled Stable Diffusion inference
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- local: using-diffusers/reproducibility
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@@ -24,325 +24,32 @@ This pipeline was contributed by [clarencechen](https://github.com/clarencechen)
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## Tips
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* The pipeline can generate masks that can be fed into other inpainting pipelines. Check out the code examples below to know more.
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* In order to generate an image using this pipeline, both an image mask (manually specified or generated using `generate_mask`)
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and a set of partially inverted latents (generated using `invert`) _must_ be provided as arguments when calling the pipeline to generate the final edited image.
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Refer to the code examples below for more details.
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* The function `generate_mask` exposes two prompt arguments, `source_prompt` and `target_prompt`,
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* The pipeline can generate masks that can be fed into other inpainting pipelines.
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* In order to generate an image using this pipeline, both an image mask (source and target prompts can be manually specified or generated, and passed to [`~StableDiffusionDiffEditPipeline.generate_mask`])
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and a set of partially inverted latents (generated using [`~StableDiffusionDiffEditPipeline.invert`]) _must_ be provided as arguments when calling the pipeline to generate the final edited image.
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* The function [`~StableDiffusionDiffEditPipeline.generate_mask`] exposes two prompt arguments, `source_prompt` and `target_prompt`
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that let you control the locations of the semantic edits in the final image to be generated. Let's say,
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you wanted to translate from "cat" to "dog". In this case, the edit direction will be "cat -> dog". To reflect
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this in the generated mask, you simply have to set the embeddings related to the phrases including "cat" to
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`source_prompt_embeds` and "dog" to `target_prompt_embeds`. Refer to the code example below for more details.
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`source_prompt` and "dog" to `target_prompt`.
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* When generating partially inverted latents using `invert`, assign a caption or text embedding describing the
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overall image to the `prompt` argument to help guide the inverse latent sampling process. In most cases, the
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source concept is sufficently descriptive to yield good results, but feel free to explore alternatives.
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Please refer to [this code example](#generating-image-captions-for-inversion) for more details.
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* When calling the pipeline to generate the final edited image, assign the source concept to `negative_prompt`
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and the target concept to `prompt`. Taking the above example, you simply have to set the embeddings related to
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the phrases including "cat" to `negative_prompt_embeds` and "dog" to `prompt_embeds`. Refer to the code example
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below for more details.
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the phrases including "cat" to `negative_prompt` and "dog" to `prompt`.
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* If you wanted to reverse the direction in the example above, i.e., "dog -> cat", then it's recommended to:
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* Swap the `source_prompt` and `target_prompt` in the arguments to `generate_mask`.
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* Change the input prompt for `invert` to include "dog".
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* Change the input prompt in [`~StableDiffusionDiffEditPipeline.invert`] to include "dog".
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* Swap the `prompt` and `negative_prompt` in the arguments to call the pipeline to generate the final edited image.
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* Note that the source and target prompts, or their corresponding embeddings, can also be automatically generated. Please, refer to [this discussion](#generating-source-and-target-embeddings) for more details.
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## Usage example
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### Based on an input image with a caption
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When the pipeline is conditioned on an input image, we first obtain partially inverted latents from the input image using a
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`DDIMInverseScheduler` with the help of a caption. Then we generate an editing mask to identify relevant regions in the image using the source and target prompts. Finally,
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the inverted noise and generated mask is used to start the generation process.
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First, let's load our pipeline:
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```py
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import torch
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from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionDiffEditPipeline
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sd_model_ckpt = "stabilityai/stable-diffusion-2-1"
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pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
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sd_model_ckpt,
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torch_dtype=torch.float16,
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safety_checker=None,
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)
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pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
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pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
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pipeline.enable_model_cpu_offload()
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pipeline.enable_vae_slicing()
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generator = torch.manual_seed(0)
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```
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Then, we load an input image to edit using our method:
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```py
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from diffusers.utils import load_image
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img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
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raw_image = load_image(img_url).convert("RGB").resize((768, 768))
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```
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Then, we employ the source and target prompts to generate the editing mask:
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```py
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# See the "Generating source and target embeddings" section below to
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# automate the generation of these captions with a pre-trained model like Flan-T5 as explained below.
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source_prompt = "a bowl of fruits"
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target_prompt = "a basket of fruits"
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mask_image = pipeline.generate_mask(
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image=raw_image,
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source_prompt=source_prompt,
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target_prompt=target_prompt,
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generator=generator,
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)
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```
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Then, we employ the caption and the input image to get the inverted latents:
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```py
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inv_latents = pipeline.invert(prompt=source_prompt, image=raw_image, generator=generator).latents
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```
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Now, generate the image with the inverted latents and semantically generated mask:
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```py
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image = pipeline(
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prompt=target_prompt,
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mask_image=mask_image,
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image_latents=inv_latents,
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generator=generator,
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negative_prompt=source_prompt,
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).images[0]
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image.save("edited_image.png")
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```
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## Generating image captions for inversion
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The authors originally used the source concept prompt as the caption for generating the partially inverted latents. However, we can also leverage open source and public image captioning models for the same purpose.
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Below, we provide an end-to-end example with the [BLIP](https://huggingface.co/docs/transformers/model_doc/blip) model
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for generating captions.
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First, let's load our automatic image captioning model:
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```py
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import torch
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from transformers import BlipForConditionalGeneration, BlipProcessor
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captioner_id = "Salesforce/blip-image-captioning-base"
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processor = BlipProcessor.from_pretrained(captioner_id)
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model = BlipForConditionalGeneration.from_pretrained(captioner_id, torch_dtype=torch.float16, low_cpu_mem_usage=True)
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```
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Then, we define a utility to generate captions from an input image using the model:
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```py
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@torch.no_grad()
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def generate_caption(images, caption_generator, caption_processor):
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text = "a photograph of"
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inputs = caption_processor(images, text, return_tensors="pt").to(device="cuda", dtype=caption_generator.dtype)
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caption_generator.to("cuda")
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outputs = caption_generator.generate(**inputs, max_new_tokens=128)
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# offload caption generator
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caption_generator.to("cpu")
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caption = caption_processor.batch_decode(outputs, skip_special_tokens=True)[0]
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return caption
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```
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Then, we load an input image for conditioning and obtain a suitable caption for it:
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```py
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from diffusers.utils import load_image
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img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
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raw_image = load_image(img_url).convert("RGB").resize((768, 768))
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caption = generate_caption(raw_image, model, processor)
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```
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Then, we employ the generated caption and the input image to get the inverted latents:
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```py
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from diffusers import DDIMInverseScheduler, DDIMScheduler
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pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
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"stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
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)
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pipeline = pipeline.to("cuda")
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pipeline.enable_model_cpu_offload()
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pipeline.enable_vae_slicing()
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pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
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pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
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generator = torch.manual_seed(0)
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inv_latents = pipeline.invert(prompt=caption, image=raw_image, generator=generator).latents
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```
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Now, generate the image with the inverted latents and semantically generated mask from our source and target prompts:
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```py
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source_prompt = "a bowl of fruits"
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target_prompt = "a basket of fruits"
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mask_image = pipeline.generate_mask(
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image=raw_image,
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source_prompt=source_prompt,
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target_prompt=target_prompt,
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generator=generator,
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)
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image = pipeline(
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prompt=target_prompt,
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mask_image=mask_image,
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image_latents=inv_latents,
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generator=generator,
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negative_prompt=source_prompt,
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).images[0]
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image.save("edited_image.png")
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```
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## Generating source and target embeddings
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The authors originally required the user to manually provide the source and target prompts for discovering
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edit directions. However, we can also leverage open source and public models for the same purpose.
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Below, we provide an end-to-end example with the [Flan-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5) model
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for generating source an target embeddings.
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**1. Load the generation model**:
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```py
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import torch
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from transformers import AutoTokenizer, T5ForConditionalGeneration
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tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-xl")
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model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", device_map="auto", torch_dtype=torch.float16)
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```
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**2. Construct a starting prompt**:
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```py
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source_concept = "bowl"
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target_concept = "basket"
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source_text = f"Provide a caption for images containing a {source_concept}. "
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"The captions should be in English and should be no longer than 150 characters."
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target_text = f"Provide a caption for images containing a {target_concept}. "
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"The captions should be in English and should be no longer than 150 characters."
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```
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Here, we're interested in the "bowl -> basket" direction.
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**3. Generate prompts**:
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We can use a utility like so for this purpose.
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```py
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@torch.no_grad
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def generate_prompts(input_prompt):
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input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids.to("cuda")
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outputs = model.generate(
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input_ids, temperature=0.8, num_return_sequences=16, do_sample=True, max_new_tokens=128, top_k=10
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)
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return tokenizer.batch_decode(outputs, skip_special_tokens=True)
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```
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And then we just call it to generate our prompts:
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```py
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source_prompts = generate_prompts(source_text)
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target_prompts = generate_prompts(target_text)
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```
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We encourage you to play around with the different parameters supported by the
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`generate()` method ([documentation](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.generation_tf_utils.TFGenerationMixin.generate)) for the generation quality you are looking for.
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**4. Load the embedding model**:
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Here, we need to use the same text encoder model used by the subsequent Stable Diffusion model.
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```py
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from diffusers import StableDiffusionDiffEditPipeline
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pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
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"stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
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)
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pipeline = pipeline.to("cuda")
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pipeline.enable_model_cpu_offload()
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pipeline.enable_vae_slicing()
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generator = torch.manual_seed(0)
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```
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**5. Compute embeddings**:
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```py
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import torch
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@torch.no_grad()
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def embed_prompts(sentences, tokenizer, text_encoder, device="cuda"):
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embeddings = []
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for sent in sentences:
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text_inputs = tokenizer(
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sent,
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padding="max_length",
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max_length=tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=None)[0]
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embeddings.append(prompt_embeds)
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return torch.concatenate(embeddings, dim=0).mean(dim=0).unsqueeze(0)
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source_embeddings = embed_prompts(source_prompts, pipeline.tokenizer, pipeline.text_encoder)
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target_embeddings = embed_prompts(target_captions, pipeline.tokenizer, pipeline.text_encoder)
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```
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And you're done! Now, you can use these embeddings directly while calling the pipeline:
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```py
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from diffusers import DDIMInverseScheduler, DDIMScheduler
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from diffusers.utils import load_image
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pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
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pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
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img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
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raw_image = load_image(img_url).convert("RGB").resize((768, 768))
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mask_image = pipeline.generate_mask(
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image=raw_image,
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source_prompt_embeds=source_embeds,
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target_prompt_embeds=target_embeds,
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generator=generator,
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)
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inv_latents = pipeline.invert(
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prompt_embeds=source_embeds,
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image=raw_image,
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generator=generator,
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).latents
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images = pipeline(
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mask_image=mask_image,
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image_latents=inv_latents,
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prompt_embeds=target_embeddings,
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negative_prompt_embeds=source_embeddings,
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generator=generator,
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).images
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images[0].save("edited_image.png")
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```
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* The source and target prompts, or their corresponding embeddings, can also be automatically generated. Please refer to the [DiffEdit](/using-diffusers/diffedit) guide for more details.
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## StableDiffusionDiffEditPipeline
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[[autodoc]] StableDiffusionDiffEditPipeline
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- all
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- generate_mask
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- invert
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- __call__
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- __call__
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## StableDiffusionPipelineOutput
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[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
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262
docs/source/en/using-diffusers/diffedit.md
Normal file
262
docs/source/en/using-diffusers/diffedit.md
Normal file
@@ -0,0 +1,262 @@
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# DiffEdit
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[[open-in-colab]]
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Image editing typically requires providing a mask of the area to be edited. DiffEdit automatically generates the mask for you based on a text query, making it easier overall to create a mask without image editing software. The DiffEdit algorithm works in three steps:
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1. the diffusion model denoises an image conditioned on some query text and reference text which produces different noise estimates for different areas of the image; the difference is used to infer a mask to identify which area of the image needs to be changed to match the query text
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2. the input image is encoded into latent space with DDIM
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3. the latents are decoded with the diffusion model conditioned on the text query, using the mask as a guide such that pixels outside the mask remain the same as in the input image
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This guide will show you how to use DiffEdit to edit images without manually creating a mask.
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Before you begin, make sure you have the following libraries installed:
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|
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```py
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# uncomment to install the necessary libraries in Colab
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#!pip install diffusers transformers accelerate safetensors
|
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```
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|
||||
The [`StableDiffusionDiffEditPipeline`] requires an image mask and a set of partially inverted latents. The image mask is generated from the [`~StableDiffusionDiffEditPipeline.generate_mask`] function, and includes two parameters, `source_prompt` and `target_prompt`. These parameters determine what to edit in the image. For example, if you want to change a bowl of *fruits* to a bowl of *pears*, then:
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|
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```py
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source_prompt = "a bowl of fruits"
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target_prompt = "a bowl of pears"
|
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```
|
||||
|
||||
The partially inverted latents are generated from the [`~StableDiffusionDiffEditPipeline.invert`] function, and it is generally a good idea to include a `prompt` or *caption* describing the image to help guide the inverse latent sampling process. The caption can often be your `source_prompt`, but feel free to experiment with other text descriptions!
|
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|
||||
Let's load the pipeline, scheduler, inverse scheduler, and enable some optimizations to reduce memory usage:
|
||||
|
||||
```py
|
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import torch
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from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionDiffEditPipeline
|
||||
|
||||
pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
|
||||
"stabilityai/stable-diffusion-2-1",
|
||||
torch_dtype=torch.float16,
|
||||
safety_checker=None,
|
||||
use_safetensors=True,
|
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)
|
||||
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
|
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pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
|
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pipeline.enable_model_cpu_offload()
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pipeline.enable_vae_slicing()
|
||||
```
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Load the image to edit:
|
||||
|
||||
```py
|
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from diffusers.utils import load_image
|
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|
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img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
|
||||
raw_image = load_image(img_url).convert("RGB").resize((768, 768))
|
||||
```
|
||||
|
||||
Use the [`~StableDiffusionDiffEditPipeline.generate_mask`] function to generate the image mask. You'll need to pass it the `source_prompt` and `target_prompt` to specify what to edit in the image:
|
||||
|
||||
```py
|
||||
source_prompt = "a bowl of fruits"
|
||||
target_prompt = "a basket of pears"
|
||||
mask_image = pipeline.generate_mask(
|
||||
image=raw_image,
|
||||
source_prompt=source_prompt,
|
||||
target_prompt=target_prompt,
|
||||
)
|
||||
```
|
||||
|
||||
Next, create the inverted latents and pass it a caption describing the image:
|
||||
|
||||
```py
|
||||
inv_latents = pipeline.invert(prompt=source_prompt, image=raw_image).latents
|
||||
```
|
||||
|
||||
Finally, pass the image mask and inverted latents to the pipeline. The `target_prompt` becomes the `prompt` now, and the `source_prompt` is used as the `negative_prompt`:
|
||||
|
||||
```py
|
||||
image = pipeline(
|
||||
prompt=target_prompt,
|
||||
mask_image=mask_image,
|
||||
image_latents=inv_latents,
|
||||
negative_prompt=source_prompt,
|
||||
).images[0]
|
||||
image.save("edited_image.png")
|
||||
```
|
||||
|
||||
<div class="flex gap-4">
|
||||
<div>
|
||||
<img class="rounded-xl" src="https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"/>
|
||||
<figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
|
||||
</div>
|
||||
<div>
|
||||
<img class="rounded-xl" src="https://github.com/Xiang-cd/DiffEdit-stable-diffusion/blob/main/assets/target.png?raw=true"/>
|
||||
<figcaption class="mt-2 text-center text-sm text-gray-500">edited image</figcaption>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
## Generate source and target embeddings
|
||||
|
||||
The source and target embeddings can be automatically generated with the [Flan-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5) model instead of creating them manually.
|
||||
|
||||
Load the Flan-T5 model and tokenizer from the 🤗 Transformers library:
|
||||
|
||||
```py
|
||||
import torch
|
||||
from transformers import AutoTokenizer, T5ForConditionalGeneration
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-xl")
|
||||
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", device_map="auto", torch_dtype=torch.float16)
|
||||
```
|
||||
|
||||
Provide some initial text to prompt the model to generate the source and target prompts.
|
||||
|
||||
```py
|
||||
source_concept = "bowl"
|
||||
target_concept = "basket"
|
||||
|
||||
source_text = f"Provide a caption for images containing a {source_concept}. "
|
||||
"The captions should be in English and should be no longer than 150 characters."
|
||||
|
||||
target_text = f"Provide a caption for images containing a {target_concept}. "
|
||||
"The captions should be in English and should be no longer than 150 characters."
|
||||
```
|
||||
|
||||
Next, create a utility function to generate the prompts:
|
||||
|
||||
```py
|
||||
@torch.no_grad
|
||||
def generate_prompts(input_prompt):
|
||||
input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids.to("cuda")
|
||||
|
||||
outputs = model.generate(
|
||||
input_ids, temperature=0.8, num_return_sequences=16, do_sample=True, max_new_tokens=128, top_k=10
|
||||
)
|
||||
return tokenizer.batch_decode(outputs, skip_special_tokens=True)
|
||||
|
||||
source_prompts = generate_prompts(source_text)
|
||||
target_prompts = generate_prompts(target_text)
|
||||
print(source_prompts)
|
||||
print(target_prompts)
|
||||
```
|
||||
|
||||
<Tip>
|
||||
|
||||
Check out the [generation strategy](https://huggingface.co/docs/transformers/main/en/generation_strategies) guide if you're interested in learning more about strategies for generating different quality text.
|
||||
|
||||
</Tip>
|
||||
|
||||
Load the text encoder model used by the [`StableDiffusionDiffEditPipeline`] to encode the text. You'll use the text encoder to compute the text embeddings:
|
||||
|
||||
```py
|
||||
import torch
|
||||
from diffusers import StableDiffusionDiffEditPipeline
|
||||
|
||||
pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
|
||||
"stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16, use_safetensors=True
|
||||
).to("cuda")
|
||||
pipeline.enable_model_cpu_offload()
|
||||
pipeline.enable_vae_slicing()
|
||||
|
||||
@torch.no_grad()
|
||||
def embed_prompts(sentences, tokenizer, text_encoder, device="cuda"):
|
||||
embeddings = []
|
||||
for sent in sentences:
|
||||
text_inputs = tokenizer(
|
||||
sent,
|
||||
padding="max_length",
|
||||
max_length=tokenizer.model_max_length,
|
||||
truncation=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
text_input_ids = text_inputs.input_ids
|
||||
prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=None)[0]
|
||||
embeddings.append(prompt_embeds)
|
||||
return torch.concatenate(embeddings, dim=0).mean(dim=0).unsqueeze(0)
|
||||
|
||||
source_embeds = embed_prompts(source_prompts, pipeline.tokenizer, pipeline.text_encoder)
|
||||
target_embeds = embed_prompts(target_prompts, pipeline.tokenizer, pipeline.text_encoder)
|
||||
```
|
||||
|
||||
Finally, pass the embeddings to the [`~StableDiffusionDiffEditPipeline.generate_mask`] and [`~StableDiffusionDiffEditPipeline.invert`] functions, and pipeline to generate the image:
|
||||
|
||||
```diff
|
||||
from diffusers import DDIMInverseScheduler, DDIMScheduler
|
||||
from diffusers.utils import load_image
|
||||
|
||||
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
|
||||
pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
|
||||
|
||||
img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
|
||||
raw_image = load_image(img_url).convert("RGB").resize((768, 768))
|
||||
|
||||
|
||||
mask_image = pipeline.generate_mask(
|
||||
image=raw_image,
|
||||
+ source_prompt_embeds=source_embeds,
|
||||
+ target_prompt_embeds=target_embeds,
|
||||
)
|
||||
|
||||
inv_latents = pipeline.invert(
|
||||
+ prompt_embeds=source_embeds,
|
||||
image=raw_image,
|
||||
).latents
|
||||
|
||||
images = pipeline(
|
||||
mask_image=mask_image,
|
||||
image_latents=inv_latents,
|
||||
+ prompt_embeds=target_embeds,
|
||||
+ negative_prompt_embeds=source_embeds,
|
||||
).images
|
||||
images[0].save("edited_image.png")
|
||||
```
|
||||
|
||||
## Generate a caption for inversion
|
||||
|
||||
While you can use the `source_prompt` as a caption to help generate the partially inverted latents, you can also use the [BLIP](https://huggingface.co/docs/transformers/model_doc/blip) model to automatically generate a caption.
|
||||
|
||||
Load the BLIP model and processor from the 🤗 Transformers library:
|
||||
|
||||
```py
|
||||
import torch
|
||||
from transformers import BlipForConditionalGeneration, BlipProcessor
|
||||
|
||||
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
|
||||
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float16, low_cpu_mem_usage=True)
|
||||
```
|
||||
|
||||
Create a utility function to generate a caption from the input image:
|
||||
|
||||
```py
|
||||
@torch.no_grad()
|
||||
def generate_caption(images, caption_generator, caption_processor):
|
||||
text = "a photograph of"
|
||||
|
||||
inputs = caption_processor(images, text, return_tensors="pt").to(device="cuda", dtype=caption_generator.dtype)
|
||||
caption_generator.to("cuda")
|
||||
outputs = caption_generator.generate(**inputs, max_new_tokens=128)
|
||||
|
||||
# offload caption generator
|
||||
caption_generator.to("cpu")
|
||||
|
||||
caption = caption_processor.batch_decode(outputs, skip_special_tokens=True)[0]
|
||||
return caption
|
||||
```
|
||||
|
||||
Load an input image and generate a caption for it using the `generate_caption` function:
|
||||
|
||||
```py
|
||||
from diffusers.utils import load_image
|
||||
|
||||
img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
|
||||
raw_image = load_image(img_url).convert("RGB").resize((768, 768))
|
||||
caption = generate_caption(raw_image, model, processor)
|
||||
```
|
||||
|
||||
<div class="flex justify-center">
|
||||
<figure>
|
||||
<img class="rounded-xl" src="https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"/>
|
||||
<figcaption class="text-center">generated caption: "a photograph of a bowl of fruit on a table"</figcaption>
|
||||
</figure>
|
||||
</div>
|
||||
|
||||
Now you can drop the caption into the [`~StableDiffusionDiffEditPipeline.invert`] function to generate the partially inverted latents!
|
||||
Reference in New Issue
Block a user