text stringlengths 0 5.54k |
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"DeepFloyd/IF-I-IF-v1.0", |
text_encoder=text_encoder, # pass the previously instantiated 8bit text encoder |
unet=None, |
device_map="auto", |
) |
prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"' |
prompt_embeds, negative_embeds = pipe.encode_prompt(prompt) |
# Remove the pipeline so we can re-load the pipeline with the unet |
del text_encoder |
del pipe |
gc.collect() |
torch.cuda.empty_cache() |
pipe = IFPipeline.from_pretrained( |
"DeepFloyd/IF-I-IF-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16, device_map="auto" |
) |
generator = torch.Generator().manual_seed(0) |
image = pipe( |
prompt_embeds=prompt_embeds, |
negative_prompt_embeds=negative_embeds, |
output_type="pt", |
generator=generator, |
).images |
pt_to_pil(image)[0].save("./if_stage_I.png") |
# Remove the pipeline so we can load the super-resolution pipeline |
del pipe |
gc.collect() |
torch.cuda.empty_cache() |
# First super resolution |
pipe = IFSuperResolutionPipeline.from_pretrained( |
"DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16, device_map="auto" |
) |
generator = torch.Generator().manual_seed(0) |
image = pipe( |
image=image, |
prompt_embeds=prompt_embeds, |
negative_prompt_embeds=negative_embeds, |
output_type="pt", |
generator=generator, |
).images |
pt_to_pil(image)[0].save("./if_stage_II.png") |
Available Pipelines: |
Pipeline |
Tasks |
Colab |
pipeline_if.py |
Text-to-Image Generation |
- |
pipeline_if_superresolution.py |
Text-to-Image Generation |
- |
pipeline_if_img2img.py |
Image-to-Image Generation |
- |
pipeline_if_img2img_superresolution.py |
Image-to-Image Generation |
- |
pipeline_if_inpainting.py |
Image-to-Image Generation |
- |
pipeline_if_inpainting_superresolution.py |
Image-to-Image Generation |
- |
IFPipeline |
class diffusers.IFPipeline |
< |
source |
> |
( |
tokenizer: T5Tokenizer |
text_encoder: T5EncoderModel |
unet: UNet2DConditionModel |
scheduler: DDPMScheduler |
safety_checker: typing.Optional[diffusers.pipelines.deepfloyd_if.safety_checker.IFSafetyChecker] |
feature_extractor: typing.Optional[transformers.models.clip.image_processing_clip.CLIPImageProcessor] |
watermarker: typing.Optional[diffusers.pipelines.deepfloyd_if.watermark.IFWatermarker] |
requires_safety_checker: bool = True |
) |
__call__ |
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