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Revert "add image-preview to sdxl" #72

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merged 1 commit into from
Aug 27, 2023
Merged

Revert "add image-preview to sdxl" #72

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77 changes: 4 additions & 73 deletions spice_agent/inference/actions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,13 +12,6 @@
StableDiffusionXLPipeline,
StableDiffusionXLImg2ImgPipeline,
)
from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
from diffusers.models.attention_processor import (
AttnProcessor2_0,
LoRAAttnProcessor2_0,
LoRAXFormersAttnProcessor,
XFormersAttnProcessor,
)
from gql import gql
from gql.transport.exceptions import TransportQueryError

Expand Down Expand Up @@ -339,7 +332,7 @@ def callback_for_stable_diffusion(
""" # noqa

# Need access to vae to decode here:
if self.pipe and self.inference_job_id and self.pipe_input and step > 0:
if self.pipe and self.inference_job_id and self.pipe_input:
# Update progress on backend
self._update_inference_job(
inference_job_id=self.inference_job_id,
Expand All @@ -352,67 +345,6 @@ def callback_for_stable_diffusion(
},
)

def callback_for_stable_diffusion_xl(
self, step: int, timestep: int, latents: torch.FloatTensor
) -> None:
"""
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
""" # noqa

# Need access to vae to decode here:
if self.pipe and self.inference_job_id and self.pipe_input and step > 0:
# Send preview images
with torch.no_grad():
# make sure the VAE is in float32 mode, as it overflows in float16
self.pipe.vae.to(dtype=torch.float32)

use_torch_2_0_or_xformers = isinstance(
self.pipe.vae.decoder.mid_block.attentions[0].processor,
(
AttnProcessor2_0,
XFormersAttnProcessor,
LoRAXFormersAttnProcessor,
LoRAAttnProcessor2_0,
),
)
if use_torch_2_0_or_xformers:
self.pipe.vae.post_quant_conv.to(latents.dtype)
self.pipe.vae.decoder.conv_in.to(latents.dtype)
self.pipe.vae.decoder.mid_block.to(latents.dtype)
else:
latents = latents.float() # type: ignore

file_name = f"{self.inference_job_id}-preview-step-{step}.png"
save_at = Path(SPICE_INFERENCE_DIRECTORY / file_name)

image = self.pipe.vae.decode(
latents / self.pipe.vae.config.scaling_factor, return_dict=False
)[0]
image = self.pipe.watermark.apply_watermark(image)
image = self.pipe.image_processor.postprocess(image, output_type="pil")

image = StableDiffusionXLPipelineOutput(images=image)[0][0]
image.save(save_at)

upload_file_response = self.spice.uploader.upload_file_via_api(
path=save_at
)
file_id = upload_file_response.json()["data"]["uploadFile"]["id"]
self._update_inference_job(
inference_job_id=self.inference_job_id,
status="COMPLETE",
file_outputs_ids=file_id,
status_details={
"progress": (
(step + 1)
/ self.pipe_input.inference_options.num_inference_steps
)
* 100,
"current_image_file_name": file_name,
},
)

def run_pipeline(
self,
inference_job_id: str,
Expand Down Expand Up @@ -472,8 +404,7 @@ def run_pipeline(
prompt = text_input
negative_prompt = options.get("negative_prompt", "")
generator = self._get_generator(int(options.get("seed", -1)))

options["callback_steps"] = 10
callback = self.callback_for_stable_diffusion

was_guarded = False
if not save_at.exists():
Expand Down Expand Up @@ -537,7 +468,7 @@ def run_pipeline(
**asdict(stable_diffusion_pipeline_input.inference_options),
**asdict(stable_diffusion_pipeline_input.output),
generator=generator,
callback=self.callback_for_stable_diffusion,
callback=callback,
) # type:ignore
# Configure MOE for xl diffusion base + refinement TASK
elif isinstance(pipe, StableDiffusionXLPipeline):
Expand Down Expand Up @@ -584,7 +515,7 @@ def run_pipeline(
),
**asdict(stable_diffusion_pipeline_xl_input.output),
generator=generator,
callback=self.callback_for_stable_diffusion_xl,
callback=callback,
).images # type: ignore

refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
Expand Down