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fix: guidance_scale not work in sd (#1488)

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Signed-off-by: hibobmaster <32976627+hibobmaster@users.noreply.github.com>
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BobMaster 2023-12-25 02:24:52 +08:00 committed by GitHub
parent 6597881854
commit 7e2d101a46
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1 changed files with 19 additions and 26 deletions
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45
backend/python/diffusers/backend_diffusers.py
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@ -149,9 +149,9 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
local = False
modelFile = request.Model
cfg_scale = 7
self.cfg_scale = 7
if request.CFGScale != 0:
cfg_scale = request.CFGScale
self.cfg_scale = request.CFGScale
clipmodel = "runwayml/stable-diffusion-v1-5"
if request.CLIPModel != "":
@ -173,17 +173,14 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
if (request.PipelineType == "StableDiffusionImg2ImgPipeline") or (request.IMG2IMG and request.PipelineType == ""):
if fromSingleFile:
self.pipe = StableDiffusionImg2ImgPipeline.from_single_file(modelFile,
torch_dtype=torchType,
guidance_scale=cfg_scale)
torch_dtype=torchType)
else:
self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained(request.Model,
torch_dtype=torchType,
guidance_scale=cfg_scale)
torch_dtype=torchType)
elif request.PipelineType == "StableDiffusionDepth2ImgPipeline":
self.pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(request.Model,
torch_dtype=torchType,
guidance_scale=cfg_scale)
torch_dtype=torchType)
## img2vid
elif request.PipelineType == "StableVideoDiffusionPipeline":
self.img2vid=True
@ -197,38 +194,32 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
self.pipe = AutoPipelineForText2Image.from_pretrained(request.Model,
torch_dtype=torchType,
use_safetensors=SAFETENSORS,
variant=variant,
guidance_scale=cfg_scale)
variant=variant)
elif request.PipelineType == "StableDiffusionPipeline":
if fromSingleFile:
self.pipe = StableDiffusionPipeline.from_single_file(modelFile,
torch_dtype=torchType,
guidance_scale=cfg_scale)
torch_dtype=torchType)
else:
self.pipe = StableDiffusionPipeline.from_pretrained(request.Model,
torch_dtype=torchType,
guidance_scale=cfg_scale)
torch_dtype=torchType)
elif request.PipelineType == "DiffusionPipeline":
self.pipe = DiffusionPipeline.from_pretrained(request.Model,
torch_dtype=torchType,
guidance_scale=cfg_scale)
torch_dtype=torchType)
elif request.PipelineType == "VideoDiffusionPipeline":
self.txt2vid=True
self.pipe = DiffusionPipeline.from_pretrained(request.Model,
torch_dtype=torchType,
guidance_scale=cfg_scale)
torch_dtype=torchType)
elif request.PipelineType == "StableDiffusionXLPipeline":
if fromSingleFile:
self.pipe = StableDiffusionXLPipeline.from_single_file(modelFile,
torch_dtype=torchType, use_safetensors=True,
guidance_scale=cfg_scale)
torch_dtype=torchType,
use_safetensors=True)
else:
self.pipe = StableDiffusionXLPipeline.from_pretrained(
request.Model,
torch_dtype=torchType,
use_safetensors=True,
variant=variant,
guidance_scale=cfg_scale)
variant=variant)
if CLIPSKIP and request.CLIPSkip != 0:
self.clip_skip = request.CLIPSkip
@ -384,12 +375,12 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
image = image.resize((1024, 576))
generator = torch.manual_seed(request.seed)
frames = self.pipe(image, decode_chunk_size=CHUNK_SIZE, generator=generator).frames[0]
frames = self.pipe(image, guidance_scale=self.cfg_scale, decode_chunk_size=CHUNK_SIZE, generator=generator).frames[0]
export_to_video(frames, request.dst, fps=FPS)
return backend_pb2.Result(message="Media generated successfully", success=True)
if self.txt2vid:
video_frames = self.pipe(prompt, num_inference_steps=steps, num_frames=int(FRAMES)).frames
video_frames = self.pipe(prompt, guidance_scale=self.cfg_scale, num_inference_steps=steps, num_frames=int(FRAMES)).frames
export_to_video(video_frames, request.dst)
return backend_pb2.Result(message="Media generated successfully", success=True)
@ -398,13 +389,15 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
conditioning = self.compel.build_conditioning_tensor(prompt)
kwargs["prompt_embeds"]= conditioning
# pass the kwargs dictionary to the self.pipe method
image = self.pipe(
image = self.pipe(
guidance_scale=self.cfg_scale,
**kwargs
).images[0]
else:
# pass the kwargs dictionary to the self.pipe method
image = self.pipe(
prompt,
prompt,
guidance_scale=self.cfg_scale,
**kwargs
).images[0]